Registration and Coffee

Introduction, Overview & Introduction to Sponsors - Business Drivers and Use Case for Real-Time Analytics
Mike Ferguson, CEO, Intelligent Business Strategies

This short introductory session introduces the conference and discusses why real-time analytics are in demand. It cites some popular use cases and sets the scene for other sessions discussed at the conference.

Real-Time Analytics in the Enterprise: Tools and Techniques to Extend Your Analytical Capabilities
Mike Ferguson, CEO, Intelligent Business Strategies

This session looks at where real-time analytics fits in your analytical architecture and what tools and techniques can be used to implement real-time analytics. In particular it looks at:

• Types of streaming data
• The need for in-memory data and scalable analytical applications
• First generation technologies - Complex Event Processing
• Building custom real-time analytical applications - Streaming analytics tools for developers – IBM InfoSphere Streams, Apache Storm, Spark Streaming and more
• Simplifying access to streaming data using SQL–based tools e.g. ParStream, SQLStream
• Decision Management
• Combining streaming analytics with other analytical workloads
• Integrating streaming analytics into your existing set-up

Coffee

Streaming Data Analysis: the Frontier of Big Data
Christoforos Anagnostopoulos, Mentat Innovations

Data streams constitute without doubt the frontier of Big Data analysis. The main challenge in streaming data analysis is twofold. On one hand, one needs to update their model on-the-fly, without revisiting the data history, so as to be able to offer real-time actionable insights. On the other, as the world is inherently dynamic and unpredictable, things often change, so that real-time methods that lack the benefit of hindsight must be able to swiftly adapt to changing circumstances and flexibly represent the current state of the world. This talk will give general insights on this problem, and will also introduce the ADAPT platform, Mentat Innovation's uniquely designed platform for real-time machine learning.

Case Study: The CERN Data Analytics Project: Improving CERN Accelerator Complex Operations with Data Analytics
Antonio Romero, CERN

The CERN Accelerator Complex is one of the most sophisticated systems in the world consisting in a succession of machines that accelerate particles to high energies and close to the speed of light. This unique complex is composed by millions of sensors, a large number of control devices, multiple critical subsystems and IT supporting infrastructure which generate a large amount of data that CERN has been gathering and storing over the years.

Exploring the Business Value of Real -Time Visualisation
Rob McNeill, Datawatch and Charles Radclyffe, BIPB

Datawatch provides a rich, real-time visual data discovery platform, empowering users to easily acquire, prepare, automate, govern and discover value in their data.  Here, we present a number of relevant client use cases - and welcome business partner BIPB to co-present with us as a thought leadership piece around the business drivers for introducing real time analytics - and the move away from static reporting.

Lunch

Panel Discussion - Deriving Value from Interaction Data – Clickstream, GPS, Twitter and other online sources
All speakers; led by Mike Ferguson, CEO, Intelligent Business Strategies

Case Study: Continuous Analytics & Optimisation using Apache Spark
Michael Cutler, Tumra

This presentation will illustrate how businesses can leverage the open-source technology Apache Spark to solve previously intractable problems at scale without some of the challenges or performance problems associated with Hadoop Map/Reduce.
Topics covered include: - Basic concepts, usage and performance characteristics- Deployment options (on premise, AWS)- Integrating data from external sources (RDBMS, NoSQL) - Applying machine learning algorithms across data- Operating on batch (file) input as well as streaming data - Compatibility with existing Business Intelligence tools.

Tea

Data Stream Algorithms in Storm and R
Radek Maciaszek, DataMine Lab

Storm and Spark are stream processing frameworks that can power a range of big data calculations on the fly and provide distributed data platforms at a global scale. In this talk we address the critical issues of why and how to use Storm and Spark, in particular we will focus on:

• Comparison between stream processing and Hadoop batch processing.
• Implementation and architecture of both Storm and Spark
• Use cases showing how to process hundreds of millions of events a day in (near) real time.

Summary; closing Q&A session

Complimentary Drinks Reception