Data Warehousing and Knowledge Discovery from Sensors and Streams Track of the 22th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2010) October 27-29, 2010, Arras, France   Selected papers from the track will be invited for submission to a special issue of Journal of Computer and System Sciences Elsevier

[Aim and Scope | Conference Location | Submission Guidelines and Instructions | Paper Publication | Important Dates | Program Committee]

ACCEPTED PAPERS

• L. Khan (Invited Speaker), Data Stream Mining: Chellenges and Techniques
   

• M.S. Ahmed, L. Khan, M. Rajeswari, Using Correlation Based Subspace Clustering for Multi-Label Text Data Classification
   

• M.M. Gaber, S. Krishnaswamy, B. Gillick, N. Nicoloudis, J. Liono, H. Al Taiar, A. Zaslavsky, Adapative Clutter-Aware Visualization for Mobile Data Stream Mining
   

• F. Stahl, M.M. Gaber, M. Bramer, P.S. Yu, Pocket Data Mining: Towards Collaborative Data Mining in Mobile Computing Environments
   

• G. Chatzimilioudis, A. Cuzzocrea, D. Gunopulos, Optimizing Query Routing Trees in Wireless Sensor Networks
   

• L. Copin, H. Rey, X. Vasquez, A. Laurent, M. Teisseire, Intelligent Energy Data Warehouses: What Challenges?
   

• B. Pogorelc, M. Gams, Discovery of Gait Anomalies from Motion Sensor Data

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Aim and Scope

During last years, the issue of effectively and efficiently supporting data warehousing and knowledge discovery from sensor networks, and, more generally, data stream sources, which can be reasonably intended as a meaningfully generalization of the former kind of networks, is gaining a more and more great deal of interest from the data warehousing and knowledge discovery research community. Main research issues in this scientific field arise from the clear and well-recognized unsuitability of traditional data warehousing and knowledge discovery methodologies, techniques and algorithms in dealing with the new challenges posed by sensor network data and, more generally, data streams. Indeed, traditional approaches are meant for multi-step methodologies and techniques, and multi-scan algorithms, which cannot be straightforwardly applied to sensor network data and data streams, due to well-known limitations such as bounded memory, online/timely data processing, need for one-pass techniques, energy consumption issues etc.

Starting from these limitations, a plethora of data warehousing and knowledge discovery methodologies, techniques and algorithms have been proposed during these last years, and, simultaneously, a more and more large number of research events have focused their attention to this leading research challenge. Following this actual trend and the previous successful related events represented by the book Intelligent Techniques for Warehousing and Mining Sensor Network Data and the First International Workshop on Data Warehsouing and Knowledge Discovery from Sensors and Streams of the 5th International Conference on Distributed Computing in Sensor Systems (DCOSS 2009), held in Marina del Rey, CA, USA, during June 8-10, 2009, the Track Data Warehousing and Knowledge Discovery from Sensors and Streams of the 22th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2010), which will be held in Arras, France, during October 27-29, 2010, will provide a leading forum for researchers and practitioners interested in data warehousing and knowledge discovery from sensor network data and data streams, to meet and exchange preliminary ideas and mature results, with emphasis on both the theoretical and practical point of view.

The Track Data Warehousing and Knowledge Discovery from Sensors and Streams of IEEE ICTAI 2010 will address all topics of data warehousing and knowledge discovery from sensor network data and data streams, including:

• Foundations of Warehousing Sensor-Network-Data/Data-Streams

• Foundations of Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Theories for Warehousing Sensor-Network-Data/Data-Streams

• Theories for Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Methodologies for Warehousing Sensor-Network-Data/Data-Streams

• Methodologies for Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Techniques for Warehousing Sensor-Network-Data/Data-Streams

• Techniques for Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Algorithms for Warehousing Sensor-Network-Data/Data-Streams

• Algorithms for Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Integration Techniques for Warehousing Sensor-Network-Data/Data-Streams

• Mediators for Warehousing Sensor-Network-Data/Data-Streams

• ETL for Sensor-Network-Data/Data-Streams

• OLAP for Sensor-Network-Data/Data-Streams

• OLAM for Sensor-Network-Data/Data-Streams

• BI for Sensor-Network-Data/Data-Streams

• Distributed Warehousing Sensor-Network-Data/Data-Streams

• Real-Time Warehousing Sensor-Network-Data/Data-Streams

• Load-Balancing Issues in Distributed and Real-Time Warehousing Sensor-Network-Data/Data-Streams

• Classification from Sensor-Network-Data/Data-Streams

• Clustering from Sensor-Network-Data/Data-Streams

• Association Rule Mining from Sensor-Network-Data/Data-Streams

• Outlier Detection from Sensor-Network-Data/Data-Streams

• Machine Learning from Sensor-Network-Data/Data-Streams

• Statistical Learning from Sensor-Network-Data/Data-Streams

• Distributed Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Real-Time Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Scalable Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Feature Selection Techniques for Sensor-Network-Data/Data-Streams

• Dimensionality Reduction Techniques for Sensor-Network-Data/Data-Streams

• Concept Drifting Problems for Sensor-Network-Data/Data-Streams

• Change Detection Techniques for Sensor-Network-Data/Data-Streams

• Knowledge Acquisition Models and Techniques for Sensor-Network-Data/Data-Streams

• Knowledge Visualization Models and Techniques for Sensor-Network-Data/Data-Streams

• Knowledge Provisioning Models and Techniques for Sensor-Network-Data/Data-Streams

• Knowledge Fusion Models and Techniques for Sensor-Network-Data/Data-Streams

• Knowledge Reasoning Models and Techniques for Sensor-Network-Data/Data-Streams

• Spatio-Temporal Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Location-Aware Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Knowledge Discovery from Uncertain Sensor-Network-Data/Data-Streams

• Knowledge Discovery from Incomplete Sensor-Network-Data/Data-Streams

• Knowledge Discovery from Probabilistic Sensor-Network-Data/Data-Streams

• Knowledge Discovery from Multiple Sensor-Network-Data/Data-Streams

• Adaptive Knowledge Discovery Models and Techniques from Sensor-Network-Data/Data-Streams

• Resource-Aware Algorithms for Large-Scale Scalable Data Warehsouing and Knowledge Discovery from Sensor-Network-Data/Data-Streams

• Advanced Techniques for Efficient Scalable Knowledge Discovery from Sensor-Network-Data/Data-Streams (e.g., Histograms and Summaries over Sensor-Network-Data/Data-Streams, Semantics-based Compression of Sensor-Network-Data/Data-Streams, Correlation Discovery Techniques for Sensor-Network-Data/Data-Streams etc)

• Applications of Warehousing Sensor-Network-Data/Data-Streams

• Applications of Knowledge Discovery from Sensor-Network-Data/Data-Streams

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Conference Location

Arras, France.

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Submission Guidelines and Instructions

Contributions are invited from prospective authors with interests in the indicated session topics and related areas of application. All contributions should be high quality, original and not published elsewhere or submitted for publication during the review period.

Submitted papers should strictly follow the IEEE CS Conference Paper Formatting Instructions and Templates. Maximum regular camera-ready paper length allowed is 8 pages. Submitted papers will be thoroughly reviewed by members of the Track Program Committee for quality, correctness, originality and relevance. Notification and reviews will be communicated via e-mail. All accepted papers must be presented by one of the authors, who must register.

Abstracts (deadline July 8, 2010) should be sent by e-mail to the Program Chair Alfredo Cuzzocrea at cuzzocrea@si.deis.unical.it. Abstracts must include paper title, abstract, list of keywords, and list of authors with full names and affiliations. One of the authors must be designated as the primary contact point to receive notification and reviews.

Papers (deadline July 15, 2010) should be submitted in PDF or Postscript format by e-mail to the Program Chair Alfredo Cuzzocrea at cuzzocrea@si.deis.unical.it.

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Paper Publication

Accepted papers will appear in the IEEE ICTAI 2010 conference proceeding book.

Authors of selected papers from the workshop will be invited to submit an extended version of their paper to a special issue of Journal of Computer and System Sciences, Elsevier. Selected papers from the previous edition have been invited to a special issue of Knowledge and Information Systems, Springer.

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Important Dates

Abstract submission: July 8, 2010
Paper submission: July 15, 2010
Notification of acceptance: August 23, 2010
Camera-ready paper due: August 31, 2010
Conference: October 27-29, 2010

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Program Committee Chair

Alfredo Cuzzocrea, ICAR-CNR & University of Calabria, Italy

Program Committee

Jesus Aguilar, Pablo de Olavide University, Spain
Giuseppe Amato, ISTI-CNR, Italy
Andre Carvalho, University of Sao Paulo, Brazil
Sanjay Chawla, University of Sidney, Australia
Francisco Ferrer, University of Seville, Spain
Mohamed Gaber, Monash University, Australia
Joao Gama, University of Porto, Portugal
Auroop Ganguly, Oak Ridge National Laboratory, USA
Dimitrios Gunopulos, University of California Riverside, USA
Paul Havinga, University of Twente, The Netherlands
Yan Huang, University of North Texas, USA
Mark Last, Ben-Gurion University, Israel
Donato Malerba, University of Bari, Italy
Michael May, Fraunhofer IAIS, Germany
Silvia Nittel, University of Maine, USA
Olufemi Omitaomu, Oak Ridge National Laboratory, USA
Apostolos Papadopoulos, Aristotle University, Greece
Dino Pedreschi, University of Pisa, Italy
Jean-Marc Petit, University of Lyon 1, France
Mark Roantree, Dublin City University, Ireland
Josep Roure, Technical University of Catalonia, Spain
Lorenza Saitta, University of Piemonte Orientale, Italy
Claudio Sartori, University of Bologna, Italy
Yucel Saygin, Sabanci University, Turkey
Bernhard Seeger, University of Marburg, Germany
Maarten van Someren, University of Amsterdam, The Netherlands
Eduardo Spinosa, University of Sao Paulo, Brazil
Ranga Vatsavai, Oak Ridge National Laboratory, USA
Jeff Vitter, Purdue University, USA
David J. Yates, Bentley University, USA
Eiko Yoneki, University of Cambridge, UK
Jeffrey Yu Xu, Chinese University of Hong Kong, China

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