Adaptive Process Management in Cyber-Physical Domains

The increasing application of process-oriented approaches in new challenging cyber-physical domains beyond business computing (e.g., personalized healthcare, emergency management, factories of the future, home automation, etc.) has led to reconsider the level of flexibility and support required to manage complex processes in such domains. A cyber-physical domain is characterized by the presence of a cyber-physical system coordinating heterogeneous ICT components (PCs, smartphones, sensors, actuators) and involving real world entities (humans, machines, agents, robots, etc.) that perform complex tasks in the “physical” real world to achieve a common goal. The physical world, however, is not entirely predictable, and processes enacted in cyber-physical domains must be robust to unexpected conditions and adaptable to unanticipated exceptions. This demands a more flexible approach in process design and enactment, recognizing that in real-world environments it is not adequate to assume that all possible recovery activities can be predefined for dealing with the exceptions that can ensue. In this chapter, we tackle the above issue and we propose a general approach, a concrete framework and a process management system implementation, called SmartPM, for automatically adapting processes enacted in cyber-physical domains in case of unanticipated exceptions and exogenous events. The adaptation mechanism provided by SmartPM is based on declarative task specifications, execution monitoring for detecting failures and context changes at run-time, and automated planning techniques to self-repair the running process, without requiring to predefine any specific adaptation policy or exception handler at design-time

Self-managed Workflows for Cyber-physical Systems

Workflows are a well-established concept for describing business logics and processes in web-based applications and enterprise application integration scenarios on an abstract implementation-agnostic level. Applying Business Process Management (BPM) technologies to increase autonomy and automate sequences of activities in Cyber-physical Systems (CPS) promises various advantages including a higher flexibility and simplified programming, a more efficient resource usage, and an easier integration and orchestration of CPS devices. However, traditional BPM notations and engines have not been designed to be used in the context of CPS, which raises new research questions occurring with the close coupling of the virtual and physical worlds. Among these challenges are the interaction with complex compounds of heterogeneous sensors, actuators, things and humans; the detection and handling of errors in the physical world; and the synchronization of the cyber-physical process execution models. Novel factors related to the interaction with the physical world including real world obstacles, inconsistencies and inaccuracies may jeopardize the successful execution of workflows in CPS and may lead to unanticipated situations. This thesis investigates properties and requirements of CPS relevant for the introduction of BPM technologies into cyber-physical domains. We discuss existing BPM systems and related work regarding the integration of sensors and actuators into workflows, the development of a Workflow Management System (WfMS) for CPS, and the synchronization of the virtual and physical process execution as part of self-* capabilities for WfMSes. Based on the identified research gap, we present concepts and prototypes regarding the development of a CPS WFMS w.r.t. all phases of the BPM lifecycle. First, we introduce a CPS workflow notation that supports the modelling of the interaction of complex sensors, actuators, humans, dynamic services and WfMSes on the business process level. In addition, the effects of the workflow execution can be specified in the form of goals defining success and error criteria for the execution of individual process steps. Along with that, we introduce the notion of Cyber-physical Consistency. Following, we present a system architecture for a corresponding WfMS (PROtEUS) to execute the modelled processes-also in distributed execution settings and with a focus on interactive process management. Subsequently, the integration of a cyber-physical feedback loop to increase resilience of the process execution at runtime is discussed. Within this MAPE-K loop, sensor and context data are related to the effects of the process execution, deviations from expected behaviour are detected, and compensations are planned and executed. The execution of this feedback loop can be scaled depending on the required level of precision and consistency. Our implementation of the MAPE-K loop proves to be a general framework for adding self-* capabilities to WfMSes. The evaluation of our concepts within a smart home case study shows expected behaviour, reasonable execution times, reduced error rates and high coverage of the identified requirements, which makes our CPS~WfMS a suitable system for introducing workflows on top of systems, devices, things and applications of CPS.:1. Introduction 15 1.1. Motivation 15 1.2. Research Issues 17 1.3. Scope & Contributions 19 1.4. Structure of the Thesis 20 2. Workflows and Cyber-physical Systems 21 2.1. Introduction 21 2.2. Two Motivating Examples 21 2.3. Business Process Management and Workflow Technologies 23 2.4. Cyber-physical Systems 31 2.5. Workflows in CPS 38 2.6. Requirements 42 3. Related Work 45 3.1. Introduction 45 3.2. Existing BPM Systems in Industry and Academia 45 3.3. Modelling of CPS Workflows 49 3.4. CPS Workflow Systems 53 3.5. Cyber-physical Synchronization 58 3.6. Self-* for BPM Systems 63 3.7. Retrofitting Frameworks for WfMSes 69 3.8. Conclusion & Deficits 71 4. Modelling of Cyber-physical Workflows with Consistency Style Sheets 75 4.1. Introduction 75 4.2. Workflow Metamodel 76 4.3. Knowledge Base 87 4.4. Dynamic Services 92 4.5. CPS-related Workflow Effects 94 4.6. Cyber-physical Consistency 100 4.7. Consistency Style Sheets 105 4.8. Tools for Modelling of CPS Workflows 106 4.9. Compatibility with Existing Business Process Notations 111 5. Architecture of a WfMS for Distributed CPS Workflows 115 5.1. Introduction 115 5.2. PROtEUS Process Execution System 116 5.3. Internet of Things Middleware 124 5.4. Dynamic Service Selection via Semantic Access Layer 125 5.5. Process Distribution 126 5.6. Ubiquitous Human Interaction 130 5.7. Towards a CPS WfMS Reference Architecture for Other Domains 137 6. Scalable Execution of Self-managed CPS Workflows 141 6.1. Introduction 141 6.2. MAPE-K Control Loops for Autonomous Workflows 141 6.3. Feedback Loop for Cyber-physical Consistency 148 6.4. Feedback Loop for Distributed Workflows 152 6.5. Consistency Levels, Scalability and Scalable Consistency 157 6.6. Self-managed Workflows 158 6.7. Adaptations and Meta-adaptations 159 6.8. Multiple Feedback Loops and Process Instances 160 6.9. Transactions and ACID for CPS Workflows 161 6.10. Runtime View on Cyber-physical Synchronization for Workflows 162 6.11. Applicability of Workflow Feedback Loops to other CPS Domains 164 6.12. A Retrofitting Framework for Self-managed CPS WfMSes 165 7. Evaluation 171 7.1. Introduction 171 7.2. Hardware and Software 171 7.3. PROtEUS Base System 174 7.4. PROtEUS with Feedback Service 182 7.5. Feedback Service with Legacy WfMSes 213 7.6. Qualitative Discussion of Requirements and Additional CPS Aspects 217 7.7. Comparison with Related Work 232 7.8. Conclusion 234 8. Summary and Future Work 237 8.1. Summary and Conclusion 237 8.2. Advances of this Thesis 240 8.3. Contributions to the Research Area 242 8.4. Relevance 243 8.5. Open Questions 245 8.6. Future Work 247 Bibliography 249 Acronyms 277 List of Figures 281 List of Tables 285 List of Listings 287 Appendices 28

Mobiilse värkvõrgu protsessihaldus

Värkvõrk, ehk Asjade Internet (Internet of Things, lüh IoT) edendab lahendusi nagu nn tark linn, kus meid igapäevaselt ümbritsevad objektid on ühendatud infosüsteemidega ja ka üksteisega. Selliseks näiteks võib olla teekatete seisukorra monitoorimissüsteem. Võrku ühendatud sõidukitelt (nt bussidelt) kogutakse videomaterjali, mida seejärel töödeldakse, et tuvastada löökauke või lume kogunemist. Tavaliselt hõlmab selline lahendus keeruka tsentraalse süsteemi ehitamist. Otsuste langetamiseks (nt milliseid sõidukeid parasjagu protsessi kaasata) vajab keskne süsteem pidevat ühendust kõigi IoT seadmetega. Seadmete hulga kasvades võib keskne lahendus aga muutuda pudelikaelaks. Selliste protsesside disaini, haldust, automatiseerimist ja seiret hõlbustavad märkimisväärselt äriprotsesside halduse (Business Process Management, lüh BPM) valdkonna standardid ja tööriistad. Paraku ei ole BPM tehnoloogiad koheselt kasutatavad uute paradigmadega nagu Udu- ja Servaarvutus, mis tuleviku värkvõrgu jaoks vajalikud on. Nende puhul liigub suur osa otsustustest ja arvutustest üksikutest andmekeskustest servavõrgu seadmetele, mis asuvad lõppkasutajatele ja IoT seadmetele lähemal. Videotöötlust võiks teostada mini-andmekeskustes, mis on paigaldatud üle linna, näiteks bussipeatustesse. Arvestades IoT seadmete üha suurenevat hulka, vähendab selline koormuse jaotamine vähendab riski, et tsentraalne andmekeskust ülekoormamist. Doktoritöö uurib, kuidas mobiilsusega seonduvaid IoT protsesse taoliselt ümber korraldada, kohanedes pidevalt muutlikule, liikuvate seadmetega täidetud servavõrgule. Nimelt on ühendused katkendlikud, mistõttu otsuste langetus ja planeerimine peavad arvestama muuhulgas mobiilseadmete liikumistrajektoore. Töö raames valminud prototüüpe testiti Android seadmetel ja simulatsioonides. Lisaks valmis tööriistakomplekt STEP-ONE, mis võimaldab teadlastel hõlpsalt simuleerida ja analüüsida taolisi probleeme erinevais realistlikes stsenaariumites nagu seda on tark linn.The Internet of Things (IoT) promotes solutions such as a smart city, where everyday objects connect with info systems and each other. One example is a road condition monitoring system, where connected vehicles, such as buses, capture video, which is then processed to detect potholes and snow build-up. Building such a solution typically involves establishing a complex centralised system. The centralised approach may become a bottleneck as the number of IoT devices keeps growing. It relies on constant connectivity to all involved devices to make decisions, such as which vehicles to involve in the process. Designing, automating, managing, and monitoring such processes can greatly be supported using the standards and software systems provided by the field of Business Process Management (BPM). However, BPM techniques are not directly applicable to new computing paradigms, such as Fog Computing and Edge Computing, on which the future of IoT relies. Here, a lot of decision-making and processing is moved from central data-centers to devices in the network edge, near the end-users and IoT sensors. For example, video could be processed in mini-datacenters deployed throughout the city, e.g., at bus stops. This load distribution reduces the risk of the ever-growing number of IoT devices overloading the data center. This thesis studies how to reorganise the process execution in this decentralised fashion, where processes must dynamically adapt to the volatile edge environment filled with moving devices. Namely, connectivity is intermittent, so decision-making and planning need to involve factors such as the movement trajectories of mobile devices. We examined this issue in simulations and with a prototype for Android smartphones. We also showcase the STEP-ONE toolset, allowing researchers to conveniently simulate and analyse these issues in different realistic scenarios, such as those in a smart city.  https://www.ester.ee/record=b552551

Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided