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» Warehousing and Analyzing Massive RFID Data Sets
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SOFSEM
2001
Springer
14 years 2 days ago
How Can Computer Science Contribute to Knowledge Discovery?
Knowledge discovery, that is, to analyze a given massive data set and derive or discover some knowledge from it, has been becoming a quite important subject in several fields incl...
Osamu Watanabe
BIBM
2010
IEEE
139views Bioinformatics» more  BIBM 2010»
13 years 5 months ago
Scalable, updatable predictive models for sequence data
The emergence of data rich domains has led to an exponential growth in the size and number of data repositories, offering exciting opportunities to learn from the data using machin...
Neeraj Koul, Ngot Bui, Vasant Honavar
ICASSP
2009
IEEE
14 years 2 months ago
Identification of neurons participating in cell assemblies
Chances to detect assembly activity are expected to increase if the spiking activities of large numbers of neurons are recorded simultaneously. Although such massively parallel re...
Sonja Grün, Denise Berger, Christian Borgelt
VLDB
2001
ACM
100views Database» more  VLDB 2001»
14 years 2 days ago
Improving Business Process Quality through Exception Understanding, Prediction, and Prevention
Business process automation technologies are being increasingly used by many companies to improve the efficiency of both internal processes as well as of e-services offered to cus...
Daniela Grigori, Fabio Casati, Umeshwar Dayal, Min...
ICDM
2006
IEEE
86views Data Mining» more  ICDM 2006»
14 years 1 months ago
Turning Clusters into Patterns: Rectangle-Based Discriminative Data Description
The ultimate goal of data mining is to extract knowledge from massive data. Knowledge is ideally represented as human-comprehensible patterns from which end-users can gain intuiti...
Byron J. Gao, Martin Ester