Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Hancock: a language for extracting signatures from data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining high-speed data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
A General Method for Scaling Up Machine Learning Algorithms and its Application to Clustering
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Better streaming algorithms for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Clustering binary data streams with K-means
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Scaling Clustering Algorithms for Massive Data Sets using Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Fast window correlations over uncooperative time series
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Nile-PDT: a phenomenon detection and tracking framework for data stream management systems
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Data Streaming in Telepresence Environments
IEEE Transactions on Visualization and Computer Graphics
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Stream window join: tracking moving objects in sensor-network databases
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Detection and tracking of discrete phenomena in sensor-network databases
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
SlidingWindow based Multi-Join Algorithms over Distributed Data Streams
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Online clustering of parallel data streams
Data & Knowledge Engineering
Scalability Management in Sensor-Network PhenomenaBases
SSDBM '06 Proceedings of the 18th International Conference on Scientific and Statistical Database Management
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
Unsupervised Clustering In Streaming Data
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A Distributed Algorithm for Joins in Sensor Networks
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
Streaming queries over streaming data
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Maximizing the output rate of multi-way join queries over streaming information sources
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Scheduling for shared window joins over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Processing sliding window multi-joins in continuous queries over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Adaptive, hands-off stream mining
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Grid-based subspace clustering over data streams
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
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A phenomenon appears in a sensor network when a group of sensors continuously produces similar readings (i.e., data streams) over a period of time. This involves the processing of hundreds and maybe thousands of data streams in real-time. This paper focuses on detecting environmental phenomena and determining possible correlation between such phenomena.This paper proposes an efficient scheme for a detecting and tracking phenomena, e.g., air pollution and oil spills. To achieve fast online response, the proposed algorithms use a Discrete Fourier Transformation (DFT) to reduce the dimensionality of the streams. Each stream is represented by a point in a multidimensional grid in the frequency domain. The algorithm uses an improved unsupervised grid-based clustering technique to detect similar streams and to form clusters. The paper also proposes an efficient algorithm for detecting correlation among phenomena. The proposed algorithm calculates the correlation coefficient in the frequency domain. It makes use of the DFT coefficients that are calculated for detecting the phenomena. The proposed correlation detection algorithm uses only few DFT coefficients in the frequency domain.Experiments on synthetic data streams show that the proposed algorithm for detecting and tracking phenomena is much faster than the DBSCAN clustering technique, which is based on the R-tree index. At the same time, the proposed phenomena detection algorithm produces the same quality as that of the DBSCAN by only using two DFT coefficients in most of the cases. The experimental results also showed that the proposed technique for detecting the correlation among phenomena performs as good as the traditional Pearson correlation formula but it is much faster.