Smart Data Analysis – from Data to Insights and Actions
Speaker:Mr. Jian-Guang LOU
Microsoft Research Asia
Date & Time:20 Nov 2017 (Monday) 15:30 - 16:15
Venue:E11-4045 (University of Macau)
Organized by:Department of Computer and Information Science

Abstract

The value of BigData lies in the insights we can obtain from the data to help our business. But, big data by itself does not directly tell you any insights. Data analysis is a key step to extract valuable insights from data. However, given the size and large number of dimensions in the data, data analysis is often very expensive, labor intensive, and inefficient. There is a great demand of smart analysis tools to help data scientists or analysts to discover and explore insights from data efficiently. Can we automate or semi-automate data analysis with the help of AI technologies? In this talk, we will introduce the technologies that Software Analytics group in Microsoft Research Asia has been innovating in the area of auto-data insights, data visualization, conversational data analysis, etc. Most of these technologies have been integrated with Microsoft’s products including Quick Insight in PowerBI, Auto Insight in Excel, Conversational data analysis in PowerBI, and AnnaTalk in Excel. A set of demos in this talk will show you how AI technologies can help users to quickly explore their data, automatically discover insights from data, drive actions through natural and intuitive interactions (e.g., natural language conversation).

Biography

Jian-Guang Lou is now a senior researcher in the Software Analytics Group, Microsoft Research Asia. He joined Microsoft Research in 2003. During the past decade, he has worked on a set of cross-discipline research projects. He designed the world’s first interactive multi-view video system in 2005, and developed a large scale peer-to-peer streaming system for CCTV.com to broadcast 2008 Olympic game to millions of concurrent online users in China. His research on data driven service analytics (e.g., anomaly detection, auto-diagnosis, log mining and failure prediction) has been applied and deployed in different Microsoft product teams (such as SharePoint online, Exchange online, and, Window Azure) for online service management. His current research mainly focuses on machine learning and data mining algorithms for software and service analytics, business intelligence, and conversational data analysis.