Interconnecting processes through IoT in a health-care scenario

In the context of health and social care, it is important to provide assistance to individuals in their own home by coordinating the activities of several actors. The coordination of processes of different organizations is a complex task which requires continuous attention. Process mining techniques have been widely used to discover hidden information useful to understand the behaviour of a single process. Thanks to the spread of Internet of Things (IoT), this complex activity can benefit also from the huge amount of data collected for other purposes. In this paper we propose techniques for exploiting the information available in the environment, considering several sources of information to analyse the interconnections of business processes. We apply the proposed approach to a health-care scenario enriched with IoT devices. The proposed techniques allow the discovery of interconnections between processes and external factors which have an impact on them.

[1]  Wil M. P. van der Aalst,et al.  Discovering signature patterns from event logs , 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[2]  Barbara Pernici,et al.  PiE - Processes in Events: Interconnections in Ambient Assisted Living , 2015, OTM Workshops.

[3]  Salima Benbernou,et al.  On the Evolution of Services , 2012, IEEE Transactions on Software Engineering.

[4]  Diogo R. Ferreira,et al.  Business process analysis in healthcare environments: A methodology based on process mining , 2012, Inf. Syst..

[5]  Yehia Taher,et al.  Adaptation of Web Service Interactions Using Complex Event Processing Patterns , 2011, ICSOC.

[6]  Wil M.P. van der Aalst,et al.  Process Mining Put into Context , 2012, IEEE Internet Computing.

[7]  Wil M. P. van der Aalst,et al.  An alignment-based framework to check the conformance of declarative process models and to preprocess event-log data , 2015, Inf. Syst..

[8]  Mathias Weske,et al.  Matching of events and activities: an approach based on behavioral constraint satisfaction , 2015, SAC.

[9]  P. Priya,et al.  Dealing With Concept Drifts in Process Mining , .

[10]  Alessandro Sperduti,et al.  Heuristics Miners for Streaming Event Data , 2012, ArXiv.

[11]  Jan Mendling,et al.  The ROAD from Sensor Data to Process Instances via Interaction Mining , 2016, CAiSE.

[12]  Jan Recker,et al.  Supporting Context-Aware Process Design: Learnings from a Design Science Study , 2010, Business Process Management Workshops.

[13]  Giordano Tamburrelli,et al.  Learning from the past: automated rule generation for complex event processing , 2014, DEBS '14.

[14]  Wil M. P. van der Aalst,et al.  Process Mining: Overview and Opportunities , 2012, ACM Trans. Manag. Inf. Syst..

[15]  Tak-Chung Fu,et al.  A review on time series data mining , 2011, Eng. Appl. Artif. Intell..

[16]  Hajo A. Reijers,et al.  Extending Process Logs with Events from Supplementary Sources , 2014, Business Process Management Workshops.

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

[18]  Alessandro Margara,et al.  Processing flows of information: From data stream to complex event processing , 2012, CSUR.

[19]  Moe Thandar Wynn,et al.  Change your history: Learning from event logs to improve processes , 2015, 2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[20]  Hongyan Ma,et al.  Process-aware information systems: Bridging people and software through process technology , 2007, J. Assoc. Inf. Sci. Technol..