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  • 1.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Gustafsson, Jörgen
    Ericsson AB.
    Shaikh, Junaid
    Ericsson AB.
    Outlier Detection for Video Session Data Using Sequential Pattern Mining2018In: ACM SIGKDD Workshop On Outlier Detection De-constructed, 2018Conference paper (Refereed)
    Abstract [en]

    The growth of Internet video and over-the-top transmission techniqueshas enabled online video service providers to deliver highquality video content to viewers. To maintain and improve thequality of experience, video providers need to detect unexpectedissues that can highly affect the viewers’ experience. This requiresanalyzing massive amounts of video session data in order to findunexpected sequences of events. In this paper we combine sequentialpattern mining and clustering to discover such event sequences.The proposed approach applies sequential pattern mining to findfrequent patterns by considering contextual and collective outliers.In order to distinguish between the normal and abnormal behaviorof the system, we initially identify the most frequent patterns. Thena clustering algorithm is applied on the most frequent patterns.The generated clustering model together with Silhouette Index areused for further analysis of less frequent patterns and detectionof potential outliers. Our results show that the proposed approachcan detect outliers at the system level.

  • 2.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Ickin, Selim
    Ericsson, SWE.
    Gustafsson, Jörgen
    Ericsson, SWE.
    A Minimum Spanning Tree Clustering Approach for Outlier Detection in Event Sequences2018In: The 17th IEEE International Conference on Machine Learning and Applications Special Session on Machine Learning Algorithms, Systems and Applications, IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    Outlier detection has been studied in many domains. Outliers arise due to different reasons such as mechanical issues, fraudulent behavior, and human error. In this paper, we propose an unsupervised approach for outlier detection in a sequence dataset. The proposed approach combines sequential pattern mining, cluster analysis, and a minimum spanning tree algorithm in order to identify clusters of outliers. Initially, the sequential pattern mining is used to extract frequent sequential patterns. Next, the extracted patterns are clustered into groups of similar patterns. Finally, the minimum spanning tree algorithm is used to find groups of outliers. The proposed approach has been evaluated on two different real datasets, i.e., smart meter data and video session data. The obtained results have shown that our approach can be applied to narrow down the space of events to a set of potential outliers and facilitate domain experts in further analysis and identification of system level issues.

  • 3.
    Abghari, Shahrooz
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    García Martín, Eva
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Johansson, Christian
    NODA Intelligent Systems AB, Sweden.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Trend analysis to automatically identify heat program changes2017In: Energy Procedia, Elsevier, 2017, p. 407-415Conference paper (Refereed)
    Abstract [en]

    The aim of this study is to improve the monitoring and controlling of heating systems located at customer buildings through the use of a decision support system. To achieve this, the proposed system applies a two-step classifier to detect manual changes of the temperature of the heating system. We apply data from the Swedish company NODA, active in energy optimization and services for energy efficiency, to train and test the suggested system. The decision support system is evaluated through an experiment and the results are validated by experts at NODA. The results show that the decision support system can detect changes within three days after their occurrence and only by considering daily average measurements.

  • 4.
    Adlemo, Anders
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Hilletofth, Per
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Fuzzy logic based decision-support for reshoring decisions2018In: Proceedings of the 8th International Conference on Operations and Supply Chain Management, 2018Conference paper (Refereed)
  • 5.
    Adlemo, Anders
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics. Jönköping University / School of Engineering.
    Tan, He
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Test case quality as perceived in Sweden2018In: Proceedings - International Conference on Software Engineering / [ed] Michael Unterkalmsteiner, ACM Digital Library, 2018, p. 9-12Conference paper (Refereed)
    Abstract [en]

    In order to reach an acceptable level of confidence in the quality of a software product, testing of the software is paramount. To obtain "good" quality software it is essential to rely on "good" test cases. To define the criteria for what make up for a "good" test case is not a trivial task. Over the past 15 years, a short list of publications have presented criteria for "good" test cases but without ranking them based on their importance. This paper presents a non-exhaustive and non-authoritative tentative list of 15 criteria and a ranking of their relative importance. A number of the criteria come from previous publications but also from discussions with our industrial partners. The ranking is based on results collected via a questionnaire that was sent out to a limited number of randomly chosen respondents in the Swedish software industry. This means that the results are more indicative than conclusive.

  • 6.
    Adlemo, Anders
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Hilletofth, Per
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Eriksson, David
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Knowledge intensive decision support for reshoring decisions2018In: Proceedings of the 30th Annual NOFOMA Conference: Relevant Logistics and Supply Chain Management Research, Kolding, 2018Conference paper (Refereed)
  • 7.
    Adlemo, Anders
    et al.
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Hilletofth, Per
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Eriksson, David
    Jönköping University, School of Engineering, JTH, Supply Chain and Operations Management.
    Reshoring decision support in a Swedish context2018Conference paper (Refereed)
    Abstract [en]

    This paper presents a decision-support system for reshoring decision-making based on fuzzy logic. The construction and functionality of the decision-support system are described, and the functionality is evaluated in a high cost environment exemplified through a Swedish context. Ten different reshoring scenarios, provided by Swedish reshoring experts, are entered into the decision-support system and the decision recommendations provided by the system are presented. The confidence that can be put on the recommendations is demonstrated by comparing them with those of the reshoring experts. The positive results obtained indicate that fuzzy logic is both feasible and that the quality of the results are sufficiently good for reshoring decision-making.

  • 8.
    Ahlberg, Ernst
    et al.
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Winiwarter, Susanne
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Boström, Henrik
    Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Sweden.
    Löfström, Tuve
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Sweden.
    Engkvist, Ola
    External Sciences, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Hammar, Oscar
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Bendtsen, Claus
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Cambridge, UK.
    Carlsson, Lars
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Using conformal prediction to prioritize compound synthesis in drug discovery2017In: Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, Machine Learning Research , 2017, p. 174-184Conference paper (Refereed)
    Abstract [en]

    The choice of how much money and resources to spend to understand certain problems is of high interest in many areas. This work illustrates how computational models can be more tightly coupled with experiments to generate decision data at lower cost without reducing the quality of the decision. Several different strategies are explored to illustrate the trade off between lowering costs and quality in decisions.

    AUC is used as a performance metric and the number of objects that can be learnt from is constrained. Some of the strategies described reach AUC values over 0.9 and outperforms strategies that are more random. The strategies that use conformal predictor p-values show varying results, although some are top performing.

    The application studied is taken from the drug discovery process. In the early stages of this process compounds, that potentially could become marketed drugs, are being routinely tested in experimental assays to understand the distribution and interactions in humans.

  • 9.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Modelling Network-based Defence: Success and Failure of an Enterprise Modelling Endeavour2010In: The Practice of Enterprise Modeling: Third IFIP WG 8.1 Working Conference / [ed] Patrick Van Bommel, Stijn Hoppenbrouwers, Sietse Overbeek, Erik Proper, Joseph Barjis, Berlin: Springer , 2010, p. 121-129Conference paper (Refereed)
    Abstract [en]

    Research projects have an inherent risk of failure, and learning howto cope with the risk is an important task for everyone involved. In order to doso it is necessary to share the knowledge of the experiences done during andafter the project. This paper investigates a recently completed enterprisemodeling research project and contributes with lessons learned andrecommendations for future enterprise modeling projects.

  • 10.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    The Practice of Competence Modelling2010In: The Practice of Enterprise Modeling: Third IFIP WG 8.1 Working Conference / [ed] Patrick Van Bommel, Stijn Hoppenbrouwers, Sietse Overbeek, Erik Proper, Joseph Barjis, Berlin: Springer , 2010, p. 106-120Conference paper (Refereed)
    Abstract [en]

    A clear understanding of the organizational competences of anenterprise and the underlying individual competences and the competencedevelopment needs has become more and more important for many industrialareas as a foundation for competence supply processes and adjustment tochanging market conditions. Competence modelling, i.e. the use of enterprisemodelling techniques for capturing existing and describing desiredorganisational and individual competences in enterprises, offers importantcontributions to this. In the last years, the authors of the paper have performed anumber of competence modelling cases, which revealed different characteristicsand resulted in lessons learned. This paper presents an examination of differentcharacteristics of competence modelling cases, and recommendations andlessons learned from these cases for the practice of competence modelling.

  • 11.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Towards Competence Modeling and Competence Matching for Network-based Defense2007In: Proc. The 3rd International Conference on Military Technology, MilTech3, June 2007, Stockholm, 2007Conference paper (Other academic)
  • 12.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Towards Competence Modeling and Competence Matching for Network-Based Defense2008In: Stockholm contributions in Military-Technology 2007 / [ed] M. Norsell, Stockholm: Swedish National Defence College , 2008, p. 9-22Chapter in book (Other academic)
  • 13.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Competence demand modeling: case study2009Report (Other academic)
  • 14.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Competence modeling and matching: frame concept2009Report (Other academic)
  • 15.
    Albertsen, Thomas
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Seigerroth, Ulf
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Modellering av 312. luftburna kompaniet: KOMO - Projektrapport2009Report (Other academic)
  • 16.
    Ali Fareedi, Abid
    et al.
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering.
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Modelling of the Ward Round Process in a Healthcare Unit2011In: The Practice of Enterprise Modeling: 4th IFIP WG 8.1 Working Conference, PoEM 2011 Oslo, Norway, November 2-3, 2011 Proceedings / [ed] Paul Johannesson, John Krogstie and Andreas L. Opdahl, Springer Berlin/Heidelberg, 2011, Vol. 92, p. 223-237Conference paper (Refereed)
    Abstract [en]

    Information systems (IS) are nowadays extensively used to support all kinds of activities in healthcare organisations. Enterprise modelling can help to make the use of IS in healthcare more effective by providing process and domain models reflecting a particular healthcare unit. This paper proposes a model of the ward round process in a healthcare unit. The proposed model identifies the roles of medical professionals, tasks that can be performed according to the personnel’s competences, and activities that are carried out as part of the tasks to achieve goals of the ward round process. A formal approach has been used to implement the modelling results in the form of an ontology. Such formal ontologies can support improvement and development of IS in healthcare. We learned that modelling workshops are important for development of models that can be formalized in a machine-readable form.

  • 17.
    Alirezaie, Marjan
    et al.
    Örebro University.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Blomqvist, Eva
    SICS - East Swedish ICT.
    SmartEnv as a Network of Ontology Patterns2018In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 9, no 6, p. 903-918Article in journal (Refereed)
    Abstract [en]

    In this article we outline the details of an ontology, called SmartEnv, proposed as a representational model to assist the development process of smart (i.e., sensorized) environments. The SmartEnv ontology is described in terms of its modules representing different aspects including physical and conceptual aspects of a smart environment. We propose the use of the Ontology Design Pattern (ODP) paradigm in order to modularize our proposed solution, while at the same time avoiding strong dependencies between the modules in order to manage the representational complexity of the ontology. The ODP paradigm and related methodologies enable incremental construction of ontologies by first creating and then linking small modules. Most modules (patterns) of the SmartEnv ontology are inspired by, and aligned with, the Semantic Sensor Network (SSN) ontology, however with extra interlinks to provide further precision and cover more representational aspects. The result is a network of 8 ontology patterns together forming a generic representation for a smart environment. The patterns have been submitted to the ODP portal and are available on-line at stable URIs.

  • 18.
    Alirezaie, Marjan
    et al.
    Örebro Universitet.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). RISE SICS East AB, Linköping, Sweden.
    Blomqvist, Eva
    Linköpings Universitet.
    Nyström, Mikael
    Linköpings Universitet.
    Ivanova, Valentina
    Linköpings Universitet.
    SmartEnv Ontology in E-care@home2018In: SSN 2018 - Semantic Sensor Networks Workshop: Proceedings of the 9th International Semantic Sensor Networks Workshopco-located with 17th International Semantic Web Conference (ISWC 2018) / [ed] Maxime Lefrançois, Raúl Garcia Castro, Amélie Gyrard, Kerry Taylor, CEUR-WS , 2018, Vol. 2213, p. 72-79Conference paper (Refereed)
    Abstract [en]

    In this position paper we briefly introduce SmartEnv ontology which relies on SEmantic Sensor Network (SSN) ontology and is used to represent different aspects of smart and sensorized environments. We will also talk about E-carehome project aiming at providing an IoT-based health-care system for elderly people at their homes. Furthermore, we refer to the role of SmartEnv in Ecarehome and how it needs to be further extended to achieve semantic interoperability as one of the challenges in development of autonomous health care systems at home.

  • 19.
    Angelova, Milena
    et al.
    Technical University of sofia, BUL.
    Vishnu Manasa, Devagiri
    Blekinge Tekniska Högskola, Institutionen för datavetenskap.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datavetenskap.
    Linde, Peter
    Blekinge Tekniska Högskola, Biblioteket.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datavetenskap.
    An Expertise Recommender System based on Data from an Institutional Repository (DiVA)2019In: Connecting the Knowledge Common from Projects to sustainable Infrastructure: The 22nd International conference on Electronic Publishing - Revised Selected Papers / [ed] Leslie Chan & Pierre Mounier, OpenEdition Press , 2019, p. 135-149Chapter in book (Refereed)
    Abstract [en]

    Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors in academy.

  • 20.
    Angelova, Milena
    et al.
    Technical University of Sofia-branch Plovdiv, BUL.
    Vishnu Manasa, Devagiri
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Linde, Peter
    Blekinge Tekniska Högskola, Biblioteket.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    An Expertise Recommender SystemBased on Data from an Institutional Repository (DiVA)2018In: Proceedings of the 22nd edition of the International Conference on ELectronic PUBlishing, 2018Conference paper (Refereed)
    Abstract [en]

    Finding experts in academics is an important practical problem, e.g. recruiting reviewersfor reviewing conference, journal or project submissions, partner matching for researchproposals, finding relevant M. Sc. or Ph. D. supervisors etc. In this work, we discuss anexpertise recommender system that is built on data extracted from the Blekinge Instituteof Technology (BTH) instance of the institutional repository system DiVA (DigitalScientific Archive). DiVA is a publication and archiving platform for research publicationsand student essays used by 46 publicly funded universities and authorities in Sweden andthe rest of the Nordic countries (www.diva-portal.org). The DiVA classification system isbased on the Swedish Higher Education Authority (UKÄ) and the Statistic Sweden's (SCB)three levels classification system. Using the classification terms associated with studentM. Sc. and B. Sc. theses published in the DiVA platform, we have developed a prototypesystem which can be used to identify and recommend subject thesis supervisors inacademy.

  • 21.
    Blomqvist, Eva
    et al.
    Linköping University, Department of Computer and Information Science; STLab, ISTC-CNR.
    Gangemi, AldoSTLab, ISTC-CNR.Hammar, KarlJönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).Suárez-Figueroa, Mari CarmenOntology Engineering Group, Universidad Politécnica de Madrid.
    WOP 2012: Proceedings of the 3rd Workshop on Ontology Patterns2012Conference proceedings (editor) (Refereed)
  • 22.
    Blomqvist, Eva
    et al.
    Linköping University, Linköping, Sweden.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Presutti, Valentina
    ISTC-CNR, Rome, Italy.
    Engineering Ontologies with Patterns - The eXtreme Design Methodology2016In: Ontology Engineering with Ontology Design Patterns / [ed] Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti, IOS Press, 2016, p. 23-50Chapter in book (Refereed)
    Abstract [en]

    When using Ontology Design Patterns (ODPs) for modelling new parts of an ontology, i.e., new ontology modules, or even an entire ontology from scratch, ODPs can be used both as inspiration for different modelling solutions, as well as concrete templates or even “building blocks” reused directly in the new solution. This chapter discusses how ODPs, and in particular Content ODPs, can be used in ontology engineering. In particular, a specific ontology engineering methodology is presented, which was originally developed for supporting ODP use. However, this methodology, the eXtreme Design (XD), also has some characteristics that set it apart from most other ontology engineering methodologies, and which may be interesting to consider regardless of how much emphasis is put on the ODP usage. Towards the end of the chapter some XD use cases are also reported and discussed, as well as lessons learned from applying XD. The chapter is concluded through a summary and discussion about future work.

  • 23.
    Blomqvist, Eva
    et al.
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Levashova, Tatiana
    Öhgren, Annika
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Sandkuhl, Kurt
    Jönköping University, School of Engineering, JTH. Research area Information Engineering.
    Smirnov, Alexander
    Tarasov, Vladimir
    Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Configuration of Dynamic SME Supply Chains Based on Ontologies2005In: Holonic and Multi-Agent Systems for Manufacturing: Second International Conference on Industrial Applications of Holonic and Multi-Agent Systems, HoloMAS 2005, Copenhagen, Denmark, August 22-24, 2005. Proceedings / [ed] V. Marik, R. W. Brennan & M. Pechoucek, Springer Berlin/Heidelberg, 2005, p. 246-256Conference paper (Refereed)
    Abstract [en]

    Due to the increasing implementation of agile and networked manufacturing, supply chain has entered a new phase, virtual supply chain. The phase is characterized by the integration of activities, operations, and functions carried out at different and geographically distributed supply chain stages. The paper proposes an approach to the configuration of a network of small and medium-sized enterprises (SMEs) being integrated into a supply chain. The SME supply chain configuration is based on a shared domain ontology for supply chain management, offering the configuration task as a function of supply chain management. Principles of the development of the shared ontology and possible ways of matching between enterprise and domain ontologies are considered.

  • 24.
    Boeva, Veselka
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Angelova, Milena
    Technical University Sofia, BUL.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Rosander, Oliver
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Tsiporkova, Elena
    Collective Center for the Belgian Technological Industry, BEL.
    Evolutionary clustering techniques for expertise mining scenarios2018In: ICAART 2018 - Proceedings of the 10th International Conference on Agents and Artificial Intelligence, Volume 2 / [ed] van den Herik J.,Rocha A.P., SciTePress, 2018, p. 523-530Conference paper (Refereed)
    Abstract [en]

    The problem addressed in this article concerns the development of evolutionary clustering techniques that can be applied to adapt the existing clustering solution to a clustering of newly collected data elements. We are interested in clustering approaches that are specially suited for adapting clustering solutions in the expertise retrieval domain. This interest is inspired by practical applications such as expertise retrieval systems where the information available in the system database is periodically updated by extracting new data. The experts available in the system database are usually partitioned into a number of disjoint subject categories. It is becoming impractical to re-cluster this large volume of available information. Therefore, the objective is to update the existing expert partitioning by the clustering produced on the newly extracted experts. Three different evolutionary clustering techniques are considered to be suitable for this scenario. The proposed techniques are initially evaluated by applying the algorithms on data extracted from the PubMed repository. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

  • 25.
    Boström, Henrik
    et al.
    Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Borås, Sweden.
    Löfström, Tuwe
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Information Technology, University of Borås, Borås, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Accelerating difficulty estimation for conformal regression forests2017In: Annals of Mathematics and Artificial Intelligence, ISSN 1012-2443, E-ISSN 1573-7470, Vol. 81, no 1-2, p. 125-144Article in journal (Refereed)
    Abstract [en]

    The conformal prediction framework allows for specifying the probability of making incorrect predictions by a user-provided confidence level. In addition to a learning algorithm, the framework requires a real-valued function, called nonconformity measure, to be specified. The nonconformity measure does not affect the error rate, but the resulting efficiency, i.e., the size of output prediction regions, may vary substantially. A recent large-scale empirical evaluation of conformal regression approaches showed that using random forests as the learning algorithm together with a nonconformity measure based on out-of-bag errors normalized using a nearest-neighbor-based difficulty estimate, resulted in state-of-the-art performance with respect to efficiency. However, the nearest-neighbor procedure incurs a significant computational cost. In this study, a more straightforward nonconformity measure is investigated, where the difficulty estimate employed for normalization is based on the variance of the predictions made by the trees in a forest. A large-scale empirical evaluation is presented, showing that both the nearest-neighbor-based and the variance-based measures significantly outperform a standard (non-normalized) nonconformity measure, while no significant difference in efficiency between the two normalized approaches is observed. The evaluation moreover shows that the computational cost of the variance-based measure is several orders of magnitude lower than when employing the nearest-neighbor-based nonconformity measure. The use of out-of-bag instances for calibration does, however, result in nonconformity scores that are distributed differently from those obtained from test instances, questioning the validity of the approach. An adjustment of the variance-based measure is presented, which is shown to be valid and also to have a significant positive effect on the efficiency. For conformal regression forests, the variance-based nonconformity measure is hence a computationally efficient and theoretically well-founded alternative to the nearest-neighbor procedure. 

  • 26.
    Buendia, Ruben
    et al.
    Department of Information Technology, University of Borås, Borås, Sweden.
    Kogej, Thierry
    Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Engkvist, Ola
    Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Carlsson, Lars
    Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Borås, Sweden.
    Johansson, Ulf
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Toccaceli, Paolo
    Department of Computer Science, Royal Holloway, University of London, Egham, Surrey, United Kingdom.
    Ahlberg, Ernst
    Data Science and AI, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Accurate Hit Estimation for Iterative Screening Using Venn-ABERS Predictors2019In: Journal of Chemical Information and Modeling, ISSN 1549-9596, E-ISSN 1549-960X, Vol. 59, no 3, p. 1230-1237Article in journal (Refereed)
    Abstract [en]

    Iterative screening has emerged as a promising approach to increase the efficiency of high-throughput screening (HTS) campaigns in drug discovery. By learning from a subset of the compound library, inferences on what compounds to screen next can be made by predictive models. One of the challenges of iterative screening is to decide how many iterations to perform. This is mainly related to difficulties in estimating the prospective hit rate in any given iteration. In this article, a novel method based on Venn - ABERS predictors is proposed. The method provides accurate estimates of the number of hits retrieved in any given iteration during an HTS campaign. The estimates provide the necessary information to support the decision on the number of iterations needed to maximize the screening outcome. Thus, this method offers a prospective screening strategy for early-stage drug discovery.

  • 27.
    Coppens, Sam
    et al.
    Ghent University.
    Hammar, KarlJönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).Knuth, MagnusUniversity of Potsdam.Neumann, MarcoKONA LLC, New York, USA.Ritze, DominiqueUniversity of Mannheim.Sack, HaraldUniversity of Potsdam.Vander Sande, MielGhent University.
    WaSABi 2013: Semantic Web Enterprise Adoption and Best Practice: Proceedings of the Workshop on Semantic Web Enterprise Adoption and Best Practice Co-located with 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, October 22, 20132013Conference proceedings (editor) (Refereed)
  • 28.
    Dórea, Fernanda C.
    et al.
    Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
    Vial, Flavie
    Epi-Connect, Skogås, Sweden.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Department of Computer and Information Science, Linköping University, Sweden.
    Lindberg, Ann
    Department of Disease Control and Epidemiology, National Veterinary Institute, Sweden.
    Lambrix, Patrick
    Department of Computer and Information Science, Linköping University, Sweden.
    Blomqvist, Eva
    Department of Computer and Information Science, Linköping University, Sweden.
    Revie, Crawford W.
    Atlantic Veterinary College, University of Prince Edward Island, Canada.
    Drivers for the development of an Animal Health Surveillance Ontology (AHSO)2019In: Preventive Veterinary Medicine, ISSN 0167-5877, E-ISSN 1873-1716, Vol. 166, p. 39-48Article in journal (Refereed)
    Abstract [en]

    Comprehensive reviews of syndromic surveillance in animal health have highlighted the hindrances to integration and interoperability among systems when data emerge from different sources. Discussions with syndromic surveillance experts in the fields of animal and public health, as well as computer scientists from the field of information management, have led to the conclusion that a major component of any solution will involve the adoption of ontologies. Here we describe the advantages of such an approach, and the steps taken to set up the Animal Health Surveillance Ontological (AHSO) framework. The AHSO framework is modelled in OWL, the W3C standard Semantic Web language for representing rich and complex knowledge. We illustrate how the framework can incorporate knowledge directly from domain experts or from data-driven sources, as well as by integrating existing mature ontological components from related disciplines. The development and extent of AHSO will be community driven and the final products in the framework will be open-access.

  • 29.
    Gangemi, Aldo
    et al.
    Université Paris 13.
    Gruninger, MichaelUniversity of Toronto.Hammar, KarlJönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).Lefort, LaurentCSIRO ICT Centre.Presutti, ValentinaSTLab (ISTC-CNR).Scherp, AnsgarUniversity of Mannheim.
    Proceedings of the 4th Workshop on Ontology and Semantic Web Patterns2014Conference proceedings (editor) (Refereed)
  • 30.
    García Martín, Eva
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Energy Efficiency Analysis of the Very Fast Decision Tree Algorithm2017In: Trends in Social Network Analysis: Information Propagation, User Behavior Modeling, Forecasting, and Vulnerability Assessment / [ed] Rokia Missaoui, Talel Abdessalem, Matthieu Latapy, Cham, Switzerland: Springer, 2017, p. 229-252Chapter in book (Refereed)
    Abstract [en]

    Data mining algorithms are usually designed to optimize a trade-off between predictive accuracy and computational efficiency. This paper introduces energy consumption and energy efficiency as important factors to consider during data mining algorithm analysis and evaluation. We conducted an experiment to illustrate how energy consumption and accuracy are affected when varying the parameters of the Very Fast Decision Tree (VFDT) algorithm. These results are compared with a theoretical analysis on the algorithm, indicating that energy consumption is affected by the parameters design and that it can be reduced significantly while maintaining accuracy.

  • 31.
    García Martín, Eva
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Identification of Energy Hotspots: A Case Study of the Very Fast Decision Tree2017In: GPC 2017: Green, Pervasive, and Cloud Computing / [ed] Au M., Castiglione A., Choo KK., Palmieri F., Li KC., Cham, Switzerland: Springer , 2017, p. 267-281Conference paper (Refereed)
    Abstract [en]

    Large-scale data centers account for a significant share of the energy consumption in many countries. Machine learning technology requires intensive workloads and thus drives requirements for lots of power and cooling capacity in data centers. It is time to explore green machine learning. The aim of this paper is to profile a machine learning algorithm with respect to its energy consumption and to determine the causes behind this consumption. The first scalable machine learning algorithm able to handle large volumes of streaming data is the Very Fast Decision Tree (VFDT), which outputs competitive results in comparison to algorithms that analyze data from static datasets. Our objectives are to: (i) establish a methodology that profiles the energy consumption of decision trees at the function level, (ii) apply this methodology in an experiment to obtain the energy consumption of the VFDT, (iii) conduct a fine-grained analysis of the functions that consume most of the energy, providing an understanding of that consumption, (iv) analyze how different parameter settings can significantly reduce the energy consumption. The results show that by addressing the most energy intensive part of the VFDT, the energy consumption can be reduced up to a 74.3%.

  • 32.
    García Martín, Eva
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Hoeffding Trees with nmin adaptation2018In: The 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA 2018), IEEE, 2018, p. 70-79Conference paper (Refereed)
    Abstract [en]

    Machine learning software accounts for a significant amount of energy consumed in data centers. These algorithms are usually optimized towards predictive performance, i.e. accuracy, and scalability. This is the case of data stream mining algorithms. Although these algorithms are adaptive to the incoming data, they have fixed parameters from the beginning of the execution. We have observed that having fixed parameters lead to unnecessary computations, thus making the algorithm energy inefficient.In this paper we present the nmin adaptation method for Hoeffding trees. This method adapts the value of the nmin pa- rameter, which significantly affects the energy consumption of the algorithm. The method reduces unnecessary computations and memory accesses, thus reducing the energy, while the accuracy is only marginally affected. We experimentally compared VFDT (Very Fast Decision Tree, the first Hoeffding tree algorithm) and CVFDT (Concept-adapting VFDT) with the VFDT-nmin (VFDT with nmin adaptation). The results show that VFDT-nmin consumes up to 27% less energy than the standard VFDT, and up to 92% less energy than CVFDT, trading off a few percent of accuracy in a few datasets.

  • 33.
    García Martín, Eva
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    How to Measure Energy Consumption in Machine Learning Algorithms2019In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, 2019, p. 243-255Conference paper (Refereed)
    Abstract [en]

    Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

  • 34.
    García Martín, Eva
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Grahn, Håkan
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Casalicchio, Emiliano
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Boeva, Veselka
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    How to Measure Energy Consumption in Machine Learning Algorithms2018In: Green Data Mining, International Workshop on Energy Efficient Data Mining and Knowledge Discovery: ECMLPKDD 2018: European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases Workshops. Lecture Notes in Computer Science. Springer, Cham, 2018Conference paper (Refereed)
    Abstract [en]

    Machine learning algorithms are responsible for a significant amount of computations. These computations are increasing with the advancements in different machine learning fields. For example, fields such as deep learning require algorithms to run during weeks consuming vast amounts of energy. While there is a trend in optimizing machine learning algorithms for performance and energy consumption, still there is little knowledge on how to estimate an algorithm’s energy consumption. Currently, a straightforward cross-platform approach to estimate energy consumption for different types of algorithms does not exist. For that reason, well-known researchers in computer architecture have published extensive works on approaches to estimate the energy consumption. This study presents a survey of methods to estimate energy consumption, and maps them to specific machine learning scenarios. Finally, we illustrate our mapping suggestions with a case study, where we measure energy consumption in a big data stream mining scenario. Our ultimate goal is to bridge the current gap that exists to estimate energy consumption in machine learning scenarios.

  • 35.
    García-Martín, Eva
    et al.
    Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Lavesson, Niklas
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Blekinge Tekniska Högskola, Institutionen för datalogi och datorsystemteknik.
    Is it ethical to avoid error analysis?2017Conference paper (Refereed)
    Abstract [en]

    Machine learning algorithms tend to create more accurate models with the availability of large datasets. In some cases, highly accurate models can hide the presence of bias in the data. There are several studies published that tackle the development of discriminatory-aware machine learning algorithms. We center on the further evaluation of machine learning models by doing error analysis, to understand under what conditions the model is not working as expected. We focus on the ethical implications of avoiding error analysis, from a falsification of results and discrimination perspective. Finally, we show different ways to approach error analysis in non-interpretable machine learning algorithms such as deep learning.

  • 36.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Linköpings universitet, Interaktiva och kognitiva system.
    Content Ontology Design Patterns: Qualities, Methods, and Tools2017Doctoral thesis, monograph (Other academic)
    Abstract [en]

    Ontologies are formal knowledge models that describe concepts and relationships and enable data integration, information search, and reasoning. Ontology Design Patterns (ODPs) are reusable solutions intended to simplify ontology development and support the use of semantic technologies by ontology engineers. ODPs document and package good modelling practices for reuse, ideally enabling inexperienced ontologists to construct high-quality ontologies. Although ODPs are already used for development, there are still remaining challenges that have not been addressed in the literature. These research gaps include a lack of knowledge about (1) which ODP features are important for ontology engineering, (2) less experienced developers' preferences and barriers for employing ODP tooling, and (3) the suitability of the eXtreme Design (XD) ODP usage methodology in non-academic contexts.

    This dissertation aims to close these gaps by combining quantitative and qualitative methods, primarily based on five ontology engineering projects involving inexperienced ontologists. A series of ontology engineering workshops and surveys provided data about developer preferences regarding ODP features, ODP usage methodology, and ODP tooling needs. Other data sources are ontologies and ODPs published on the web, which have been studied in detail. To evaluate tooling improvements, experimental approaches provide data from comparison of new tools and techniques against established alternatives.

    The analysis of the gathered data resulted in a set of measurable quality indicators that cover aspects of ODP documentation, formal representation or axiomatisation, and usage by ontologists. These indicators highlight quality trade-offs: for instance, between ODP Learnability and Reusability, or between Functional Suitability and Performance Efficiency. Furthermore, the results demonstrate a need for ODP tools that support three novel property specialisation strategies, and highlight the preference of inexperienced developers for template-based ODP instantiation---neither of which are supported in prior tooling. The studies also resulted in improvements to ODP search engines based on ODP-specific attributes. Finally, the analysis shows that XD should include guidance for the developer roles and responsibilities in ontology engineering projects, suggestions on how to reuse existing ontology resources, and approaches for adapting XD to project-specific contexts.

  • 37.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    DC Proposal: Towards an ODP Quality Model2011In: The Semantic Web – ISWC 2011: 10th International Semantic Web Conference, Bonn, Germany, October 23-27, 2011, Proceedings, Part II / [ed] Lora Aroyo, Chris Welty, Harith Alani, Jamie Taylor, Abraham Bernstein, Lalana Kagal, Natasha Noy and Eva Blomqvist, Berlin: Springer , 2011, p. 277-284Conference paper (Refereed)
    Abstract [en]

    The study of ontology design patterns (ODPs) is a fairly recent development. Such patterns simplify ontology development by codifying and reusing known best practices, thus lowering the barrier to entry of ontology engineering. However, while ODPs appear to be a promising addition to research and while such patterns are being presented and used, work on patterns as artifacts of their own, i.e. methods of developing, identifying and evaluating them, is still uncommon. Consequently, little is known about what ODP features or characteristics are beneficial or harmful in different ontology engineering situations. The presented PhD project aims to remedy this by studying ODP quality characteris- tics and what impact these characteristics have on the usability of ODPs themselves and on the suitability of the resulting ontologies.

  • 38.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    How to Document Ontology Design Patterns: Supporting Data Part 22016Data set
    Abstract [en]

    Survey data presented and discussed in the paper 'How to Document Ontology Design Patterns' presented at the Workshop on Ontology and Semantic Web Patterns in conjunction with the International Semantic Web Conference 2016.

  • 39.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Modular Semantic CEP for Threat Detection2012In: Operations Research and Data Mining ORADM 2012 workshop proceedings / [ed] Luis Villa-Vargas, Leonid Sheremetov, and Hans-Dietrich Haasis, Mexico City: National Polytechnic Institute , 2012Conference paper (Refereed)
    Abstract [en]

    This paper introduces a generic architecture for semantic complex event processing (CEP) over sensor data, as exemplified by a reference implementation system in development for the security and surveillance domain. The system gathers data from a number of sensor subsystems and classifies this data according to ontology-based situation models and rules. The output of the system is alerts about threat situations that supports human operators in deciding when and how to deploy security personnel to manage these threats. The novelty of the proposed approach lies in the use of modular ontology design patterns for system configuration, which enable non-technical users to rapidly configure the system for particular scenarios.

  • 40.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Motivating and Evaluating Template-Based Content ODP Instantiation: Evaluation Dataset for WOP 2016 Submission2016Data set
  • 41.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Ontology Design Pattern Property Specialisation Strategies2014In: Knowledge Engineering and Knowledge Management: 19th International Conference, EKAW 2014, Linköping, Sweden, November 24-28, 2014. Proceedings / [ed] Krzysztof Janowicz, Stefan Schlobach, Patrick Lambrix, Eero Hyvönen, Berlin: Springer Berlin/Heidelberg, 2014, p. 165-180Conference paper (Refereed)
    Abstract [en]

    Ontology Design Patterns (ODPs) show potential in enabling simpler, faster, and more correct Ontology Engineering by laymen and experts. For ODP adoption to take off, improved tool support for ODP use in Ontology Engineering is required. This paper studies and evaluates the effects of strategies for object property specialisation in ODPs, and suggests tool improvements based on those strategies. Results indicate the existence of three previously unstudied strategies for ODP specialisation, the uses of which affect reasoning performance and integration complexity of resulting ontologies.

  • 42.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Ontology Design Patterns: Adoption Challenges and Solutions2014In: Joint Proceedings of the Second International Workshop on Semantic Web Enterprise Adoption and Best Practice and Second International Workshop on Finance and Economics on the Semantic Web, EUR-WS , 2014, p. 23-32Conference paper (Refereed)
    Abstract [en]

    Ontology Design Patterns (ODPs) are intended to guide non-experts in performing ontology engineering tasks successfully. While being the topic of significant research efforts, the uptake of these ideas outside the academic community is limited. This paper summarises some issues preventing broader adoption of Ontology Design Patterns among practitioners, suggests research directions that may help overcome these issues, and presents early results of work in these directions.

  • 43.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Ontology Design Patterns: Improving Findability and Composition2014In: The Semantic Web: ESWC 2014 Satellite Events: ESWC 2014 Satellite Events, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers / [ed] Valentina Presutti, Eva Blomqvist, Raphael Troncy, Harald Sack, Ioannis Papadakis, Anna Tordai, Berlin: Springer Berlin/Heidelberg, 2014, p. 3-13Conference paper (Refereed)
    Abstract [en]

    Ontology Design Patterns (ODPs) are intended to guide non-experts in performing ontology engineering tasks successfully. While being the topic of significant research efforts, the uptake of these ideas outside the academic community is limited. This paper summarises issues preventing broader adoption of Ontology Design Patterns among practitioners, with an emphasis on finding and composing such patterns, and presents early results of work aiming to overcome these issues.

  • 44.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Ontology Design Patterns in Use: Lessons Learnt from an Ontology Engineering Case2012In: Proceedings of the 3rd Workshop on Ontology Patterns, 2012Conference paper (Refereed)
    Abstract [en]

    Ontology Design Patterns show promise in enabling simpler, faster, more correct Ontology Engineering by laymen and experts alike. Evaluation of such patterns has typically been performed in experiments set up with artificial scenarios and measured by quantitative metrics and surveys. This paper presents an observational case study of content pattern usage in configuration of an event processing system. Results indicate that while structural characteristics of patterns are of some importance, greater emphasis needs to be put on pattern metadata and the development of pattern catalogue features.

  • 45.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Ontology Design Patterns in WebProtégé2015In: Proceedings of the ISWC 2015 Posters & Demonstrations Track co-located with the 14th International Semantic Web Conference (ISWC-2015), Betlehem, USA, October 11, 2015 / [ed] Serena Villata, Jeff Z. Pan, Mauro Dragoni, CEUR-WS , 2015Conference paper (Refereed)
    Abstract [en]

    The use of Ontology Design Patterns (ODPs) in ontology engineering has been shown to have beneficial effects on the quality of developed ontologies, and promises increased interoperability of those same ontologies. Unfortunately, the lack of user-friendly integrated ODP tooling has prevented the adoption of pattern use. This paper demonstrates an extension to the WebProt´eg´e ontology engineering environment supporting the finding, specialisation, and integration of ODPs. The extension combines existing approaches with new developments in ODP search, specialisation strategies, and alignment.

  • 46.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL). Linkoping University.
    Quality of Content Ontology Design Patterns2016In: Ontology Engineering with Ontology Design Patterns / [ed] Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti, IOS Press, 2016, p. 51-71Chapter in book (Refereed)
  • 47.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Reasoning Performance Indicators for Ontology Design Patterns2014In: Proceedings of the 4th Workshop on Ontology and Semantic Web Patterns, CEUR-WS , 2014Conference paper (Refereed)
    Abstract [en]

    Ontologies are increasingly used in systems where performance is an important requirement. While there is a lot of work on reasoning performance-altering structures in ontologies, how these structures appear in Ontology Design Patterns (ODPs) is as of yet relatively unknown. This paper surveys existing literature on performance indicators in ontologies applicable to ODPs, and studies how those indicators are expressed in patterns published on two well known ODP portals. Based on this, it proposes recommendations and design principles for the development of new ODPs.

  • 48.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    The State of Ontology Pattern Research2011In: Perspectives in Business Informatics Research: Associated Workshops and Doctoral Consortium / [ed] Laila Niedrite, Renate Strazdina, Benkt Wangler, Riga, Latvia: Riga Technical University , 2011, p. 29-37Conference paper (Refereed)
    Abstract [en]

    Semantic web ontologies have several advantages over other knowledge representation formats that make them appropriate for information logistics architectures and applications. However, the construction of ontologies is still time-consuming and error prone for practitioners. One recent development that aims to remedy this situation is the introduction of ontology design patterns, codifying best practices and promoting reuse. This paper presents a literature survey into the state of research on ontology patterns, and suggests the use of such patterns for modeling information demand and distribution.

  • 49.
    Hammar, Karl
    Jönköping University, School of Engineering, JTH, Computer and Electrical Engineering. Jönköping University, School of Engineering, JTH. Research area Information Engineering. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Towards an Ontology Design Pattern Quality Model2013Licentiate thesis, monograph (Other academic)
    Abstract [en]

    The use of semantic technologies and Semantic Web ontologies in particular have enabled many recent developments in information integration, search engines, and reasoning over formalised knowledge. Ontology Design Patterns have been proposed to be useful in simplifying the development of Semantic Web ontologies by codifying and reusing modelling best practices.

    This thesis investigates the quality of Ontology Design Patterns. The main contribution of the thesis is a theoretically grounded and partially empirically evaluated quality model for such patterns including a set of quality characteristics, indicators, measurement methods and recommendations. The quality model is based on established theory on information system quality, conceptual model quality, and ontology evaluation. It has been tested in a case study setting and in two experiments.

    The main findings of this thesis are that the quality of Ontology Design Patterns can be identified, formalised and measured, and furthermore, that these qualities interact in such a way that ontology engineers using patterns need to make tradeoffs regarding which qualities they wish to prioritise. The developed model may aid them in making these choices.

    This work has been supported by Jönköing University.

  • 50.
    Hammar, Karl
    et al.
    Jönköping University, School of Engineering, JTH. Research area Computer Science and Informatics. Jönköping University, School of Engineering, JTH, Computer Science and Informatics, JTH, Jönköping AI Lab (JAIL).
    Blomqvist, Eva
    Linköping University.
    Carral, David
    Wright State University.
    Van Erp, Marieke
    Vrije Universiteit Amsterdam.
    Fokkens, Antske
    Vrije Universiteit Amsterdam.
    Gangemi, Aldo
    CNR-ISTC.
    Van Hage, Willem Robert
    Vrije Universiteit Amsterdam.
    Hitzler, Pascal
    Wright State University.
    Janowicz, Krzysztof
    University of California, Santa Barbara.
    Karima, Nazifa
    Wright State University.
    Krisnadhi, Adila
    Wright State University.
    Narock, Tom
    Marymount University.
    Segers, Roxane
    Vrije Universiteit Amsterdam.
    Solanki, Monika
    University of Oxford.
    Svatek, Vojtech
    University of Economics, Prague.
    Collected Research Questions Concerning Ontology Design Patterns2016In: Ontology Engineering with Ontology Design Patterns / [ed] Pascal Hitzler, Aldo Gangemi, Krzysztof Janowicz, Adila Krisnadhi, Valentina Presutti, IOS Press, 2016, p. 189-198Chapter in book (Refereed)
    Abstract [en]

    This chapter lists and discusses open challenges for the ODP community in the coming years, both in terms of research questions that will need be answered, and in terms of tooling and infrastructure that will need be developed to increase adoption of ODPs in academia and industry. The chapter is organised into three sections: Section 1 focuses on issues pertaining to the patterns themselves, including understanding their features and qualities, and developing pattern languages and standards. Section 2 concerns the evaluation and development of methods for using, constructing, and extracting ODPs. Finally, Section 3 focuses on tooling and infrastructure development, including Ontology Engineering environments that support pattern use, pattern repository development, and sustainability and versioning issues.

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