A Knowledge-Based Approach for Business Process Risk Management

In order to support effective and efficient business process management, it is imperative that the process management lifecycle be integrated with risk management knowledge. In this regard, this article presents a knowledge-based approach to integrating risk management with business process management. The adopted approach is based on conversational case-based reasoning (CCBR) with the objective to provide support in developing an appropriate risk management strategy for an ongoing workflow instance. This approach builds on the notion of integrating risks within business process models. A prototype is currently under development, which will assess the feasibility of this approach. We then intend to validate this approach using case studies

Service discovery and composition : PreDiCtS approach

The proliferation of Web Services is fostering the need for service-discovery and composition tools to provide more personalisation during the service retrieval process. In this paper, we describe the motivating details behind PreDiCtS, a framework for personalised service-retrieval. In our approach we consider that similar service composition problems can be tackled in a similar manner by reusing and adapting past composition best practices or templates. The proposed retrieval process uses a mixed- initiative technique based on Conversational Case-Based Reasoning (CCBR), that provides i) for a clearer identification of the user’s service requirements and ii) based on these requirements, finds suitable service templates that satisfy the user’s goal. We discuss how retrieval can vary through the use of different CCBR algorithms and how adaptation can be performed over the retrieved templates thus providing the personalisation feature in PreDiCtS.peer-reviewe

CCBR-Driven Business Process Evolution

Process-aware information systems (PAIS) allow coordinating the execution of business processes by providing the right tasks to the right people at the right time. In order to support a broad spectrum of business processes, PAIS must be flexible at run-time. Ad-hoc deviations from the predefined process schema as well as the quick adaptation of the process schema itself due to changes of the underlying business processes must be supported. This paper presents an integrated approach combining the concepts and methods provided by the process management systems ADEPT and CBRFlow. Integrating these two systems enables ad-hoc modifications of single process instances, the memorization of these modifications using conversational case-based reasoning, and their reuse in similar future situations. In addition, potential process type changes can be derived from cases when similar ad-hoc modifications at the process instance level occur frequently

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

A Personalized System for Conversational Recommendations

Searching for and making decisions about information is becoming increasingly difficult as the amount of information and number of choices increases. Recommendation systems help users find items of interest of a particular type, such as movies or restaurants, but are still somewhat awkward to use. Our solution is to take advantage of the complementary strengths of personalized recommendation systems and dialogue systems, creating personalized aides. We present a system -- the Adaptive Place Advisor -- that treats item selection as an interactive, conversational process, with the program inquiring about item attributes and the user responding. Individual, long-term user preferences are unobtrusively obtained in the course of normal recommendation dialogues and used to direct future conversations with the same user. We present a novel user model that influences both item search and the questions asked during a conversation. We demonstrate the effectiveness of our system in significantly reducing the time and number of interactions required to find a satisfactory item, as compared to a control group of users interacting with a non-adaptive version of the system

Providing Integrated Life Cycle Support in Process-Aware Information Systems

The need for more flexibility of process-aware information systems (PAISs) has been discussed for several years and different approaches for adaptive process management have emerged. However, only few of them provide support for both changes of individual process instances and the propagation of process type changes to a collection of related process instances. Furthermore, knowledge about process changes has not yet been exploited by any of these systems. This paper presents the ProCycle approach which overcomes this practical limitation by capturing the whole process life cycle and all kinds of changes in an integrated way. Users are not only allowed to deviate from the predefined process in exceptional situations, but are also assisted in retrieving and reusing knowledge about previously performed changes in this context. If similar instance deviations occur frequently, process engineers will be supported in deriving improved process models from them. This, in turn, allows engineers to evolve the PAIS (including the knowledge about the changes) over time. Feasability of the ProCycle approach is demonstrated by a proof-of-concept prototype which combines adaptive process management technology with concepts and methods provided by case-based reasoning (CBR) technology

Integrating Case-Based Reasoning with Adaptive Process Management

The need for more flexiblity of process-aware information systems (PAIS) has been discussed for several years and different approaches for adaptive process management have emerged. Only few of them provide support for both changes of individual process instances and the propagation of process type changes to a collection of related process instances. The knowledge about changes has not yet been exploited by any of these systems. To overcome this practical limitation, PAIS must capture the whole process life cycle and all kinds of changes in an integrated way. They must allow users to deviate from the predefined process in exceptional situations, and assist them in retrieving and reusing knowledge about previously performed changes. In this report we present a proof-of concept implementation of a learning adaptive PAIS. The prototype combines the ADEPT2 framework for dynamic process changes with concepts and methods provided by case-based reasoning(CBR) technology

Using Analytics to Transform a Problem-Based Case Library: An Educational Design Research Approach

This article describes the iterative design, development, and evaluation of a case-based learning environment focusing on an ill-structured sales management problem. We discuss our processes and situate them within the broader framework of educational design research. The learning environment evolved over the course of three design phases. A semisummative evaluation of student concept maps after the third phase revealed unsatisfactory learning outcomes. This paper focuses on how we investigated design flaws that contributed to poor learning performance. A specific focus of our investigation was the use of Google Analytics data, which uncovered weaknesses in our design. Based on our findings, we used a rapid prototyping process to redesign the learning environment, emphasizing interactive and multimedia-rich elements. Processes and methods are reported along with discussion of implications for case-based reasoning, including relevant design principles. This article will provide insights into resolving design tensions for researchers and practitioners seeking to advance theory and practice in similar domains