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3. The Challenges of Big Data - Contributions in the Field of Data Quality and Artificial Intelligence Applications

Author

Szubartowicz, Michael

Subjects
ddc:004, 650 Management, ddc:330, 330 Wirtschaft, ddc:650, data quality, recommender systems, business process management, natural language processing, 004 Informatik
Abstract

The term "big data" has been characterized by challenges regarding data volume, velocity, variety and veracity. Solving these challenges requires research effort that fits the needs of big data. Therefore, this cumulative dissertation contains five paper aiming at developing and applying AI approaches within the field of big data as well as managing data quality in big data., Der Begriff "big data" wird durch Herausforderungen im Bezug auf die Menge, Geschwindigkeit, Vielfältigkeit sowie Richtigkeit von Daten charakterisiert. Um diese Herausforderungen zu lösen werden Forschungsaufwände benötigt, welche den Bedürfnissen von big data genügen. Daher enthält diese kumulative Dissertation fünf Forschungsarbeiten, welche zum Ziel haben, KI-Ansätze im Bereich big data zu entwickeln und anzuwenden, sowie die Datenqualität im Bereich big data zu verwalten.

Published
2022

6. GroupFM: Enabling Context-Aware Group Recommendations with Factorization Machines.

Author

Szubartowicz, Michael

Abstract

During the last decade, factorization machines have proven to be a highly versatile recommendation technique, capable of incorporating additional information such as the rating context. This versatility makes factorization machines a highly promising choice for capturing group dynamics in a group recommender scenario. Therefore, we present with GroupFM three independent approaches (PseudoU, MultiU and GC) to enable group recommendations with factorization machines. To evaluate GroupFM, we used the CAMRa2011 data set as well as a newly collected data set. The new data set contains contextually annotated ratings from both single users and groups and is made public to support the evaluation of future context-aware group recommender systems. Based on our results on both data sets, each approach was able to outperform existing group recommender systems given sufficient group ratings. Therefore, we discuss the individual benefits and characteristics of each approach depending on the number of available group ratings. [ABSTRACT FROM AUTHOR]

Published
2021

9. Requirements for Data Quality Metrics

Author

Heinrich, Bernd, Hristova, Diana, Klier, Mathias, Schiller, Alexander, and Szubartowicz, Michael

Abstract

Data quality and especially the assessment of data quality have been intensively discussed in research and practice alike. To support an economically oriented management of data quality and decision making under uncertainty, it is essential to assess the data quality level by means of well-founded metrics. However, if not adequately defined, these metrics can lead to wrong decisions and economic losses. Therefore, based on a decision-oriented framework, we present a set of five requirements for data quality metrics. These requirements are relevant for a metric that aims to support an economically oriented management of data quality and decision making under uncertainty. We further demonstrate the applicability and efficacy of these requirements by evaluating five data quality metrics for different data quality dimensions. Moreover, we discuss practical implications when applying the presented requirements.

Published
2018
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