January 20 to 22 2016, Santa Clara, USA.

Speakers

Speaker "Alok Aggarwal" Details

Name :
alok aggarwal
Company :
Title :
CEO
Topic :

Building End-To-End Solutions for Big Data Science Problems

Abstract :

Substantial amount of time and money that is being spent by startup companies in Big Data Science area is in building databases (e.g., Tokutek) or making programming faster (e.g., Splice Machine) or improving underlying algorithms (e.g., H2O or Oxdata). Indeed, all this is extremely important since it provides the required “plumbing” for development platforms in this area and for growing the overall ecosystem. However, the eventual aim of Big Data Science and Analytics is to improve key performance indicators for an organization, which include improving cash-flow, timeliness, quality, customer experience, and parameters related to compliance and risk.

In this technical session, we discuss as to how to build end-to-end solutions in Big Data Science. In this regard, we take an example of improving the overall efficiency for a typical paper mill (all the way from “taking in paper pulp” to “providing finished paper” to the end-clients), and we discuss step by step as to how such a solution may be built. We will also go into detail regarding the four steps that are required to design a typical solution, wherein the first step involves creating a data cleansing and munging module (using Extract-Transform-Load and machine learning algorithms) for harmonizing data, the second step consists of building math, statistics and computer science algorithms for providing descriptive, predictive and prescriptive analytics modules, the third step consists of connecting these modules together, and the fourth and final step consists of ensuring that the entire solution runs on an appropriate operating system and so that bugs can be fixed quickly. This session assumes a basic knowledge of Open Source Algorithms and Systems such as Linux, Java, Hadoop, Spark, Mahout, and Storm. Understanding of math, computer science and statistics algorithms will also be helpful.

Profile :
Brief Bio-data of Dr. Alok Aggarwal Dr. Alok Aggarwal received his B. Tech. from IIT Delhi in 1980 in Electrical Engineering and his Ph. D. from Johns Hopkins University in Electrical Engineering and Computer Science in 1984, after which he joined IBM’s T. J. Watson Research Center in New York. Since then, he has published 95 research articles and has been granted 8 patents from the US Patents and Trademark Office. During 1984 and 1993, he also won two innovation awards from IBM. Futhermore, during 1984 and 1996, Dr. Aggarwal served as a program chairman for a number of conferences, including Symposium on Theory of Computing, Foundations of Computer Science, and Symposium on Computational Geometry. He also served as a Chairperson of the IEEE Computer Society's Technical Committee on Mathematical Foundations of Computing and was on the editorial boards of SIAM Journal of Computing, Algorithmica, and Journal of Symbolic Computation. Finally, during the fall of 1988 and 1989, he was on sabbatical from IBM and taught two courses at the Massachusetts Institute of Technology (MIT) and supervised two Ph.D. students. During 1993 and 1996, along with other researchers at IBM, he built and sold a "Supply Chain Management Solution" for paper mills and steel mills. By optimizing schedules for paper machines, trimmers, winders, storage in warehouses, loading of trucks and rail-cars, and transportation, their solution was able to save a typical paper mill around 1.75% of revenue in operating costs. Hence, they published a seminal paper titled, “Cooperative Multi-objective Decision Support for the Paper Industry,” for which they won the Daniel H. Wagner prize for Excellence in Operations Research Practice from INFORMS in 1998. In July 1997, Dr. Aggarwal "founded" IBM’s India Research Laboratory, which he set-up inside the Indian Institute of Technology (IIT), Delhi. This Lab was inaugurated by the Indian Minister for Human Resources, Dr. Alagh, and the US Ambassador to India, Mr. Richard Celeste. Dr. Aggarwal started this Laboratory from "ground zero" and grew it to a 70-member team (with 35 PhDs and 35 Masters in Electrical Engineering, Computer Science, and in Business Administration) by July 2000. In August 2000, Dr. Aggarwal became the Director of Emerging Business Opportunities for IBM Research Division worldwide, and in this capacity, his responsibilities included converting technology innovations into business models and taking them to market to create profitable ventures. During 1998-2000, Dr. Aggarwal was a member of Executive Committee on Information Technology of the Confederation of the Indian Industry (CII) and the Telecom Committee of Federation of Indian Chamber of Commerce and Industry (FICCI). During 2002-2005, he was a charter member of The Indus Entrepreneur (TiE) organization and on the executive board of its New York chapter. In 2008, he received Distinguished Alumnus Award from IIT Delhi. In December 2000, he “co-founded” Evalueserve - a company that provides research, intellectual property, and analytics services to clients in North America, Europe and Asia Pacific from its five research centers in Delhi-Gurgaon, India; Shanghai, China; Cluj, Romania, Santiago-Valparaiso, Chile; and Raleigh, North Carolina. He was Evalueserve’s chairman until December 2013, and in 2003, along with his colleagues at Evalueserve, he pioneered the concept of “Knowledge Process Outsourcing (KPO)” and wrote the first article regarding KPO. Today, KPO is a well-known term in the outsourcing industry and there are more than 200 KPO companies in India alone; hence, Dr. Aggarwal has been quoted extensively by Wall Street Journal and other newspapers and magazines. In February 2014, Dr. Aggarwal founded Scry Analytics, a company that codifies different kinds of work-flows in various industries and uses artificial intelligence, machine learning, data mining as well as statistical analysis to improve their efficiency with respect to timeliness, quality, money earned, customer experience, compliance and aggregated risks.
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