LACIS 2010 - Workshop on Large-scale Analytics for Complex Instrumented Systems (LACIS 2010)

Complex instrumented systems are ubiquitous around the world, such as civil engineering, information systems, health care systems, biological and life science, financial engineering and social networks. The fundamental notion of complex instrumented systems is about collecting, monitoring, analyzing data from everywhere and generating real-time insight to help people to make the informed decisions. Learning and mining from complex instrumented systems, such as environment monitoring, oil drilling, health care, sensor network, has emerged as one of the most important and challenging areas for sustainable development. Large-scale and heterogeneity are the key properties of data in complex instrumented systems. Real-time response and incremental model update are the key requirements for the analysis in complex instrumented systems. This raises great challenges to the existing algorithms on machine learning and data mining. In this workshop, we are interested in investigating the scalability and efficiency of machine learning and data mining algorithms with respect to both theoretical and experimental perspectives mining from complex instrumented systems. We are also interested in real world data mining applications and case studies related to complex instrumented systems. We seek papers in the following topics:

Systems and frameworks for large-scale data mining
Methodologies for online data mining or stream mining
Real-time decision support and mining
Parallel data mining methods and applications
Scalable data mining algorithms and systems over heterogeneous data sources
Data Mining Methods and Systems for Manufacturing and Heavy Industry applications
Data mining methods and systems for life science, biological applications
Data mining for medical informatics and health care applications
Data mining for environmental applications such as climate modeling
Data mining for emerging applications such as web mining, social network analysis
Data mining applications in other domains such as civil engineering, financial engineering
Invited Speaker

Eamonn Keogh from University of California at Riverside, USA
Important Date

Submission deadline: TBD
Review period: approximately 3 weeks
Notification date: TBD
Final version submission date: TBD
Submissions

Please prepare your paper not more than 10 pages in PDF file, with IEEE camera‐ready template: http://wi-lab.com/cyberchair/icdm09/scripts/submit....

All papers must be submitted in Adobe Portable Document Format (PDF). Please ensure that any special fonts used are included in the submitted documents. Please use the following link to submit your paper here. If you cannot submit there, please send to us by email .

Workshop Co-Chairs

Chid Apte, IBM TJ Watson
Wray Buntine, Canberra Research Laboratory
Yan Liu, IBM TJ Watson
Jimeng Sun, IBM TJ Watson
Jie Tang, Tsinghua University
Program Committee

Alex Gray, Geogia Tech
Rayid Ghani, Accenture
Charles Elkan, University of California, San Diego
Jennifer Neville, Purdue University
Mohammed Zaki, Rensselaer Polytechnic Institute
Dong Zhang, Google Inc.
Lei Zhang, Microsoft Research Asia
Zhong Su, IBM, CRL
Xifeng Yan, University of California, San Babara
Spiros Papadimitriou, IBM
Tamara Kolda, Sandia National Labs
Petros Drineas, RPI
Edwin Pednault, IBM Research
Elad Yom-Tov, IBM Research
Philip S. Yu, University of Illinois at Chicago
Qiang Yang, Hong Kong University of Science and Technology
Huiming Qu, IBM TJ Watson
Sanjay Chawla, University of Sydney
Yu-Ru Lin, ASU
Contact us

Yan Liu, IBM TJ Watson Research Center, liuya-AT-us.ibm.com, 1-914-945-2128