Home
SIGMOD Keynotes Download
PODS Keynote and Invited Tutorials Download
Pictures

Organization
Conference Officers
  SIGMOD | PODS
Program Committee
  SIGMOD | PODS

Program
Program At A Glance
Conference Program
  SIGMOD | PODS
Keynote Program
  SIGMOD | PODS
Tutorial Program
  SIGMOD | PODS
Accepted Papers
  SIGMOD | PODS
Social Events

Student Scholarship
Student Scholarship

Hotel/Travel
About Beijing
Visa
Travel
Hotel
Food

Links
Archive
Previous Conference
SIGMOD
APWEB/WAIM 2007



SIGMOD Tutorials and Speakers

Event Processing Using Database Technology
Mani Chandy (California Institute of Technology), Dieter Gawlick (Oracle)

This tutorial deals with applications that help systems and individuals respond to critical conditions in their environments. The identification of critical conditions requires correlating vast amounts of data within and outside an enterprise. Conditions that signal opportunities or threats are defined by complex patterns of data over time, space and other attributes. Systems and individuals have models (expectations) of behaviors of their environments, and applications notify them when reality ĘC as determined by measurements and estimates ĘC deviate from their expectations. Components of event systems are also sent information to validate their current models and when specific responses are required. Valuable information is that which supports or contradicts current expectations or that which requires an action on the part of the receiver. A major problem today is information overload; this problem can be solved by identifying what information is critical, complementing existing pull technology with sophisticated push technology, and filtering out non-critical data.

Chandy got his PhD from MIT in Electrical Engineering and Operations Research in 1969. He taught at the University of Texas at Austin from 1970 to 1987 and from then at the California Institute of Technology where he currently holds the Simon Ramo Chair of Computer Science. Awards received include the IEEE Koji Kobayashi Award and the CMG A.A. Michelson Award. He is a member of the U.S. National Academy of Engineering.

Dieter joined IBM in 1968. As member of the IMS development team he proposed, architected, and implemented products that enabled high-end transaction technology. Core database/transaction technologies such as 2phase commit, group-commit, partitioning, data replication, online utilities, escrow technology, and hot standby are among achievements of this effort. Dieter joined Amdahl in 1984 and worked on I/O related problems. He developed methods for the usage of electronic data storage. In 1988, he joined Digital. His team developed and shipped the first workflow manager with full integration into database and transaction technology. Dieter joined Oracle in 1994. He architected Oracle/AQ (Advanced Queuing) and was a key contributor to Oracle's integration and sensor technologies. Dieter's current focus is leveraging and evolving database technologies to accelerate the evolution of event processing. He works on improvements for event creation, event processing, event dissemination and continuous analytics. Additionally, Dieter works on database support for long running transactions. Dieter has the MS equivalent from Muenster (Germany). He has published a series of articles, holds various patents, served on many program committees and co-chair of a working group of OGF.

Provenance in Databases
Peter Buneman (University of Edinburgh), Wang-Chiew Tan (University of California, Santa Cruz)

The provenance of data has recently been recognized as central to the trust one places in data. It is also important to annotation, to data integration and to probabilistic databases. Three workshops have been held on the topic, and it has been the focus of several research projects and prototype systems. This tutorial will attempt to provide an overview of research in provenance in databases with a focus on recent database research and technology in this area. This tutorial is aimed at a general database research audience and at people who work with scientific data.

Peter Buneman is Professor of Database Systems at the U niversity of Edinburgh. He has worked in several areas of databases including query languages, semistructured data and, recently, a number of issues in scientific data.

Wang-Chiew Tan is an Assistant Professor at the Computer Science Department at University of California, Santa Cruz since September 2002.Her current research interests include data provenance, annotations, archiving, and information integration.

Mining Large Graphs and Streams using Matrix and Tensor Tools
Christos Faloutsos (Carnegie Mellon University), Tamara G. Kolda (Sandia National Labs), Jimeng Sun (Carnegie Mellon University)

Coevolving streams of numerical measurements, as well as time evolving graphs, can well be represented as tensors. Here we review the fundamental matrix and tensors tools for the analysis and mining of large scale streams and graphs.

Christos Faloutsos is a Professor at Carnegie Mellon University. He has received the Research Contributions Award in ICDM 2006, nine "best paper" awards and several teaching awards. His research interests include data mining for streams and networks, fractals, indexing for multimedia and bio-informatics data bases, and performance.

Tamara G. Kolda is a researcher at Sandia National Laboratories in Livermore, California and has received the Presidential Early Career Award for Scientists and Engineers (2003). She has published over 25 refereed articles and released several software packages including the MATLAB Tensor Toolbox. She is an associate editor for the SIAM Journal on Scientific Computing. Her research interests include multilinear algebra and tensor decompositions, data mining, optimization, nonlinear solvers, graph algorithms, parallel computing and the design of scientific software.

Jimeng Sun is a PhD candidate in Computer Science Department at Carnegie Mellon University. His rearch interests include data mining on streams, graphs and tensors, anomaly detection.

Streaming in a Connected World: Querying and Tracking Distributed Data Streams
Graham Cormode (AT&T Labs), Minos Garofalakis (Yahoo! Research)

Large-scale event-monitoring systems require fast or continuous query answering in a world where the data is streaming and inherently distributed. The key challenge is to minimize both communication and processing burden while ensuring accuracy and timeliness of answers. We discuss example application domains, including sensor networks, network monitoring, and P2P networks. We also cover basic (centralized) data-streaming models and results, and outline the key dimensions of distributed data-streaming problems: (1) Querying Model: One-shot vs. continuous, exact vs. approximate, deterministic vs. randomized; (2) Communication Model: Single-level, hierarchical, or fullydistributed (e.g., DHT-based P2P systems), other communication constraints (e.g., network loss, intermittent connectivity); and, (3) Class of Queries: Holistic vs. non-holistic aggregates, duplicate sensitive vs. insensitive aggregates, more complex queries (e.g., inference models, set-valued results).

Graham Cormode is a Principal Member of Technical Staff in the Database Management Group at AT&T Shannon Laboratories in New Jersey. Previously, he was a researcher at Bell Labs, after postdoctoral study at the DIMACS center in Rutgers University from 2002-2004. His PhD was granted by the University of Warwick in 2002. He works on data stream algorithms, large-scale data mining, and applied algorithms, with applications to databases, networks, and fundamentals of communications and computation.

Minos Garofalakis is a Principal Research Scientist with the Community Systems group at Yahoo! Research in Santa Clara, California, and an Adjunct Associate Professor of Computer Science at the University of California, Berkeley. Previously, he was a Senior Researcher at Intel Research Berkeley (2005-2007), and a Member of Technical Staff at Bell Laboratories (1998-2005). He obtained his PhD from the University of Wisconsin-Madison in 1998. His current research interests include data streaming, approximate query processing, probabilistic databases, network-data management, and XML databases.

Mobile and Embedded Databases
Anil K. Nori (Microsoft)

Recent advances in device technology and connectivity have paved the way for next generation applications that are datadriven, whose data can reside anywhere, can be accessed at any time, from any client. Also, advances in memory technology are driving the capacities of RAM and Flash higher, and their costs down. These trends lead to applications that are mobile, embedded, and data-centric.
This tutorial presents an overview of the mobile and embedded database systems and their applications.

Anil Nori is a Distinguished Engineer at Microsoft. He is an architect in the SQL Server organization, focusing on data and application platform technologies. He is part of the senior leadership overseeing the overall vision, strategy, and architecture for the Microsoft data platforms. He has over 25 years of experience in building complex database and application systems. Before coming to Microsoft, Anil was the CTO of Asera, which he co-founded with Vinod Khosla of Kleiner Perkins.Asera pioneered Composite Applications and the composite application platform.
Prior to Asera, Anil was at Oracle as an Architect for the Oracle database system, where he was responsible for Oracle object-relational and extensible technology, Internet and multi-media DBMS development, and XML technology. Before joining Oracle, Anil was a Database Architect for DEC Rdb database products, where he was involved in the development of centralized and distributed DBMS products. Prior to DEC, Anil was a Computer Scientist at Computer Corporation of America, a leader in Database Research. Anil is active and well known in the database and applications community.

System Design Issues in Sensor Databases
Qiong Luo (HKUST), Hejun Wu (HKUST)

In-network sensor query processing systems (ISQPs), or sensor databases, have been developed to acquire, process and aggregate data from wireless sensor networks (WSNs). Because WSNs are resource-limited and involve multiple layers of embedded software, the system design issues have a significant impact on the performance of sensor databases. Therefore, we propose this tutorial to study the state of the art on these issues with a focus on their interaction with query processing techniques. Our goal is to present the challenges and efforts in developing holistic, efficient ISQPs. Specifically, we will cover architectural design, scheduling, data-centric routing, and wireless medium access control. This tutorial is intended for database researchers who are interested in sensor networks.

Qiong Luo is an assistant professor at the Computer Science and Engineering Department, the Hong Kong University of Science and Technology (HKUST). Her research interests are database systems, with a focus on data management and analysis techniques related to network applications. Qiong received her Ph.D. in Computer Sciences from the University of Wisconsin-Madison in 2002 and her B.S.and M.S. in Computer Sciences from Beijing (Peking) University, China in 1992 and 1997 respectively.

Hejun Wu is a PhD student at the Computer Science and Engineering Department, the Hong Kong University of Science and Technology (HKUST). His research interests are wireless sensor networks, distributed systems, and embedded systems.


Organized by Tsinghua University Renmin University Peking University
Copyright ©2006 by SIGMOD/PODS 2007.