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SIGMOD
2004
ACM
209views Database» more  SIGMOD 2004»
14 years 7 months ago
MAIDS: Mining Alarming Incidents from Data Streams
Real-time surveillance systems, network and telecommunication systems, and other dynamic processes often generate tremendous (potentially infinite) volume of stream data. Effectiv...
Y. Dora Cai, David Clutter, Greg Pape, Jiawei Han,...
GRC
2008
IEEE
13 years 7 months ago
MovStream: An Efficient Algorithm for Monitoring Clusters Evolving in Data Streams
Monitoring cluster evolution in data streams is a major research topic in data streams mining. Previous clustering methods for evolving data streams focus on global clustering res...
Liang Tang, Chang-jie Tang, Lei Duan, Chuan Li, Ye...
GIS
2010
ACM
13 years 5 months ago
A data stream-based evaluation framework for traffic information systems
Traffic information systems based on mobile, in-car sensor technology are a challenge for data management systems as a huge amount of data has to be processed in real-time. Data m...
Sandra Geisler, Christoph Quix, Stefan Schiffer
WSDM
2012
ACM
245views Data Mining» more  WSDM 2012»
12 years 3 months ago
The early bird gets the buzz: detecting anomalies and emerging trends in information networks
In this work we propose a novel approach to anomaly detection in streaming communication data. We first build a stochastic model for the system based on temporal communication pa...
Brian Thompson
ICDE
2008
IEEE
192views Database» more  ICDE 2008»
14 years 9 months ago
Verifying and Mining Frequent Patterns from Large Windows over Data Streams
Mining frequent itemsets from data streams has proved to be very difficult because of computational complexity and the need for real-time response. In this paper, we introduce a no...
Barzan Mozafari, Hetal Thakkar, Carlo Zaniolo