Fast discovery of association rules
Advances in knowledge discovery and data mining
Scaling up dynamic time warping for datamining applications
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Locally adaptive dimensionality reduction for indexing large time series databases
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
Knowledge Discovery from Telecommunication Network Alarm Databases
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Distance Measures for Effective Clustering of ARIMA Time-Series
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Finding Informative Rules in Interval Sequences
IDA '01 Proceedings of the 4th International Conference on Advances in Intelligent Data Analysis
An Index-Based Approach for Similarity Search Supporting Time Warping in Large Sequence Databases
Proceedings of the 17th International Conference on Data Engineering
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding surprising patterns in a time series database in linear time and space
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Evolutionary Rule Mining in Time Series Databases
Machine Learning
Efficient mining of iterative patterns for software specification discovery
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Knowledge construction from time series data using a collaborative exploration system
Journal of Biomedical Informatics
Classification of software behaviors for failure detection: a discriminative pattern mining approach
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Toward a simulation-generated knowledge base of service performance
Proceedings of the 4th International Workshop on Middleware for Service Oriented Computing
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Administering service-oriented architecture (SOA) systems could require sophisticated rules to decide for instance whether to add or remove servers and when. Rule construction often necessitates experts to study patterns that contribute to changes or events. This is a time consuming and error-prone process for complex software systems. In this paper we test the feasibility of automating this process by mining historical data such as past service requests (in time series) and server change events that the administrator committed. We propose a new method to relate frequent patterns in a given time series to changes recorded in the event's history. We implemented and tested our method on a simulation system for SOA applications. First, we use Euclidean distance, DTW, and FastDTW to identify frequent patterns in a time series that represents performance metric of a SOA simulation system. Then, we calculate the confidence and support of frequent patterns that contribute to changes to identify a set of rules for automating changes. We tested rules that are generated using the proposed method in a training set on a testing set. The average accuracy of generated rules for the change event "remove" exceeded 80% in our experiments.