NDLI: Knowledge discovery from sensor data (SensorKDD) (SKDD, Vol. 12, Issue. 2)

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Volume 12, Issue 2, December 2010

On the importance of sharing negative results

Topic-based social network analysis for virtual communities of interests in the dark web

Latent semantic indexing (LSI) fails for TREC collections

Tenth international workshop on multimedia data mining

Activity recognition using cell phone accelerometers

On cross-validation and stacking

MultiClust 2010

Fully decentralized computation of aggregates over data streams

Limitations of matrix completion via trace norm minimization

Knowledge discovery from sensor data (SensorKDD)

A framework for mining interesting pattern sets

On exploiting hierarchical label structure with pairwise classifiers

Novel data stream pattern mining report on the StreamKDD'10 workshop

Unexpected results in automatic list extraction on the web

Useful patterns (UP'10) ACM SIGKDD workshop report

Unexpected results in online controlled experiments

Computation in large-scale scientific and internet data applications is a focus of MMDS 2010

Inactive learning?

Eighth workshop on mining and learning with graphs

Volume 12, Issue 1, June 2010

Knowledge discovery from sensor data (SensorKDD) (SKDD, Vol. 12, Issue. 2)

Content Provider	ACM Digital Library
Author	Chawla, Nitesh V. Omitaomu, Olufemi A. Chandola, Varun Vatsavai, Ranga R. Gaber, Mohamed M. Gama, Joao Ganguly, Auroop R.
Abstract	Sensor data is being collected at an unprecedented rate across a variety of domains from a broad spectrum of sources, such as wide-area sensor infrastructures, remote sensing instruments, RFIDs, and wireless sensor networks. With the recent proliferation of smart-phones, and similar GPS enabled mobile devices, collection of sensor data is no longer limited to scientific communities, but has reached general public. With massive volumes of such disparate, dynamic, and geographically distributed data available, many high-priority applications have been identified that involve analysis of such data to solve real world problems such as understanding climate change and its impacts, electric grid monitoring, disaster preparedness and management, national or homeland security, and the management of critical infrastructures. Given the unique characteristics of sensor data, particularly its spatiotemporal nature and presence of constraints associated with the data collection and computational resources, there have been many research efforts to analyze the sensor data which build upon the general research in the data mining community but are significantly different in terms of how they address the specific challenges encountered when dealing with sensor data. In particular, the raw data from sensors needs to be efficiently managed and transformed to usable information through data fusion, which in turn must be converted to predictive insights via knowledge discovery, ultimately facilitating automated or humaninduced tactical decisions or strategic policy based on decision sciences and decision support systems. Keeping in view the requirements of the emerging field of knowledge discovery from sensor data, we took initiative to develop a community of researchers with common interests and scientific goals, which culminated into the organization of SensorKDD series of workshops in conjunction with the prestigious ACM SIGKDD International Conference of Knowledge Discovery and Data Mining. In this report, we summarize events at the Fourth ACM-SIGKDD International Workshop on Knowledge Discovery form Sensor Data (SensorKDD 2010).
Starting Page	50
Ending Page	53
Page Count	4
File Format	PDF
ISSN	19310145 19310153
DOI	10.1145/1964897.1964911
Journal	ACM SIGKDD Explorations Newsletter (SKDD)
Volume Number	12
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2000-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article

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