Scientific Publications

2025
3.		Burkhardt, Dirk; Ristow, Gerald Enabling Smart Manufacturing with Visual Analytics for Plant Workers Proceedings Article In: Klettke, Meike; Schenkel, Ralf; Henrich, Andreas; Nicklas, Daniela; Schüle, Maximilian E.; Meyer-Wegener, Klaus (Ed.): Datenbanksysteme für Business, Technologie und Web (BTW 2025), pp. 665-678, Gesellschaft für Informatik, Bonn, 2025, ISSN: 2944-7682. Abstract \| Links \| BibTeX \| Tags: Human-Computer Interaction, Internet of Things, Smart Manufacturing, User-Centered Design, Visual Analytics @inproceedings{Burkhardt2025, title = {Enabling Smart Manufacturing with Visual Analytics for Plant Workers}, author = {Dirk Burkhardt and Gerald Ristow}, editor = {Meike Klettke and Ralf Schenkel and Andreas Henrich and Daniela Nicklas and Maximilian E. Schüle and Klaus Meyer-Wegener}, url = {https://dirk.burkhardt.xyz/wp-content/uploads/2025/03/2025btw.pdf, Paper as PDF}, doi = {10.18420/BTW2025-33}, issn = {2944-7682}, year = {2025}, date = {2025-02-28}, urldate = {2025-02-28}, booktitle = {Datenbanksysteme für Business, Technologie und Web (BTW 2025)}, pages = {665-678}, publisher = {Gesellschaft für Informatik}, address = {Bonn}, series = {Lecture Notes in Informatics (LNI) - Proceedings, Volume P-361}, abstract = {Smart manufacturing is increasingly making use of visual analytics to optimize production or to identify early problem signs. However, current solutions and approaches require professionals, especially from the data science area, to make use of it, which is for most production companies not affordable. In this paper, we describe first a best practice to sensorize plants from the wood and beverage industry to enable smart manufacturing in general. Second, we describe a new approach that aims at providing easy-to-use visual analytics functionalities that are designed to be used directly by plant workers. Plant workers usually have encompassing experience in the production and the plant, but lack of computer experience and corresponding mathematical knowledge for data analysis. Through lowering the barriers for plant workers in performing data analysis of the IoT sensors with simplified and almost automated analysis functions would give them the ability to gain insights into the production and achieve similar production optimizations and problem preventions as data science experts could. The main contributions of this article are on the one hand the best practice of how production lines of the wood and beverage industry could be made ready for smart manufacturing, but also an approaches that enable non-data scientists, especially plant workers, to perform sufficient analysis about optimal production settings and early problem cause identification.}, keywords = {Human-Computer Interaction, Internet of Things, Smart Manufacturing, User-Centered Design, Visual Analytics}, pubstate = {published}, tppubtype = {inproceedings} } Close Smart manufacturing is increasingly making use of visual analytics to optimize production or to identify early problem signs. However, current solutions and approaches require professionals, especially from the data science area, to make use of it, which is for most production companies not affordable. In this paper, we describe first a best practice to sensorize plants from the wood and beverage industry to enable smart manufacturing in general. Second, we describe a new approach that aims at providing easy-to-use visual analytics functionalities that are designed to be used directly by plant workers. Plant workers usually have encompassing experience in the production and the plant, but lack of computer experience and corresponding mathematical knowledge for data analysis. Through lowering the barriers for plant workers in performing data analysis of the IoT sensors with simplified and almost automated analysis functions would give them the ability to gain insights into the production and achieve similar production optimizations and problem preventions as data science experts could. The main contributions of this article are on the one hand the best practice of how production lines of the wood and beverage industry could be made ready for smart manufacturing, but also an approaches that enable non-data scientists, especially plant workers, to perform sufficient analysis about optimal production settings and early problem cause identification. Close Paper as PDF doi:10.18420/BTW2025-33 Close
2017
2.		Nazemi, Kawa; Burkhardt, Dirk; Kuijper, Arjan Analyzing the Information Search Behavior and Intentions in Visual Information Systems Journal Article In: Journal of Computer Science Technology Updates (JCSTU), vol. 4, no. 2, pp. 19–27, 2017, ISSN: 2410-2938. Abstract \| Links \| BibTeX \| Tags: Adaptive Visualization, Distant Supervision, Human-Computer Interaction, Information Retrieval, Information Search Behavior, Interactive Search, Predictive Analysis, User-Centered Design @article{Nazemi2017, title = {Analyzing the Information Search Behavior and Intentions in Visual Information Systems}, author = {Kawa Nazemi and Dirk Burkhardt and Arjan Kuijper}, url = {https://dirk.burkhardt.xyz/wp-content/uploads/2018/11/2017jcstu.pdf, Paper as PDF}, doi = {10.15379/2410-2938.2017.04.02.02}, issn = {2410-2938}, year = {2017}, date = {2017-12-26}, journal = {Journal of Computer Science Technology Updates (JCSTU)}, volume = {4}, number = {2}, pages = {19--27}, abstract = {Visual information search systems support different search approaches such as targeted, exploratory or analytical search. Those visual systems deal with the challenge of composing optimal initial result visualization sets that face the search intention and respond to the search behavior of users. The diversity of these kinds of search tasks require different sets of visual layouts and functionalities, e.g. to filter, thrill-down or even analyze concrete data properties. This paper describes a new approach to calculate the probability towards the three mentioned search intentions, derived from users’ behavior. The implementation is realized as a web-service, which is included in a visual environment that is designed to enable various search strategies based on heterogeneous data sources. In fact, based on an entered search query our developed search intention analysis web-service calculates the most probable search task, and our visualization system initially shows the optimal result set of visualizations to solve the task. The main contribution of this paper is a probability-based approach to derive the users’ search intentions based on the search behavior enhanced by the application to a visual system.}, keywords = {Adaptive Visualization, Distant Supervision, Human-Computer Interaction, Information Retrieval, Information Search Behavior, Interactive Search, Predictive Analysis, User-Centered Design}, pubstate = {published}, tppubtype = {article} } Close Visual information search systems support different search approaches such as targeted, exploratory or analytical search. Those visual systems deal with the challenge of composing optimal initial result visualization sets that face the search intention and respond to the search behavior of users. The diversity of these kinds of search tasks require different sets of visual layouts and functionalities, e.g. to filter, thrill-down or even analyze concrete data properties. This paper describes a new approach to calculate the probability towards the three mentioned search intentions, derived from users’ behavior. The implementation is realized as a web-service, which is included in a visual environment that is designed to enable various search strategies based on heterogeneous data sources. In fact, based on an entered search query our developed search intention analysis web-service calculates the most probable search task, and our visualization system initially shows the optimal result set of visualizations to solve the task. The main contribution of this paper is a probability-based approach to derive the users’ search intentions based on the search behavior enhanced by the application to a visual system. Close Paper as PDF doi:10.15379/2410-2938.2017.04.02.02 Close
2016
1.		Burkhardt, Dirk; Pattan, Sachin; Nazemi, Kawa; Kuijper, Arjan Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications Journal Article In: Procedia Computer Science. ICTE 2016, Riga Technical University, Latvia, vol. 104, pp. 539 - 547, 2016, ISSN: 1877-0509. Abstract \| Links \| BibTeX \| Tags: Human-Computer Interaction, Information Retrieval, Interactive Search, Predictive Analysis, User-Centered Design @article{Burkhardt2016, title = {Search Intention Analysis for Task- and User-Centered Visualization in Big Data Applications}, author = {Dirk Burkhardt and Sachin Pattan and Kawa Nazemi and Arjan Kuijper}, editor = {Egils Ginters and Jörn Kohlhammer}, url = {https://dirk.burkhardt.xyz/wp-content/uploads/2018/11/2016icte.pdf, Paper as PDF}, doi = {10.1016/j.procs.2017.01.170}, issn = {1877-0509}, year = {2016}, date = {2016-01-30}, journal = {Procedia Computer Science. ICTE 2016, Riga Technical University, Latvia}, volume = {104}, pages = {539 - 547}, abstract = {A new approach for classifying users’ search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data.}, keywords = {Human-Computer Interaction, Information Retrieval, Interactive Search, Predictive Analysis, User-Centered Design}, pubstate = {published}, tppubtype = {article} } Close A new approach for classifying users’ search intentions is described in this paper. The approach uses the parameters: word frequency, query length and entity matching for distinguishing the user's query into exploratory, targeted and analysis search. The approach focuses mainly on word frequency analysis, where different sources for word frequency data are considered such as the Wortschatz frequency service by the University of Leipzig and the Microsoft Ngram service (now part of the Microsoft Cognitive Services). The model is evaluated with the help of a survey tool and few machine learning techniques. The survey was conducted with more than one hundred users and on evaluating the model with the collected data, the results are satisfactory. In big data applications the search intention analysis can be used to identify the purpose of a performed search, to provide an optimal initially set of visualizations that respects the intended task of the user to work with the result data. Close Paper as PDF doi:10.1016/j.procs.2017.01.170 Close