Artifact-Driven Business Process Monitoring: A Novel Approach to Transparently Monitor Business Proc

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.