An Architecture for Querying Business Process, Business Process Instances, and Business Data Models

Business data are usually managed by means of business processes during process instances. These viewpoints (business, instances and data) are strongly related because the life-cycle of business data objects need to be aligned with the business process and process instance models. However, current approaches do not provide a mechanism to integrate these three viewpoints nor to query them all together while maintaining the information in the distributed, heterogeneous systems where they have been created. In this paper, we propose the integration of the business process, business process instance, and business data models by using their metamodels and also an architecture to support this integration. The goal of this integration is to make the most of the three models and the technologies that support them in an isolated way. In our approach, it is not necessary to change the source data formats nor transforming them into a common one. Furthermore, the proposed architecture allows us to query the three models even though they come from three different technologies.

[1]  Boualem Benatallah,et al.  Enabling the Analysis of Cross-Cutting Aspects in Ad-Hoc Processes , 2013, CAiSE.

[2]  Wil M. P. van der Aalst,et al.  Process querying: Enabling business intelligence through query-based process analytics , 2017, Decis. Support Syst..

[3]  Stijn Heymans,et al.  Semantic Business Process Repository , 2007, SBPM.

[4]  Tao Jin,et al.  Efficient querying of large process model repositories , 2013, Comput. Ind..

[5]  Piergiorgio Bertoli,et al.  Semantic-Based Process Analysis , 2014, International Semantic Web Conference.

[6]  Sherif Sakr,et al.  A framework for querying graph-based business process models , 2010, WWW '10.

[7]  Katalina Grigorova,et al.  Object relational business process repository , 2012, CompSysTech '12.

[8]  Daniela Grigori,et al.  Process Analytics - Concepts and Techniques for Querying and Analyzing Process Data , 2016 .

[9]  Hajo A. Reijers,et al.  Connecting databases with process mining: a meta model and toolset , 2016, Software & Systems Modeling.

[10]  Mark von Rosing,et al.  Business Process Model and Notation - BPMN , 2015, The Complete Business Process Handbook, Vol. I.

[11]  Hajo A. Reijers,et al.  Everything You Always Wanted to Know About Your Process, but Did Not Know How to Ask , 2016, Business Process Management Workshops.

[12]  Manfred Reichert,et al.  Process and Data: Two Sides of the Same Coin? , 2012, OTM Conferences.

[13]  Catriel Beeri,et al.  BP-Mon: query-based monitoring of BPEL business processes , 2008, SGMD.

[14]  Injun Choi,et al.  An XML‐based process repository and process query language for integrated process management , 2007 .

[15]  Jianmin Wang,et al.  APQL: A Process-Model Query Language , 2013, AP-BPM.

[16]  María Teresa Gómez López,et al.  Process Instance Query Language to Include Process Performance Indicators in DMN , 2016, 2016 IEEE 20th International Enterprise Distributed Object Computing Workshop (EDOCW).

[17]  Catriel Beeri,et al.  Querying Business Processes with BP-QL , 2005, VLDB.

[18]  María Teresa Gómez López,et al.  Data State Description for the Migration to Activity-Centric Business Process Model Maintaining Legacy Databases , 2014, BIS.

[19]  Claudia Kocian,et al.  Geschäftsprozessmodellierung mit BPMN 2.0 - Business Process Model and Notation im Methodenvergleich. , 2011 .

[20]  Hye-Young Paik,et al.  BPIM: A Multi-view Model for Business Process Instances , 2015, APCCM.

[21]  Mathias Weske,et al.  Business Process Management: Concepts, Languages, Architectures , 2007 .

[22]  María Teresa Gómez López,et al.  Guiding the Creation of Choreographed Processes with Multiple Instances Based on Data Models , 2016, Business Process Management Workshops.

[23]  Philippe Desfray,et al.  Viewpoint-Based Modeling-Towards Defining the Viewpoint Concept and Implications for Supporting Modeling Tools , 2012, EMISA.

[24]  Patrick Valduriez,et al.  Towards the efficient development of model transformations using model weaving and matching transformations , 2009, Software & Systems Modeling.