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Publikation KPIs 4 Workplace Learning(Springer, 2016) Emmenegger, Sandro; Hinkelmann, Knut; Thönssen, Barbara; Witschel, Hans Friedrich; Ana, Fred; Aveiro, DavidEnterprises and Public Administrations alike need to ensure that newly hired employees are able to learn the ropes fast. Employers also need to support continuous workplace learning. Work-place learning should be strongly related to business goals and thus, learning goals should direct-ly add to business goals. To measure achievement of both learning and business goals we pro-pose augmented Key Performance Indicators (KPI). In our research we applied model driven engineering. Hence we developed a model for a Learning Scorecard comprising of business and learning goals and their KPIs represented in an ontology. KPI performance values and scores are calculated with formal rules based on the SPARQL Inferencing Notation. Results are presented in a dashboard on an individual level as well as on a team/group level. Requirements, goals and KPIs as well as performance measurement were defined in close co-operation with Marche Region, business partner in Learn PAd.04B - Beitrag KonferenzschriftPublikation Ensuring Action: Identifying Unclear Actor Specifications in Textual Business Process Descriptions(SciTePress, 2016) Sanne, Ulf; Witschel, Hans Friedrich; Ferrari, Alessio; Gnesi, Stefania; Ana, Fred; Aveiro, DavidIn many organisations, business process (BP) descriptions are available in the form of written procedures, or operational manuals. These documents are expressed in informal natural language, which is inherently open to different interpretations. Hence, the content of these documents might be incorrectly interpreted by those who have to put the process into practice. It is therefore important to identify language defects in written BP descriptions, to ensure that BPs are properly carried out. Among the potential defects, one of the most relevant for BPs is the absence of clear actors in action-related sentences. Indeed, an unclear actor might lead to a missing responsibility, and, in turn, to activities that are never performed. This paper aims at identifying unclear actors in BP descriptions expressed in natural language. To this end, we define an algorithm named ABIDE, which leverages rule-based natural language processing (NLP) techniques. We evaluate the algorithm on a manually annotated data-set of 20 real-world BP descriptions (1,029 sentences). ABIDE achieves a recall of 87%, and a precision of 56%. We consider these results promising. Improvements of the algorithm are also discussed in the paper.04B - Beitrag KonferenzschriftPublikation Where Did I(t) put it? A holistic solution to the automatic construction of topic trees for navigation(2014) Thönssen, Barbara; Witschel, Hans Friedrich; Lutz, JonasManaging information based on hierarchical structures is prevailing, be it by storing documents physically in a file structure like MS explorer or virtually in topic trees as in many web applications. The problem is that the structure evolves over time, created individually and hence reflecting individual opinions of how information objects should be grouped. This leads to time consuming searches and error prone retrieval results since relevant documents might be stored elsewhere. Our approach aims at solving the problem by replacing or complementing the manually created navigation structures by automatically created ones. We consider existing approaches for clustering and labelling and focus on yet unrewarding aspects like having information objects in inner nodes (as it is common in folder hierarchies) and cognitively adequate labelling for textual and non-textual resources. Evaluation was done by knowledge experts based on a comparison of retrieval time for finding given documents in manually and automatic generated information structures and showed the advantage of automatically created topic trees.04B - Beitrag KonferenzschriftPublikation Breaking free from your information prison - A recommender based on semantically enriched context descriptions(2013) Lutz, Jonas; Thönssen, Barbara; Witschel, Hans FriedrichInformation repositories, implemented as Enterprise Portals (EP) on the intranet, are increasingly popular in companies of all sizes. Enterprise Portals allow for structuring information in a way that resembles the organization of paper copies, i.e. simulating folders and registries and furthermore, provide simple routines for publishing and collaborating. Hence, in general, such kind of information management is not much different from paper management: electronic documents must be uploaded into the Enterprise Portal manually, filed into folders (which have to be created manually, too), tagged and related to other information objects if need be. With this approach information structuring remains subject to the individual user leading to the well-known problems of multiple filing, overlooking relevant information and incomprehensible Folder structure. The SEEK!sem project aims at improving such kind of information system by automatically identifying and recommending related information resources to be added to a folder. The recommendations are based on rules, exploiting content and context similarity of information resources. Rules can be created upfront, based on explicitly defined Relations between information objects. They can also be machine learned, i.e. the recommender exploits the existing linkage between documents, folders and other objects to learn “relatedness rules”. In either case, potential new connections are inferred by applying the rules in a reasoning step. Recommended new connections are ranked by the sum of the scores of all applied rules – the rule scores, again, can either be provided by experts or machinelearned. The applied rules can serve as an explanation of a recommendation, i.e. they can assist users in understanding why a particular connection is suggested.04B - Beitrag KonferenzschriftPublikation Energy saving in smart homes based on consumer behavior: A case study(IEEE, 2015) Zehnder, Michael; Wache, Holger; Witschel, Hans Friedrich; Zanatta, Danilo; Rodriguez, MiguelThis paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that mines consumer behavior data only and applies machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system looks for frequent and periodic patterns in the event data provided by the digitalSTROM home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects opportunities to save energy without decreasing the comfort level, it sends a recommendation to the inhabitants.04B - Beitrag Konferenzschrift