TextDB: Declarative and Scalable Text Analytics on Large Data Sets
Speaker: Chen Li
Title / Affiliation:
Professor, School of Information and Computer Sciences
University of California, Irvine
Talk Abstract:
We are developing an open source system called "TextDB" for text analytics on large data sets. The goal is to build a text-centric data-management system to enable declarative and scalable query processing. It supports common text computation as operators, such as keyword search, dictionary-based matching, similarity search, regular expressions, and natural language processing. It supports index-based operators without scanning all the documents one by one. These operators can be used to compose more complicated query plans to do advanced text analytics. In the talk we will give an overview of the system, and present details about these operators and query plans. We will also report our initial results of using the system to do information extraction. The system is available at https://github.com/TextDB/textdb/wiki
Biography:
Chen Li is a professor in the Department of Computer Science at the University of California, Irvine. He received his Ph.D. degree in Computer Science from Stanford University in 2001, and his M.S. and B.S. in Computer Science from Tsinghua University, China, in 1996 and 1994, respectively. His research interests are in the field of data management, including data cleaning, data integration, data-intensive computing and text analytics. He was a recipient of an NSF CAREER Award, several test-of-time publication awards, and many other grants and industry gifts. He was once a part-time Visiting Research Scientist at Google. He founded a company SRCH2 to develop an open source search engine with high performance and advanced features from ground up using C++.
About the Forum:
The IBM THINKLab Distinguished Speaker Series brings together IBM and external researchers and practitioners to share their expertise in all aspects of analytics. This global bi-weekly event features a wide range of scientific topics which appeal to a broad audience interested in the latest technology for analytics, and how analytics is being used to gain insights from data.