Artifact-Driven Business Process Monitoring, 1st ed. 2019
A Novel Approach to Transparently Monitor Business Processes, Supported by Methods, Tools, and Real-World Applications
Lecture Notes in Business Information Processing Series, Vol. 368

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Language: English

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This book proposes a novel technique, named artifact-driven process monitoring, by which multi-party processes, involving non-automated activities, can be continuously and autonomously monitored. This technique exploits the Internet of Things (IoT) paradigm to make the physical objects, participating in a process, smart. Being equipped with sensors, a computing device, and a communication interface, such smart objects can then become self-aware of their own conditions and of the process they participate in, and exchange this information with the other smart objects and the involved organizations. To allow organizations to reuse preexisting process models, a method to instruct smart objects given Business Process Model and Notation (BPMN) collaboration diagrams is also presented.

The work constitutes a revised version of the PhD dissertation written by the author at the PhD School of Information Engineering of Politecnico di Milano, Italy. In 2019, the PhD dissertation won the ?CAiSE PhD award?, granted to outstanding PhD theses in the field of Information Systems Engineering.

 


1 Introduction.- 1.1 Motivations.- 1.2 Research Challenges.- 1.3 Research Questions.- 1.4 Major Contributions.- 1.5 Book Structure.- 2 Related Work.- 2.1 Business Process Monitoring.- 2.1.1 Process Monitoring Based on Sensor Data.- 2.1.2 Event Data Logging.- 2.1.3 Business Activity Monitoring.- 2.1.4 Conformance and Compliance Checking.- 2.2 Declarative Languages.- 2.2.1 Constraint-based Languages.- 2.2.2 Artifact-centric Languages.- 2.2.3 Case Management Languages.- 2.2.4 Imperative to Declarative Model Translators.- 2.3 The Internet of Things.- 2.3.1 Enabling technologies of the IoT.- 2.3.2 Ontologies for the IoT.- 2.3.3 Synergies between the IoT and BPM.- 3 Artifact-driven Process Monitoring Overview.- 3.1 Motivating Example.- 3.2 Introducing Artifact-driven Process Monitoring.- 3.3 Reference Architecture.- 3.4 Summary.- 4 E-GSM: an Artifact-centric Language for Process Monitoring.- 4.1 The Guard-Stage-Milestone Artifact-centric Language.- 4.2 Extending GSM.- 4.3 Assessing the severity of Constraints Violations.- 4.4 E-GSM Expressiveness.- 4.4.1 Activity Exclusion.- 4.4.2 Activity Overlap.- 4.4.3 Responded Existence.- 4.4.4 Constrained Iteration.- 4.5 Summary.- 5 A Method to Easily Configure the Monitoring Platform.- 5.1 Steps.- 5.1.1 Enriching the BPMN Process Model With Artifacts.- 5.1.2 Extracting the Artifact-oriented Process View.- 5.1.3 Generating the E-GSM Process Model.- 5.1.4 Generating the E-GSM Artifact Lifecycle Model.- 5.1.5 Generating the Artifact-to-object Mapping Criteria.- 5.2 Proof of Correctness.- 5.2.1 Trace Conformance.- 5.2.2 Execution Flow Alignment.- 5.2.3 Artifact Lifecycle Alignment.- 5.3 Summary.- 6 Assessing and Improving Process Monitorability.- 6.1 Formalizing the Capabilities of the Smart Objects.- 6.1.1 Smart Objects Ontology.- 6.1.2 State Detection Rules Ontology.- 6.2 Problem Setting.- 6.3 Process Monitorability Assessment.- 6.4 Process Monitorability Improvement.- 6.4.1 Process model improvement.- 6.4.2 State detection rules improvement.- 6.4.3 Infrastructure improvement.- 6.5 Summary.- 7 Implementing and Evaluating Artifact-driven Process Monitoring.- 7.1 SMARTifact: an Artifact-driven Monitoring Platform.- 7.2 Simulated Environment.- 7.3 Field Evaluation.- 7.4 Summary.- 8 Conclusions.- 8.1 Answers to the Research Questions.- 8.2 Achievements in Runtime Process Monitoring.- 8.3 Achievements in the Integration Among BPM and IoT.- 8.4 Current Limitations and Future Work.- A Criteria to Evaluate the Integration Among BPM and IoT.- A.1 Placing sensors in a process-oriented way (IC1).- A.2 Monitoring manual activities (IC2).- A.3 Connecting analytical processes with the IoT (IC3).- A.4 Exploiting the IoT to do process correctness check (IC4).- A.5 Dealing with unstructured environments (IC5).- A.6 Managing the links between micro processes (IC6).- A.7 Breaking down end-to-end processes (IC7).- A.8 Detecting new processes from data (IC8).- A.9 Specifying the autonomy level of smart objects (IC9).- A.10 Specifying the socialroles of smart objects (IC10).- A.11 Concretizing abstract process models (IC11).- A.12 Dealing with new situations (IC12).- A.13 Bridging the gap between process-based and event-based systems (IC13).- A.14 Improving online conformance checking (IC14).- A.15 Improving resource utilization optimization (IC15).- A.16 Improving resource monitoring and quality of task execution (IC16).- B BPMN to E-GSM Translation Proof of Correctness.- B.1 Process Model.- B.1.1 Data Component.- B.1.2 Blocks.- B.1.3 Process Model.- B.2 Trace Conformance.- B.3 Conformance Preservation of the Translation.- References.

Revised PhD dissertation

Received the CAiSE PhD award for an outstanding thesis in the field of Information Systems Engineering

Proposes a novel technique which allows to monitor multi-party processes, involving non-automated activities