Data-Driven Process Discovery and Analysis: 8th IFIP WG 2.6 International Symposium, SIMPDA 2018, Seville, Spain, December 13–14, 2018, and 9th International Symposium, SIMPDA 2019, Bled, Slovenia, September 8, 2019, Revised Selected Papers

This article aims at introducing a new process-centric, trusted, configurable and multipurpose electronic voting service based on the blockchain infrastructure. The objective is to design an e-voting service using blockchain able to automatically translate service configuration defined by the end-user into a cloud-based deployable bundle, automating business logic definition, blockchain configuration, and cloud service provider selection. The architecture includes process mining by design in order to optimize process performance and configuration. The article depicts all the components of the architecture and discusses the impact of the proposed solution.

[1]  David K. Y. Yau,et al.  Privacy vulnerability of published anonymous mobility traces , 2010, MobiCom.

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

[3]  Matthias Weidlich,et al.  PRETSA: Event Log Sanitization for Privacy-aware Process Discovery , 2019, 2019 International Conference on Process Mining (ICPM).

[4]  Konstantinos Markantonakis,et al.  E-Voting With Blockchain: An E-Voting Protocol with Decentralisation and Voter Privacy , 2018, 2018 IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData).

[5]  Martin Bichler,et al.  Responsible Data Science , 2017, Bus. Inf. Syst. Eng..

[6]  María Teresa Gómez López,et al.  An Architecture for Querying Business Process, Business Process Instances, and Business Data Models , 2017, Business Process Management Workshops.

[7]  Catriel Beeri,et al.  Querying business processes , 2006, VLDB.

[8]  Vesna Bosilj-Vuksic,et al.  Supporting Performance Management with Business Process Management and Business Intelligence: A Case Analysis of Integration and Orchestration , 2013, Int. J. Inf. Manag..

[9]  Jianwen Su,et al.  Towards Formal Analysis of Artifact-Centric Business Process Models , 2007, BPM.

[10]  Tatiana Gavrilova,et al.  Mind Mapping and Spreadsheets in Collaborative Design of Manufacturing Assembly Units' Knowledge Graphs , 2018, BIR Workshops.

[11]  Ingo Weber,et al.  User-Friendly Semantic Annotation in Business Process Modeling , 2007, WISE Workshops.

[12]  Patrick Delfmann,et al.  The generic model query language GMQL - Conceptual specification, implementation, and runtime evaluation , 2015, Inf. Syst..

[13]  Ahmed Awad,et al.  Deducing Case IDs for Unlabeled Event Logs , 2015, Business Process Management Workshops.

[14]  Marlon Dumas,et al.  Predictive Business Process Monitoring with LSTM Neural Networks , 2016, CAiSE.

[15]  Tao Jin,et al.  Querying Business Process Models Based on Semantics , 2011, DASFAA.

[16]  Jonathan Katz,et al.  Cryptographic Counters and Applications to Electronic Voting , 2001, EUROCRYPT.

[17]  Alessandro Sperduti,et al.  LSTM networks for data-aware remaining time prediction of business process instances , 2017, 2017 IEEE Symposium Series on Computational Intelligence (SSCI).

[18]  Wil M. P. van der Aalst,et al.  Hierarchical performance analysis for process mining , 2018, ICSSP.

[19]  Zekeriya Erkin,et al.  Privacy-Preserving Alpha Algorithm for Software Analysis , 2016 .

[20]  Irene Teinemaa,et al.  An interdisciplinary comparison of sequence modeling methods for next-element prediction , 2018, Software and Systems Modeling.

[21]  Dirk Fahland,et al.  Behavioral Conformance of Artifact-Centric Process Models , 2011, BIS.

[22]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[23]  C. Humby,et al.  Process Mining: Data science in Action , 2014 .

[24]  Armand Toguyéni,et al.  A semi-formal approach to build the functional graph of an automated production system for supervision purposes , 2006, Int. J. Comput. Integr. Manuf..

[25]  Jörg Becker,et al.  Supporting Information Systems Analysis Through Conceptual Model Query - The Diagramed Model Query Language (DMQL) , 2015, Commun. Assoc. Inf. Syst..

[26]  Wil M. P. van der Aalst,et al.  Scenario-Based Prediction of Business Processes Using System Dynamics , 2019, OTM Conferences.

[27]  Emanuele Bellini,et al.  A blockchain based Trusted Persistent Identifier system for Big Data in Science , 2019, Foundations of Computing and Decision Sciences.

[28]  Frank Budinsky,et al.  EMF: Eclipse Modeling Framework 2.0 , 2009 .

[29]  Sören Auer,et al.  Enterprise Knowledge Graphs: A Backbone of Linked Enterprise Data , 2016, 2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI).

[30]  Wil M. P. van der Aalst,et al.  Repairing Outlier Behaviour in Event Logs , 2018, BIS.

[31]  Vincent Rijmen,et al.  The Design of Rijndael: AES - The Advanced Encryption Standard , 2002 .

[32]  Giancarlo Fortino,et al.  Process Querying in Apromore , 2015, BPM.

[33]  Markku Hinkka,et al.  Exploiting Event Log Event Attributes in RNN Based Prediction , 2019, SIMPDA.

[34]  Boudewijn F. van Dongen,et al.  Replaying history on process models for conformance checking and performance analysis , 2012, WIREs Data Mining Knowl. Discov..

[35]  Wilfred Ng,et al.  Efficient query processing on graph databases , 2009, TODS.

[36]  Fernando Silva Parreiras.  Marrying Ontology and Model‐Driven Engineering , 2012 .

[37]  Manfred Reichert,et al.  A visual language for modeling multiple perspectives of business process compliance rules , 2017, Software & Systems Modeling.

[38]  Wil M. P. van der Aalst.  Process Mining: The Missing Link , 2016 .

[39]  Sobah Abbas Petersen,et al.  Privacy Challenges for Process Mining in Human-Centered Industrial Environments , 2018, 2018 14th International Conference on Intelligent Environments (IE).

[40]  Wei Song,et al.  Collaborative material and production tracking in toy manufacturing , 2013, Proceedings of the 2013 IEEE 17th International Conference on Computer Supported Cooperative Work in Design (CSCWD).

[41]  Jorge Bernardino,et al.  Graph Databases Comparison: AllegroGraph, ArangoDB, InfiniteGraph, Neo4J, and OrientDB , 2018, DATA.

[42]  Wil M. P. van der Aalst,et al.  Ensuring Confidentiality in Process Mining , 2018, SIMPDA.

[43]  Parameswaran Seshan.  Process-Centric Architecture for Enterprise Software Systems , 2010 .

[44]  Max Mühlhäuser,et al.  BINet: Multivariate Business Process Anomaly Detection Using Deep Learning , 2018, BPM.

[45]  Mohit Singh,et al.  Selection of manufacturing process using graph theoretic approach , 2011, Int. J. Syst. Assur. Eng. Manag..

[46]  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).

[47]  Wil M. P. van der Aalst,et al.  Process discovery from event data: Relating models and logs through abstractions , 2018, WIREs Data Mining Knowl. Discov..

[48]  Matthias Steinhorst,et al.  The generic model query language GMQL , 2014 .

[49]  Sherif Sakr,et al.  Encyclopedia of Big Data Technologies , 2019, Springer International Publishing.

[50]  Sven Apel,et al.  Variability-aware performance prediction: A statistical learning approach , 2013, 2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[51]  Paolo Ceravolo,et al.  Designing Process-Centric Blockchain-Based Architectures: A Case Study in e-voting as a Service , 2018, SIMPDA.

[52]  Alfred Menezes,et al.  Handbook of Applied Cryptography , 2018 .

[53]  Fabrizio Maria Maggi,et al.  Tell me what’s ahead? Predicting remaining activity sequences of business process instances , 2016 .

[54]  King-Hang Wang,et al.  A Review of Contemporary E-voting : Requirements , Technology , Systems and Usability , 2017 .

[55]  Richard Hull,et al.  Introducing the Guard-Stage-Milestone Approach for Specifying Business Entity Lifecycles , 2010, WS-FM.

[56]  Peter Dadam,et al.  Design and Verification of Instantiable Compliance Rule Graphs in Process-Aware Information Systems , 2010, CAiSE.

[57]  Marcelo Arenas,et al.  Foundations of Modern Query Languages for Graph Databases , 2016, ACM Comput. Surv..

[58]  David Chaum,et al.  Untraceable electronic mail, return addresses, and digital pseudonyms , 1981, CACM.

[59]  Jon Espen Ingvaldsen,et al.  Preprocessing Support for Large Scale Process Mining of SAP Transactions , 2007, Business Process Management Workshops.

[60]  Marwa El Abri.  Probabilistic relational models learning from graph databases. (Apprentissage des modèles probabilistes relationnels à partir des bases de données graphe) , 2018 .

[61]  Andrea Burattin,et al.  A framework for semi-automated process instance discovery from decorative attributes , 2011, 2011 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[62]  Wil M. P. van der Aalst,et al.  Extracting Object-Centric Event Logs to Support Process Mining on Databases , 2018, CAiSE Forum.

[63]  Wil M. P. van der Aalst,et al.  Responsible Data Science: Using Event Data in a "People Friendly" Manner , 2016, ICEIS.

[64]  Paolo Tonella,et al.  Crosscutting Concern Documentation by Visual Query of Business Processes , 2008, Business Process Management Workshops.

[65]  C. Furlanello,et al.  Recursive feature elimination with random forest for PTR-MS analysis of agroindustrial products , 2006 .

[66]  Hans-Peter Fröschle,et al.  Customer relationship management , 2019, Digital Marketing Fundamentals.

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

[68]  Konstantinos Limniotis,et al.  Blockchain Solutions for Forensic Evidence Preservation in IoT Environments , 2019, 2019 IEEE Conference on Network Softwarization (NetSoft).

[69]  Virginia Ivanov,et al.  Applications Of The Graph Theory For Optimization In Manufacturing Environment Of The Electrical Equipments , 2014, ECMS.

[70]  Diego Calvanese,et al.  Ontology-Driven Extraction of Event Logs from Relational Databases , 2015, Business Process Management Workshops.

[71]  María Teresa Gómez López,et al.  Tactical Business-Process-Decision Support based on KPIs Monitoring and Validation , 2018, Comput. Ind..

[72]  Manfred Reichert,et al.  PQL - A Descriptive Language for Querying, Abstracting and Changing Process Models , 2015, BMMDS/EMMSAD.

[73]  Ying Liu,et al.  A static compliance-checking framework for business process models , 2007, IBM Syst. J..

[74]  Doina Caragea,et al.  Graph Databases , 2019, Encyclopedia of Big Data Technologies.

[75]  Jan Mendling,et al.  Untrusted Business Process Monitoring and Execution Using Blockchain , 2016, BPM.

[76]  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.

[77]  Mike P. Papazoglou,et al.  Formalizing and appling compliance patterns for business process compliance , 2016, Software & Systems Modeling.

[78]  Georgios Theodoropoulos,et al.  e-Voting Requirements and Implementation , 2007, The 9th IEEE International Conference on E-Commerce Technology and The 4th IEEE International Conference on Enterprise Computing, E-Commerce and E-Services (CEC-EEE 2007).

[79]  Salim Jouili,et al.  An Empirical Comparison of Graph Databases , 2013, 2013 International Conference on Social Computing.

[80]  Joseph K. Liu,et al.  Robust Receipt-Free Election System with Ballot Secrecy and Verifiability , 2008, NDSS.

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

[82]  Chris H. Q. Ding,et al.  Minimum redundancy feature selection from microarray gene expression data , 2003, Computational Systems Bioinformatics. CSB2003. Proceedings of the 2003 IEEE Bioinformatics Conference. CSB2003.

[83]  Rik Eshuis,et al.  Verifying the manipulation of data objects according to business process and data models , 2020, Knowledge and Information Systems.

[84]  J. Alex Halderman.  Practical Attacks on Real-World E-Voting , 2016 .

[85]  Fabrizio Maria Maggi,et al.  Predictive Process Monitoring Methods: Which One Suits Me Best? , 2018, BPM.

[86]  Tim Menzies,et al.  On the value of user preferences in search-based software engineering: A case study in software product lines , 2013, 2013 35th International Conference on Software Engineering (ICSE).

[87]  Peter Plapper,et al.  An IoT Framework for Assembly Tracking and Scheduling in Manufacturing SME , 2019, ICINCO.

[88]  Laurian M. Chirica,et al.  The entity-relationship model: toward a unified view of data , 1975, SIGF.

[89]  Kazimierz Subieta,et al.  Process Query Language: A Way to Make Workflow Processes More Flexible , 2004, ADBIS.

[90]  Jana-Rebecca Rehse,et al.  Predicting process behaviour using deep learning , 2016, Decis. Support Syst..

[91]  Steffen Becker,et al.  Quantitative Evaluation of Model-Driven Performance Analysis and Simulation of Component-Based Architectures , 2015, IEEE Transactions on Software Engineering.

[92]  Agata Filipowska,et al.  Semantic Business Process Management: A Lifecycle Based Requirements Analysis , 2007, SBPM.

[93]  Ahmed Awad,et al.  Runtime deduction of case ID for unlabeled business process execution events , 2015, 2015 IEEE/ACS 12th International Conference of Computer Systems and Applications (AICCSA).

[94]  Dimitris Gritzali,et al.  Principles and requirements for a secure e-voting system , 2002, Comput. Secur..

[95]  Mathias Weske,et al.  Blockchains for Business Process Management - Challenges and Opportunities , 2017, ACM Trans. Manag. Inf. Syst..

[96]  C. Andrew Neff,et al.  A verifiable secret shuffle and its application to e-voting , 2001, CCS '01.

[97]  Wil M. P. van der Aalst,et al.  Composite State Machine Miner: Discovering and Exploring Multi-perspective Processes , 2016, BPM.

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

[99]  Fabrizio Maria Maggi,et al.  Clustering-Based Predictive Process Monitoring , 2015, IEEE Transactions on Services Computing.

[100]  Daniel Pustka,et al.  Tracking of manufacturing tools with cylindrical markers , 2012, VRST '12.

[101]  Yi Zhang,et al.  Performance Prediction of Configurable Software Systems by Fourier Learning (T) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[102]  Maurizio Proietti,et al.  Ontology-Based Querying of Composite Services , 2010, BSME.

[103]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[104]  Béatrice Bouchou-Markhoff,et al.  Semantic mediator querying , 2014, IDEAS.

[105]  Hajo A. Reijers,et al.  Discovering Social Networks from Event Logs , 2005, Computer Supported Cooperative Work (CSCW).

[106]  Stan Matwin,et al.  Privacy-Preserving Collaborative Sequential Pattern Mining , 2004 .

[107]  Stefan Jablonski,et al.  Deep Learning Process Prediction with Discrete and Continuous Data Features , 2018, ENASE.

[108]  Richard Hull,et al.  Business artifacts with guard-stage-milestone lifecycles: managing artifact interactions with conditions and events , 2011, DEBS '11.

[109]  Richard Hull,et al.  Business Artifacts: A Data-centric Approach to Modeling Business Operations and Processes , 2009, IEEE Data Eng. Bull..

[110]  Giuliano Casale,et al.  An Uncertainty-Aware Approach to Optimal Configuration of Stream Processing Systems , 2016, 2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS).

[111]  Steffen Lamparter,et al.  Graph-based predictions and recommendations in flexible manufacturing systems , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[112]  Markku Hinkka,et al.  Focusing Business Improvements Using Process Mining Based Influence Analysis , 2016, BPM.

[113]  Andrew W. Moore,et al.  X-means: Extending K-means with Efficient Estimation of the Number of Clusters , 2000, ICML.

[114]  Harald Störrle,et al.  VMQL: A visual language for ad-hoc model querying , 2011, J. Vis. Lang. Comput..

[115]  Sven Apel,et al.  Cost-Efficient Sampling for Performance Prediction of Configurable Systems (T) , 2015, 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE).

[116]  P. Rousseeuw.  Silhouettes: a graphical aid to the interpretation and validation of cluster analysis , 1987 .

[117]  Don S. Batory,et al.  Finding near-optimal configurations in product lines by random sampling , 2017, ESEC/SIGSOFT FSE.

[118]  H. Wang,et al.  A graph-based data structure for assembly dimensional variation control at a preliminary phase of product design , 2009, Int. J. Comput. Integr. Manuf..

[119]  Mauro Conti,et al.  Toward an Anonymous Process Mining , 2015, 2015 3rd International Conference on Future Internet of Things and Cloud.

[120]  Periklis Andritsos,et al.  Accurate and Transparent Path Prediction Using Process Mining , 2019, ADBIS.

[121]  Pascal Paillier,et al.  Public-Key Cryptosystems Based on Composite Degree Residuosity Classes , 1999, EUROCRYPT.

[122]  Sebastian Krieter,et al.  Feature-Model Interfaces: The Highway to Compositional Analyses of Highly-Configurable Systems , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).

[123]  Sanaz Mostaghim,et al.  A Survey on Graph-based Systems in Manufacturing Processes , 2018, 2018 IEEE Symposium Series on Computational Intelligence (SSCI).

[124]  Alexander Jung,et al.  Structural Feature Selection for Event Logs , 2017, Business Process Management Workshops.

[125]  Paolo Bouquet,et al.  Interoperability Knowledge Base for Persistent Identifiers Interoperability Framework , 2012, 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems.

[126]  Daniela Cabiddu,et al.  slice2mesh: A meshing tool for the simulation of additive manufacturing processes , 2019, Comput. Graph..

[127]  Phininder Balaghan.  An exploration of graph algorithms and graph databases , 2019 .

[128]  Wil M. P. van der Aalst,et al.  Discovering and Exploring State-Based Models for Multi-perspective Processes , 2016, BPM.

[129]  Grzegorz J. Nalepa,et al.  Overview of Selected Business Process Semantization Techniques , 2017 .

[130]  Wil M. P. van der Aalst,et al.  Mining Roles from Event Logs While Preserving Privacy , 2019, Business Process Management Workshops.

[131]  Maguelonne Teisseire,et al.  Privacy preserving sequential pattern mining in distributed databases , 2006, CIKM '06.

[132]  Jorge Martínez Gil.  Automated knowledge base management: A survey , 2015, Comput. Sci. Rev..

[133]  Diego Calvanese,et al.  Ontop: Answering SPARQL queries over relational databases , 2016, Semantic Web.

[134]  Lu Wang,et al.  Optimal lot-sizing policy for a manufacturer with defective items in a supply chain with up-stream and down-stream trade credits , 2013, Comput. Ind. Eng..

[135]  Mohsen Mohammadi,et al.  A Review of SOA Modeling Approaches for Enterprise Information Systems , 2013 .

[136]  Sherif Sakr,et al.  A Query Language for Analyzing Business Processes Execution , 2011, BPM.

[137]  Sven Apel,et al.  Performance-influence models for highly configurable systems , 2015, ESEC/SIGSOFT FSE.

[138]  Ahmed Awad,et al.  BPMN-Q: A Language to Query Business Processes , 2007, EMISA.

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

[140]  Jon M. Kleinberg,et al.  Challenges in mining social network data: processes, privacy, and paradoxes , 2007, KDD '07.

[141]  Sander J. J. Leemans,et al.  Scalable process discovery and conformance checking , 2016, Software & Systems Modeling.

[142]  Yehuda Lindell,et al.  Introduction to Modern Cryptography , 2004 .

[143]  Pingyu Jiang,et al.  Manufacturing Knowledge Graph: A Connectivism to Answer Production Problems Query With Knowledge Reuse , 2019, IEEE Access.

[144]  Grzegorz J. Nalepa,et al.  Opportunities for Business Process semantization in open-source process execution environments , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[145]  Varun Grover,et al.  Special Section: Toward a Theory of Business Process Change Management , 1995, J. Manag. Inf. Syst..

[146]  Ronald Cramer,et al.  A Secure and Optimally Efficient Multi-Authority Election Scheme ( 1 ) , 2000 .

[147]  Alexander Jung,et al.  Classifying Process Instances Using Recurrent Neural Networks , 2018, Business Process Management Workshops.

[148]  Hajo A. Reijers,et al.  Guided Process Discovery - A pattern-based approach , 2018, Inf. Syst..

[149]  Jorge Martínez Gil,et al.  Design and Implementation of a Graph-Based Solution for Tracking Manufacturing Products , 2019, ADBIS.

[150]  Grzegorz J. Nalepa,et al.  Unifying business concepts for SMEs with Prosecco ontology , 2015, 2015 Federated Conference on Computer Science and Information Systems (FedCSIS).

[151]  Günter Müller,et al.  On the exploitation of process mining for security audits: the process discovery case , 2013, SAC '13.

[152]  Artem Polyvyanyy,et al.  Business Process Querying , 2019, Encyclopedia of Big Data Technologies.

[153]  Qingtian Zeng,et al.  Towards Comprehensive Support for Privacy Preservation Cross-Organization Business Process Mining , 2019, IEEE Transactions on Services Computing.

[154]  Gregor Engels,et al.  Activity diagram patterns for modeling quality constraints in business processes , 2005, MoDELS'05.