2 research outputs found

    Monitoring interactions across multi business processes with token carried data

    Get PDF
    The rapid development of web service provides many opportunities for companies to migrate their business processes to the Internet for wider accessibility and higher collaboration efficiency. However, the open, dynamic and ever-changing Internet also brings challenges in protecting these business processes. There are certain process monitoring methods and the recently proposed ones are based on state changes of process artifacts or places, however, they do not mention defending process interactions from outer tampering, where events could not be detected by process systems, or saving fault-handling time. In this paper, we propose a novel Token-based Interaction Monitoring framework based on token carried data to safeguard process collaboration and reduce problem solving time. Token is a more common data entity in processes than process artifacts and they cover all tasks’ executions. Comparing to detecting places’ state change, we set security checking points at both when tokens are just produced and to be consumed. This will ensure that even if data is tampered after being created it would be detected before being used

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

    Get PDF
    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
    corecore