XRel: a path-based approach to storage and retrieval of XML documents using relational databases
ACM Transactions on Internet Technology (TOIT)
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Querying business processes with BP-QL
VLDB '05 Proceedings of the 31st international conference on Very large data bases
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Structural Matching of BPEL Processes
ECOWS '07 Proceedings of the Fifth European Conference on Web Services
On the suitability of BPMN for business process modelling
BPM'06 Proceedings of the 4th international conference on Business Process Management
Search, adapt, and reuse: the future of scientific workflows
ACM SIGMOD Record
On efficient processing of BPMN-Q queries
Computers in Industry
FNet: an index for advanced business process querying
BPM'12 Proceedings of the 10th international conference on Business Process Management
A graph distance based metric for data oriented workflow retrieval with variable time constraints
Expert Systems with Applications: An International Journal
Querying business process model repositories
World Wide Web
| Hi-index | 0.00 |
With the rapid and incremental increase in the number of process models developed by different process designers, it becomes crucial for business process designers to be able to look up the repository for models that could handle a similar situation before developing new ones. In this paper, we present an approach for querying repositories of graph-based business process models. Our approach is based on a visual query language for business processes called BPMN-Q. BPMN-Q is used to query business process models by matching the structure of a graph query to that of a process model. The query engine of our system is built on top of traditional RDBMS. We make use of the robust relational indexing infrastructure in order to achieve an efficient and scalable query evaluation performance.