Developing provenance-aware query systems: an occurrence-centric approach

In recent years, research on provenance has increased exponentially, and such studies in the field of business process monitoring have been especially remarkable. Business process monitoring deals with recording information about the actual execution of processes to then extract valuable knowledge that can be utilized for business process quality improvement. In prior research, we developed an occurrence-centric approach built on our notion of occurrence that provides a holistic perspective of system dynamics. Based on this concept, more complex structures are defined herein, namely Occurrence Base (OcBase) and Occurrence Management System (OcSystem), which serve as scaffolding to develop business process monitoring systems. This paper focuses primarily on the critical provenance task of extracting valuable knowledge from such systems by proposing an Occurrence Query Framework that includes the definition of an Occurrence Base Metamodel and an Occurrence Query Language based on this metamodel. Our framework provides a way of working for the construction of business process monitoring systems that are provenance aware. As a proof of concept, a tool implementing the various components of the framework is presented. This tool has been tested against a real system in the context of biobanks.

[1]  Wilhelm Hasselbring,et al.  Capturing provenance information with a workflow monitoring extension for the Kieker framework , 2012, SWPM@ESWC.

[2]  Sérgio Manuel Serra da Cruz,et al.  Monitoring SOA-based applications with business provenance , 2013, SAC '13.

[3]  Manfred Reichert,et al.  The Proviado Access Control Model for Business Process Monitoring Components , 2010, Enterp. Model. Inf. Syst. Archit. Int. J. Concept. Model..

[4]  Margo I. Seltzer,et al.  Choosing a Data Model and Query Language for Provenance , 2008, IPAW 2008.

[5]  Gintaras V. Reklaitis,et al.  A workflow modeling system for capturing data provenance , 2014, Comput. Chem. Eng..

[6]  Nirmal Mukhi,et al.  Monitoring Unmanaged Business Processes , 2010, OTM Conferences.

[7]  Beth Plale,et al.  Temporal representation for mining scientific data provenance , 2014, Future Gener. Comput. Syst..

[8]  Bokyoung Kang,et al.  Real-time business process monitoring method for prediction of abnormal termination using KNNI-based LOF prediction , 2012, Expert Syst. Appl..

[9]  Dongwoo Kang,et al.  An OWL-based semantic business process monitoring framework , 2009, Expert Syst. Appl..

[10]  Yolanda Gil,et al.  PROV-DM: The PROV Data Model , 2013 .

[11]  Cris Kobryn,et al.  Architectural Patterns for Metamodeling: A Hitchhikers Guide to the UML Metaverse , 2000, UML.

[12]  Michele Risi,et al.  Design pattern recovery through visual language parsing and source code analysis , 2009, J. Syst. Softw..

[13]  Eladio Domínguez,et al.  QRP: a CMMI Appraisal Tool for Project Quality Management , 2013 .

[14]  Gabor Karsai,et al.  Design Guidelines for Domain Specific Languages , 2014, ArXiv.

[15]  Juliana Freire,et al.  Tackling the Provenance Challenge one layer at a time , 2008, Concurr. Comput. Pract. Exp..

[16]  Peter Buneman,et al.  Data provenance – the foundation of data quality , 2010 .

[17]  Jean Bézivin,et al.  Model Driven Engineering: An Emerging Technical Space , 2005, GTTSE.

[18]  Parag Agrawal,et al.  Trio: a system for data, uncertainty, and lineage , 2006, VLDB.

[19]  José M Ordovás,et al.  Aragon workers’ health study – design and cohort description , 2012, BMC Cardiovascular Disorders.

[20]  Yogesh L. Simmhan,et al.  The Open Provenance Model core specification (v1.1) , 2011, Future Gener. Comput. Syst..

[21]  Vasa Curcin,et al.  Implementing interoperable provenance in biomedical research , 2014, Future Gener. Comput. Syst..

[22]  Luigi Coppolino,et al.  A business process monitor for a mobile phone recharging system , 2008, J. Syst. Archit..

[23]  Anthony C. Bloesch,et al.  ConQuer: A Conceptual Query Language , 1996, ER.

[24]  Shiyong Lu,et al.  RDFProv: A relational RDF store for querying and managing scientific workflow provenance , 2010, Data Knowl. Eng..

[25]  Luc Moreau,et al.  The Foundations for Provenance on the Web , 2010, Found. Trends Web Sci..

[26]  Val Tannen,et al.  Querying data provenance , 2010, SIGMOD Conference.

[27]  Michael J. Lutz,et al.  Undergraduate software engineering , 2014, CACM.

[28]  Marta Mattoso,et al.  Provenance management in Swift , 2011, Future Gener. Comput. Syst..

[29]  Wil M. P. van der Aalst,et al.  Exploring the CSCW spectrum using process mining , 2007, Adv. Eng. Informatics.

[30]  Margo I. Seltzer,et al.  A primer on provenance , 2014, CACM.

[31]  Wang Chiew Tan,et al.  DBNotes: a post-it system for relational databases based on provenance , 2005, SIGMOD '05.

[32]  Klaus R. Dittrich,et al.  Data Provenance: A Categorization of Existing Approaches , 2007, BTW.

[33]  E. Rodríguez-Sánchez,et al.  Ambulatory arterial stiffness indices and target organ damage in hypertension , 2012, BMC Cardiovascular Disorders.

[34]  Jianwen Su,et al.  Static Analysis of Business Artifact-centric Operational Models , 2007, IEEE International Conference on Service-Oriented Computing and Applications (SOCA '07).

[35]  Eladio Domínguez,et al.  Occurrence-Oriented Design Strategy for Developing Business Process Monitoring Systems , 2014, IEEE Transactions on Knowledge and Data Engineering.

[36]  Ryan K. L. Ko,et al.  A computer scientist's introductory guide to business process management (BPM) , 2009, ACM Crossroads.

[37]  Yurdaer N. Doganata,et al.  Business Provenance - A Technology to Increase Traceability of End-to-End Operations , 2008, OTM Conferences.

[38]  Gustavo Alonso,et al.  Using SQL for Efficient Generation and Querying of Provenance Information , 2013, In Search of Elegance in the Theory and Practice of Computation.

[39]  Paul T. Groth,et al.  The provenance of electronic data , 2008, CACM.

[40]  Yogesh L. Simmhan,et al.  A survey of data provenance in e-science , 2005, SGMD.

[41]  Owen Molloy,et al.  Towards a Semantic Framework for Business Activity Monitoring and Management , 2008, AAAI Spring Symposium: AI Meets Business Rules and Process Management.

[42]  Wil M. P. van der Aalst,et al.  Workflow mining: discovering process models from event logs , 2004, IEEE Transactions on Knowledge and Data Engineering.

[43]  Cláudio T. Silva,et al.  Provenance for Computational Tasks: A Survey , 2008, Computing in Science & Engineering.