Introduction and Overview - 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.

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 of 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.

The CERN Data Analytics project aims to profit from that data investment to improve and optimize the systems in the Accelerator Complex by using Data Analytics.

This presentation will show the challenges and the work being carried out within the project, as well as the implementation of some real use cases and potential applications of Real-Time Data Analytics.

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

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

Data stream algorithms in Storm and R
Radek Maciaszek, DataMine Lab
Streaming data presents new challenges for statistics and machine learning on extremely large data sets. Tools such as Storm, a stream processing framework, can power range of data analytics but lack advanced statistical capabilities. In this talk we will discuss developing streaming algorithms with the flexibility of both Storm and R, a statistical programming language.

We will address the critical issues of why and how to use Storm and R to develop streaming algorithms; in particular we will focus on:

• Streaming algorithms
• Online machine learning algorithms
• Use cases showing how to process hundreds of millions of events a day in (near) real time

Panel Session: Deriving Value from Interaction Data – Clickstream, GPS, Twitter and other online sources.
All above speakers plus other guest panellists to be announced.