Artificial Intelligence - Machine Learning Applications

June 2017

The New York Times is leveraging Jigsaw’s machine learning prowess to open up comments on more articles.
https://www.fastcompany.com/40430603/alphabet-jigsaw-new-york-times-comments-robot-ai-machine-learning

Microsoft Power BI

What is Power BI?

Power BI is a suite of business analytics tools that deliver insights throughout your organization. Connect to hundreds of data sources, simplify data prep, and drive ad hoc analysis. Produce beautiful reports, then publish them for your organization to consume on the web and across mobile devices. Everyone can create personalized dashboards with a unique, 360-degree view of their business. And scale across the enterprise, with governance and security built-in.

https://powerbi.microsoft.com/en-us/


The rise of self service BI

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Microsoft Power BI

Dark Vision - Artificial Intelligence Applied on Videos

Related videos can be identified. Advertisements related to the topics in the video can be provided.

9 March 2016

https://www.ibm.com/blogs/bluemix/2016/03/openwhisk-and-watson-video-analysis-app/



https://github.com/IBM-Bluemix/openwhisk-darkvisionapp

Cloudera Altus - Data Engineering Solution

A cloud service for engineers

For data engineers
Focus on the workload. Cloudera Altus elevates data pipeline operations over cluster operations

Leave the management to Altus. Data engineering experience is delivered as a service. Altus takes care of cluster management and operations

Analyze and troubleshoot jobs. Don’t waste time sifting through logs to find the root cause of a failed job. Monitor your work as it’s running, or let us help you troubleshoot

Simple migration and backward compatibility. Easily move your on-prem workloads to and from the cloud with minimal risk. Leverage new platform versions without breaking compatibility with existing applications

Data engineering made easy
Cloudera Altus is a managed service that makes it easier than ever to execute data pipelines. Launch your cluster in minutes on AWS, and start exploring and extracting value from all your data. Over time Cloudera plans to expand Altus to support other leading public clouds such as Microsoft Azure, etc.


https://www.cloudera.com/products/altus.html


The initial rollout of Cloudera Altus includes support for Apache Spark, Apache Hive on MapReduce2, and Hive on Spark. It is available today in most Amazon Web Services (AWS) regions.

https://www.cloudera.com/more/news-and-events/press-releases/2017-05-24-cloudera-launches-altus-to-simplify-big-data-workloads-in-the-cloud.html

Google BIGQUERY - Enterprise Cloud Data Warehouse

BigQuery is Google's fully managed, petabyte scale, low cost enterprise data warehouse for analytics. BigQuery is serverless. There is no infrastructure to manage and you don't need a database administrator, so you can focus on analyzing data to find meaningful insights using familiar SQL. BigQuery is a powerful Big Data analytics platform used by all types of organizations, from startups to Fortune 500 companies.

Speed & Scale

BigQuery can scan TB in seconds and PB in minutes. Load your data from Google Cloud Storage or Google Cloud Datastore, or stream it into BigQuery to enable real-time analysis of your data. With BigQuery you can easily scale your database from GBs to PBs.

https://cloud.google.com/bigquery/


Building and scaling new business models to gain insights from disparate data faster, while reducing IT costs, requires an architecture that can go from prototype to petabyte scale as your needs evolve. Google BigQuery’s serverless architecture can help ensure that your enterprise data warehouse withstands growth at any scale. Informatica helps you unlock the power of hybrid data with high performance, highly scalable data management solutions that efficiently move and manage large volumes of data to Google BigQuery. Informatica and Google BigQuery is the best combination for modernizing your data architecture.

https://www.brighttalk.com/webcast/10477/258043/modernize-your-data-architecture-with-google-bigquery-and-informatica

Blockchain Technology - Introduction and Bibliography

What is Blockchain Technology?


“The blockchain is an incorruptible digital ledger of economic transactions that can be programmed to record not just financial transactions but virtually everything of value.”
Don & Alex Tapscott, authors Blockchain Revolution (2016)


Alex Tapscott: "Blockchain Revolution" | Talks at Google
11 July 2016
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Talks at Google


http://blockchain-revolution.com/

Dan Tapscott

https://www.linkedin.com/pulse/whats-next-generation-internet-surprise-its-all-don-tapscott





What is Blockchain Technology? A Step-by-Step Guide For Beginners
An in-depth guide by BlockGeeks
https://blockgeeks.com/guides/what-is-blockchain-technology/

Network Analysis and Social Network Analysis - Bibliography and Videos

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http://snap.stanford.edu/

http://snap.stanford.edu/class/cs224w-2015/projects.html

https://web.stanford.edu/class/cs224w/

https://web.stanford.edu/class/cs224w/intro_handout/intro_handout.pdf

http://historicalnetworkresearch.org/resources/first-steps/

Network Structure Inference, A Survey: Motivations, Methods, and
Applications
Ivan Brugere, University of Illinois at Chicago
Brian Gallagher, Lawrence Livermore National Laboratory
Tanya Y. Berger-Wolf, University of Illinois at Chicago
https://arxiv.org/pdf/1610.00782.pdf


Social Network Analysis: Methods and Applications

Stanley Wasserman, Katherine Faust
Cambridge University Press, 25-Nov-1994 - Social Science - 825 pages


Social network analysis, which focuses on relationships among social entities, is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. As the first book to provide a comprehensive coverage of the methodology and applications of the field, this study is both a reference book and a textbook.
https://books.google.co.in/books?hl=en&lr=&id=CAm2DpIqRUIC

The structure and function of complex networks
M. E. J. Newman
Department of Physics, University of Michigan, Ann Arbor, MI 48109, U.S.A. and
Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501, U.S.A.

http://www-personal.umich.edu/~mejn/courses/2004/cscs535/review.pdf

https://en.wikipedia.org/wiki/Network_theory

http://distance.ufl.edu/media/distanceufledu/documents/ANG5420_network_analysis.pdf

Analyzing Participation of Students in Online Courses Using Social

Network Analysis Techniques

Reihaneh Rabbany k., Mansoureh Takaffoli and Osmar R. Za¨ıane,

Department of Computing Science, University of Alberta, Canada

rabbanyk,takaffol,zaiane@ualberta.ca

http://webdocs.cs.ualberta.ca/~rabbanyk/research/Papers/edm11.pdf

Social Network Analysis and Mining to Support the

Assessment of On-line Student Participation

http://webdocs.cs.ualberta.ca/~rabbanyk/research/Papers/ExplorationsRabbany.pdf

http://webdocs.cs.ualberta.ca/~rabbanyk/

Big Data over Networks

Shuguang Cui, Alfred O. Hero, III, Zhi-Quan Luo

Cambridge University Press, 14-Jan-2016 - Computers - 457 pages

Utilising both key mathematical tools and state-of-the-art research results, this text explores the principles underpinning large-scale information processing over networks and examines the crucial interaction between big data and its associated communication, social and biological networks. Written by experts in the diverse fields of machine learning, optimisation, statistics, signal processing, networking, communications, sociology and biology, this book employs two complementary approaches: first analysing how the underlying network constrains the upper-layer of collaborative big data processing, and second, examining how big data processing may boost performance in various networks. Unifying the broad scope of the book is the rigorous mathematical treatment of the subjects, which is enriched by in-depth discussion of future directions and numerous open-ended problems that conclude each chapter. Readers will be able to master the fundamental principles for dealing with big data over large systems, making it essential reading for graduate students, scientific researchers and industry practitioners alike.

https://books.google.co.in/books?id=Rb8wCwAAQBAJ

Networks, Crowds, and Markets: Reasoning about a Highly Connected World

Full Book

David Easley

Dept. of Economics

Cornell University

Jon Kleinberg

Dept. of Computer Science

Cornell University

Cambridge University Press, 2010

Draft version: June 10, 2010.

https://www.cs.cornell.edu/home/kleinber/networks-book/networks-book.pdf

Fundamentals of Predictive Text Mining

Sholom M. Weiss, Nitin Indurkhya, Tong Zhang

Springer, 07-Sep-2015 - Computers - 239 pages

This successful textbook on predictive text mining offers a unified perspective on a rapidly evolving field, integrating topics spanning the varied disciplines of data science, machine learning, databases, and computational linguistics. Serving also as a practical guide, this unique book provides helpful advice illustrated by examples and case studies.

This highly anticipated second edition has been thoroughly revised and expanded with new material on deep learning, graph models, mining social media, errors and pitfalls in big data evaluation, Twitter sentiment analysis, and dependency parsing discussion. The fully updated content also features in-depth discussions on issues of document classification, information retrieval, clustering and organizing documents, information extraction, web-based data-sourcing, and prediction and evaluation.

Topics and features: presents a comprehensive, practical and easy-to-read introduction to text mining; includes chapter summaries, useful historical and bibliographic remarks, and classroom-tested exercises for each chapter; explores the application and utility of each method, as well as the optimum techniques for specific scenarios; provides several descriptive case studies that take readers from problem description to systems deployment in the real world; describes methods that rely on basic statistical techniques, thus allowing for relevance to all languages (not just English); contains links to free downloadable industrial-quality text-mining software and other supplementary instruction material.

Fundamentals of Predictive Text Mining is an essential resource for IT professionals and managers, as well as a key text for advanced undergraduate computer science students and beginning graduate students.

https://books.google.co.in/books?id=FUiGCgAAQBAJ

http://www.leonidzhukov.net/hse/2016/sna/


Advanced Database Marketing: Innovative Methodologies and Applications for Managing Customer Relationships

Koen W. De Bock
Routledge, Mar 23, 2016 - 348 pages
https://books.google.co.in/books?id=4hHPCwAAQBAJ