Intelligent Techniques for Warehousing and Mining Sensor Network Data

Intelligent Techniques for Warehousing and Mining Sensor Network Data

Indexed In: SCOPUS
Release Date: December, 2009|Copyright: © 2010 |Pages: 424
DOI: 10.4018/978-1-60566-328-9
ISBN13: 9781605663289|ISBN10: 160566328X|EISBN13: 9781605663296
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Description & Coverage
Description:

Sensor network data management poses new challenges outside the scope of conventional systems where data is represented and regulated.

Intelligent Techniques for Warehousing and Mining Sensor Network Data presents fundamental and theoretical issues pertaining to data management. Covering a broad range of topics on warehousing and mining sensor networks, this advanced title provides significant industry solutions to those in database, data warehousing, and data mining research communities.

Coverage:

The many academic areas covered in this publication include, but are not limited to:

  • Efficient compression techniques
  • Geographic routing
  • Integrated intelligence
  • Intelligent acquisition techniques
  • Location-based sensor data
  • Mining sensor network data
  • Mobile object observations
  • Multidimensional data streams
  • Outlier detection techniques
  • Peer-to-peer data clustering
  • Radio Frequency Identification
  • Self-organizing sensor networks
  • Streaming sensor data
Reviews & Statements

This book expands the sensor networks research field by putting the basis for novel research trends in the context of warehousing and mining sensor network data, via addressing topics that are, at now, rarely investigated, such as data mining query languages for sensor data. Indeed, the most important unique characteristic of this book is represented by its interdisciplinarity across different research fields spanning from traditional DBMS to Data Warehousing and Data Mining, all concerned with the innovative research theme of sensor network data management.

– Alfredo Cuzzocrea, University of Calabria, Italy
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Editor/Author Biographies
Alfredo Cuzzocrea is a researcher at the Institute of High Performance Computing and Networking of the Italian National Research Council, Italy, and an adjunct professor at the Department of Electronics, Computer Science and Systems of the University of Calabria, Italy. His research interests include multidimensional data modelling and querying, data stream modelling and querying, data warehousing and OLAP, OLAM, XML data management, Web information systems modelling and engineering, knowledge representation and management models and techniques, Grid and P2P computing. He is author or co-author of more than 100 papers in referred international conferences (including EDBT, SSDBM, ISMIS, ADBIS, DEXA, DaWaK, DOLAP, IDEAS, SEKE, WISE, FQAS, SAC) and international journals (including DKE, JIIS, IJDWM, WIAS). He serves as program committee member of referred international conferences (including ICDM, SDM, PKDD, PAKDD, CIKM, ICDCS, ER, WISE, DASFAA, FQAS, SAC) and as review board member of referred international journals (including TODS, TKDE, TSMC, IS, DKE, JIIS, IPL, TPLP, COMPJ, DPDB, KAIS, INS, IJSEKE, FGCS). He also serves as PC Chair in several international conferences and as Guest Editor in international journals like JCSS, DKE, KAIS, IJBIDM, IJDMMM and JDIM.
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Editorial Advisory Board
  • Elena Baralis, Politecnico di Torino, Italy
  • Sharma Chakravarthy, University of Texas at Arlington, USA
  • Shi-Kuo Chang, University of Pittsburgh, USA
  • Joao Gama, University of Porto, Portugal
  • Jiawei Han, University of Illinois at Urbana-Champaign, USA
  • Sotiris Nikoletseas, University of Patras, Greece