Publications and Talks

@conference{Wicaksono2020,
title = {Context-sensitive Assistance in Requirements-based Knowledge Management},
author = {Simon Fritz; Matthias Jaenicke; Jivka Ovtcharova; Hendro Wicaksono
},
url = {https://doi.org/10.1145/3443279.3443306},
doi = {10.1145/3443279.3443306},
year = {2020},
date = {2020-12-17},
booktitle = {NLPIR 2020: Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval},
pages = {47–54},
publisher = {ACM},
abstract = {In this paper, a concept of a digital assistance system is presented which, based on computer linguistic methods, supports the user in the tasks of requirement-based knowledge management. The concept is divided into six modules that offer context-sensitive support in the identification, documentation, linking, modification and reuse of requirements and the associated knowledge. Since this concept was developed as part of the BMBF-funded SME Innovative Project DAM4KMU, which is primarily aimed at German SMEs, the concept developed was specially designed for processing German-language texts.

The digital assistance system pursues the goal, on the one hand, of increasing the quality of the documentation by supporting the user in the creation of complete formulations. On the other hand, with the help of the most modern language models, possible relationships between the information should be identified and linked to each other in a partially automated manner. In addition, the integration of web crawling technologies should make the knowledge available on the Internet available in a context-sensitive manner, in order to lift possible innovations on the one hand and not to forget possible non-considered boundary conditions on the other.

The automatic linking of all information is intended to ensure a continuous exchange of knowledge, which should reduce misunderstandings and non-communicated changes to requirements or goals to a minimum.

},
keywords = {knowledge management, machine learning, requirement engineering},
pubstate = {published},
tppubtype = {conference}
}

@article{Wicaksono2014,
title = {Energy efficiency evaluation in manufacturing through an ontology-represented knowledge base},
author = {Hendro Wicaksono and Fabian Jost and Sven Rogalski and Jivka Ovtcharova},
url = {http://onlinelibrary.wiley.com/doi/10.1002/isaf.1347/abstract},
doi = {10.1002/isaf.1347},
year = {2014},
date = {2014-04-01},
journal = {Intelligent Systems in Accounting, Finance and Management},
volume = {21},
number = {1},
pages = {59-69},
abstract = {Improving energy efficiency in a manufacturing company through an energy management system requires active participation of different stakeholders and involvement of different organizational entities and technical processes. Interoperability of stakeholders and entities is the key factor to achieve a successful implementation of an energy management system. Researchers have been developing approaches in applying ontologies to address interoperability issues among humans as well as machines. Ontologies have also been used for knowledge representation in different domains, such as energy management and manufacturing. In recent years, researchers have developed knowledge-based intelligent energy management systems in buildings, especially households, which use ontologies for knowledge representation. In the manufacturing domain, ontologies have been used for knowledge management in order to provide a common formal understanding between the stakeholders, who have different background knowledge. This paper proposes an approach to apply ontology to allow knowledge-based energy efficiency evaluation in manufacturing companies. The ontology provides a formal knowledge representation that addresses the interoperability issues due to different human stakeholders as well as machines involved in the energy management system of the company. This paper also describes the methods used to construct and to process the ontology. },
keywords = {energy efficiency, knowledge base, knowledge management, manufacturing, Ontology},
pubstate = {published},
tppubtype = {article}
}

@inproceedings{Wicaksono2013e,
title = {Intelligent knowledge generation for energy management in buildings },
author = {Hendro Wicaksono and Preslava Dobreva},
editor = {Wolfgang Breh},
url = {https://publikationen.bibliothek.kit.edu/1000036425/2888062},
doi = {10.5445/KSP/1000036425},
isbn = {978-3-7315-0097-1},
year = {2013},
date = {2013-06-13},
booktitle = {Impulse für die Zukunft der Energie : Wissenschaftliche Beiträge des KIT zur 2. Jahrestagung des KIT-Zentrums Energie. Doktorandensymposium, 13.06.2013},
pages = {73-78},
publisher = {KIT Scientific Publishing},
address = {Karlsruhe},
organization = {Zentrum Energie (Karlsruhe)},
abstract = {Energy consumption in buildings is currently representing a significant percentage of the whole energy consumption on earth. The EU responds to this trend by dedicated policy making and financially supporting research activities in the field of efficiency improvement without decreasing inhabitants comfort. This paper describes a method for intelligent energy management in public buildings, going behind classical data-driven approaches commonly used by BMS solutions. An ontology based approach for energy analysis offering extended concept for automated population, self-learning mechanisms and integration to other systems. Furthermore we demonstrate how the energy performance analysis is improved using the ontology based approach. },
keywords = {building energy management, energy efficiency, energy performance indicator, knowledge management, Ontology, ontology engineering, ontology population},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Wicaksono2013f,
title = {An Integrated Method for ICT Supported Energy Efficiency Improvement in Manufacturing },
author = {Hendro Wicaksono},
editor = {Wolfgang Breh},
url = {http://digbib.ubka.uni-karlsruhe.de/volltexte/1000036425},
doi = {10.5445/KSP/1000036425},
isbn = {978-3-7315-0097-1},
year = {2013},
date = {2013-06-13},
booktitle = {Impulse für die Zukunft der Energie : Wissenschaftliche Beiträge des KIT zur 2. Jahrestagung des KIT-Zentrums Energie. Doktorandensymposium, 13.06.2013},
pages = {67-72},
publisher = {KIT Scientific Publishing},
address = {Karlsruhe},
organization = {Zentrum Energie (Karlsruhe)},
abstract = {Energy and resource efficiency have been developing into one of the most crucial issues of the 21st century. Manufacturers are demanded to improve their energy efficiency by regulating their energy consumption. Energy management system helps the manufacturers to improve their energy efficiency. Most of the manufacturing companies face problems in implementing the energy management standards mostly due to the lack of ICT support, especially to help to evaluate the current energy performance. This paper presents an ICT based holistic approach to help manufacturing companies in the implementation of energy management system. The approach uses an ontological knowledge base containing the structures and rules representing best practices as reference of energy efficiency to support the qualitative evaluation. In the approach, we also develop measurement figures called Energy Performance Indicators (EPI) to determine the energy efficiency degrees in different resource units and organizational parts of the company. The knowledge management approach and EPI support quantitative and qualitative energy efficiency evaluation of manufacturing operations. Furthermore this paper introduces the method to improve the energy efficiency in production process planning.},
keywords = {energy efficiency, energy management, energy performance indicator, knowledge based energy management, knowledge management, Ontology},
pubstate = {published},
tppubtype = {inproceedings}
}

@inproceedings{Wicaksono2012b,
title = {Energy Consumption Regulation and Optimization in Discrete Manufacturing through Semi-automatic Knowledge Generation using Data Mining},
author = {Hendro Wicaksono and Jivka Ovtcharova },
year = {2012},
date = {2012-11-02},
booktitle = {Proceeding 10th Global Conference of Sustainable Manufacturing (GCSM)},
abstract = {The rapid growth of industrialization has led to a significant increase of energy demand that results in a constantly increasing of energy prices. Meanwhile, the changes of social, technical, and economic conditions in the market have challenged manufacturers to deal with the requirements for various and complex products. This has made production processes more sophisticated and energy intensive thus it leads to expensive production costs. This paper discusses a knowledge based approach to regulate the energy consumption in processing the customer orders in discrete manufacturing. The knowledge base consists of a rule set, which determines the choices of machines to process the products based on the given characteristics. Generally, the construction of such a knowledge base is a time-consuming task. This paper presents a semi-automatic rule generation using data mining. It extracts the energy consumption pattern based on relation of different parameter, such as product properties, machine profile, production processes, and surrounding variables.},
keywords = {data mining, discrete manufacturing, energy efficiency, knowledge capturing, knowledge management, machine learning},
pubstate = {published},
tppubtype = {inproceedings}
}

@article{Wicaksono2012d,
title = {Knowledge Management Approach to improve Energy Efficiency in Small Medium Enterprises},
author = {Hendro Wicaksono and Sven Rogalski and Jivka Ovtcharova},
year = {2012},
date = {2012-09-13},
abstract = {Energy efficiency in accordance with the economization of production costs is an important com-petitive factor in the energy-intensive industry. Energy management is a way to achieve this, but most of the manufacturing companies face problems in implementing it due to the lack of standard-izations in their operation portfolio. Energy related information are managed separately and in an unstructured manner. Managements have low visibility to the usage of energy in the operation due to the knowledge gap between managers and operators. Operators are often not aware whether their activities and decisions create excessive energy usage because of the different knowledge among them. This paper introduces a novel method based on knowledge management approach to address the problem using an ontology knowledge base and semi-automatic knowledge acquisition using data mining technique. In this paper the application of the approach in a small medium sized stain-less steel manufacturer will be presented. },
keywords = {data mining, energy efficiency, energy management, knowledge management, machine learning, Ontology},
pubstate = {published},
tppubtype = {article}
}

@article{Wicaksono2010,
title = {Wissensbasierte Energieanalyse – Verbesserte Energieeffizienz in Gebäuden des Privat- und Geschäftsbereichs},
author = {Hendro Wicaksono and Sven Rogalski},
url = {http://www.hanser-elibrary.com/doi/10.3139/104.110342},
doi = {10.3139/104.110342},
year = {2010},
date = {2010-06-30},
journal = {ZWF Zeitschrift für wirtschaftlichen Fabrikbetrieb},
volume = {105},
number = {6},
pages = {551-555},
abstract = {Vor dem Hintergrund einer wesentlich verbesserten Energieeffizienz in Gebäuden des Privat- und Geschäftsbereichs – trotz bestehender Gebäudeautomationssysteme – startete im Mai 2009 das Projekt „KEHL – Kontrollierte-Energie-Haushalts-Lösungen“ im Rahmen des Programms „KMU-innovativ“. Die wesentliche Forschungsherausforderung ist dabei, es dem Nutzer auf Basis einer einzigartigen Raum-, Zeit-, Ereignis-Relation zu ermöglichen, gebäudebezogene Gesamtverbräuche in Gebäuden bis auf die Geräteebene aufzusplitten und energetische Auswertungen in Abhängigkeit zum Nutzungsverhalten durchzuführen. Hierdurch werden nutzungsabhängige Energieverbrauchsanalysen ermöglicht, die eine automatisierte energieeffizientere Ansteuerung der vorhandenen Automationssysteme bewirken. Im nachstehenden Beitrag sollen unter anderem auf bisher erzielte Projektergebnisse eingegangen sowie weiterführende Forschungsansätze herausgestellt werden. Schwerpunkt der Ausführungen bilden insbesondere die KEHL-Wissensbasis und die darauf aufbauenden Energieanalysen.},
keywords = {data mining, Energy efficient building, knowledge management, machine learning},
pubstate = {published},
tppubtype = {article}
}