The Big Data dilemma

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Most of you will have interacted with several algorithms already today. Algorithms are of course simply sets of rules for solving problems, and existed long before computers. But algorithms are now everywhere in digital services. An algorithm decided the results of your internet searches today. If you used Google Maps to get here, an algorithm proposed your route. Algorithms decided the news you read on your news feed and the ads you saw.


Matching While Learning

arXiv.org Machine Learning

We consider the problem faced by a service platform that needs to match supply with demand, but also to learn attributes of new arrivals in order to match them better in the future. We introduce a benchmark model with heterogeneous workers and jobs that arrive over time. Job types are known to the platform, but worker types are unknown and must be learned by observing match outcomes. Workers depart after performing a certain number of jobs. The payoff from a match depends on the pair of types and the goal is to maximize the steady-state rate of accumulation of payoff. Our main contribution is a complete characterization of the structure of the optimal policy in the limit that each worker performs many jobs. The platform faces a trade-off for each worker between myopically maximizing payoffs (exploitation) and learning the type of the worker (\emph{exploration}). This creates a multitude of multi-armed bandit problems, one for each worker, coupled together by the constraint on the availability of jobs of different types (capacity constraints). We find that the platform should estimate a shadow price for each job type, and use the payoffs adjusted by these prices, first, to determine its learning goals and then, for each worker, (i) to balance learning with payoffs during the "exploration phase", and (ii) to myopically match after it has achieved its learning goals during the "exploitation phase."


Talend Recognized in CRN's Big Data 100 List for Third Consecutive Year - NASDAQ.com

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REDWOOD CITY, Calif., May 22, 2018 (GLOBE NEWSWIRE) -- Talend (NASDAQ: TLND), a leader in cloud data integration solutions, has been named to the 2018 CRN Big Data 100 list, a brand of The Channel Company. This annual list recognizes vendors that have demonstrated an ability to innovate in bringing to market products and services that help businesses work with one of the most dynamic, fastest growing segments of the IT industry - Big Data. As a result of Talend's inclusion in the CRN Big Data 100, Solutions Review selected Talend Open Studio for Data Integration and Talend Cloud among its list of "7 Data Integration Tools We Recommend". The data explosion in recent years has fueled a vibrant big data technology industry. Businesses need innovative products and services to capture, integrate, manage and analyze the massive volumes of data they are grappling with every day.


Getting Personal with Big Data in Insurance: Strategies for Mastering Massive Amounts of Data - Global IQX

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Telemetry, IoT, wearables, AI, chatbots and drones are tools that help group Insurers better engage with customers and improve business processes. There is one thing that all of these technologies have in common: data. Personal data to be precise. Exactly how insurers will mine, manage and utilize the massive amounts of data now available from various internal and external sources may mean the difference between data mastery and data mystery for many carriers. In this blog, I'll outline a few things carriers can start to think about as they incorporate big data into their corporate strategies.