Parallel sequence mining on shared-memory machines
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Probabilistic discovery of time series motifs
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Parallel tree-projection-based sequence mining algorithms
Parallel Computing
Parallel mining of closed sequential patterns
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Anytime Classification Using the Nearest Neighbor Algorithm with Applications to Stream Mining
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Knowledge construction from time series data using a collaborative exploration system
Journal of Biomedical Informatics
Declarative querying for biological sequences
Declarative querying for biological sequences
Finding Motifs of Financial Data Streams in Real Time
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Discovering multivariate motifs using subsequence density estimation and greedy mixture learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Proceedings of the 2008 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
| Hi-index | 0.00 |
Time series motifs are an integral part of diverse data mining applications including classification, summarization and near-duplicate detection. These are used across wide variety of domains such as image processing, bioinformatics, medicine, extreme weather prediction, the analysis of web log and customer shopping sequences, the study of XML query access patterns, electroencephalograph interpretation and entomological telemetry data mining. Exact Motif discovery in soft real-time over 100K time series is a challenging problem. We present novel parallel algorithms for soft real-time exact motif discovery on multi-core architectures. Experimental results on large scale P6 SMP system, using real life and synthetic time series data, demonstrate the scalability of our algorithms and their ability to discover motifs in soft real-time. To the best of our knowledge, this is the first such work on parallel scalable soft real-time exact motif discovery.