Process Querying Methods

This book presents a framework for developing as well as a comprehensive collection of state-of-the-art process querying methods. Process querying combines concepts from Big Data and Process Modeling and Analysis with Business Process Intelligence and Process Analytics to study techniques for retrieving and manipulating models of real-world and envisioned processes to organize and extract process-related information for subsequent systematic use. The book comprises sixteen contributed chapters distributed over four parts and two auxiliary chapters. The auxiliary chapters by the editor provide an introduction to the area of process querying and a summary of the presented methods, techniques, and applications for process querying. The introductory chapter also examines a process querying framework. The contributed chapters present various process querying methods, including discussions on how they instantiate the framework components, thus supporting the comparison of themethods. The four parts are due to the distinctive features of the methods they include. The first three are devoted to querying event logs generated by IT-systems that support business processes at organizations, querying process designs captured in process models, and methods that address querying both event logs and process models. The methods in these three parts usually define a language for specifying process queries. The fourth part discusses methods that operate over inputs other than event logs and process models, e.g., streams of process events, or do not develop dedicated languages for specifying queries, e.g., methods for assessing process model similarity. This book is mainly intended for researchers. All the chapters in this book are contributed by active researchers in the research disciplines of business process management, process mining, and process querying. They describe state-of-the-art methods for process querying, discuss use cases of process querying, and suggest directions for future work for advancing the field. Yet, also other groups like business or data scientists and other professionals, lecturers, graduate students, and tool vendors will find relevant information for their distinctive needs. Chapter "Celonis PQL: A Query Language for Process Mining " is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com. Foreword References Preface References Contents Contributors Acronyms Introduction to Process Querying 1 Introduction 2 Process Querying 2.1 Objective 2.2 Definition 2.3 Methods 3 Process Querying Framework 3.1 Framework 3.2 Design Decisions 3.3 Challenges and Compromise 3.3.1 Challenges 3.3.2 Compromise 4 Conclusion References Part I Event Log Querying BP-SPARQL: A Query Language for Summarizing and Analyzing Big Process Data 1 Introduction 2 Background and Contributions Overview 3 Process Abstractions 4 Summarizing Big Process Data 5 Querying Big Process Data 5.1 Entity-Level Queries 5.2 Summarization Queries 5.3 Regular Expression Queries 5.3.1 Path Condition Queries 5.3.2 Path Node Queries 5.4 Metadata Queries 5.5 User-Defined Queries 6 Scalable Analysis Using MapReduce 7 Implementation 8 Process Querying Framework 9 Conclusion References Data-Aware Process Oriented Query Language 1 Introduction 2 Preliminaries 3 DAPOQ-Lang 3.1 Syntax 3.1.1 Terminal Meta Model Elements 3.1.2 Elements Related to Elements 3.1.3 Computation of Temporal Values 3.1.4 Temporal Interval Algebra 3.1.5 Operators on Attributes of Elements 3.1.6 Abstract Syntax 3.2 Semantics 4 Implementation and Evaluation 5 Application and Use Cases 5.1 Business Questions in Process Mining 5.2 Exporting Logs 5.3 Specialized Sublogs 5.4 Metrics, Artifacts, and Provenance 5.5 DAPOQ-Lang vs. SQL 6 DAPOQ-Lang and the Process Querying Framework 7 Conclusion References Process Instance Query Language and the Process Querying Framework 1 Introduction 2 Background 3 Motivating Scenario 4 Process Instance Query Language 4.1 Syntax 4.2 Semantics 4.3 Patterns and Predicates 5 Implementation 6 Application 6.1 Dashboard Enriched with PIQL 6.2 DMN Enriched with PIQL 6.3 Dataflow Enriched with PIQL 7 Framework 8 Conclusions and Future Work References Part II Process Model Querying The Diagramed Model Query Language 2.0: Design, Implementation, and Evaluation 1 Introduction 2 Preliminaries 3 The Generic Model Query Language (GMQL) 3.1 Syntax 3.2 Semantics, Notation, and Query Example 3.3 The Transition from GMQL to DMQL 4 The Diagramed Model Query Language (DMQL) 4.1 Syntax 4.2 Notation 4.3 Semantics 4.4 Query Example 4.5 DMQL 2.0 5 Evaluation 5.1 Runtime Complexity 5.2 Performance 5.3 Utility 6 GMQL, DMQL, and the Process Querying Framework 7 Conclusion References VM*: A Family of Visual Model Manipulation Languages 1 Introduction 2 Examples 2.1 High-Level Process Models Expressed as Use Case Diagrams 2.2 Low-Level Process Models Expressed as Activity Diagrams 2.3 Low-Level Process Models Expressed as BPMN Diagrams 3 Query Language 3.1 Abstract Syntax 3.2 Concrete Syntax 3.3 Semantics 4 Implementation 5 Usability Evaluation 6 Applications and Use Cases 7 VM* and PQF 8 Conclusion References The BPMN Visual Query Language and Process Querying Framework 1 Introduction 2 Background 3 BPMN VQL 3.1 Syntax 3.2 Semantics and Notation 4 Implementation and Evaluation 4.1 Implementation 4.2 Performance Evaluation 4.3 Empirical Evaluation 4.3.1 Experiment Definition, Planning, and Design 4.3.2 Experimental Results 4.3.3 Discussion 5 Framework 6 Conclusion and Future Work References Retrieving, Abstracting, and Changing Business Process Models with PQL 1 Introduction 2 Use Cases 3 Fundamentals of Process Model Abstractions 3.1 Process Model 3.2 Changing Process Models 3.3 Process Model Abstractions 3.4 Updating Process Models Based on Model Abstractions 4 The PQL Language 4.1 Overview 4.2 Selecting Process Models and Process Elements 4.3 Changing Process Models 4.4 Abstracting Process Models 4.5 Handling Process Views with PQL 4.5.1 Creating, Updating, and Deleting Process Views 4.5.2 Changing Abstracted Process Models 5 Implementation 5.1 Software Architecture 5.2 Processing Pipeline 5.3 PQL Lexer and Parser 6 PQL and the Process Querying Framework 6.1 Part 1: Model, Simulate, Record, and Correlate 6.2 Part 2: Prepare 6.3 Part 3: Execute 6.4 Part 4: Interpret 7 Conclusion References QuBPAL: Querying Business Process Knowledge 1 Introduction 2 Business Process Knowledge Base 2.1 Business Process Schemas 2.2 Behavioral Semantics 2.3 Semantic Annotations 2.3.1 Rule-Based Ontologies 2.3.2 Terminological Annotations 2.3.3 Functional Annotations 3 Querying the Business Process Knowledge Base 3.1 Syntax Predicates of the WHERE Statement 3.2 Semantics 3.3 Query Examples 4 Use Cases 5 Implementation 5.1 Graphical User Interface 5.2 Application Logic 5.3 Knowledge Layer 6 Framework 7 Conclusions and Future Work References CRL and the Design-Time Compliance Management Framework 1 Introduction 2 CRL Framework 2.1 ``Model'' Part 2.2 ``Knowledge'' Part 2.3 ``Execute'' Part 2.4 ``Interpret'' Part 3 Case Study 4 Linear Temporal Logic 5 Compliance Request Language 5.1 Syntax, Notation, and Semantics 5.2 Atomic Patterns 5.3 Resource Patterns 5.4 Composite Patterns 5.5 Timed Patterns 6 Implementation 7 Validation and Evaluation 8 Discussion and Conclusion References Process Query Language 1 Introduction 2 Motivating Examples 3 Process Query Language 3.1 Process Querying 3.1.1 Behavioral Predicates 3.1.2 Scenarios 3.2 Process Manipulation 4 Process Querying Framework 5 Implementation 6 Discussion 6.1 Querying and Manipulation 6.2 Quality 6.3 Suitability 6.4 Decidability and Efficiency 7 Conclusion References Part III Event Log and Process Model Querying Business Process Query Language 1 Introduction 2 Business Process Metamodel 3 Query Language 3.1 Syntax 3.2 Semantics 3.2.1 Architecture of the Query Evaluation Mechanism 3.2.2 Environment Stack 3.2.3 Query Result Stack 3.2.4 Query Evaluation Procedure 3.2.5 Collections and Structs 3.2.6 Literals and Names 3.2.7 Algebraic Operators 3.2.8 Non-algebraic Operators 3.2.9 Imperative Constructs 3.2.10 Procedures and Functions 3.2.11 Predefined Context-Dependent Functions 4 Monitoring Functions 4.1 Settlement of Travel Expenses Example 5 Architecture and Standardization 5.1 BPQL Embedded in BPMN 5.2 Architecture 6 Case Study 7 Conclusion References Celonis PQL: A Query Language for Process Mining 1 Introduction 2 Background 2.1 Process Mining 2.2 Architecture Overview 2.3 History of Celonis PQL 2.4 Design Goals 3 Applications 4 The Celonis Process Query Language 4.1 Language Overview 4.2 Source and Target Operators 4.3 Variant Computation 4.4 Conformance Checking 5 Use Cases 5.1 Working Capital Optimization by On-Time Payment of Invoices 5.2 Identifying Ping-Pong-Cases for Ticket Resolution Time Reduction 5.3 Fraud Prevention by Identifying Segregation of Duties Violations 6 Implementation 7 Celonis PQL and the Process Querying Framework 8 Conclusion and Future Work References Part IV Other Process Querying Methods Process Querying Using Process Model Similarity 1 Introduction 2 Measures of Business Process Similarity 2.1 Preliminaries 2.2 Activity-Based Similarity Measures 2.3 Structure-Based Similarity Measures 2.4 Behavior-Based Similarity Measures 3 Indexing Structures for Business Process Similarity 3.1 Tree-Based Index and Proper Metrics 3.2 F-Net 4 Use Case: Finding Optimal Outsourcing Partners 4.1 Scenarios and Requirements for Business Process Outsourcing 4.2 Matching and Similarity Measures 4.3 Post-Matching 4.4 Similarity Measures in Business Process Outsourcing 5 Process Similarity Querying and the Process Querying Framework 6 Conclusion References Logic-Based Approaches for Process Querying 1 Introduction 2 Background 2.1 Business Process Model and Notation 2.2 The Soundness Property 3 Process Querying Using Prolog 3.1 Expressing the Model as Logic Facts 3.2 Checking Syntactical Correctness 3.3 Checking for Proper Layout 3.4 Locating Patterns Indicating a Soundness Violation 3.5 Locating Incorrect and Ambiguous Labels 3.6 Suggesting Process Model Refactoring 3.7 Suggesting Process Improvements 4 Process Querying Using Semantic Technologies 4.1 Querying Process Models Stored as Ontologies 4.2 Querying Process Models Stored in Graph-Oriented DB 5 Process Querying Framework 6 Conclusion References Process Model Similarity Techniques for Process Querying 1 Introduction 2 Foundations 2.1 Business Process Model 2.2 Business Process Instances 2.3 Business Process Model Matching 2.4 Business Process Model Similarity 2.5 Evaluation Measures 3 Process Model Querying and Similarity-Based Search 4 Selection of Similarity Techniques 4.1 Latent Semantic Analysis-Based Similarity Search 4.2 Similarity Score Based on Common Activity Names 4.3 Causal Footprints 4.4 Feature-Based Similarity Estimation 4.5 La Rosa Similarity 4.6 Longest Common Sets of Traces 5 Evaluation 5.1 Dataset 5.2 Query Results 5.3 Evaluation Results 5.4 Discussion and Limitations 6 Conclusion and Outlook References Complex Event Processing Methods for Process Querying 1 Introduction 2 The Context of Event-Based Process Querying 2.1 Use Cases 2.2 The Context of Event-Based Process Querying 2.3 An Example Scenario 3 Complex Event Processing 3.1 Event Streams 3.2 Event Query Languages 3.3 Event Query Evaluation 4 Methods for Process Querying 4.1 Event–Activity Correlation 4.2 Model-Based Query Generation 4.2.1 Overview 4.2.2 Query Derivation 4.3 Discovery of Event Queries 4.3.1 The Problem of Event Query Discovery 4.3.2 Discovery Algorithms 4.4 Diagnostics for Event Query Matches 4.4.1 Diagnostics for Exclusiveness Violations 4.4.2 Diagnostics for Order Violations 4.4.3 Diagnostics on the Violation Context 5 Discussion References Process Querying: Methods, Techniques, and Applications 1 Introduction 2 Foundations 3 Process Querying Methods 3.1 Log Querying 3.2 Model Querying 3.3 Log and Model Querying 4 Process Querying Techniques 5 Process Querying Applications 6 Past, Present, and Future of Process Querying References Index This book presents a framework for developing as well as a comprehensive collection of state-of-the-art process querying methods. Process querying combines concepts from Big Data and Process Modeling and Analysis with Business Process Intelligence and Process Analytics to study techniques for retrieving and manipulating models of real-world and envisioned processes to organize and extract process-related information for subsequent systematic use. The book comprises sixteen contributed chapters distributed over four parts and two auxiliary chapters. The auxiliary chapters by the editor provide an introduction to the area of process querying and a summary of the presented methods, techniques, and applications for process querying. The introductory chapter also examines a process querying framework. The contributed chapters present various process querying methods, including discussions on how they instantiate the framework components, thus supporting the comparison of the methods. The four parts are due to the distinctive features of the methods they include. The first three are devoted to querying event logs generated by IT-systems that support business processes at organizations, querying process designs captured in process models, and methods that address querying both event logs and process models. The methods in these three parts usually define a language for specifying process queries. The fourth part discusses methods that operate over inputs other than event logs and process models, e.g., streams of process events, or do not develop dedicated languages for specifying queries, e.g., methods for assessing process model similarity. This book is mainly intended for researchers. All the chapters in this book are contributed by active researchers in the research disciplines of business process management, process mining, and process querying. They describe state-of-the-art methods for process querying, discuss use cases of process querying, and suggest directions for future work for advancing the field. Yet, also other groups like business or data scientists and other professionals, lecturers, graduate students, and tool vendors will find relevant information for their distinctive needs. Chapter "Celonis PQL: A Query Language for Process Mining" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com