Preliminary Book Information
Kluwer Academic Publishers

Editors' Names and Addresses

  

Nauman Chaudhry – nauman AT cs.uno.edu

Computer Science Department
University of New Orleans

K. Shaw – shaw AT nrlssc.navy.mil
Naval Research Laboratory, Stennis Space Center - Mississippi

M. Abdelguerfi – mahdi AT cs.uno.edu
Computer Science Department
University of New Orleans

Title of Manuscript

  

Stream Data Management

Production

  

Tentative Chapters Submission Date:

June 1, 2004


Copyright Form

  

Download the Copyright Form in PDF Format:

Download


Description and Purpose of Manuscript:

  

Researchers in data management have recently recognized the importance of a new class of data-intensive applications. Data for these applications is best modeled via streams of data that requires continuous monitoring, rather than persistent data stored in relational tables. Many application domains require management of data streams. These include finance, web applications, security, networking, and sensor monitoring.

Consider the domain of sensor monitoring. Cheap micro-sensor technology is expected to soon enable most objects of interest to report their location and state in real time. These large number of sensors distributed in the physical world would generate streams of data which would need to be combined, monitored and analyzed. Examples of such sensor networks include environmental monitoring systems, battlefield monitoring systems, Gamma ray detection in astrophysics.

Key differences of data streams from conventional database systems (that focus on stored data) can be specified as:

The data stream is potentially unbounded in size.

·        After processing an element from the data stream, this element is discarded or archived. This means that later retrieval of this element, if possible, would be fairly expensive.

·        The arrival rate of the data can vary over time. Indeed the arrival may be in bursts.

·        Certain queries will be continuously evaluated over the continuously arriving data.

·        Decisions could be made based on the data and would need to be acted upon.

Management of data streams poses a number of interesting challenges for research in database management systems. Adequate mechanisms will have to be developed to query data streams, including means to express queries, develop operators to process these queries and algorithms to optimize such queries. Additionally, to monitor data streams and alert humans (or other software) of abnormal activity, stream management systems need to move from the currently dominant model of being a passive repository to become more active. An additional need is for systems managing stream data is to cater to real-time requirements since the decisions made on the basis of the data need to be acted upon in close to real-time, probably with some Quality-of-Service (QoS) guarantees.

Content of Manuscript:

  

This edited manuscript will be composed of chapters from experts in the emerging field of stream data management. The manuscript will gather recent knowledge related to various issues related to managing stream data, including systems, languages and algorithms for managing and querying stream data. Currently this knowledge is scattered across disparate research papers and will appeal to researchers and students interested in this area.

Chapter Submission

 

The Editors of the book are soliciting chapters from researchers in the Stream Management Area by explicit invitation.

Guidelines for Chapter Preparation:

  

The LaTeX template for chapter preparation can be downloaded from:

http://www.wkap.nl/subjects/single_column_edited_volume

Your manuscript must be prepared using this template before submission to nauman AT cs.uno.edu

Alternately you can use Word (please let me know if you are using Word). Word templates (and instructions about the template) for chapter preparation can be downloaded from: http://www.wkap.nl/prod/a/vbaKAPedvo.zip.

Please note that chapters are limited to no more than 20 pages each. Chapters are tentatively due by June 1, 2004.

About Royalties:

  

All expected royalties resulting from this manuscript will be donated (directly by the publisher) to a charitable organization "habitat for humanity". Indeed, many participants in this project are US government employees who are not permitted to receive royalties as part of their regular duties.

Proposed Chapters

 

1. Introduction and Overview:

Nauman Chaudhry (Univ. of New Orleans) and Kevin Shaw (Naval Research Lab)

 

2. Architectures of Data Stream Systems

      Krithi Ramamritham  (IIT Bombay, India)


3. Query Execution and Optimization

     Stratis Viglas (Univ. of Edinburgh, U.K.)

 

4. Filters, Punctuation and Synopsis

     David Maier (Oregon Graduate Institute), Pete Tucker (Oregon Graduate Institute), Minos Garofalakis (Lucent Bell Labs)

 

5.  XML & Data Streams

     Nick Koudas and Divesh Srivastava (AT & T)

 

6. Efficient Support for Time Series Queries in Data Stream Management Systems

     Carlo Zaniolo and colleagues (UCLA)

 

7. CAPE : The Adaptive Constraint-Driven Stream Processing Engine

      Elke A. Rundensteiner, Luping Ding, Yali Zhu, Timothy Sutherland, Brad Pielech (Worcester Polytechnic Institute)

 

8. Cougar Data Management System

      Johannes Gehrke, Alan Demers, Niki Trigoni and Yong Yao (Cornell Univ.) and Rajmohan Rajaraman (Northeastern University)

 

9. Managing Distributed Geographical Data Streams with the GIDB Portal

        John T. Sample, Frank P. McCreedy, Micheal Thomas (Naval Reserch Lab)