On a relation between graph edit distance and maximum common subgraph
Pattern Recognition Letters
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Taverna: lessons in creating a workflow environment for the life sciences: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Provenance in collection-oriented scientific workflows
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
PASSing the provenance challenge
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
Provenance trails in the Wings-Pegasus system
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
Addressing the provenance challenge using ZOOM
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
Tackling the Provenance Challenge one layer at a time
Concurrency and Computation: Practice & Experience - The First Provenance Challenge
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Research on Ontology-Based Measuring Semantic Similarity
ICICSE '08 Proceedings of the 2008 International Conference on Internet Computing in Science and Engineering
Towards Case-Based Support for e-Science Workflow Generation by Mining Provenance
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Application of named graphs towards custom provenance views
TAPP'09 First workshop on on Theory and practice of provenance
Differencing Provenance in Scientific Workflows
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Provenance Information Model of Karma Version 3
SERVICES '09 Proceedings of the 2009 Congress on Services - I
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Connected substructure similarity search
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Provenance-based trustworthiness assessment in sensor networks
Proceedings of the Seventh International Workshop on Data Management for Sensor Networks
The Open Provenance Model core specification (v1.1)
Future Generation Computer Systems
Supervised learning for provenance-similarity of binaries
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Improving workflow fault tolerance through provenance-based recovery
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
Semantic service integration for water resource management
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Case-Based Trust Evaluation from Provenance Information
TRUSTCOM '11 Proceedings of the 2011IEEE 10th International Conference on Trust, Security and Privacy in Computing and Communications
Provenance explorer – customized provenance views using semantic inferencing
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Provenance collection support in the kepler scientific workflow system
IPAW'06 Proceedings of the 2006 international conference on Provenance and Annotation of Data
On presenting apropos provenance for situation awareness and data forensics
IPAW'12 Proceedings of the 4th international conference on Provenance and Annotation of Data and Processes
FlowRecommender: a workflow recommendation technique for process provenance
AusDM '09 Proceedings of the Eighth Australasian Data Mining Conference - Volume 101
High efficiency and quality: large graphs matching
The VLDB Journal — The International Journal on Very Large Data Bases
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A process trace describes the steps taken in a workflow to generate a particular result. Understanding a process trace is critical to be able to verify data, enable its re-use and to make appropriate decisions. Given many process traces, each with a large amount of very low level information, it is a challenge to make process traces meaningful to different users. It is more challenging to compare two complex process traces generated by heterogeneous systems and having different levels of granularity. In this paper, we present a novel notion of multi-granularity process trace that attempts to capture the conceptual abstraction of large process traces at different levels of granularity by leveraging ontology information. Based on this notion, graph matching based algorithms with semantic filtering are developed to efficiently and effectively compute the similarity between two process traces by considering both structural similarity and semantic similarity. Our experiment using both real world and synthetic datasets demonstrates that our techniques provide a practical approach for process trace similarity measurement.