Martin, Andreas
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Martin, Andreas
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- PublikationArchiMEO: A standardized enterprise ontology based on the ArchiMate conceptual model(2020) Hinkelmann, Knut; Laurenzi, Emanuele; Martin, Andreas; Montecchiari, Devid; Spahic, Maja; Thönssen, Barbara; Hammoudi, Slimane; Ferreira Pires, Luis; Selić, Bran [in: Proceedings of the 8th International Conference on Model-Driven Engineering and Software Development]Many enterprises face the increasing challenge of sharing and exchanging data from multiple heterogeneous sources. Enterprise Ontologies can be used to effectively address such challenge. In this paper, we present an Enterprise Ontology called ArchiMEO, which is based on an ontological representation of the ArchiMate standard for modeling Enterprise Architectures. ArchiMEO has been extended to cover various application domains such as supply risk management, experience management, workplace learning and business process as a service. Such extensions have successfully proven that our Enterprise Ontology is beneficial for enterprise applications integration purposes.04B - Beitrag Konferenzschrift
- PublikationTraining and re-using human experience: a recommender for more accurate cost estimates in project planning(SciTePress, 2018) Rudolf von Rohr, Christian; Witschel, Hans Friedrich; Martin, Andreas [in: IC3K 2018 - Proceedings of the 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management]In many industries, companies deliver customised solutions to their (business) customers within projects. Estimating the human effort involved in such projects is a difficult task and underestimating efforts can lead to non-billable hours, i.e. financial loss on the side of the solution provider. Previous work in this area has focused on automatic estimation of the cost of software projects and has largely ignored the interaction between automated estimation support and human project leads. Our main hypothesis is that an adequate design of such interaction will increase the acceptance of automatically derived estimates and that it will allow for a fruitful combination of data-driven insights and human experience. We therefore build a recommender that is applicable beyond software projects and that suggests job positions to be added to projects and estimated effort of such positions. The recommender is based on the analysis of similar cases (case-based reasoning), "explains" derived similarities and allows human intervention to manually adjust the outcomes. Our experiments show that recommendations were considered helpful and that the ability of the system to explain and adjust these recommendations was heavily used and increased the trust in the system. We conjecture that the interaction of project leads with the system will help to further improve the accuracy of recommendations and the support of human learning in the future.04B - Beitrag Konferenzschrift
- PublikationRandom walks on human knowledge: incorporating human knowledge into data-driven recommenders(2018) Witschel, Hans Friedrich; Martin, Andreas; Bernardino, Jorge; Salgado, Ana; Filipe, Joaquim [in: IC3K 2018. 10th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management. Proceedings]We explore the use of recommender systems in business scenarios such as consultancy. In these situations, apart from personal preferences of users, knowledge about objective business-driven criteria plays a role. We investigate strategies for representing and incorporating such knowledge into data-driven recommenders. As a baseline, we choose a robust and flexible paradigm that is based on a simple graph-based representation of past customer cases and choices, in combination with biased random walks. On a real data set from a business intelligence consultancy firm, we study how the incorporation of two important types of explicit human knowledge – namely taxonomic and associative knowledge – impacts the effectiveness of a data-driven recommender. Our results show no consistent improvement for taxonomic knowledge, but quite substantial and significant gains when using associative knowledge.04B - Beitrag Konferenzschrift
- PublikationA Case Modelling Language for Process Variant Management in Case-based Reasoning(2015) Cognini, Riccardo; Hinkelmann, Knut; Martin, Andreas [in: AdaptiveCM 2015 – 4th International Workshop on Adaptive Case Management and other non-workflow approaches to BPM]04B - Beitrag Konferenzschrift
- PublikationIntegrating an Enterprise Architecture Ontology in a Case-Based Reasoning Approach for Project Knowledge(08.11.2013) Martin, Andreas; Emmenegger, Sandro; Wilke, Gwendolin04B - Beitrag Konferenzschrift
- PublikationMining of Agile Business Processes(21.03.2011) Brander, Simon; Hinkelmann, Knut; Martin, Andreas; Thönssen, Barbara [in: Proceedings of the AAAI 2011 Spring Symposium]Organizational agility is a key challenge in today's business world. The Knowledge-Intensive Service Support approach tackles agility by combining process modeling and business rules. In the paper at hand, we present five approaches of process mining that could further increase the agility of processes by improving an existing process model.04B - Beitrag Konferenzschrift
- PublikationRefining Process Models through the Analysis of Informal Work Practice(2011) Brander, Simon; Hinkelmann, Knut; Hu, Bo; Martin, Andreas; Riss, Uwe; Thönssen, Barbara; Witschel, Hans FriedrichThe work presented in this paper explores the potential of leveraging the traces of informal work and collaboration in order to improve business processes over time. As process executions often differ from the original design due to individual preferences, skills or competencies and exceptions, we propose methods to analyse personal preferences of work, such as email communication and personal task execution in a task management application. Outcome of these methods is the detection of internal substructures (subtasks or branches) of activities on the one hand and the recommendation of resources to be used in activities on the other hand, leading to the improvement of business process models. Our first results show that even though human intervention is still required to operationalise these insights it is indeed possible to derive interesting and new insights about business processes from traces of informal work and infer suggestions for process model changes.04B - Beitrag Konferenzschrift
- PublikationAgile Process Execution with KISSmir(03.06.2010) Brun, Roman; Martin, AndreasIn this paper, we describe an approach for agile business process execution and its developed prototype. In a rapidly changing environment an enterprise must be flexible and able to quickly react. Traditional business process modelling approaches are too rigid and inflexible. To achieve more agility, the modelling during build-time must be less strict and more open in a way that users are able to perform individual adaptations during run-time, which leads to more flexibility. Being able to react fast is also depending on the enterprise knowledge. Employees must be aware of it and able to access it in an easy way. The approach proposes to use ontologies to store information and appropriate services to receive context-relevant information to tackle these challenges.04B - Beitrag Konferenzschrift
- PublikationApplying Organizational Learning to Enterprise Knowledge Maturing(04.09.2009) Martin, Andreas; Brun, RomanWe first describe the state of the art of organizational learning, mentioning the theories and types of it. The need of organizational learning, contributing processes and the main processes are further explained. Various methods of organizational learning are introduced. A template for a short description is proposed, which gives an overview about existing methods. The template then offers the possibility to indicate which method can be applied on Enterprise Knowledge Maturing.04B - Beitrag Konferenzschrift