Abstract
Network-based services are becoming a ubiquitous part of our lives, to the point where individuals and businesses have often come to critically rely on them. Building and maintaining such reliable, high performance network and service infrastructures requires the ability to rapidly investigate and resolve complex service and performance impacting issues. To achieve this, it is important to collect, correlate and analyze massive amounts of data, such as network element fault logs, configuration and topology information, performance data and traffic measurements, from a diverse collection of data sources in real time.

We have designed and implemented a variety of data systems at AT&T Labs Research to build highly scalable databases that support real time data collection, correlation and analysis, including (a) the Daytona database management system, (b) the GS data stream management system, (c) the Data Depot stream warehouse generator and (d) the Bistro data feed manager. Together, these data systems have enabled the creation and maintenance of a database and data analysis infrastructure for troubleshooting complex issues in the network. This talk describes these data systems, presents their key research contributions, and identifies technical challenges that are currently being addressed.

BIO
Divesh Srivastava is the head of the Database Research Department at AT&T Labs-Research. He received his Ph.D. from the University of Wisconsin, Madison, and his B.Tech from the Indian Institute of Technology, Bombay. He is a Fellow of the ACM, on the board of trustees of the VLDB Endowment, and an associate editor of the ACM Transactions on Database Systems. He has served as the associate Editor-in-Chief of the IEEE Transactions on Knowledge and Data Engineering, and the program committee co-chair of many conferences, including VLDB 2007. He has presented keynote talks at several conferences, including VLDB 2010. His research interests span a variety of topics in data management.