Exploring multi-dimensional hierarchical data is a long-standing problem present in a wide range of fields such as bioinformatics, software systems, social sciences and business intelligence. While each hierarchical dimension within these data structures can be explored in isolation, critical information lies in the relationships between dimensions. Existing approaches can either simultaneously visualize multiple non-hierarchical dimensions, or only one or two hierarchical dimensions. Yet, the challenge of visualizing multi-dimensional hierarchical data remains open. To address this problem, we developed a novel data visualization approach -- Parallel Hierarchies -- that we demonstrate on a real-life SAP SE product called SAP Product Lifecycle Costing. The starting point of the research is a thorough customer-driven requirement engineering phase including an iterative design process. To avoid restricting ourselves to a domain-specific solution, we abstract the data and tasks gathered from users, and demonstrate the approach generality by applying Parallel Hierarchies to datasets from bioinformatics and social sciences. Moreover, we report on a qualitative user study conducted in an industrial scenario with 15 experts from 9 different companies. As a result of this co-innovation experience, several SAP customers requested a product feature out of our solution. Moreover, Parallel Hierarchies integration as a standard diagram type into SAP Analytics Cloud platform is in progress. This thesis further introduces different uncertainty representation methods applicable to Parallel Hierarchies and in general to flow diagrams. We also present a visual comparison taxonomy for time-series of hierarchically structured data with one or multiple dimensions. Moreover, we propose several visual solutions for comparing hierarchies employing flow diagrams. Finally, after presenting two application examples of Parallel Hierarchies on industrial datasets, we detail two validation methods to examine the effectiveness of the visualization solution. Particularly, we introduce a novel design validation table to assess the perceptual aspects of eight different visualization solutions including Parallel Hierarchies.:1 Introduction 1.1 Motivation and Problem Statement 1.2 Research Goals 1.3 Outline and Contributions 2 Foundations of Visualization 2.1 Information Visualization 2.1.1 Terms and Definition 2.1.2 What: Data Structures 2.1.3 Why: Visualization Tasks 2.1.4 How: Visualization Techniques 2.1.5 How: Interaction Techniques 2.2 Visual Perception 2.2.1 Visual Variables 2.2.2 Attributes of Preattentive and Attentive Processing 2.2.3 Gestalt Principles 2.3 Flow Diagrams 2.3.1 Classifications of Flow Diagrams 2.3.2 Main Visual Features 2.4 Summary 3 Related Work 3.1 Cross-tabulating Hierarchical Categories 3.1.1 Visualizing Categorical Aggregates of Item Sets 3.1.2 Hierarchical Visualization of Categorical Aggregates 3.1.3 Visualizing Item Sets and Their Hierarchical Properties 3.1.4 Hierarchical Visualization of Categorical Set Aggregates 3.2 Uncertainty Visualization 3.2.1 Uncertainty Taxonomies 3.2.2 Uncertainty in Flow Diagrams 3.3 Time-Series Data Visualization 3.3.1 Time & Data 3.3.2 User Tasks 3.3.3 Visual Representation 3.4 Summary ii Contents 4 Requirement Engineering Phase 4.1 Introduction 4.2 Environment 4.2.1 The Product 4.2.2 The Customers and Development Methodology 4.2.3 Lessons Learned 4.3 Visualization Requirements for Product Costing 4.3.1 Current Visualization Practice 4.3.2 Visualization Tasks 4.3.3 Data Structure and Size 4.3.4 Early Visualization Prototypes 4.3.5 Challenges and Lessons Learned 4.4 Data and Task Abstraction 4.4.1 Data Abstraction 4.4.2 Task Abstraction 4.5 Summary and Outlook 5 Parallel Hierarchies 5.1 Introduction 5.2 The Parallel Hierarchies Technique 5.2.1 The Individual Axis: Showing Hierarchical Categories 5.2.2 Two Interlinked Axes: Showing Pairwise Frequencies 5.2.3 Multiple Linked Axes: Propagating Frequencies 5.2.4 Fine-tuning Parallel Hierarchies through Reordering 5.3 Design Choices 5.4 Applying Parallel Hierarchies 5.4.1 US Census Data 5.4.2 Yeast Gene Ontology Annotations 5.5 Evaluation 5.5.1 Setup of the Evaluation 5.5.2 Procedure of the Evaluation 5.5.3 Results from the Evaluation 5.5.4 Validity of the Evaluation 5.6 Summary and Outlook 6 Visualizing Uncertainty in Flow Diagrams 6.1 Introduction 6.2 Uncertainty in Product Costing 6.2.1 Background 6.2.2 Main Causes of Bad Quality in Costing Data 6.3 Visualization Concepts 6.4 Uncertainty Visualization using Ribbons 6.4.1 Selected Visualization Techniques 6.4.2 Study Design and Procedure 6.4.3 Results 6.4.4 Discussion 6.5 Revised Visualization Approach using Ribbons 6.5.1 Application to Sankey Diagram 6.5.2 Application to Parallel Sets 6.5.3 Application to Parallel Hierarchies 6.6 Uncertainty Visualization using Nodes 6.6.1 Visual Design of Nodes 6.6.2 Expert Evaluation 6.7 Summary and Outlook 7 Visual Comparison Task 7.1 Introduction 7.2 Comparing Two One-dimensional Time Steps 7.2.1 Problem Statement 7.2.2 Visualization Design 7.3 Comparing Two N-dimensional Time Steps 7.4 Comparing Several One-dimensional Time Steps 7.5 Summary and Outlook 8 Parallel Hierarchies in Practice 8.1 Application to Plausibility Check Task 8.1.1 Plausibility Check Process 8.1.2 Visual Exploration of Machine Learning Results 8.2 Integration into SAP Analytics Cloud 8.2.1 SAP Analytics Cloud 8.2.2 Ocean to Table Project 8.3 Summary and Outlook 9 Validation 9.1 Introduction 9.2 Nested Model Validation Approach 9.3 Perceptual Validation of Visualization Techniques 9.3.1 Design Validation Table 9.3.2 Discussion 9.4 Summary and Outlook 10 Conclusion and Outlook 10.1 Summary of Findings 10.2 Discussion 10.3 Outlook A Questionnaires of the Evaluation B Survey of the Quality of Product Costing Data C Questionnaire of Current Practice Bibliography

In recent years businesses around the world have been facing the challenges of a rapidly changing business and technology environment. As a result, organisations are paying more attention to supporting business process management by adapting to the dynamic environment. With the increased complexity and uncertainty in business operations, adaptive and collaborative business process and exception management (EM) are gaining attention. In the logistics industry, the growing importance of logistics worldwide as well as the increasing complexity of logistics networks and the service requirement of customers has become a challenge. The current logistics exceptions are managed using human brain power together with the traditional workflow technology-based supply-chain management or other logistics tools. The traditional workflow technology models and manages business processes and anticipated exceptions based on predefined logical procedures of activities from a centralised perspective. This situation offers inadequate decision support for flexibility and adaptability in logistics EM. The traditional workflow technology is also limited to monitoring the logistics activities in real-time to detect and resolve exceptions in a timely manner. To mitigate these problems, an intelligent agent incorporating business activity monitoring (BAM) decision support approach in logistics EM has been proposed and investigated in this research. This research creates and evaluates two IT designed artefacts (conceptual framework and prototype) intended to efficiently and automatically monitor and handle logistics exceptions. It follows a design science research strategy. The design, development, and evaluation adhere to the principles enunciated in the design science literature. The aim of this research is to solve the important logistics EM problem in a more effective and efficient manner. Two designed artefacts were strictly informed by, and incorporated with, three different theories. An exploratory case study and a later confirmatory case study assisted in the rigorous derivation of the design and framework. The results of the confirmatory case study were used in particular to refine the designed artefacts. Such a build-and-evaluate loop iterated several times before the final designed artefacts were generated. The designed artefacts were then evaluated empirically via a field experiment. The research included both a technical presentation and a practical framing in terms of application in the logistics exception monitoring and handling domain. In this study, there were three interrelated research phases. In the first research phase, a decision-making conceptual framework (an artefact) for design and development of real-time logistics EM system was developed. To enable more efficient decision support practices for logistics EM, the characteristics of logistics exceptions were first examined and identified. The logistics exception analysis was conducted through a comprehensive literature review and an exploratory case study conducted in a major logistics company in Australia. The logistics exceptions were then classified into known and knowable categories, based on the Cynefin sense-making framework (Snowden, 2002). On the basis of the logistics analysis, informed by Gartner’s three-layer BAM architecture (Dresner, 2003), the Cynefin sense-making framework decision models (Snowden, 2002), and Simon’s (1977) decision-making/problem-solving process, the real-time logistics EM conceptual framework was depicted. The BAM architecture provided the real-time decision support. Based on Cynefin’s decision model, adaptive business process flow was chosen for known and knowable logistics exceptions to speed up the decision-making process. In addition, Simon’s process theory was deployed to model the diagnosing process for known and knowable logistics exceptions. This conceptual model guided the analysis, design, and development for real-time logistics EM systems. In the second research phase, based on the logistics EM conceptual framework, a Web-service-multi-agent-based real-time logistics EM system (an artefact) was designed and developed. Intelligent agent technology was applied to deal with the complex, dynamic, and distributed logistics EM processes. Web-services techniques were proposed for more interoperability and scalability in network-based business environment. By integrating agent technology with Web-services to make use of the advantages from both, this approach provided a more intelligent, flexible, autonomous, and comprehensive solution to real-time logistics EM. In the third research phase, two designed artefacts were evaluated via a confirmatory case study and a field experiment. The confirmatory case study was conducted to collect feedback on the two designed artefacts (i.e., conceptual framework and prototype system) to refine them. The field experiment was then conducted to investigate the proposed logistics EM prototype system decision support effectiveness and efficiency by comparing the human decision-making performance with/without the logistics EM decision support facility. The evaluation results indicated that the proposed logistics EM prototype outperformed the one without logistics EM decision support in terms of more efficient decision process, higher decision outcome quality, and better user perception. The two designed artefacts were the major contributions of this research. They add knowledge to decision theory and practice. The artefacts are the real-time extension for Simon’s (1977) classic decision-making/problem-solving process model in logistics EM by incorporating BAM (Dresner, 2003). In addition, by adding the Cynefin sense-making framework (Snowden, 2002), the artefacts provide a more efficient decision-making routine for logistics EM. This research provides the first attempt (to the best of the researcher’s knowledge) to design a real-time logistics EM decision support mechanism based on decision science theories. To demonstrate the usability of the proposed conceptual framework, a logistics EM decision support prototype was designed, developed, and evaluated. For practice, the logistics exceptions classification, logistics EM conceptual framework, and incorporating agent technologies into logistics EM all will assist logistics companies to develop their logistics exception handling decision-making strategies and solutions.

In recent years businesses around the world have been facing the challenges of a rapidly changing business and technology environment. As a result, organisations are paying more attention to supporting business process management by adapting to the dynamic environment. With the increased complexity and uncertainty in business operations, adaptive and collaborative business process and exception management (EM) are gaining attention. In the logistics industry, the growing importance of logistics worldwide as well as the increasing complexity of logistics networks and the service requirement of customers has become a challenge. The current logistics exceptions are managed using human brain power together with the traditional workflow technology-based supply-chain management or other logistics tools. The traditional workflow technology models and manages business processes and anticipated exceptions based on predefined logical procedures of activities from a centralised perspective. This situation offers inadequate decision support for flexibility and adaptability in logistics EM. The traditional workflow technology is also limited to monitoring the logistics activities in real-time to detect and resolve exceptions in a timely manner. To mitigate these problems, an intelligent agent incorporating business activity monitoring (BAM) decision support approach in logistics EM has been proposed and investigated in this research. This research creates and evaluates two IT designed artefacts (conceptual framework and prototype) intended to efficiently and automatically monitor and handle logistics exceptions. It follows a design science research strategy. The design, development, and evaluation adhere to the principles enunciated in the design science literature. The aim of this research is to solve the important logistics EM problem in a more effective and efficient manner. Two designed artefacts were strictly informed by, and incorporated with, three different theories. An exploratory case study and a later confirmatory case study assisted in the rigorous derivation of the design and framework. The results of the confirmatory case study were used in particular to refine the designed artefacts. Such a build-and-evaluate loop iterated several times before the final designed artefacts were generated. The designed artefacts were then evaluated empirically via a field experiment. The research included both a technical presentation and a practical framing in terms of application in the logistics exception monitoring and handling domain. In this study, there were three interrelated research phases. In the first research phase, a decision-making conceptual framework (an artefact) for design and development of real-time logistics EM system was developed. To enable more efficient decision support practices for logistics EM, the characteristics of logistics exceptions were first examined and identified. The logistics exception analysis was conducted through a comprehensive literature review and an exploratory case study conducted in a major logistics company in Australia. The logistics exceptions were then classified into known and knowable categories, based on the Cynefin sense-making framework (Snowden, 2002). On the basis of the logistics analysis, informed by Gartner’s three-layer BAM architecture (Dresner, 2003), the Cynefin sense-making framework decision models (Snowden, 2002), and Simon’s (1977) decision-making/problem-solving process, the real-time logistics EM conceptual framework was depicted. The BAM architecture provided the real-time decision support. Based on Cynefin’s decision model, adaptive business process flow was chosen for known and knowable logistics exceptions to speed up the decision-making process. In addition, Simon’s process theory was deployed to model the diagnosing process for known and knowable logistics exceptions. This conceptual model guided the analysis, design, and development for real-time logistics EM systems. In the second research phase, based on the logistics EM conceptual framework, a Web-service-multi-agent-based real-time logistics EM system (an artefact) was designed and developed. Intelligent agent technology was applied to deal with the complex, dynamic, and distributed logistics EM processes. Web-services techniques were proposed for more interoperability and scalability in network-based business environment. By integrating agent technology with Web-services to make use of the advantages from both, this approach provided a more intelligent, flexible, autonomous, and comprehensive solution to real-time logistics EM. In the third research phase, two designed artefacts were evaluated via a confirmatory case study and a field experiment. The confirmatory case study was conducted to collect feedback on the two designed artefacts (i.e., conceptual framework and prototype system) to refine them. The field experiment was then conducted to investigate the proposed logistics EM prototype system decision support effectiveness and efficiency by comparing the human decision-making performance with/without the logistics EM decision support facility. The evaluation results indicated that the proposed logistics EM prototype outperformed the one without logistics EM decision support in terms of more efficient decision process, higher decision outcome quality, and better user perception. The two designed artefacts were the major contributions of this research. They add knowledge to decision theory and practice. The artefacts are the real-time extension for Simon’s (1977) classic decision-making/problem-solving process model in logistics EM by incorporating BAM (Dresner, 2003). In addition, by adding the Cynefin sense-making framework (Snowden, 2002), the artefacts provide a more efficient decision-making routine for logistics EM. This research provides the first attempt (to the best of the researcher’s knowledge) to design a real-time logistics EM decision support mechanism based on decision science theories. To demonstrate the usability of the proposed conceptual framework, a logistics EM decision support prototype was designed, developed, and evaluated. For practice, the logistics exceptions classification, logistics EM conceptual framework, and incorporating agent technologies into logistics EM all will assist logistics companies to develop their logistics exception handling decision-making strategies and solutions.