Enabling Real-Time Business Intelligence

September 5, 2016 - New Delhi, India

Celebrating 10 years of bridging Academic and Industrial Innovation!

Keynote speakers

To celebrate BIRTE's 10th anniversary we have a special program with three keynotes by distinguished speakers!

Rakesh Agrawal

A Tale of Quest for Real-Time Business Intelligence

We present the story of a research expedition (code-named WaveFour) into building an enterprise-scale, real-time business intelligence system over social data. We cover what drove us to undertake this journey, the system prototypes we built, some of the successful outcomes, and the lessons learned.


Rakesh Agrawal is the President and Founder of the Data Insights Laboratories. He is a member of the National Academy of Engineering, a Fellow of ACM, and a Fellow of IEEE. He has been both an IBM Fellow and a Microsoft Fellow. ACM SIGKDD awarded him its inaugural Innovations Award and ACM SIGMOD the Edgar F. Codd Award. He was named to the Scientific American's First list of top 50 Scientists. Rakesh has been granted 80+ patents and published 200+ papers, including the 1st and 2nd highest cited in databases and data mining. Four of his papers have received "test-of-time" awards. His research formed the nucleus of IBM Intelligent Miner that led the creation of data mining as a new software category. Besides Intelligent Miner, several other commercial products incorporate his work, including IBM DB2 and WebSphere and Microsoft Bing.

Surajit Chaudhuri

Data Exploration Challenges in the Age of Big Data

As we continue to accumulate variety of data at an unprecedented rate, the complexity of formulating data exploration queries as well as the cost of executing such queries continue to grow. Therefore, reducing both the complexity of query formulation as well as the cost of executing exploratory queries is becoming increasingly important. We begin by discussing an example-driven approach to formulating data exploration queries to simplify the challenge of query formulation. We then reflect on why approximate query processing techniques that promise to reduce cost of exploratory queries have so far failed to gain adoption. We comment on implications of some of the recent developments in approximate query processing and conclude with a few open challenges.

Surajit Chaudhuri

Surajit Chaudhuri is a Distinguished Scientist at Microsoft Research and leads the Data Management, Exploration and Mining group. As a Deputy Managing Director of MSR Redmond Lab, he also has oversight of Distributed Systems, Networking, Security, Programming languages and Software Engineering groups. His current areas of interest are enterprise data analytics, data discovery, self-manageability and cloud database services. Working with his colleagues in Microsoft Research, he helped incorporate the Index Tuning Wizard (and subsequently Database Engine Tuning Advisor) and data cleaning technology into Microsoft SQL Server. Surajit is an ACM Fellow, a recipient of the ACM SIGMOD Edgar F. Codd Innovations Award, ACM SIGMOD Contributions Award, a VLDB 10-year Best Paper Award, and an IEEE Data Engineering Influential Paper Award. Surajit received his Ph.D. from Stanford University in 1992.

C. Mohan

Hybrid Transaction and Analytics Processing (HTAP): State of the Art

Abstract: Traditionally, database processing has been broadly classified into two categories: online transaction processing (OLTP) and online analytical processing (OLAP). OLTP systems preceded the emergence of relational database management systems (RDBMSs). OLAP, which was enabled by the arrival of RDBMSs and SQL, and enhancements to them, has gained even more attention in the last decade or so with the emergence of column stores and Big Data technologies like Map/Reduce, Hadoop and Spark. Data generated by OLTP systems are periodically moved in a batched fashion into OLAP systems for analytical processing. In the last few years, increasingly organizations want to be able to base their decisions on the latest set of raw data and the real-time analytics derived from them. This has meant that the capabilities of OLTP and OLAP have had to be combined in a single system with essentially a single copy of the data being used for both purposes. The term HTAP is being used to refer to such Hybrid Transaction and Analytics Processing systems. Currently, there is intense focus on HTAP systems in industry and academia. In this talk, I will discuss the problems, technologies and systems that relate to HTAP.

Dr. C. Mohan

Dr. C. Mohan has been an IBM researcher for 34 years in the database area, impacting numerous IBM and non-IBM products, the research and academic communities, and standards, especially with his invention of the ARIES family of database locking and recovery algorithms, and the Presumed Abort commit protocol. This IBM (1997), and ACM/IEEE (2002) Fellow has also served as the IBM India Chief Scientist for 3 years. In addition to receiving the ACM SIGMOD Innovation Award (1996), the VLDB 10 Year Best Paper Award (1999) and numerous IBM awards, Mohan was elected to the US and Indian National Academies of Engineering (2009), and was named an IBM Master Inventor (1997). This Distinguished Alumnus of IIT Madras (1977) received his PhD at the University of Texas at Austin (1981). He is an inventor of 50 patents. He has served on the advisory board of IEEE Spectrum, and on numerous conference and journal boards. Mohan is a frequent speaker in North America, Europe and India, and has given talks in 40 countries. He is very active on social media and has a huge following. More information can be found at http://bit.ly/CMnMDS