Lahar: Warehousing Markovian Streams

March 29, 2010
Julie Letchner | University of Washington

In this talk, I present Lahar, a warehousing system for a general class of imprecise, sequential data called Markovian streams. These imprecise streams are commonly used to model location sequences inferred from noisy sensors such as RFID/GPS, text inferred from spoken audio, etc. In the context of Lahar, I introduce algorithms for supporting sophisticated analytics on these streams (e.g. “How many coffee breaks did Bob take in May that lasted over an hour?” or “Find the start/end timestamps of every podcast snippet containing the phrase ‘health care’.”) The rich semantics of both queries and data in Lahar pose serious efficiency challenges. In this talk, I present several techniques to address these challenges, including novel indexing and approximation approaches.

Speaker Details

Julie is a Ph.D candidate in the Computer Science & Engineering department at the University of Washington. Her thesis research focuses on scalable management of imprecise data, and her work is implemented in the Lahar system. More broadly, she is interested in developing algorithms and scalability techniques for extracting utility from low-level data (sensor streams, multimedia data, web logs, etc.). Prior to her graduate studies, Julie earned B.S. and M.S. degrees in Computer Science from Stanford University, where her master’s work focused on Artificial Intelligence and Robotics techniques.