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

A possibilistic treatment of data quality measurement

The ever growing capabilities of data storage systems have created the need to assess the quality of data in an efficient manner. In this paper, we consider a framework of data quality measurement that relies on basic predicates formulated on the data. It is then motivated that in some cases, the evaluation of predicates is hindered due to a lack of information. As a result, the truth value of a predicate can not be determined with complete certainty. In this paper, it is first shown how such uncertainty about the evaluation of predicates can be modelled. Such uncertainty can then be propagated throughout the measurement process. This establishes a possibilistic measurement of data quality