A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions
Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions
Data integration: the teenage years
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
Mining compressed commodity workflows from massive RFID data sets
CIKM '06 Proceedings of the 15th ACM international conference on Information and knowledge management
Event-driven rules for sensing and responding to business situations
Proceedings of the 2007 inaugural international conference on Distributed event-based systems
Bigtable: a distributed storage system for structured data
OSDI '06 Proceedings of the 7th USENIX Symposium on Operating Systems Design and Implementation - Volume 7
BHUNT: automatic discovery of Fuzzy algebraic constraints in relational data
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Data & Knowledge Engineering
Process Mining of RFID-Based Supply Chains
CEC '09 Proceedings of the 2009 IEEE Conference on Commerce and Enterprise Computing
Semantic Event Correlation Using Ontologies
OTM '09 Proceedings of the Confederated International Conferences, CoopIS, DOA, IS, and ODBASE 2009 on On the Move to Meaningful Internet Systems: Part II
A Business Centric End-to-End Monitoring Approach for Service Composites
SCC '10 Proceedings of the 2010 IEEE International Conference on Services Computing
Automated correlation discovery for semi-structured business processes
ICDEW '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering Workshops
Event correlation and pattern detection in CEDR
EDBT'06 Proceedings of the 2006 international conference on Current Trends in Database Technology
Deriving a unified fault taxonomy for event-based systems
Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems
Business process mining from e-commerce web logs
BPM'13 Proceedings of the 11th international conference on Business Process Management
Enhancing declare maps based on event correlations
BPM'13 Proceedings of the 11th international conference on Business Process Management
Hi-index | 0.00 |
In this paper we describe an algorithm to discover event correlation rules from arbitrary data sources. Correlation rules can be useful for determining relationships between events in order to isolate instances of a running business process for the purposes of monitoring, discovery and other applications. We have implemented our algorithm and validate our approach on events generated by a simulator that implements a real-world inspired export compliance regulations scenario consisting of 24 activities and corresponding event types. This simulated scenario involves a wide range of heterogeneous systems (e.g. Order Management, Document Management, E-Mail, and Export Violation Detection Services) as well as workflow-supported human-driven interactions (Process Management System). Experimental results demonstrate that our algorithm achieves a high level of accuracy in the detection of correlation rules. This paper confirms that our algorithm is a step towards semi-automating the task of detecting correlations. We also demonstrate how correlation rules discovered by our algorithm can be used to create aggregation nodes that allow more efficient querying, filtering and analytics. The results in this paper encourage future directions such as distributed statistics calculation, and scalability in terms of handling massive data sets.