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Mukesh Mohania

Qualifications: Ph.D.

Title: ACM Distinguished Scientist

Affiliation: IBM Research–India

Contact Details:

Short CV:
Mukesh Mohania has worked extensively in the areas of distributed databases, data warehousing, data integration, and autonomic computing. He has received several awards within IBM, such as "Best of IBM", "Excellence in People Management", "Outstanding Innovation Award", "Technical Accomplishment Award", "Leadership By Doing", and many more. He has published more than 120 papers and also filed more than 60 patents in these or related areas and more than 25 have already been granted. Mukesh is an IBM Distinguished Engineer, IBM Master Inventor and a member of IBM Academy of Technology.

Title of Talk 1: Big Data Analytics for Personalized Education
Synopsis: Online courses and learning systems have been gained tremendous popularity over the last few years. While their ease of access and availability make them a very useful medium for knowledge sharing and learning, they do not keep the learners and their learning abilities in mind. The "one size fits all" approach to learning content does not work in a large virtual classroom consisting of diverse students with different skill profiles, learning styles, aptitude and capability. In a traditional classroom, teachers interact closely with students are in a position to evaluate the pace and depth of the curriculum being taught and can also suggest learning content to students not being able to cope with the general classroom teaching. Such suggestion and guidance is absent in current online learning systems. In this talk we aim to address this gap through learning content analytics and automatic content tagging that enables the adaptive and personalized education on Big Data platform.

Title of Talk 2: Cloud Computing and Big Data Analytics: What Is New from DB Perspective?
Synopsis: Cloud computing offers an exciting opportunity to bring on-demand applications to customers and is being used for delivering hosted services over the Internet and/or processing massive amount of data for business intelligence. In this talk, we will discuss the architecture of cloud computing, MapReduce, and Hadoop. We then discuss how the cloud infrastructure can be used for data management services, and various Big Data applications.

Title of Talk 3: Analyzing Data and Content Together
Synopsis: The growth of organizations invariably leads to creation of multiple isolated data sources which are totally disconnected from each other. This leads to reduced efficiency and lack of complete knowledge of the enterprise and its customers while making critical business decisions. This is the classical Information integration problem which has become the biggest pain point for enterprises today, and performing Business Intelligence (BI) over data and content together remains a big challenge. BI refers to methodologies for the collection, integration, and analysis of all relevant information from a business process for the purpose of better business decision making. This involves discovering different variables crucial for business and their correlation against variables that define success of the business.

In this talk we discuss how BI can be derived from combining data and unstructured content. We will show that actionable insights derived using such models can be fed back into the system to achieve measurable gains in the business.

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