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Database Management

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  • Toward Internet of Everything: Architectures, Standards, & Interoperability
    Toward Internet of Everything: Architectures, Standards, & Interoperability Ram D. Sriram, Chief of the Software and Systems Division, IT Lab at National Institute of Standards and Technology Recorded: Jun 21 2017 63 mins
    In this talk, Ram will provide a unified framework for Internet of Things, Cyber-Physical Systems, and Smart Networked Systems and Societies, and then discuss the role of ontologies for interoperability.

    The Internet, which has spanned several networks in a wide variety of domains, is having a significant impact on every aspect of our lives. These networks are currently being extended to have significant sensing capabilities, with the evolution of the Internet of Things (IoT). With additional control, we are entering the era of Cyber-physical Systems (CPS). In the near future, the networks will go beyond physically linked computers to include multimodal-information from biological, cognitive, semantic, and social networks.

    This paradigm shift will involve symbiotic networks of people (social networks), smart devices, and smartphones or mobile personal computing and communication devices that will form smart net-centric systems and societies (SNSS) or Internet of Everything. These devices – and the network -- will be constantly sensing, monitoring, interpreting, and controlling the environment.

    A key technical challenge for realizing SNSS/IoE is that the network consists of things (both devices & humans) which are heterogeneous, yet need to be interoperable. In other words, devices and people need to interoperate in a seamless manner. This requires the development of standard terminologies (or ontologies) which capture the meaning and relations of objects and events. Creating and testing such terminologies will aid in effective recognition and reaction in a network-centric situation awareness environment.

    Before joining the Software and Systems Division (his current position), Ram was the leader of the Design and Process group in the Manufacturing Systems Integration Division, Manufacturing Engineering Lab, where he conducted research on standards for interoperability of computer-aided design systems.
  • The Ways Machine Learning and AI Can Fail
    The Ways Machine Learning and AI Can Fail Brian Lange, Partner and Data Scientist, Datascope Recorded: Apr 13 2017 48 mins
    Good applications of machine learning and AI can be difficult to pull off. Join Brian Lange, Partner and Data Scientist at data science firm Datascope, as he discusses a variety of ways machine learning and AI can fail (from technical to human factors) so that you can avoid repeating them yourself.
  • Logistics Analytics: Predicting Supply-Chain Disruptions
    Logistics Analytics: Predicting Supply-Chain Disruptions Dmitri Adler, Chief Data Scientist, Data Society Recorded: Feb 16 2017 47 mins
    If a volcano erupts in Iceland, why is Hong Kong your first supply chain casualty? And how do you figure out the most efficient route for bike share replacements?

    In this presentation, Chief Data Scientist Dmitri Adler will walk you through some of the most successful use cases of supply-chain management, the best practices for evaluating your supply chain, and how you can implement these strategies in your business.
  • Unlock real-time predictive insights from the Internet of Things
    Unlock real-time predictive insights from the Internet of Things Sam Chandrashekar, Program Manager, Microsoft Recorded: Feb 16 2017 60 mins
    Continuous streams of data are generated in every industry from sensors, IoT devices, business transactions, social media, network devices, clickstream logs etc. Within these streams of data lie insights that are waiting to be unlocked.

    This session with several live demonstrations will detail the build out of an end-to-end solution for the Internet of Things to transform data into insight, prediction, and action using cloud services. These cloud services enable you to quickly and easily build solutions to unlock insights, predict future trends, and take actions in near real-time.

    Samartha (Sam) Chandrashekar is a Program Manager at Microsoft. He works on cloud services to enable machine learning and advanced analytics on streaming data.
  • Bridging the Data Silos
    Bridging the Data Silos Merav Yuravlivker, Chief Executive Officer, Data Society Recorded: Feb 15 2017 48 mins
    If a database is filled automatically, but it's not analyzed, can it make an impact? And how do you combine disparate data sources to give you a real-time look at your environment?

    Chief Executive Officer Merav Yuravlivker discusses how companies are missing out on some of their biggest profits (and how some companies are making billions) by aggregating disparate data sources. You'll learn about data sources available to you, how you can start automating this data collection, and the many insights that are at your fingertips.
  • Strategies for Successful Data Preparation
    Strategies for Successful Data Preparation Raymond Rashid, Senior Consultant Business Intelligence, Unilytics Corporation Recorded: Feb 14 2017 33 mins
    Data scientists know, the visualization of data doesn't materialize out of thin air, unfortunately. One of the most vital preparation tactics and dangerous moments happens in the ETL process.

    Join Ray to learn the best strategies that lead to successful ETL and data visualization. He'll cover the following and what it means for visualization:

    1. Data at Different Levels of Detail
    2. Dirty Data
    3. Restartability
    4. Processing Considerations
    5. Incremental Loading

    Ray Rashid is a Senior Business Intelligence Consultant at Unilytics, specializing in ETL, data warehousing, data optimization, and data visualization. He has expertise in the financial, manufacturing and pharmaceutical industries.
  • HPE ALM Standardization as a Precursor for Data Warehousing
    HPE ALM Standardization as a Precursor for Data Warehousing Tuomas Leppilampi , Assure Recorded: Feb 9 2017 59 mins
    Agenda:
    Data warehousing at a glance
    Wild West vs Enterprise HPE ALM Template
    Planning and configuring the template
    Customer use case: Standardization project walkthrough
    How to maintain a standardized environment
    Next steps with HPE ALM
  • Building Enterprise Scale Solutions for Healthcare with Modern Data Architecture
    Building Enterprise Scale Solutions for Healthcare with Modern Data Architecture Ramu Kalvakuntla, Sr. Principal, Big Data Practice, Clarity Solution Group Recorded: Nov 10 2016 47 mins
    We all are aware of the challenges enterprises are having with growing data and silo’d data stores. Businesses are not able to make reliable decisions with un-trusted data and on top of that, they don’t have access to all data within and outside their enterprise to stay ahead of the competition and make key decisions for their business.

    This session will take a deep dive into current Healthcare challenges businesses are having today, as well as, how to build a Modern Data Architecture using emerging technologies such as Hadoop, Spark, NoSQL datastores, MPP Data stores and scalable and cost effective cloud solutions such as AWS, Azure and BigStep.
  • Data at the corner of SAP and AWS
    Data at the corner of SAP and AWS Frank Stienhans, CTO, Ocean9 Recorded: Nov 9 2016 48 mins
    Past infrastructures provided compute, storage and network enabling static enterprise deployments which changed every few years. This talk will analyze the consequences of a world where production SAP and Spark clusters including data can be provisioned in minutes with the push of a button.

    What does it mean for the IT architecture of an enterprise? How to stay in control in a super agile world?
  • 3 Critical Data Preparation Mistakes and How-to Avoid them
    3 Critical Data Preparation Mistakes and How-to Avoid them Mark Vivien, Business Development, Big Data Recorded: Oct 20 2016 32 mins
    Whether you're just starting out or a seasoned solution architect, developer, or data scientist, there are most likely key mistakes that you've probably made in the past, may be making now, or will most likely make in the future. In fact, these same mistakes are most likely impacting your company's overall success with their analytics program.

    Join us for our upcoming webinar, 3 Critical Data Preparation Mistakes and How to avoid them, as we discuss 3 of the most critical, fundamental pitfalls and more!

    • Importance of early and effective business partner engagement
    • Importance of business context to governance
    • Importance of change and learning to your development methodology

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