-------- Original-Nachricht -------- Betreff: Call for Papers: ACM SenSys 2013 SenseMine Workshop (Submission Deadline: Sep 6, 2013) Datum: Wed, 14 Aug 2013 07:39:33 +1000 Von: salil kanhere salilk@CSE.UNSW.EDU.AU Antwort an: salil kanhere salilk@CSE.UNSW.EDU.AU An: SIGMOBILE-MEMBERS@LISTSERV.ACM.ORG

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-------------------------------------------------------- ACM SenSys 2013 SenseMine Workshop --------------------------------------------------------

SenseMine 2013 First International Workshop on Sensing and Big Data Mining

November 14, Rome, Italy

http://www2.research.att.com/sensemine2013/

------- CALL FOR PAPERS

The numbers and modalities of digital information sources being captured to monitor our traffic, weather, power, personal context, goods, factories, utilities, ports, health, IT infrastructure, and social networks, is continuing to grow at an incredible rate. Commercial, government organizations, and individuals depend on the ability to automatically mine data from different types of sensor platforms (from large sensor networks to an individual's smartphone) in order to monitor, alert, learn from, and in some cases affect and control our surroundings.

The research involved in developing applications for these classes of problems lies at the intersection of several diverse disciplines, including sensing systems, signal processing, machine learning and data mining, data management, and large-scale distributed systems - for both online as well as offline analysis. †In this workshop, co-located with AMC SenSys 2013, we will include state-of-the-art approaches and technical solutions in the area of extracting knowledge, by mining data from sensor networks in large-scale settings. The goal of the workshop is to establish a new research community and a venue for researchers, practitioners, and academics to present their results in these disciplines. We expect this workshop to be a long-term, continued venue for this research community, and to also lead to the setup of appropriate special issues and journals.

As part of this workshop, we will include both peer-reviewed and invited papers. All accepted papers will be included in the ACM digital library. Original contributions, previously unpublished, and not currently under review by another journal or conference, are solicited in relevant areas, including, but not limited to:

1. Mining data from large-scale sensor networks and smartphone sensing deployments a. Distributed, Parallel, and Scalable Mining Algorithms b. Multi-Modal Mining Algorithms c. Resource-Adaptive (power, network, compute) Mining d. Mining sparsely sampled, noisy, and untrustworthy data e. Mining big data collected from large-scale smartphone sensing deployments f. Supporting visualization and user interaction

2. Distributed Processing for Sensor Network Data a. Distributed Processing at edge, and core of sensor network b. Stream Processing Systems, Hadoop/MapReduce, Cloud, Cross-Platform computing

3. Sensor Systems and Machine-to-Machine architecture Design a. Large-scale sensor network and Machine-to-Machine architectures b. Smartphone sensing deployments for big data collection

4. Novel Applications of Mining Sensor Network Data: Energy, Healthcare, Security, Transportation, P2P Systems, Enterprise Environments, Social Networks, Smartphones

-------------------- Submission Instructions: Submission deadline: September 6, 2013 Notification of acceptance: October 11, 2013

Please email your submission to the workshop TPC chairs (miluzzo at research.att.com http://research.att.com/ and turaga atus.ibm.com http://atus.ibm.com/) with the following text in the subject line of your email: [SenseMine 2013 submission]

-------------------- Workshop Chairs: Emiliano Miluzzo, AT&T Labs Research Deepak S. Turaga, IBM Research

-------------------- TPC Committee (Preliminary):

Deborah Estrin (Cornell) Andrew Campbell (Dartmouth) Yanyong Zhang (Rutgers) Marco Conti (CNR Italy) Immanel Schweizer (Technische Universitat Darmstadt) Jin Gao (SUNY Buffalo) Wei Fan (Huawei) Tarek Abdelzahar (UIUC) Charu Aggarwal (IBM Research)

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