Bursty and hierarchical structure in streams
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Asynchronous Periodic Patterns in Time Series Data
IEEE Transactions on Knowledge and Data Engineering
MAIDS: mining alarming incidents from data streams
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Adaptive event detection with time-varying poisson processes
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Suppressing model overfitting in mining concept-drifting data streams
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Detecting changes in large data sets of payment card data: a case study
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-dimensional regression analysis of time-series data streams
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
A regression-based temporal pattern mining scheme for data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Intervention Events Detection and Prediction in Data Streams
APWeb/WAIM '09 Proceedings of the Joint International Conferences on Advances in Data and Web Management
PGG: an online pattern based approach for stream variation management
Journal of Computer Science and Technology
Stop Chasing Trends: Discovering High Order Models in Evolving Data
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
| Hi-index | 0.00 |
Huge time-series stream data are collected every day from many areas, and their trends may be impacted by outside events, hence biased from its normal behavior. This phenomenon is referred as intervention. Intervention rule mining is a new research direction in data mining with great challenges. To solve these challenges, this study makes the following contributions: (a) Proposes a framework to detect intervention events in time-series streams, (b) Proposes approaches to evaluate the impact of intervention events, and (c) Conducts extensive experiments both on real data and on synthetic data. The results of the experiments show that the newly proposed methods reveal interesting knowledge and perform well with good accuracy and efficiency.