Accepted ContributionsIntelligent
Techniques for Warehousing
and Mining
Sensor Network Data
Editor
Alfredo Cuzzocrea
cuzzocrea@si.deis.unical.it
ICAR Institute and DEIS Department
University of Calabria
Publisher
DISSEMINATOR of KNOWLEDGE
[Introduction | Keywords | Recommended Main Topics | Timetable | Submission Guidelines and Instructions | Objectives | Target Audience | Book Publication]
Accepted Contributions
Marcos M. Campos, Oracle Data Mining
Technologies, USA
Boriana L. Milenova, Oracle Data Mining Technologies, USA
Integrated Intelligence – Separating the Wheat from
the Chaff in Sensor Data
Alfredo Cuzzocrea, ICAR-CNR & University
of Calabria, ITALY
Filippo Furfaro, University of Calabria, ITALY
Elio Masciari, ICAR-CNR, ITALY
Domenico Saccà, University of Calabria, ITALY
Improving OLAP Analysis of Multidimensional Data
Streams via Efficient Compression Techniques
Hector Gonzalez, Department of Computer
Science, University of Illinois at Urbana-Champaign, USA
Jiawei Han, Department of Computer Science, University of Illinois at
Urbana-Champaign, USA
Hong Cheng, Department of Computer Science, University of Illinois at
Urbana-Champaign, USA
Tianyi Wu, Department of Computer Science, University of Illinois at
Urbana-Champaign, USA
Warehousing RFID and Location-Based Sensor Data
Salvatore Orlando, Dipartimento di
Informatica, Università Ca’ Foscari di Venezia, ITALY
Alessandra Raffaetà, Dipartimento di Informatica, Università Ca’ Foscari di
Venezia, ITALY
Alessandra Roncato, Dipartimento di Informatica, Università Ca’ Foscari di
Venezia, ITALY
Claudio Silvestri, Dipartimento di Informatica e Comunicazione, Università
di Milano, ITALY
Warehousing and Mining Streams of Mobile Object
Observations
Alec Pawling, University of Notre Dame,
USA
Ping Yan, University of Notre Dame, USA
Julián Candia, Northeastern University, USA
Tim Schoenharl, University of Notre Dame, USA
Greg Madey, University of Notre Dame, USA
Anomaly Detection in Streaming Sensor Data
Pedro Pereira Rodrigues, LIAAD - INESC
Porto L.A. & Faculty of Sciences, University of Porto, PORTUGAL
João Gama, LIAAD - INESC Porto L.A. & Faculty of Economics, University of
Porto, PORTUGAL
Luís Lopes, CRACS - INESC Porto L.A. & Faculty of Sciences, University of
Porto, PORTUGAL
Knowledge Discovery for Sensor Network
Comprehension
Yang Zhang, Department of Computer
Science, University of Twente, THE NETHERLANDS
Nirvana Meratnia, Department of Computer Science, University of Twente, THE
NETHERLANDS
Paul Havinga, Department of Computer Science, University of Twente, THE
NETHERLANDS
Why General Outlier Detection Techniques do not
Suffice for Wireless Sensor Networks?
Elena Baralis, Dipartimento di
Automatica e Informatica, Politecnico di Torino, ITALY
Tania Cerquitelli, Dipartimento di Automatica e Informatica, Politecnico di
Torino, ITALY
Vincenzo D'Elia, Dipartimento di Automatica e Informatica, Politecnico di
Torino, ITALY
Intelligent Acquisition Techniques for Sensor
Network Data
Stefano Lodi, Dipartimento di
Elettronica, Informatica e Sistemistica, University of Bologna, ITALY
Gabriele Monti, Dipartimento di Elettronica, Informatica e Sistemistica,
University of Bologna, ITALY
Gianluca Moro, Dipartimento di Elettronica, Informatica e Sistemistica,
University of Bologna, ITALY
Claudio Sartori, Dipartimento di Elettronica, Informatica e Sistemistica,
University of Bologna, ITALY
Peer-To-Peer Data Clustering in Self-Organizing
Sensor Networks
Shi-Kuo Chang, Department of Computer
Science, University of Pittsburgh, USA
Gennaro Costagliola, Dipartimento di Matematica ed Informatica, Università
di Salerno, ITALY
Erland Jungert, Swedish Defense Research Agency, SWEDEN
Karin Camara, Swedish Defense Research Agency, SWEDEN
Intelligent Querying Techniques for Sensor Data
Fusion
Mark Roantree, Interoperable Systems
Group, Dublin City University, IRELAND
Alan F. Smeaton, CLARITY: Centre for Sensor Network Technologies, Dublin
City University, IRELAND
Noel E. O'Connor, CLARITY: Centre for Sensor Network Technologies, Dublin
City University, IRELAND
Vincent Andrieu, Interoperable Systems Group, Dublin City University,
IRELAND
Nicolas Legeay, Interoperable Systems Group, Dublin City University, IRELAND
Fabrice Camous, Interoperable Systems Group, Dublin City University, IRELAND
Query Optimisation for Data Mining in Peer-to-Peer
Sensor Networks
Sotiris Nikoletseas, Computer Technology
Institute and Department of Computer Engineering & Informatics, University
of Patras, GREECE
Olivier Powell, Centre Universitaire d' Informatique, University of Geneva,
SWITZERLAND
Jose Rolim, Centre Universitaire d' Informatique, University of Geneva,
SWITZERLAND
Geographic Routing of Sensor Data around Voids and
Obstacles
David J. Yates, Computer Information
Systems Department, Bentley College, USA
Jennifer Xu, Computer Information Systems Department, Bentley College, USA
Sensor Field Resource Management for Sensor Network
Data Mining
Qingchun Jiang, Oracle Corporation, USA
Raman Adaikkalavan, CIS Department, IU South Bend, USA
Sharma Chakravarthy, CSE Department, UT Arlington, USA
Event/Stream Processing for Advanced Applications
Biswajit Panja, Department of
Mathematics & Computer Science, Morehead State University, USA
Sanjay Kumar Madria, Department of Computer Science, Missouri University of
Science and Technology, USA
A Survey of Dynamic Key Management Schemes in
Sensor Networks
Call for Chapters
The book Intelligent Techniques for Warehousing and Mining Sensor Network Data is focused on the warehousing and mining sensor network data research theme, which is attracting a lot of attention from the Database, Data Warehousing and Data Mining research communities. The book Intelligent Techniques for Warehousing and Mining Sensor Network Data is oriented to fundamentals and theoretical issues as well as sensor network applications, which have become of relevant interest for next-generation intelligent information systems. Sensor network applications are manifolds: from environmental data collection/management to alerting/alarming systems, from intelligent tools for monitoring/managing IP networks to novel RFID-based applications etc.
Basically, sensor network data management poses new challenges that are outside the scope of capabilities of conventional DBMS, where data are represented and managed according to a tuple-oriented scheme. As an example, DBMS expose a limited memory that is not compatible with the unbounded-memory requirement of managing sensor network data, which, ideally, originate an unbounded data flow, also referred as data stream or intermittent sources of information. In this respect, collecting and querying sensor network data is questioning, and it cannot be accomplished via conventional DBMS-inspired methodologies. Also, time is completely neglected in DBMS, whereas it plays a leading role in sensor network data management.
With a broader view, sensor network data are a specialized class of data streams. As a consequence, the above-mentioned issues become the guidelines for the design and development of next-generation Data Stream Management Systems (DSMS), which can be reasonably intended as the next challenge for data management research. Therefore, under another perspective, warehousing and mining sensor network data, and, more generally, data streams can be viewed as a collection of methodologies and techniques on top of DSMS, oriented to extend data-intensive capabilities of such systems. A similar evolution has been observed in the context of OLAP and Data Mining tools implemented on top of DBMS.
Warehousing and mining sensor network data research initiative can be also roughly indented as the application of traditional warehousing and mining techniques developed in the context of DBMS for relational data as well as non-conventional data (e.g., textual data, raw data, XML data etc) to novel scenarios drawn by sensor networks.
Despite this, models and algorithms developed for conventional data warehousing and mining technologies cannot be applied “as-they-are” to the novel context of sensor network data management, as the former are not suitable to innovative requirements of sensor network data such as: time-oriented processing, multiple-rate arrivals, unbounded memory, single-pass processing etc. From this evidence, it follows the need for designing and developing models and algorithms able to deal with previously-unrecognized characteristics of sensor network intelligent information systems, thus overcoming actual limitations of data warehousing and mining systems and platforms.
Based on these aims, the book Intelligent Techniques for Warehousing and Mining Sensor Network Data will cover a broad range of topics: data warehousing models for sensor network data, intelligent acquisition techniques for sensor network data, ETL processes over sensor network data, advanced techniques for processing sensor network data, efficient storage solutions for sensor network data, collecting sensor network data, querying sensor network data, query languages for sensor network data, fusion and integration techniques for heterogeneous sensor network data. cleaning techniques over sensor network data, mining sensor network data, frequent item set mining over sensor network data, intelligent mining techniques over sensor network data, OLAP over sensor network data, mining outliers and deviants over sensor network data, discovery of complex knowledge patterns from sensor network data, privacy preserving issues of warehousing and mining sensor network data etc.
- Sensor
network data
- Data warehousing on sensor network data
- Data mining from sensor network data
- Representing sensor network data
- Querying sensor network data
- Mining sensor network data
- Knowledge representation and extraction from sensor network data
- Intelligent techniques for representing, querying and mining sensor network
data
- Sensor Network Data: Fundamentals and Basic Definitions
- Warehousing Sensor Network Data: Models, Issues, Techniques
- Advances in Warehousing Sensor Network Data
- Mining Sensor Network Data: Models, Issues, Techniques
- Advances in Mining Sensor Network Data
- Combining Data Warehousing and Data Mining Techniques for Improving Knowledge Representation and Extraction from Sensor Network Data
- Designing and Developing High-Performance Architectures for Warehousing and Mining Sensor Network Data
- Theoretical Aspects of Warehousing and Mining Sensor Network Data
- Novel Issues: Distributed Warehousing and Mining of Sensor Network Data
- Novel Issues: Privacy Preserving Aspects of Warehousing and Mining Sensor Network Data
- Novel Issues: Time and Synchronization in Warehousing and Mining Sensor Network Data
- Warehousing and Mining Sensor Network Data: Open Problems and Future Trends
Chapter proposal submission deadline: November 15, 2007 (EXTENDED)
Notification of the acceptance/rejection of chapter proposal: December 15, 2007
Chapter submission deadline: March 30, 2008 (EXTENDED)
Chapter review: June 30, 2008 (EXTENDED)
Revised chapter submission deadline: August 31, 2008 (EXTENDED)
Revised chapter review: September 31, 2008 (EXTENDED)
Final chapter submission deadline: October 31, 2008 (EXTENDED)
The mission of this book is the achievement of a highly-referred publication on fundamentals, state-of-the-art techniques and future trends of warehousing and mining from sensor network data research. In this respect, this book will stimulate the submission of chapters from highly-referred researchers in the field.
Researchers in Computer Science and Computer Science Engineering, PhD students in Computer Science and Computer Science Engineering, graduate students in Computer Science and Computer Science Engineering, practioners in Computer Science and Computer Science Engineering.
Submission Guidelines and Instructions
Individuals interested in submitting chapters on the above-suggested main topics or other related topics in their area of interest should submit via e-mail to Alfredo Cuzzocrea, editor, a 2-3 page manuscript proposal clearly explaining the mission and concerns of the proposed chapter by November 15, 2007. We strongly encourage other topics that have not been listed in our suggested list, particularly if the topic is related to the research area in which you have expertise. Upon acceptance of your proposal (notification December 15, 2007), you will have until March 30, 2008, to prepare your chapter of 8,000-10,000 words and 7-10 related terms and their appropriate definitions. Guidelines for preparing your chapter and terms and definitions will be sent to you upon acceptance of your proposal.
The book Intelligent Techniques for Warehousing and Mining Sensor Network Data will be published by IGI Global (formerly Idea Group Inc.), an imprint of Information Science Reference (formerly Idea Group Reference), in 2009.
For more information and any inquire, please contact Alfredo Cuzzocrea, ICAR Institute and DEIS Department, University of Calabria, Italy, at cuzzocrea@si.deis.unical.it