Discovering cross-organizational business rules from the cloud

Cloud computing is rapidly emerging as a new information technology that aims at providing improved efficiency in the private and public sectors, as well as promoting growth, competition, and business dynamism. Cloud computing represents, today, an opportunity also from the perspective of business process analytics since data recorded by process-centered cloud systems can be used to extract information about the underlying processes. Cloud computing architectures can be used in cross-organizational environments in which different organizations execute the same process in different variants and share information about how each variant is executed. If the process is characterized by low predictability and high variability, business rules become the best way to represent the process variants. The contribution of this paper consists in providing: (i) a cloud computing multi-tenancy architecture to support cross-organizational process executions; (ii) an approach for the systematic extraction/composition of distributed data into coherent event logs carrying process-related information of each variant; (iii) the integration of online process mining techniques for the runtime extraction of business rules from event logs representing the process variants running on the infrastructure. The proposed architecture has been implemented and applied for the execution of a real-life process for acknowledging an unborn child performed in four different Dutch municipalities.

[1]  Fabrizio Maria Maggi,et al.  Declarative Process Mining with the Declare Component of ProM , 2013, BPM.

[2]  Alessandro Sperduti,et al.  Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models , 2013, OTM Conferences.

[3]  Richard Wolski,et al.  The Eucalyptus Open-Source Cloud-Computing System , 2009, 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid.

[4]  Mohamed Medhat Gaber,et al.  Knowledge discovery from data streams , 2009, IDA 2009.

[5]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[6]  Wil M. P. van der Aalst Configurable Services in the Cloud: Supporting Variability While Enabling Cross-Organizational Process Mining , 2010, OTM Conferences.

[7]  Wil M. P. van der Aalst,et al.  Process Mining - Discovery, Conformance and Enhancement of Business Processes , 2011 .

[8]  Alessandro Sperduti,et al.  A Lossy Counting Based Approach for Learning on Streams of Graphs on a Budget , 2013, IJCAI.

[9]  Wil M. P. van der Aalst,et al.  Declarative workflows: Balancing between flexibility and support , 2009, Computer Science - Research and Development.

[10]  Bikram Sengupta,et al.  Engineering multi-tenant software-as-a-service systems , 2011, PESOS '11.

[11]  Wil M. P. van der Aalst Business Process Configuration in the Cloud: How to Support and Analyze Multi-tenant Processes? , 2011, 2011 IEEE Ninth European Conference on Web Services.

[12]  Boudewijn F. van Dongen,et al.  Towards Cross-Organizational Process Mining in Collections of Process Models and Their Executions , 2011, Business Process Management Workshops.

[13]  Steve Bobrowski,et al.  Optimal Multitenant Designs for Cloud Apps , 2011, 2011 IEEE 4th International Conference on Cloud Computing.

[14]  Fabrizio Maria Maggi,et al.  Lights, Camera, Action! Business Process Movies for Online Process Discovery , 2014, Business Process Management Workshops.

[15]  G. G. Meyer,et al.  Lecture notes in business information processing , 2009 .

[16]  Jesús S. Aguilar-Ruiz,et al.  Knowledge discovery from data streams , 2009, Intell. Data Anal..

[17]  Chandra Krintz,et al.  AppScale: Scalable and Open AppEngine Application Development and Deployment , 2009, CloudComp.

[18]  Wil M. P. van der Aalst,et al.  Configurable Declare: Designing Customisable Flexible Process Models , 2012, OTM Conferences.

[19]  Yun-Shiow Chen,et al.  Perspectives on process mining within cloud computing , 2011, 2011 3rd International Conference on Advanced Computer Control.

[20]  Lukasz Golab,et al.  Issues in data stream management , 2003, SGMD.

[21]  Rajeev Motwani,et al.  Approximate Frequency Counts over Data Streams , 2012, VLDB.

[22]  Wil M. P. van der Aalst,et al.  Intra- and Inter-Organizational Process Mining: Discovering Processes within and between Organizations , 2011, PoEM.

[23]  Florian Gottschalk,et al.  Configurable process models , 2009 .

[24]  Ricardo Seguel,et al.  Process Mining Manifesto , 2011, Business Process Management Workshops.