Automated process recognition architecture for cyber-physical systems

ABSTRACT Advances in pervasive computing, sensor networks and activity recognition have opened several paths to achieve novel solutions for applying model-driven data-intensive techniques. Complex system integrating computers, communication and control systems, generate new challenges for Enterprise Systems in the area of data modelling and processing. This paper is focused on the analysis of data intensive streams using process mining techniques, in the context of Enterprise Information Systems. An automated process recognition method based on data acquired from sensor networks, able to generate a process model describing dynamic aspects of the monitored environment, is proposed and analyzed.

[1]  Wil M. P. van der Aalst,et al.  Process Mining Applied to the Test Process of Wafer Scanners in ASML , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Andrew W. H. Ip,et al.  Design and development of inventory knowledge discovery system , 2017, Enterp. Inf. Syst..

[3]  Llanos Cuenca,et al.  Towards the development of the framework for inter sensing enterprise architecture , 2016, J. Intell. Manuf..

[4]  Marlon Dumas,et al.  Process Mining Reloaded: Event Structures as a Unified Representation of Process Models and Event Logs , 2015, Petri Nets.

[5]  King Lun Choy,et al.  An IoT-based cargo monitoring system for enhancing operational effectiveness under a cold chain environment , 2017 .

[6]  Elena Gorbacheva,et al.  Towards a typology of business process management professionals: identifying patterns of competences through latent semantic analysis , 2016, Enterp. Inf. Syst..

[7]  Chenxia Jin,et al.  A new attribute reduction method and its application in covering information systems , 2017, Enterp. Inf. Syst..

[8]  Hervé Panetto,et al.  An approach for formalising the supply chain operations , 2011, Enterp. Inf. Syst..

[9]  Jan Vanthienen,et al.  Exploring business process modelling paradigms and design-time to run-time transitions , 2016, Enterp. Inf. Syst..

[10]  Christopher W. Geib,et al.  The meaning of action: a review on action recognition and mapping , 2007, Adv. Robotics.

[11]  Wil M. P. van der Aalst,et al.  Event Abstraction for Process Mining using Supervised Learning Techniques , 2016, IntelliSys.

[12]  Wil M. P. van der Aalst,et al.  Abstractions in Process Mining: A Taxonomy of Patterns , 2009, BPM.

[13]  Josep Carmona,et al.  Unfolding-Based Process Discovery , 2015, ATVA.

[14]  Wil M. P. van der Aalst,et al.  Process-Aware Information Systems: Lessons to Be Learned from Process Mining , 2009, Trans. Petri Nets Other Model. Concurr..

[15]  Wil M.P. van der Aalst.  Process Mining: Overview and Opportunities , 2012, TMIS.

[16]  Wil M. P. van der Aalst,et al.  Fuzzy Mining - Adaptive Process Simplification Based on Multi-perspective Metrics , 2007, BPM.

[17]  Ricardo Jardim-Gonçalves,et al.  Knowledge framework for intelligent manufacturing systems , 2011, J. Intell. Manuf..

[18]  Liming Chen,et al.  Activity Recognition: Approaches, Practices and Trends , 2011 .

[19]  Simon A. Dobson,et al.  Situation identification techniques in pervasive computing: A review , 2012, Pervasive Mob. Comput..

[20]  Rama Chellappa,et al.  Machine Recognition of Human Activities: A Survey , 2008, IEEE Transactions on Circuits and Systems for Video Technology.

[21]  Lukás Chrpa,et al.  Towards Getting Domain Knowledge: Plans Analysis through Investigation of Actions Dependencies , 2008, FLAIRS.

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

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

[24]  Hajo A. Reijers,et al.  From Low-Level Events to Activities - A Pattern-Based Approach , 2016, BPM.

[25]  Hajo A. Reijers,et al.  Business Process Mining Success , 2013, ECIS.

[26]  Arkady B. Zaslavsky,et al.  Context-Aware Sensor Search, Selection and Ranking Model for Internet of Things Middleware , 2013, 2013 IEEE 14th International Conference on Mobile Data Management.

[27]  Aleksander Slominski,et al.  Discovering event correlation rules for semi-structured business processes , 2011, DEBS '11.

[28]  Ioan Stefan Sacala,et al.  E-Services quality assessment framework for collaborative networks , 2015, Enterp. Inf. Syst..

[29]  A Min Tjoa,et al.  Information technology for sustainable supply chain management: a literature survey , 2017, Enterp. Inf. Syst..

[30]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[31]  Lukás Chrpa,et al.  KEWI - A Knowledge Engineering Tool for Modelling AI Planning Tasks , 2014, KEOD.

[32]  Hector Geffner,et al.  Probabilistic Plan Recognition Using Off-the-Shelf Classical Planners , 2010, AAAI.

[33]  Jan Mendling,et al.  Towards the Enhancement of Business Process Monitoring for Complex Logistics Chains , 2013, Business Process Management Workshops.

[34]  Christel Baier,et al.  Principles of model checking , 2008 .

[35]  C. L. Philip Chen,et al.  A new practice-driven approach to develop software in a cyber-physical system environment , 2016, Enterp. Inf. Syst..

[36]  Ioan Dumitrache,et al.  Towards the development of semantically enabled flexible process monitoring systems , 2017, Int. J. Comput. Integr. Manuf..

[37]  Xi Vincent Wang,et al.  A cloud-based production system for information and service integration: an internet of things case study on waste electronics , 2017, Enterp. Inf. Syst..

[38]  Ricardo Jardim-Gonçalves,et al.  On the formal definition of the systems’ interoperability capability: an anthropomorphic approach , 2017, Enterp. Inf. Syst..

[39]  Changqin Quan,et al.  Exploiting salient semantic analysis for information retrieval , 2016, Enterp. Inf. Syst..