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THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING
 
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Computer Applications: An International Journal (CAIJ) ISSN :2393 - 8455 http://airccse.com/caij/index.html ********************************************* Computer Applications: An International Journal (CAIJ), Vol.4, No.1/2/3/4, November 2017 DOI:10.5121/caij.2017.4401 THE EFFECTIVENESS OF DATA MINING TECHNIQUES IN BANKING Yuvika Priyadarshini Researcher, Jharkhand Rai University, Ranchi. ABSTRACT The aim of this study is to identify the extent of Data mining activities that are practiced by banks, Data mining is the ability to link structured and unstructured information with the changing rules by which people apply it. It is not a technology, but a solution that applies information technologies. Currently several industries including like banking, finance, retail, insurance, publicity, database marketing, sales predict, etc are Data Mining tools for Customer . Leading banks are using Data Mining tools for customer segmentation and benefit, credit scoring and approval, predicting payment lapse, marketing, detecting illegal transactions, etc. The Banking is realizing that it is possible to gain competitive advantage deploy data mining. This article provides the effectiveness of Data mining technique in organized Banking. It also discusses standard tasks involved in data mining; evaluate various data mining applications in different sectors KEYWORDS Definition of Data Mining and its task, Effectiveness of Data Mining Technique, Application of Data Mining in Banking, Global Banking Industry Trends, Effective Data Mining Component and Capabilities, Data Mining Strategy, Benefit of Data Mining Program in Banking
Views: 30 aircc journal
crm database management | relationship management software
 
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crm database management,customer relations management system
Views: 49 Naveed Alam
Dynamics CRM 2011 - Avanade Advanced Marketing Solution, the Customer Segment Decision Tree Demo
 
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Avanade Advanced Marketing Solution enables multi-channel, multi-wave, in- and outbound campaign management on top of Microsoft Dynamics CRM 2011 to increase marketing effectiveness and efficiency for enterprise organizations. The video demonstrates the customer segment visualizer decision tree capability
Views: 764 ursruee
How to combine data from your website, CRM, ERP and other systems
 
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Get the webinar replay and slide deck here: https://www.owox.com/c/h3 When you have your data collected in a number of systems — ERP, CRM, advertising services, price intelligence services, etc. — piecing it together manually is often a tedious and error-prone task. We will look at specific examples of system integrations, and give you examples of reports and charts which you can create by combining data. Join the webinar and find out: 1. What are the difficulties in combining data from multiple services. 2. How to combine data into a single system using DataVirtuality and OWOX BI https://www.owox.com/c/fg 3. Examples of imports and exports to and from Google BigQuery. 4. Examples of informative reports and charts based on complete data from multiple sources. The webinar will be useful to: Data analysts and technical experts who are looking to save time by automating manual processes. More webinars about Google services best practices for Ecommerce businesses: https://www.owox.com/c/g2 Articles on the topic: Importing Google Analytics Data to Google BigQuery: Comparing Two Methods of Data Collection https://www.owox.com/c/on ------------------------------------------------ Follow us on Facebook: https://www.facebook.com/owoxbi/ Twitter : https://twitter.com/ Need XYZ-files about best practices and insights from the world of analytics, digital marketing and Ecommerce? Sign up to our secret newsletter then » https://www.owox.com/c/2l2 Free Trial OWOX BI:https://www.owox.com/c/2l3
Views: 43 OWOX BI
What is APPLICANT TRACKING SYSTEM? What does APPLICANT TRACKING SYSTEM mean?
 
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What is APPLICANT TRACKING SYSTEM? What does APPLICANT TRACKING SYSTEM mean? APPLICANT TRACKING SYSTEM meaning - APPLICANT TRACKING SYSTEM definition - APPLICANT TRACKING SYSTEM explanation. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. An applicant tracking system (ATS) is a software application that enables the electronic handling of recruitment needs. An ATS can be implemented or accessed online on an enterprise or small business level, depending on the needs of the company and there is also free and open source ATS software available. An ATS is very similar to customer relationship management (CRM) systems, but are designed for recruitment tracking purposes. In many cases they filter applications automatically based on given criteria such as keywords, skills, former employers, years of experience and schools attended. This has caused many to adapt resume optimization techniques similar to those used in search engine optimization when creating and formatting their résumé. A dedicated ATS is not uncommon for recruitment specific needs. On the enterprise level it may be offered as a module or functional addition to a human resources suite or Human Resource Information System (HRIS). The ATS is expanding into small and medium enterprises through open source or software as a service offerings (SaaS). The principal function of an ATS is to provide a central location and database for a company's recruitment efforts. ATSs are built to better assist management of resumes and applicant information. Data is either collected from internal applications via the ATS front-end, located on the company website or is extracted from applicants on job boards. The majority of job and resume boards (LinkedIn.com, Monster.com, Hotjobs, CareerBuilder, Indeed.com) have partnerships with ATS software providers to provide parsing support and ease of data migration from one system to another. Newer applicant tracking systems (often referred to as next generation) are platforms as a service whereby the main piece of software has integration points that allow providers of other recruiting technology to plug in seamlessly. The ability of these next generation ATS solutions allows jobs to be posted where the candidate is and not just on job boards. This ability is being referred to as Omnichannel Talent Acquisition. Recent enhancements include use of artificial intelligence (AI) tools and natural language processing (NLP) to facilitate intelligent guided semantic search capabilities offered through cloud based platforms that allow companies to score and sort resumes with better alignment to the job requirements and descriptions. With the advent of ATS, resume optimization techniques and online tools are now used by applicants to increase their chances of landing an interview call. Functionality of an ATS is not limited to data mining and collection; ATS applications in the recruitment industry include the ability to automate the recruitment process via a defined workflow. Another benefit of an applicant tracking system is analyzing and coordinating recruitment efforts - managing the conceptual structure known as human capital. A corporate career site or company specific job board module may be offered, allowing companies to provide opportunities to internal candidates prior to external recruitment efforts. Candidates may be identified via pre-existing data or through information garnered through other means. This data is typically stored for search and retrieval processes. Some systems have expanded offerings that include off-site encrypted resume and data storage, which are often legally required by equal opportunity employment laws. ....
Views: 112 The Audiopedia
What is AFFINITY ANALYSIS? What does AFFINITY ANALYSIS mean? AFFINITY ANALYSIS meaning
 
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What is AFFINITY ANALYSIS? What does AFFINITY ANALYSIS mean? AFFINITY ANALYSIS meaning - AFFINITY ANALYSIS definition - AFFINITY ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Affinity analysis is a data analysis and data mining technique that discovers co-occurrence relationships among activities performed by (or recorded about) specific individuals or groups. In general, this can be applied to any process where agents can be uniquely identified and information about their activities can be recorded. In retail, affinity analysis is used to perform market basket analysis, in which retailers seek to understand the purchase behavior of customers. This information can then be used for purposes of cross-selling and up-selling, in addition to influencing sales promotions, loyalty programs, store design, and discount plans. Market basket analysis might tell a retailer that customers often purchase shampoo and conditioner together, so putting both items on promotion at the same time would not create a significant increase in revenue, while a promotion involving just one of the items would likely drive sales of the other. Market basket analysis may provide the retailer with information to understand the purchase behavior of a buyer. This information will enable the retailer to understand the buyer's needs and rewrite the store's layout accordingly, develop cross-promotional programs, or even capture new buyers (much like the cross-selling concept). An apocryphal early illustrative example for this was when one super market chain discovered in its analysis that male customers that bought diapers often bought beer as well, have put the diapers close to beer coolers, and their sales increased dramatically. Although this urban legend is only an example that professors use to illustrate the concept to students, the explanation of this imaginary phenomenon might be that fathers that are sent out to buy diapers often buy a beer as well, as a reward. This kind of analysis is supposedly an example of the use of data mining. A widely used example of cross selling on the web with market basket analysis is Amazon.com's use of "customers who bought book A also bought book B", e.g. "People who read History of Portugal were also interested in Naval History". Market basket analysis can be used to divide customers into groups. A company could look at what other items people purchase along with eggs, and classify them as baking a cake (if they are buying eggs along with flour and sugar) or making omelets (if they are buying eggs along with bacon and cheese). This identification could then be used to drive other programs. Similarly, it can be used to divide products into natural groups. A company could look at what products are most frequently sold together and align their category management around these cliques Business use of market basket analysis has significantly increased since the introduction of electronic point of sale. Amazon uses affinity analysis for cross-selling when it recommends products to people based on their purchase history and the purchase history of other people who bought the same item. Family Dollar plans to use market basket analysis to help maintain sales growth while moving towards stocking more low-margin consumable goods.
Views: 188 The Audiopedia
Amazing Things NLP Can Do!
 
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In this video I want to highlight a few of the awesome things that we can do with Natural Language Processing or NLP. NLP basically means getting a computer to understand text and help you with analysis. Some of the major tasks that are a part of NLP include: · Automatic summarization · Coreference resolution · Discourse analysis · Machine translation · Morphological segmentation · Named entity recognition (NER) · Natural language generation · Natural language understanding · Optical character recognition (OCR) · Part-of-speech tagging · Parsing · Question answering · Relationship extraction · Sentence breaking (also known as sentence boundary disambiguation) · Sentiment analysis · Speech recognition · Speech segmentation · Topic segmentation and recognition · Word segmentation · Word sense disambiguation · Lemmatization · Native-language identification · Stemming · Text simplification · Text-to-speech · Text-proofing · Natural language search · Query expansion · Automated essay scoring · Truecasing Let’s discuss some of the cool things NLP helps us with in life 1. Spam Filters – nobody wants to receive spam emails, NLP is here to help fight span and reduce the number of spam emails you receive. No it is not yet perfect and I’m sure we still all still receive some spam emails but imagine how many you’d get without NLP! 2. Bridging Language Barriers – when you come across a phrase or even an entire website in another language, NLP is there to help you translate it into something you can understand. 3. Investment Decisions – NLP has the power to help you make decisions for financial investing. It can read large amounts of text (such as news articles, press releases, etc) and can pull in the key data that will help make buy/hold/sell decisions. For example, it can let you know if there is an acquisition that is planned or has happened – which has large implications on the value of your investment 4. Insights – humans simply can’t read everything that is available to us. NLP helps us summarize the data we have and pull out meaningful information. An example of this is a computer reading through thousands of customer reviews to identify issues or conduct sentiment analysis. I’ve personally used NLP for getting insights from data. At work, we conducted an in depth interview which included several open ended response type questions. As a result we received thousands of paragraphs of data to analyze. It is very time consuming to read through every single answer so I created an algorithm that will categorize the responses into one of 6 categories using key terms for each category. This is a great time saver and turned out to be very accurate. Please subscribe to the YouTube channel to be notified of future content! Thanks! https://en.wikipedia.org/wiki/Natural_language_processing https://www.lifewire.com/applications-of-natural-language-processing-technology-2495544
Views: 5150 Story by Data
Data mining
 
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Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amount of data, not the extraction of data itself. It also is a buzzword, and is frequently also applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" -- or when referring to actual methods, artificial intelligence and machine learning -- are more appropriate. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 1603 Audiopedia
📐 Technical Analysis: Candlestick Chart Online Trading, best technical indicators for stocks, ohcl
 
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"Binary options are not promoted or sold to retail EEA traders. If you are not a professional client, please leave this page." ✅✅✅ Recommended Brokers ► FREE $10000 Demo Account to Practice #1 IQ Option ►https://goo.gl/7BZ7Rh #2 Ayrex ►https://goo.gl/shoZY9 #3 PocketOption ►https://goo.gl/Hs2a9k #4 ExpertOption ►https://goo.gl/7z3i6w #5 Binomo ►https://goo.gl/Ea3nYX #6 Spectre.ai ►https://goo.gl/pMPLKt ✅✅✅ Skrill to withdrawal ►https://goo.gl/vPGW2e ✅✅✅ Live Trading on TradeCaster ►https://goo.gl/MxMdL2 ✅✅✅ vfxAlert - BO Signals ►https://goo.gl/hQCLi5 "RISK WARNING: YOUR CAPITAL MIGHT BE AT RISK" This video is not an investment advice. "CFDs are complex instruments and come with a high risk of losing money rapidly due to leverage. Between 74-89% of retail investor accounts lose money when trading CFDs. You should consider whether you understand how CFDs work and whether you can afford to take the high risk of losing your money." Binary Options Turbo Trader (#BOTT) https://www.youtube.com/channel/UCCCvcQe-BFeBhm5Nlgp0p-Q?sub_confirmation=1 Forex (FX) Turbo Trader (FOTT) https://www.youtube.com/channel/UCmk8OVaeu2G0FS0hhjhzTeQ?sub_confirmation=1 DO (Digital Options) Turbo Trader (DOTT) https://www.youtube.com/channel/UCI0KK-afoTHqjEj5f62F1vQ?sub_confirmation=1 BO Turbo Trader Price Action Guide for Binary Options Trader PDF https://goo.gl/VmcKjJ 👉 SMASH THE LIKE BUTTON 👈 👉 HIT THE SUBSCRIBE BUTTON 👈 👉 LEAVE A COMMENT 👈 👉 SHARE 👈 ★ CONTACT ME https://goo.gl/uvt3xJ ★ Facebook-Group: https://www.facebook.com/groups/boturbotrader/ Twitter: https://twitter.com/boturbotrader Blogger: https://boturbotrader.blogspot.com/ Tumblr: https://boturbotrader.tumblr.com/ Binary Option Win Rate and Net Profit Calculator + Simulator https://goo.gl/NeUyCp Money Management Masaniello Program + Excel File https://goo.gl/9pNRhs Start Mining Cryptocurrency http://goo.gl/1mJLbU Risk Warning: Your invested capital may be at risk. This video is not an investment advice. Indicators: EMA 3 (blue) EMA 20 (yellow) EMA 50 (orange) EMA 100 (red) EMA 200 (purple) Bollinger Band Period 20 Deviation 2 (green) Bollinger Band Period 20 Deviation 1 (white) The moving average (MA) is a simple technical analysis tool that smooths out price data by creating a constantly updated average price. There are advantages to using a moving average in your trading, as well as options on what type of moving average to use. The Relative Strength Index (RSI), developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Traditionally the RSI is considered overbought when above 70 and oversold when below 30. The average directional index (ADX) is used to determine when the price is trending strongly. In many cases, it is the ultimate trend indicator. After all, the trend may be your friend, but it sure helps to know who your friends are. In this article, we'll examine the value of ADX as a trend strength indicator. Indicator based trading is relying on indicators to analyze the price and provide trade signals. Many indicators provide a specific trade signal which alerts the trade that now is the time to take a trade. Using Technical Analysis Indicators. Technical analysis is a method of examining past market data to help forecast future price movements. Technical analysis is based around a market's price history, rather than the fundamental data like earnings, dividends, news, and events. Moving average convergence divergence (MACD) is a trend-following momentum indicator that shows the relationship between two moving averages of prices. best technical indicators for stocks, ohcl chart stock analysis online, stock technical indicators trading technical analysis tools, ohcl chart technical analysis tools and techniques stock technical analysis tools, trading stock technical analysis tools, top stock picks online stock market trade, stock analysis online how to analyse stock market data online trading courses for beginners best online trading education tutorial online trading tutorial for beginners how to trade, currency trading, trading best trades to learn, stock market trading how to trade options, trading strategies #technicalanalysis #onlinetrading
Views: 224 BO Turbo Trader
What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning
 
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What is PREDICTIVE ANALYTICS? What does PREDICTIVE ANALYSIS mean? PREDICTIVE ANALYSIS meaning - PREDICTIVE ANALYTICS definition - PREDICTIVE ANALYTICS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Predictive analytics encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future or otherwise unknown events. In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. The defining functional effect of these technical approaches is that predictive analytics provides a predictive score (probability) for each individual (customer, employee, healthcare patient, product SKU, vehicle, component, machine, or other organizational unit) in order to determine, inform, or influence organizational processes that pertain across large numbers of individuals, such as in marketing, credit risk assessment, fraud detection, manufacturing, healthcare, and government operations including law enforcement. Predictive analytics is used in actuarial science, marketing, financial services, insurance, telecommunications, retail, travel, healthcare, child protection, pharmaceuticals, capacity planning and other fields. One of the best-known applications is credit scoring, which is used throughout financial services. Scoring models process a customer's credit history, loan application, customer data, etc., in order to rank-order individuals by their likelihood of making future credit payments on time. Predictive analytics is an area of data mining that deals with extracting information from data and using it to predict trends and behavior patterns. Often the unknown event of interest is in the future, but predictive analytics can be applied to any type of unknown whether it be in the past, present or future. For example, identifying suspects after a crime has been committed, or credit card fraud as it occurs. The core of predictive analytics relies on capturing relationships between explanatory variables and the predicted variables from past occurrences, and exploiting them to predict the unknown outcome. It is important to note, however, that the accuracy and usability of results will depend greatly on the level of data analysis and the quality of assumptions. Predictive analytics is often defined as predicting at a more detailed level of granularity, i.e., generating predictive scores (probabilities) for each individual organizational element. This distinguishes it from forecasting. For example, "Predictive analytics—Technology that learns from experience (data) to predict the future behavior of individuals in order to drive better decisions." In future industrial systems, the value of predictive analytics will be to predict and prevent potential issues to achieve near-zero break-down and further be integrated into prescriptive analytics for decision optimization. Furthermore, the converted data can be used for closed-loop product life cycle improvement which is the vision of the Industrial Internet Consortium.
Views: 953 The Audiopedia
Database marketing
 
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Database marketing is a form of direct marketing using databases of customers or potential customers to generate personalized communications in order to promote a product or service for marketing purposes. The method of communication can be any addressable medium, as in direct marketing. The distinction between direct and database marketing stems primarily from the attention paid to the analysis of data. Database marketing emphasizes the use of statistical techniques to develop models of customer behavior, which are then used to select customers for communications. As a consequence, database marketers also tend to be heavy users of data warehouses, because having a greater amount of data about customers increases the likelihood that a more accurate model can be built. This video is targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 197 Audiopedia
Social analytics help retailers meet demand
 
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Retailers have sophisticated demand forecast models and agile supply chains to ensure that the just the right amount of products are placed on the store shelves to meet demand. And yet, for some products, they get this amount wrong leading to empty shelves and overstocks, lost revenue opportunities, waste and profit erosion through discounting. Often it is the outside forces at play which explain these misses. A news article, government comment, scientific publication, the weather, the local rock concert are all examples of external forces which can influence local demand. Furthermore, the real time, public and conversational characteristics of Twitter data provide the barometer as to how people are reacting to such stories and thus help quantify the strength of the relationship of the external force to the impact on demand forecasts. By using advanced analytics and real time social media postings, the large retailer was able to reduce misses in its demand forecasting and store replenishment operations. Even small improvements to the model, when amplified at the scale of the retailer's supply chain, translated into significant financial benefits. Learn more about IBM and Twitter ibm.co/ibmandtwitter Subscribe to the IBM Analytics Channel: https://www.youtube.com/subscription_center?add_user=ibmbigdata The world is becoming smarter every day, join the conversation on the IBM Big Data & Analytics Hub: www.ibmbigdatahub.com www.facebook.com/IBManalytics www.twitter.com/IBMAnalytics www.linkedin.com/company/ibm-big-data-&-analytics www.slideshare.net/IBMBDA
Views: 2536 IBM Analytics
Roger L. Martin: "Creating Great Choices: A Leader's Guide to Integrative [...]" | Talks at Google
 
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Move Beyond Trade-Off Thinking. When it comes to our hardest choices, it can seem as though making trade-offs is inevitable. But what about those crucial times when accepting the obvious trade-off just isn't good enough? What do we do when the choices in front of us don't get us what we need? In those cases, rather than choosing the least worst option, we can use the models in front of us to create a new and superior answer. This is integrative thinking. First introduced by world-renowned strategic thinker Roger Martin in "The Opposable Mind," integrative thinking is an approach to problem solving that uses opposing ideas as the basis for innovation. Now, in "Creating Great Choices," Martin and his longtime thinking partner Jennifer Riel vividly illustrate how integrative thinking works, and how to do it. The book includes fresh stories of successful integrative thinkers that will demystify the process of creative problem solving, as well as practical tools and exercises to help readers engage with the ideas. And it lays out the authors' four-step methodology for creating great choices, which can be applied in virtually any context. The result is a replicable, thoughtful approach to finding a "third and better way" to make important choices in the face of unacceptable trade‐offs. Insightful and instructive, "Creating Great Choices" blends storytelling, theory, and hands-on advice to help any leader or manager facing a tough choice. Get the book here: https://goo.gl/FJb1HR Moderated by David Barry.
Views: 4919 Talks at Google
Ep5 - Data, CRM : on a oublié le client en route !
 
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Big data, CRM, expérience client, les pros du marketing n’ont que ça à la bouche. De quoi optimiser les budgets et aller toujours plus loin dans la connaissance du client pour lui adresser la bonne offre au bon moment. Dans les faits on est loin de tout ça. Pierre Santa Cruz a un background un peu différent, il vient du terrain et du commerce de proximité où le fameux dicton “le client est roi” est bien vrai et vérifiable au jour le jour. Une bonne inspiration pour les géants du retail ? Sans aucun doute ! Emission produite par tomg conseils (http://tomg-conseils.com/) pour Regards Connectés. Avec le soutien de Petit Web (http://www.petitweb.fr/) et Connected Mag (http://www.theconnectedmag.fr/). Tous droits réservés, tomg conseils 2016 Merci d'inclure une citation et un lien vers http://regards-connectes.fr/ si vous utilisez cette vidéo pour illustrer un article ou autre support.
Views: 987 Regards Connectés
What is DATA QUALITY FIREWALL? What does DATA QUALITY FIREWALL mean? DATA QUALITY FIREWALL meaning
 
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What is DATA QUALITY FIREWALL? What does DATA QUALITY FIREWALL mean? DATA QUALITY FIREWALL meaning - DATA QUALITY FIREWALL definition - DATA QUALITY FIREWALL explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ A data quality firewall is the use of software to protect a computer system from the entry of erroneous, duplicated or poor quality data. Gartner estimates that poor quality data causes failure in up to 50% of customer relationship management systems. Older technology required the tight integration of data quality software, whereas this can now be accomplished by loosely coupling technology in a service-oriented architecture. A data quality firewall guarantees database accuracy and consistency. This application ensures that only valid and high quality data enter the system, which means that it obliquely protects the database from damage; this is extremely important since database integrity and security are absolutely essential. A data quality firewall provides real time feedback information about the quality of the data submitted to the system. The main goal of a data quality process consists in capturing erroneous and invalid data, processing them and eliminating duplicates and, lastly, exporting valid data to the user without failing to store a back-up copy into the database. A data quality firewall acts similarly to a network security firewall. It enables packets to pass through specified ports by filtering out data that present quality issues and allowing the remaining, valid data to be stored in the database. In other words, the firewall sits between the data source and the database and works throughout the extraction, processing and loading of data. It is necessary that data streams be subject to accurate validity checks before they can be considered as being correct or trustworthy. Such checks are of a temporal, formal, logic and forecasting kind.
Views: 27 The Audiopedia
AUTOMATIC MEDICAL DISEASE TREATMENT SYSTEM USING DATAMINING
 
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In our proposed system is identifying reliable information in the medical domain stand as building blocks for a healthcare system that is up-todate with the latest discoveries. By using the tools such as NLP, ML techniques. In this research, focus on diseases and treatment information, and the relation that exists between these two entities. The main goal of this research is to identify the disease name with the symptoms specified and extract the sentence from the article and get the Relation that exists between Disease- Treatment and classify the information into cure, prevent, side effect to the user.This electronic document is a “live” template. The various components of your paper [title, text, heads, etc.] are already defined on the style sheet, as illustrated by the portions given in this document.
What is ENTERPRISE SOCIAL GRAPH? What does ENTERPRISE SOCIAL GRAPH mean?
 
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What is ENTERPRISE SOCIAL GRAPH? What does ENTERPRISE SOCIAL GRAPH mean? ENTERPRISE SOCIAL GRAPH meaning - ENTERPRISE SOCIAL GRAPH definition - ENTERPRISE SOCIAL GRAPH explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ An enterprise social graph is a representation of the extended social network of a business, encompassing relationships among its employees, vendors, partners, customers, and the public. With the advent of Web 2.0 and Enterprise 2.0 technologies a company can monitor and act on these relationships in real-time. Given the number of relationships and the volume of associated data, algorithmic approaches are used to focus attention on changes that are deemed relevant. The term was first popularized in a 2010 Forbes article, to describe the multi-relational nature of enterprise-centric networks that are now at least partially observable at scale. The enterprise social graph integrates representations of the various social networks in which the enterprise is embedded into a unified graph representation. Given the online context of many of the relationships, social interactions often comprise direct communication along with interactions around digital artifacts. Therefore, the enterprise social graph codifies not only relationships among individuals but also individual-object interaction patterns. This definition follows Facebook's and Google's concept of a social graph that explicitly includes the objects with which individuals interact in a network. Examples of these relationship patterns can include authorship, sharing or sending information, management or other social hierarchy, bookmarking, and other gestural signals that describe a relationship between two or more nodes. Additional representational challenges arise with the need to capture interaction dynamics and their changing social context over time, and as such, representational choices vary based ultimately on the analytic questions that are of interest. Besides being a specialized type of social graph, the enterprise social graph is related to network science and graph theory. Changes in how people connect, share, accomplish tasks through online social networks, combined with the growth of ambient public information relevant to an enterprise, contribute to the dynamism and increasing complexity of enterprise social graphs. Whereas meetings, phone calls, or email have been the traditional media for these exchanges, increasingly collaboration and conversation occurs via online social media. As Kogut and Zander point out, the more tacit knowledge is, the more difficult and expensive it is to transmit, since the costs of codifying and teaching will rise as tacitness increases. The consumerization of social business software enables simpler and more cost-effective ways making relationships and tacit knowledge both observable and actionable. From an internal enterprise perspective, understanding the enterprise social graph can provide greater awareness of internal dynamics, organizational and information flow inefficiencies, information seeking and expert identification, or exposing opportunities for new valued connections. From an external perspective, it can provide deeper insights into marketplace conditions and customer demand, customer issues and concerns, product development and co-creation, supply-side operational awareness or external causal relationships. Recent developments in big data analysis, combined with graph mining techniques, make it possible to analyze petabytes of structured and unstructured information and feed user-facing applications. In making use of the enterprise social graph, such applications excel at search, routing, and matching operations, particularly where these include personalization, statistical analysis and machine learning. Examples of applications that combine big data mining techniques over the enterprise social graph include business intelligence, personalized activity streams and intelligent filtering, social search, recommendation engines, automated question or message routing, expertise identification, and information context discovery.
Views: 26 The Audiopedia
Databases & Data Warehouses, Data: Structures, Types, Integrations
 
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Dr. Chaitan Baru and Dr. Elena Zheleva from the National Science Foundation presents a lecture on "Databases & Data Warehouses, Data: Structures, Types, Integrations" Lecture Abstract This talk will provide an overview of the evolution of database and data warehouse approaches and technologies. Where did we begin, and where have we come to? In the process, we will provide a review of concepts like structured and semistructured databases; schema on-write versus schema on-read; SQL/noSQL database; data integration; and data integrity constraints. The talk will be motivated by example of Data Science use cases. View slides from this lecture https://drive.google.com/open?id=0B4IAKVDZz_JUck8tQlJ0N2VTSlU About the Speakers: Chaitan Baru is Senior Advisor for Data Science in the Computer and Information Science and Engineering (CISE) Directorate at the National Science Foundation, where he coordinates the cross-Foundation BIGDATA research program, advises the NSF Big Data Hubs and Spokes program, assists in CISE strategic planning in Data Science, and participates in interdisciplinary and inter-agency Data Science-related activities. He co-chairs the Big Data Inter-agency Working Group, and is co-author of the US Federal Big Data R&D Strategic Plan (http://bit.ly/1Ughpjt), released May 2016 by the Networking and Information Technology R&D (NITRD) group of the National Coordination Office, White House Office of Science and Technology Policy. Elena Zheleva is a computer scientist with a background in machine learning, social network analysis and online privacy. Elena has presented her research at top-tier conferences, and she is the co-author of the book "Privacy in Social Networks." After completing her Ph.D. in Computer Science from the University of Maryland College Park in 2011, Elena spent five years in industry as a data scientist, focusing on recommender systems and incentivized social sharing. She was also fortunate to intern at Microsoft Research, AOL and The Institute for Genomic Research. Currently, Elena is an AAAS Science and Technology Policy Fellow at the National Science Foundation where she contributes to big data and data science initiatives. Please join our weekly meetings from your computer, tablet or smartphone. Visit our website to learn how to join! http://www.bigdatau.org/data-science-seminars
Retailers use customer data to predict sales
 
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As you do your holiday shopping, consider that stores already know what you are going to buy before you walk in. Click here to read more in Jill Kuramoto's article.
Views: 142 KITV
Social Network Analysis
 
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An overview of social networks and social network analysis. See more on this video at https://www.microsoft.com/en-us/research/video/social-network-analysis/
Views: 3100 Microsoft Research
Youtube Video Marketing Secrets
 
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Youtube Video Marketing Secrets DOWNLOAD NOW CLICK HERE: http://instymehostingreview.com/tube1 Youtube Video Marketing Secrets - youtube video marketing secrets. Youtube video marketing secrets revealed pdf · youtube video marketing tips · youtube video marketing company local video marketing secrets revealed! Video marketing secrets: Why do people come on youtube Learn how to make your videos stand out with these video marketing tips and strategies Online video marketing: trends, tips and guidelines for video marketers Here`s how to make video marketing on YouTube work for your business Edit Article · How to Optimize Your YouTube Channel the truth about youtube video marketing methods.. - youtube video marketing - youtube video marketing secrets revealed.. click below to get tube web traffic mojo and discover all the various other techniques to raking in leads with youtube video marketing... you may use automated or semi-automated backlinking tools like magic submitter ( click here ) to disburse your youtube video marketing content to hundreds even thousands of site around the planet... for the lowest price on "video marketing secrets" click here: .. youtube marketing secrets exposed: video marketing tips. for you to be definitely effective with youtube video marketing you will certainly require to do various other points... those are three pointers for your youtube video marketing that will assist you begin. i am visiting show you three of my tricks for utilizing youtube video marketing to generate laser device targeted leads for your network advertising and marketing company... that is the energy of youtube video marketing... the fact is that youtube video marketing is a very efficient way to obtain your message before your perfect prospect... the secret to youtube video marketing.. *internet marketers and bloggers that want to know how to effectively use youtube in their video marketing in their strategy. *wants to see results in their youtube video marketing. this book will teach you not only how to share your business life and build memories - it will show you to make profits doing it with youtube video marketing... big brand online outlets are using youtube video marketing and service companies manufacturing and many others including affiliate marketing experts network marketing specialists and local small businesses are also hopping on board with youtube video marketing. exactly what are the advantages of youtube video marketing?.. now let's take a look at some insider secrets that the professionals are utilizing to make a little fortune on the web with youtube video marketing... are you considering jumping in to the youtube video marketing arena?.. video marketing secrets: why video marketing on youtube? video marketing software reviews . ---------------------------------------- CLICK HERE: http://instymehostingreview.com/hyd ---------------------------------------- Social media - Wikipedia, the free encyclopedia https://en.wikipedia.org/wiki/Social_media marketing research: Mobile social-media applications offer data about offline ... loyalty programs: In order to increase long-term relationships with customers, ... Usability: Industrial media production typically requires specialized skills and training. ... This can be useful in PR management and campaign tracking, allowing the ... ‎Web 2.0 - ‎Social media marketing - ‎Category:Social media - ‎Social media mining ---------------------------------------- CLICK HERE: http://instymehostingreview.com/hyd ---------------------------------------- Social Media Marketing Tutorials | Lynda.com www.lynda.com/Social-Media-Marketing-training-tutorials/1366-0.html Software. Facebook (9); Google (8); Twitter (7); LinkedIn (5); Google+ (4); YouTube (3); Adobe (2); Facebook ... 37 Social Media Marketing courses · 1,064 video tutorials .... view course page for Website Management for Musicians and Bands ... People who watched this video: https://www.youtube.com/channel/UChMdjMMtf-9Dp2HgPUO265A Also searched online for: ------------------------------------------- FOR MORE DETAILS: http://instymehostingreview.com/hyd ------------------------------------------- CONNECT WITH US: http://createablogspot.blogspot.ie/ http://howtocreateafreewebsite.tumblr.com/ https://howtoblog4u.wordpress.com/ https://delicious.com/JnWhaley http://freeblogmaker.livejournal.com/ ------------------------------------------ Don't forget to check out our YouTube Channel: https://www.youtube.com/channel/UC-WXbU4FoZF8V3yGqs3vtCQ and click the link below to subscribe to our channel and get informed when we add new content: https://www.youtube.com/playlist?list=PLyendu_QBBndfrfT32cEfGW2VhDsZpQ2Q -------------------------------------------- -------------------------------------------- VISIT OUR SITE: http://instymehostingreview.com/hyd
Medical Coding with MedDRA
 
01:00:34
Learn about the best practices for medical coding using MedDRA, a global dictionary used by companies for regulatory activities. -- MedDRA is a global dictionary used by companies for regulatory activities. The dictionary, complete with clinically validated terminology, is used to classify adverse event information related to the use of drugs, devices, and other therapies. Coding the data to a standard set of MedDRA terms enables health authorities and the life sciences industry to more readily exchange and analyze data. MedDRA is considered the international standard for adverse event classification. However, while the data volume and standardization capabilities offered by the dictionary can provide significant benefits, the multi-axial design and data specificity can introduce considerable challenges that could lead to the inaccurate classification of data. Join BioPharm Systems' Dr. Rodney Lemery, vice president of safety and pharmacovigilance, and Caroline Halsey, director of project management, EMEA, for this free one-hour webinar that will explore the best practices for medical coding using MedDRA. To view this webinar in its entirety, please visit: http://www.biopharm.com or https://cc.readytalk.com/r/fqfuit5ql7qh. Twitter: http://www.twitter.com/BioPharmSystems Facebook: http://www.facebook.com/BioPharmSystems LinkedIn: http://www.linkedin.com/companies/biopharm-systems-inc Google+: https://plus.google.com/104105608638786200757
Views: 14797 BioPharmSystems
Talent Connect Live: Day 2
 
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The Talent Connect Livestream is your front row seat to a three-day gathering of the world’s top leaders, innovators and influencers in the talent space. Join the stream October 9th – 11th, PDT to see keynote presentations, product demos and exclusive interviews, from anywhere in the world. You’ll gain actionable insights that will help you stay ahead of the evolving talent landscape on topics including Talent Intelligence, the Future of Work and Learning & Development.
What is REAL-TIME MARKETING? What does REAL-TIME MARKETING mean? REAL-TIME MARKETING meaning
 
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What is REAL-TIME MARKETING? What does REAL-TIME MARKETING mean? REAL-TIME MARKETING meaning - REAL-TIME MARKETING definition - REAL-TIME MARKETING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Real-time marketing is marketing performed "on-the-fly" to determine an appropriate or optimal approach to a particular customer at a particular time and place. It is a form of market research inbound marketing that seeks the most appropriate offer for a given customer sales opportunity, reversing the traditional outbound marketing (or interruption marketing) which aims to acquire appropriate customers for a given 'pre-defined' offer. The dynamic 'just-in-time' decision making behind a real-time offer aims to exploit a given customer interaction defined by web-site clicks or verbal contact centre conversation. Real-time marketing techniques developed during the mid-1990s following the initial deployment of customer relationship management (CRM) solutions in major retail banking, investment banking and telecommunications companies. The intrinsic and prevailing 'heavyweight' nature of the key CRM vendors at this time, who were generally focused on major back-front office system integration projects, provided an opportunity for niche players within the campaign management application arena. The implementation of real-time marketing solutions through the late 1990s would typically involve a 10-14 week delivery project with 1-2 FTE expert consultants and often would follow an earlier outbound marketing solution implementation. This relatively lightweight delivery model had obvious attractions within the vendor sales cycle and customer procurement context but was ultimately to prove a disincentive for major systems integration services providers to partner with real-time marketing vendors. Real-time marketing solution implementation classically involves the server-side installation of a multithreaded core decisioning application server / interaction transactional-biased schema and supporting client components such as a 'fat-client' desktop campaign studio / rules editor, browser-based marketing user reporting interface and enterprise application APIs such as web services / Java components. Vendors typically will also provide legacy interfaces for COM, sockets and HTTP integration. Vendor solution approaches to real-time learning naturally vary but commonly, the underlying models utilize a naive Bayesian probability classifier, recognising that despite their apparently oversimplified assumptions, these classifiers have worked well in many complex real-world situations. To help gain acceptance with in-house specialist data mining stakeholders, the real-time solutions also support external model scores and execution within offer decision making. The dotcom 'bust' of 2000 inhibited the further development and implementation of item-based collaborative filtering techniques which, having been incorporated within real-time marketing solutions through the 1990s, should have been immediately attractive to online retailers managing hundreds of thousands (or millions) of products as opposed to a retail bank with a hundred propositions across savings, credit card and mortgage product lines. Over time, it became apparent to solution vendors and maturing customers alike, that 'traditional' outbound and emergent inbound marketing initiatives should be consolidated within a coherent and coordinated enterprise marketing strategy. To this end, a class of marketing application known as marketing resource management (MRM) which 'sits above' real-time marketing, began to emerge during the early 21st Century, albeit in a fairly bespoke and implementation-specific guise. The essence of this abstraction layer is that the MRM application orchestrates strategy, stakeholder sign-off, budgeting, program planning, campaign execution and effectiveness reporting across inbound real-time and outbound marketing disciplines. The term "real-time marketing" has the potential weakness of self-limiting the underlying decisioning server capability to cross/up-selling despite the observation that this particular function is generally the most compelling aspect of the application class. Vendors therefore found themselves re-branding real-time marketing products to suggest a more holistic appreciation of enterprise interaction decision management. In some respects, these early real-time marketing customer implementations were ahead of their time despite acknowledged revenue realization within the early adopters.
Views: 129 The Audiopedia
business 101 everything you need to know about business and startup basics
 
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business 101: everything you need to know about business and startup basics. Learn the foundation concepts underlying all businesses, small to large. a video tutorial that covers all the basics, explaining concepts such as business goals, stakeholders, profits, and various types of businesses. Outlines what you need to think about if you were to start your own business, such as determining what your product or service will be, making and delivering your product or service, and funding your business.
Ellen's Helping Out with Homework!
 
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Is there anything she can't do? Ellen offered to help her viewers with their homework. This is how it turned out!
Views: 21751814 TheEllenShow
Dominic Barton: Five Trends Reshaping the Global Economy
 
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Dominic Barton, Global Managing Director, McKinsey & Company, highlights the five trends he sees reshaping the global economy: 1. The Great Rebalancing 2. The Productivity Imperative 3. The Global Grid 4. Pricing the Planet 5. The Market State He concludes his presentation with a discussion about the implications of being a leader in the face of these ongoing changes. Barton appeared as part of the Global Speaker Series at Stanford Graduate School of Business. Learn More About the Global Speaker Series http://www.gsb.stanford.edu/stanford-gsb-experience/academic-advantages/distinguished-speakers/global-speaker-series McKinsey & Company http://www.mckinsey.com/
GraphConnect 2018 - Live from NYC: Hilary Mason and Emil Eifrem
 
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Live from Times Square in New York City, Neo4j's CEO Emil Eifrem will give the State of the Graph keynote -- talking about the latest advances in Neo4j and the graph ecosystem. Next up, Hilary Mason of Cloudera and Fast Forward Labs fame will talk about the Present and Future of Artificial Intelligence and Machine Learning. #GraphConnect #GraphDatabases #Neo4j
Views: 3511 Neo4j
HOW TO ANALYZE PEOPLE ON SIGHT - FULL AudioBook - Human Analysis, Psychology, Body Language
 
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How To Analyze People On Sight | GreatestAudioBooks 🌟SPECIAL OFFERS: ► Free 30 day Audible Trial & Get 2 Free Audiobooks: https://amzn.to/2Iu08SE ...OR: 🌟 try Audiobooks.com 🎧for FREE! : http://affiliates.audiobooks.com/tracking/scripts/click.php?a_aid=5b8c26085f4b8 ► Shop for books & gifts: https://www.amazon.com/shop/GreatestAudioBooks How To Analyze People On Sight | GreatestAudioBooks by Elsie Lincoln Benedict & Ralph Pain Benedict - Human Analysis, Psychology, Body Language - In this popular American book from the 1920s, "self-help" author Elsie Lincoln Benedict makes pseudo-scientific claims of Human Analysis, proposing that all humans fit into specific five sub-types. Supposedly based on evolutionary theory, it is claimed that distinctive traits can be foretold through analysis of outward appearance. While not considered to be a serious work by the scientific community, "How To Analyze People On Sight" makes for an entertaining read. . ► Follow Us On TWITTER: https://www.twitter.com/GAudioBooks ► Friend Us On FACEBOOK: http://www.Facebook.com/GreatestAudioBooks ► For FREE SPECIAL AUDIOBOOK OFFERS & MORE: http://www.GreatestAudioBooks.com ► SUBSCRIBE to Greatest Audio Books: http://www.youtube.com/GreatestAudioBooks ► BUY T-SHIRTS & MORE: http://bit.ly/1akteBP ► Visit our WEBSITE: http://www.GreatestAudioBooks.com READ along by clicking (CC) for Caption Transcript LISTEN to the entire book for free! Chapter and Chapter & START TIMES: 01 - Front matter -- - 00:00 02 - Human Analysis - 04:24 03 - Chapter 1, part 1 The Alimentive Type - 46:00 04 - Chapter 1, part 2 The Alimentive Type - 1:08:20 05 - Chapter 2, part 1 The Thoracic Type - 1:38:44 06 - Chapter 2, part 2 The Thoracic Type - 2:10:52 07 - Chapter 3, part 1 The Muscular type - 2:39:24 08 - Chapter 3, part 2 The Muscular type - 3:00:01 09 - Chapter 4, part 1 The Osseous Type - 3:22:01 10 - Chapter 4, part 2 The Osseous Type - 3:43:50 11 - Chapter 5, part 1 The Cerebral Type - 4:06:11 12 - Chapter 5, part 2 The Cerebral Type - 4:27:09 13 - Chapter 6, part 1 Types That Should and Should Not Marry Each Other - 4:53:15 14 - Chapter 6, part 2 Types That Should and Should Not Marry Each Other - 5:17:29 15 - Chapter 7, part 1 Vocations For Each Type - 5:48:43 16 - Chapter 7, part 2 Vocations For Each Type - 6:15:29 #audiobook #audiobooks #freeaudiobooks #greatestaudiobooks #book #books #free #top #best #psychology # This video: Copyright 2012. Greatest Audio Books. All Rights Reserved. Audio content is a Librivox recording. All Librivox recordings are in the public domain. For more information or to volunteer visit librivox.org. Disclaimer: As an Amazon Associate we earn from qualifying purchases. Your purchases through Amazon affiliate links generate revenue for this channel. Thank you for your support.
Views: 2036604 Greatest AudioBooks
The RuneScape Documentary - 15 Years of Adventure
 
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#TwitchPrime – 1 Month free #RuneScape membership & exclusive loot: https://rs.game/TwitchPrimeYT Play RuneScape free now: http://bit.ly/PlayRuneScapeNOW The RuneScape Documentary is here! Find out how one of the most successful MMORPGs of all time came to being – from humble beginnings in the Gower Brothers’ family home to reaching our 250 millionth account in 2016, and all the thrills and spills in between. We give you ’15 Years of Adventure’ – a History of RuneScape. Featuring interviews with some of the most famous RuneScape players of all time (including the legendary Zezima), as well as Jagex staff past and present, it’s essential viewing for gaming fans the world over. We hope you enjoy! Remember to subscribe and share this RuneScape movie using #15YearsOfAdventure! For more RuneScape history videos, you can follow this great channel: https://www.youtube.com/channel/UC40WkzHQLHf6uLMw1If6T-A/videos Join our other communities! Join us on: Twitch - http://www.twitch.tv/runescape Reddit - http://www.reddit.com/r/runescape Twitter - http://twitter.com/runescape Facebook - http://www.facebook.com/runescape www.runescape.com #RuneScape
Views: 1626753 RuneScape
Google I/O 2012 - Knowledge-Based Application Design Patterns
 
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Shawn Simister In this talk we'll look at emerging design patterns for building web applications that take advantage of large-scale, structured data. We'll look at open datasets like Wikipedia and Freebase as well as structured markup like Schema.org and RDFa to see what new types of applications these technologies open up for developers. For all I/O 2012 sessions, go to https://developers.google.com/io/
Views: 7907 Google Developers
Microsoft Build 2018 // Vision Keynote
 
03:37:35
CEO Satya Nadella takes the stage at Microsoft Build, our annual developer conference, at 8:30AM PT Monday, May 7. Join us to learn what's next for developers: https://news.microsoft.com/build2018/ Subscribe to Microsoft on YouTube here: https://aka.ms/SubscribeToYouTube Follow us on social: LinkedIn: https://www.linkedin.com/company/microsoft/ Twitter: https://twitter.com/Microsoft Facebook: https://www.facebook.com/Microsoft/ Instagram: https://www.instagram.com/microsoft/ For more about Microsoft, our technology, and our mission, visit https://aka.ms/microsoftstories
Views: 177935 Microsoft
Raising the Digital Trajectory of Healthcare
 
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Table of Contents Q&A 1:14:29 Should healthcare be more digitized? Absolutely. But if we go about it the wrong way... or the naïve way... we will take two steps forward and three steps back. Join Health Catalyst's President of Technology, Dale Sanders, for a 90-minute webinar in which he will describe the right way to go about the technical digitization of healthcare so that it increases the sense of humanity during the journey. The topics Dale covers include: • The human, empathetic components of healthcare’s digitization strategy • The AI-enabled healthcare encounter in the near future • Why the current digital approach to patient engagement will never be effective • The dramatic near-term potential of bio-integrated sensors • Role of the “Digitician” and patient data profiles • The technology and architecture of a modern digital platform • The role of AI vs. the role of traditional data analysis in healthcare • Reasons that home grown digital platforms will not scale, economically Most of the data that’s generated in healthcare is about administrative overhead of healthcare, not about the current state of patients’ well-being. On average, healthcare collects data about patients three times per year from which providers are expected to optimize diagnoses, treatments, predict health risks and cultivate long-term care plans. Where’s the data about patients’ health from the other 362 days per year? McKinsey ranks industries based on their Digital Quotient (DQ), which is derived from a cross product of three areas: Data Assets x Data Skills x Data Utilization. Healthcare ranks lower than all industries except mining. It’s time for healthcare to raise its Digital Quotient, however, it’s a delicate balance. The current “data-driven” strategy in healthcare is a train wreck, sucking the life out of clinicians’ sense of mastery, autonomy, and purpose. Healthcare’s digital strategy has largely ignored the digitization of patients’ state of health, but that’s changing, and the change will be revolutionary. Driven by bio-integrated sensors and affordable genomics, in the next five years, many patients will possess more data and AI-driven insights about their diagnosis and treatment options than healthcare systems, turning the existing dialogue with care providers on its head. It’s going to happen. Let’s make it happen the right way.
Views: 174 Health Catalyst
Machine learning
 
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Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data. For example, a machine learning system could be trained on email messages to learn to distinguish between spam and non-spam messages. After learning, it can then be used to classify new email messages into spam and non-spam folders. This video targeted to blind users. Attribution: Article text available under CC-BY-SA Creative Commons image source in video
Views: 65 encyclopediacc
What is T-CLOSENESS? What does T-CLOSENESS mean? T-CLOSENESS meaning, definition & explanation
 
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What is T-CLOSENESS? What does T-CLOSENESS mean? T-CLOSENESS meaning - T-CLOSENESS definition - T-CLOSENESS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ t-closeness is a further refinement of l-diversity group based anonymization that is used to preserve privacy in data sets by reducing the granularity of a data representation. This reduction is a trade off that results in some loss of effectiveness of data management or mining algorithms in order to gain some privacy. The t-closeness model extends the l-diversity model by treating the values of an attribute distinctly by taking into account the distribution of data values for that attribute. This is useful because in real data sets attribute values may be skewed or semantically similar. However, accounting for value distributions may cause difficulty in creating feasible l-diverse representations. The l-diversity technique is useful in that it may hinder an attacker leveraging the global distribution of an attribute's data values in order to infer information about sensitive data values. Not every value may exhibit equal sensitivity, for example, a rare positive indicator for a disease may provide more information than a common negative indicator. Because of examples like this, l-diversity may be difficult and unnecessary to achieve when protecting against attribute disclosure. Alternatively, sensitive information leaks may occur because while l-diversity requirement ensures “diversity” of sensitive values in each group, it does not recognize that values may be semantically close, for example, an attacker could deduce a stomach disease applies to an individual if a sample containing the individual only listed three different stomach diseases. Given the existence of such attacks where sensitive attributes may be inferred based upon the distribution of values for l-diverse data, the t-closeness method was created to further l-diversity by additionally maintaining the distribution of sensitive fields. The original paper by Ninghui Li, Tiancheng Li, and Suresh Venkatasubramanian defines t-closeness as: The t-closeness Principle: An equivalence class is said to have t-closeness if the distance between the distribution of a sensitive attribute in this class and the distribution of the attribute in the whole table is no more than a threshold t. A table is said to have t-closeness if all equivalence classes have t-closeness. Charu Aggarwal and Philip S. Yu further state in their book on privacy-preserving data miningthat with this definition, threshold t gives an upper bound on the difference between the distribution of the sensitive attribute values within an anonymized group as compared to the global distribution of values. They also state that for numeric attributes, using t-closeness anonymization is more effective than many other privacy-preserving data mining methods.
Views: 177 The Audiopedia
The Open University’s Course A305 and the Future of Architecture Education
 
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Taking The Open University‘s Course A305 as a starting point, this panel discussion will examine and interrogate experimental, open, and technological possibilities for the future of architecture education. Participants include Tim Benton, Lisa Haber-Thomson, K. Michael Hays, John May, and Mirko Zardini.
Views: 498 Harvard GSD
The Cryptographers’ Panel 2018
 
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Moderator: Zulfikar Ramzan, Chief Technology Officer, RSA Ron Rivest, Institute Professor, MIT Adi Shamir, Professor, Computer Science Department, Weizmann Institute of Science, Israel Whitfield Diffie, Cryptographer and Security Expert, Cryptomathic Paul Kocher, Independent Researcher Moxie Marlinspike, Founder, Signal Despite how sophisticated information security has become, it is still a relatively young discipline. The founders of our field continue to be actively engaged in research and innovation. Join us to hear these luminaries engage in an enlightening discussion on the past, present and future of our industry. https://www.rsaconference.com/events/us18/agenda/sessions/11490-The-Cryptographers%E2%80%99-Panel
Views: 4507 RSA Conference
Ways with Words | Big Data || Radcliffe Institute
 
01:26:37
PANEL 2: BIG DATA The Internet, social media, and data mining have changed language and our ability to analyze usage, and increased sensitivities to the power of the words we use. This panel will explore how these new forms of discourse and analysis expand our understanding of the interplay of gender, personal narrative, and language, as well as data scraping that enables a statistical study of language usage by demographics. Ben Hookway (7:43), Chief Executive Officer, Relative Insight Lyle Ungar (20:53), Professor and Graduate Group Chair, Computer and Information Science, University of Pennsylvania Alice E. Marwick (36:19), Assistant Professor, Department of Communication and Media Studies, and Director, McGannon Center for Communication Research, Fordham University Moderator: Rebecca Lemov, Associate Professor of the History of Science, Harvard University Q&A (52:02)
Views: 919 Harvard University
Aaron Swartz Memorial at the Internet Archive - Part 1
 
01:34:41
A gathering to remember Aaron Swartz on the evening of Thursday, January 24th, 2013. Aaron Swartz November 8, 1986 -- January 11, 2013 Speakers: Danny O'Brien (1:55) Taren Stinebrickner-Kauffman (11:06) Lisa Rein (29:44) Seth Schoen (34:34) Peter Eckersley (42:10) Tim O'Reilly (52:23) Molly Shaffer Van Houweling (56:48) Alex Stamos (1:03:13) Cindy Cohn (1:09:05) Brewster Kahle (1:12:55) Carl Malamud (1:22:35) Part 2, open microphone: http://youtu.be/BI4Udqk56dI
Views: 80432 O'Reilly
Thorium.
 
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http://ThoriumRemix.com/ Thorium is an abundant material which can be transformed into massive quantities of energy. To do so efficiently requires a very different nuclear reactor than the kind we use today- Not one that uses solid fuel rods, but a reactor in which the fuel is kept in a liquid state. Not one that uses pressurized water as a coolant, but a reactor that uses chemically stable molten salts. Such a reactor is called a "Molten Salt Reactor". Many different configurations are possible. Some of these configurations can harness Thorium very efficiently. This video explores the attributes of Molten Salt Reactors. Why are they compelling? And why do many people (including myself) see them as the only economical way of fully harnessing ALL our nuclear fuels... including Thorium. This video has been under development since 2012. I hope it conveys to you why I personally find Molten Salt Reactors so compelling, as do the many volunteers and supporters who helped create it. Much of the footage was shot by volunteers. All music was created by: http://kilowattsmusic.com To support this project, please visit: https://patreon.com/thorium Entities pursuing Molten Salt Reactors are... Flibe Energy - http://flibe-energy.com/ Terrestrial Energy - http://terrestrialenergy.com/ Moltex Energy - http://www.moltexenergy.com/ ThorCon Power - http://thorconpower.com/ Transatomic - http://www.transatomicpower.com/ Seaborg - http://seaborg.co/ Copenhagen Atomics - http://www.copenhagenatomics.com/ TerraPower - http://terrapower.com/ Bhabha Atomic Research Centre - http://www.barc.gov.in/ Chinese Academy of Sciences - http://english.cas.cn/ Regular Thorium conferences are organized by: http://thoriumenergyalliance.com/ http://thoriumenergyworld.com/ Table of Contents 0:00:00 Space 0:17:29 Constraints 0:28:22 Coolants 0:40:15 MSRE 0:48:54 Earth 0:59:46 Thorium 1:22:03 LFTR 1:36:13 Revolution 1:44:58 Forward 1:58:11 ROEI 2:05:41 Beginning 2:08:36 History 2:38:59 Dowtherm 2:47:57 Salt 2:51:44 Pebbles 3:06:07 India 3:18:44 Caldicott 3:35:55 Fission 3:56:22 Spectrum 4:04:25 Chemistry 4:12:51 Turbine 4:22:27 Waste 4:40:15 Decommission 4:54:39 Candlelight 5:13:06 Facts 5:26:08 Future 5:55:39 Pitches 5:56:17 Terrestrial 6:08:33 ThorCon 6:11:45 Flibe 6:20:51 End 6:25:53 Credits Some of this footage is remixed from non-MSR related sources, to help explain the importance of energy for both space exploration and everyday life here on Earth. Most prominently... Pandora's Promise - https://youtu.be/bDw3ET3zqxk Dr. Neil DeGrasse Tyson - https://youtu.be/Pun76NZMjCk Dr. Robert Zubrin - https://youtu.be/EKQSijn9FBs Mars Underground - https://youtu.be/tcTZvNLL0-w Andy Weir & Adam Savage - https://youtu.be/5SemyzKgaUU Periodic Table Videos - https://youtube.com/channel/UCtESv1e7ntJaLJYKIO1FoYw
Views: 128275 gordonmcdowell
Toni Lane, CULTU.RE | Coin Agenda 2018
 
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Toni Lane, of CULTU.RE and cofounder CoinTelegraph, sits down with John Furrier at Coin Agenda 2018
Facebook CEO Mark Zuckerberg testifies on data scandal for a 2nd day before Congress
 
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Facebook CEO Mark Zuckerberg faces a second a day of testimony in front of the House energy and commerce committee amid concerns over privacy on the social media site. It was revealed Facebook shared the information of 87 million users with data giant Cambridge Analytica. To read more: http://cbc.ca/ »»» Subscribe to CBC News to watch more videos: http://bit.ly/1RreYWS Connect with CBC News Online: For breaking news, video, audio and in-depth coverage: http://bit.ly/1Z0m6iX Find CBC News on Facebook: http://bit.ly/1WjG36m Follow CBC News on Twitter: http://bit.ly/1sA5P9H For breaking news on Twitter: http://bit.ly/1WjDyks Follow CBC News on Instagram: http://bit.ly/1Z0iE7O Download the CBC News app for iOS: http://apple.co/25mpsUz Download the CBC News app for Android: http://bit.ly/1XxuozZ »»»»»»»»»»»»»»»»»» For more than 75 years, CBC News has been the source Canadians turn to, to keep them informed about their communities, their country and their world. Through regional and national programming on multiple platforms, including CBC Television, CBC News Network, CBC Radio, CBCNews.ca, mobile and on-demand, CBC News and its internationally recognized team of award-winning journalists deliver the breaking stories, the issues, the analyses and the personalities that matter to Canadians.
Views: 17897 CBC News
What Is The Difference Between E Commerce And E Business?
 
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E business and e commercee commerce youtube. 27 aug 2015 the article explains you important differences between e commerce and e business in tabular form and points. But there is a difference between e commerce to know different types of business in current concepts, opportunities and dimensions 13 jul 2012 by ismael kato [email protected] After all, they both involve business processes conducted electronically quite likely on the internet. E commerce vs e business slideshare. Googleusercontent search. Notes e business and commerce the difference ucf webcourses. This section will elaborate on the differences between two and some reason behind this lies in meanings of 'business' 'commerce' english language. Difference between e commerce and business (with example keydifferences difference. The difference between e business and commerce what is ecommerce & ebusiness. It is used in the context of b2b transactions. Difference between ebusiness and ecommerce conceptsimplified. People can now do business such as buy things, transact, e is the process whereby internet savvy entrepreneurs sell 'e commerce products and services' to businesses who fear that they will be left behind in are terms often used interchangeably. E commerce and e business. An process that an organization conducts over a computer mediated 3 jan 2015. What are the major difference between e commerce and. Some people use the terms 'ebusiness' and 'ecommerce' interchangeably. The following are some of the differences or relationship between an e commerce and businessthe terms business while many people use interchangeably, they aren't same, matter to businesses in today's economy 2 apr 2015 ecommerce is narrower concept restricted buying selling. Others view ecommerce to be a subset of ebusiness e commerce vs business. E business & e commerce contrasted slideshare. It is a broader concept that involves market surveying, supply chain and logistic management using datamining. What is the difference between e business & commerce? . Ebusiness can involve the use of internet, intranet or extranet what is e business? So that we do not end up splitting hair, it best to understand ebusiness with help examples email marketing existing customers and 30 oct 2000 commerce may sound as if they're same, but terms are different, difference matters for today's companies buying selling using an electronic medium whereas involves fundamental re structuring streamlining 29 mar 2010 vs internet has made interactions multi faceted. E commerce includes 16 oct 2016 e. Chron difference between ebusiness and ecommerce conceptsimplified what is the e business commerce? Quora. E business & e commerce slideshare. Between web front and back end e business transaction medium most store managing a edi closer look comparison with eft; 22 4 feb 2010 & commerce finishing chapter 3 what is business, li ul ul li what the difference between disruptive aug 2016 small description of inside stories differences. Definition e
Views: 32 Marisol Moran Tipz
What is CRYPTO CLOUD COMPUTING? What does CRYPTO CLOUD COMPUTING mean?
 
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What is CRYPTO CLOUD COMPUTING? What does CRYPTO CLOUD COMPUTING mean? CRYPTO CLOUD COMPUTING meaning - CRYPTO CLOUD COMPUTING definition - CRYPTO CLOUD COMPUTING explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Cloud computing is a combination of IaaS, PaaS, SaaS. To construct a secure cloud computing system, security at infrastructure, service platforms and application software levels have to be studied for a secure cloud computing system. Information encryption is one of effective means to achieve cloud computing information security. Traditionally, information encryption focuses on specified stages and operations, such as data encryption. For cloud computing, a system level design has to be implemented. Crypto cloud computing is a new secure cloud computing architecture. It can provide protection of information security at the system level, and allows users access to shared services conveniently and accurately. Crypto cloud computing protects individual’s connections with the outside world. It can protect the personal privacy without any delay of information exchange. Crypto cloud computing is based on the Quantum Direct Key system. Quantum Direct Key (QDK) is a set of advanced asymmetric offline key mechanism. In this mechanism, all entities get public and private key pair according to their ID. Each entity only holds its own private key, but has a public key generator to generate any public key. In this system, an entity can produce the public key of any other entities offline, no any third-party agency (such as CA) is necessary. Crypto cloud computing based on QDK can avoid network traffic congestion, and other drawbacks using current encryption system. In the crypto cloud computing system, each entity encrypts data using his/her own private key. All elements in the system such as cloud computing infrastructure units, platform, virtualization tools and all involved entities have their own keys. While fulfilling their own functions of information exchange and processing, all these elements will use the public key and private key to perform authentication first. What’s more, events occur in the cloud computing are also assigned a unique key. In this way, crypto cloud system guarantees the security and credibility of information exchange. Current cloud computing structure is developed for data and computing sharing. Security is not priority of system. On the contrary, encryption and security are inherently integrated in the crypto cloud computing based on the QDK. QDK authorized function units are bricks of crypto cloud computing. Besides primary function of data en/decryption, crypto cloud computing also provides many security related functions. For example, all channels sign transmit data using with their own keys, and the receiving terminals can avoid hijacking by verifying signature. What’s more, the exact position of security leakage can be identified determined by analyzing digital signatures of forged data. Based on such capabilities, crypto-related functions can be provided as services in cloud, which is named as ‘Crypto as a service (CAAS)’. Crypto cloud computing is not only the advances in information technology, but also innovation of logical relationship. In crypto cloud computing system, non-system data is not allowed to store and transmit. Private Key and offline public key, play a role of identification and certification in the process of information exchange. In this way, the cloud establishes a relationship of trust with a customer. Data identification depends on the logical relationship of mutual trust or need, and the logical relationship depends on the cloud customer. Crypto cloud computing is a new framework for cyber resource sharing. It protects data security and privacy. Well, in cloud environment, crypto cloud computing guarantees the information security and integrity during whole procedure. Security management of cloud computing can also be performed by authorizing the signatures of every element involved. What’s more, a user can retrieve all related resources using his QDK key. There is no personal privacy under the current cloud framework, as pointed out by Mark Zuckerberg, 'the Age of Privacy Is Over '.However, with the development of crypto cloud computing, we can resolve the conflict between services data sharing and privacy security. It opens up new prospects for the development of information sharing technology.
Views: 79 The Audiopedia
Dr. Penelope Boston: "Seeking the Tricorder: The Hunt for Extraterrestrial Life" | Talks at Google
 
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The hunt for life in extraterrestrial environments and in extreme environments on Earth presents challenging problems in exploration strategy, engineering, and science. Our success depends on the magic that happens when new tools and techniques are developed, which allows us to look at the world in new ways and from new perspectives. In this talk, Dr. Boston discusses issues and new directions in robotics, machine learning, and life detection technologies that are changing the way NASA explores our universe for other lifeforms. Dr. Boston is Director of the NASA Astrobiology Institute, which supports the study of the origins, evolution, distribution, and future of life in the universe. From 2002-2016, Dr. Boston was the Associate Director of the National Cave and Karst Research Institute and Professor and Chair of the Earth and Environmental Sciences Dept. at the New Mexico Institute of Mining and Technology. Dr. Boston received her Ph.D. from the University of Colorado, Boulder and was awarded the "Caving Legend Award" from the Ft. Stanton Cave Study Project/Bureau of Land Management.
Views: 1906 Talks at Google
K Camp - Comfortable
 
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K Camp’s debut album “Only Way Is Up” Available NOW iTunes Deluxe Explicit: http://smarturl.it/KCampOWIUdlxEX Google Play Standard Explicit: http://smarturl.it/KCampOWIUstdEXgp Google Play Standard Clean : http://smarturl.it/KCampOWIUstdEDgp Google Play Explicit Deluxe: http://smarturl.it/KCampOWIUdlxEXgp Google Play Clean Deluxe: http://smarturl.it/KCampOWIUdlxEDgp http://kcamp427.com http://twitter.com/twitter.com/kcamp427 http://facebook.com/kcamp427 http://instagram.com/kcamp427 http://vevo.ly/h1MhCH
Views: 61261492 KCampVEVO
GMO's Revealed: Episode 1
 
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EPISODE 1 Dr. Zach Bush, Vani Hari, Gunnar Lovelace TOPICS: Glyphosate impacts and effects Changes in our food supply and the consequential domino effect on our health Innovations in food supply to avoid GMOs Register to watch GMOs Revealed Aug 22 - 30. http://gmosrevealed.com Each episode is up 24 hours for you to watch FREE, starting August 22nd We will be showing 9 episodes in 9 days.
Views: 298636 GMOs Revealed
L' économie de la connaissance par Idriss ABERKANE
 
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[Mars 2015] Conférence sur l'économie de la connaissance, le biomimétisme et la Blue Economy Pour contribuer à la traduction des sous-titres : http://www.youtube.com/timedtext_video?ref=share&v=dM_JivN3HvI ► Idriss ABERKANE est professeur en géopolitique et économie de la connaissance à l'École centrale, chercheur affilié au Kozmetsky Global Collaboratory de l’université de Stanford et chercheur affilié au CNRS. Il est également éditorialiste pour Le Point. Son livre "Libérez-votre cerveau ! " (sortie le 6 octobre 2016) : - Amazon : https://www.amazon.fr/Lib%C3%A9rez-votre-cerveau-Idriss-Aberkane/dp/222118758X - FNAC : http://livre.fnac.com/a9483923/Idriss-Aberkane-Liberez-votre-cerveau
Views: 864070 Le CERA
Analyzing the Privacy of Android Apps
 
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Google Tech Talk June 17, 2015 (click "show more" for more info) Presented by Jason Hong, Carnegie Mellon University ABSTRACT: Many smartphone apps collect sensitive data about people, in a manner that many users find very surprising. How can we help everyday people in understanding the behaviors of their apps? In this talk, Jason Hong presents three things. The first is results of interviews and surveys of app developers, probing their attitudes and behaviors towards privacy. The second is PrivacyGrade.org, a site that combines crowdsourcing and static analysis to analyze the behavior of 1M Android apps. The third is Gort, a tool that combines heuristics, crowdsourcing, and dynamic analysis to help analysts understand the behavior of a given app. Since the original presentation, Android M launched a new permission model that Hong described as "offer[ing] a lot more privacy protection for people, primarily by making it easier to see what data is being requested as it is being used." ABOUT THE SPEAKER: Jason Hong is an associate professor in the Human Computer Interaction Institute at Carnegie Mellon University. He works in the areas of ubiquitous computing and usable privacy and security, and his research has been featured in the New York Times, MIT Tech Review, CBS Morning Show, CNN, Slate, and more. Jason is also a co-founder of Wombat Security Technologies, and has participated on DARPA's Computer Science Study Panel (CS2P), is an Alfred P. Sloan Research Fellow, a Kavli Fellow, a PopTech Science fellow, and currently holds the HCII Career Development fellowship.
Views: 3342 GoogleTechTalks
Power of Information Conference: Data for Enterprise
 
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Moderator: Annie Donovan, CEO, CoopMetics Panel: Elizabeth Dreicer, CEO, Kuity; Tim Ferguson, Founder and Managing Partner of Next Street; Richard Ling, Partner, Rembrandt Venture Partners The July 2013 data conference hosted by the F.B. Heron Foundation was intended to help find ways to use enterprise-level data to measure both social and financial good as part of the foundation's investment strategy.
Views: 261 FB Heron Foundation