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  • 1.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Business Process Optimization with Reinforcement Learning2019In: Lect. Notes Bus. Inf. Process., Springer Verlag , 2019, Vol. 356, p. 203-212Conference paper (Refereed)
    Abstract [en]

    We investigate the use of deep reinforcement learning to optimize business processes in a business support system. The focus of this paper is to investigate how a reinforcement learning algorithm named Q-Learning, using deep learning, can be configured in order to support optimization of business processes in an environment which includes some degree of uncertainty. We make the investigation possible by implementing a software agent with the help of a deep learning tool set. The study shows that reinforcement learning is a useful technique for business process optimization but more guidance regarding parameter setting is needed in this area. © 2019, Springer Nature Switzerland AG.

  • 2.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Component Selection with Fuzzy Decision Making2018In: Procedia Computer Science, Elsevier B.V. , 2018, Vol. 126, p. 1378-1386Conference paper (Refereed)
    Abstract [en]

    In many situations a decision maker (DM) would like to grade a component, or rank several components of the same type. Often a component type has many features, which are deemed as valuable by the DM. Other vital features are not known by the DM but are needed for the component to function. However, it should be possible to guide the DM to find the desired business solution, without putting a requirement of detailed knowledge of the component type on the DM. We propose a framework for component selection with the help of fuzzy decision making. The work is based on algorithms from fuzzy decision making, which we have adapted or extended. The framework was validated by practitioners, which found the framework useful. © 2018 The Author(s).

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  • 3.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    On Context Frames and Their Implementations2021In: BUSINESS MODELING AND SOFTWARE DESIGN (BMSD 2021) / [ed] Shishkov B, Springer, 2021, Vol. 422, p. 133-153Conference paper (Refereed)
    Abstract [en]

    When an actor is selecting an action in order to fulfill its intents, in a given context, the actor’s knowledge and beliefs about the specific context will impact the possibility to achieve a desired outcome. The context is often affected by unobserved, or unmeasured, factors, which can impact the result of the desired outcome.

    The context specific knowledge and beliefs an actor has about a domain, together with the possibilities to evaluate and learn which actions shall be taken, are packaged into a context frame. Our intention with this study is to evaluate an implementation of such a context frame. The context frame concept is meant to support actors to fulfill their intents in a given knowledge domain, by enforcing the needed, and available, actions which cause effects on the outcomes. We have built our implementation of the context frame on the OODA-loop, Pask’s conversation theory, and structural causal models, by using a Bayesian approach, and probabilistic programming.

    The research approach is based on evaluation research. We evaluated our implementation with the help of a proof of concept. During the proof of concept we used data sets containing decisions about treatment and survival analysis regarding cancer patients, information obtained during focus group interviews, and questionnaire data.

    The proof of concept used to evaluate our implementation of a context frame was regarded as successful and the concept of context frames deemed as useful.

    Our division of a context frame in three parts, supported by four different types of analysis functions, made it easier to create a solution which supports evaluation and learning.

  • 4.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Towards Intent-Driven Systems2017Licentiate thesis, comprehensive summary (Other academic)
    Abstract [en]

    Context: Software supporting an enterprise’s business, also known as a business support system, needs to support the correlation of activities between actors as well as influence the activities based on knowledge about the value networks in which the enterprise acts. This can be supported with the help of intent-driven systems. The aim of intent-driven systems is to capture stakeholders’ intents and transform these into a form that enables computer processing of them. Only then are different machine actors able to negotiate with each other on behalf of their respective stakeholders and their intents, and suggest a mutually beneficial agreement.

    Objective: When building a business support system it is critical to separate the business model of the business support system itself from the business models used by the enterprise which is using the business support system. The core idea of intent-driven systems is the possibility to change behavior of the system itself, based on stakeholder intents. This requires a separation of concerns between the parts of the system used to execute the stakeholder business, and the parts which are used to design the business based on stakeholder intents. The business studio is a software that supports the realization of business models used by the enterprise by configuring the capabilities provided by the business support system. The aim is to find out how we can support the design of a business studio which is based on intent-driven systems.

    Method: We are using the design science framework as our research frame- work. During our design science study we have used the following research methods: systematic literature review, case study, quasi experiment, and action research.

    Results: We have produced two design artifacts as a start to be able to support the design of a business studio. These artifacts are the models and quasi-experiment in Chapter 3, and the action research in Chapter 4. The models found during the case study have proved to be a valuable artifact for the stakeholder. The results from the quasi-experiment and the action research are seen as new problem solving knowledge by the stakeholder.

    Conclusion: The synthesis shows a need for further research regarding semantic interchange of information, actor interaction in intent-driven systems, and the governance of intent-driven systems.

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  • 5.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Towards Intent-Driven Systems Based on Context Frames2021Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    In this research project we investigate how machine actors can support the business intents (desired outcomes) of an enterprise via predictive execution flows, prescriptive execution flows, and bidirectional knowledge creation between human actors and machine actors. A context frame supports bidirectional knowledge creation via interventions and counterfactual analysis. An intent-driven system combines execution flows to obtain business intents, and a context frame is a component in these flows. 

    Our aim is to develop theoretical frameworks supporting intent-driven systems and context frames, and to validate the components needed to realize such frame- works. 

    We are using the design science framework as our research framework. During our design science study we have used the following research methods: systematic literature review, case study, quasi experiment, action research, and evaluation research. 

    We have created theoretical frameworks supporting intent-driven systems, and context frames, and implemented needed functionality in the involved components. The framework supports knowledge creation and knowledge validation. The possibility of using the knowledge for predictive analysis, prescriptive analysis, and counterfactual analysis, makes it possible to obtain bidirectional knowledge creation between a human actor and a machine actor. This enables a context frame to be part of an intent-driven system which supports predictive, and prescriptive, executions flows. 

    The produced artifacts provide answers to our research questions. These answers are a base for theoretical frameworks supporting intent-driven systems and context frames, and provide knowledge of how to construct the components needed to realize these frameworks. 

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  • 6.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Understanding human generated decision data2020In: Lecture Notes in Business Information Processing / [ed] Shishkov B., Springer, 2020, Vol. 391, p. 362-374Conference paper (Refereed)
    Abstract [en]

    In order to design intent-driven systems, the understanding of how the data is generated is essential. Without the understanding of the data generation process, it is not possible to use interventions, and counterfactuals. Interventions, and counterfactuals, are useful tools in order to achieve an artificial intelligence which can improve the system itself. We will create an understanding, and a model, of how data about decisions are generated, as well as used, by human decision makers. The research data were collected with the help of focus group interviews, and questionnaires. The models were built and evaluated with the help of, bayesian statistics, probability programming, and discussions with the practitioners. When we are combining, probabilistic programming models, extended machine learning algorithms, and data science processes, into a directed acyclic graph, we can mimic the process of human generated decision data. We believe the usage of a directed acyclic graph, to combine the functions and models, is a good base for mimic human generated decision data. Our next step is to evaluate if flow-based programming can be used as a framework for realization of components, useful in intent-driven systems. © Springer Nature Switzerland AG 2020.

  • 7.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Angelin, Lars
    Ericsson AB, SWE.
    Introducing intents to the OODA-loop2019In: Procedia Computer Science / [ed] Rudas, IJ; Janos, C; Toro, C; Botzheim, J; Howlett, RJ; Jain, LC, Elsevier B.V. , 2019, Vol. 159, p. 878-883Conference paper (Refereed)
    Abstract [en]

    Together with Ericsson AB, we are using the design science framework when investigating how to create an intent-driven system for their business support system and its business studio. The aim is to present our initial results on how an extended OODA-loop can be used to realize a robust, but still flexible, software architecture for an intent-driven system. We explain how an extended OODA-loop is constructed and provide suggestions of how different part of it can be implemented. The initial results are promising but further research is needed to use the extended OODA-loop as reusable components in intent-driven systems. Our next step is to extend the generic methods with knowledge representation and reasoning capabilities. © 2019 The Author(s). Published by Elsevier B.V.

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    IntroducingintentstotheOODA-loop
  • 8.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Singh, Shailesh Pratap
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science.
    Validating Trust in Human Decisions to Improve Expert Models Based on Small Data Sets2023In: Business Modeling and Software Design / [ed] Boris Shishkov, Springer Nature, 2023, Vol. 483, p. 256-267Conference paper (Refereed)
    Abstract [en]

    When a model is built based on expert knowledge, a small data set will, in many cases, form the base for the model. It must be possible to validate the trustworthiness and model improvement potential of the provided information from humans or machines. In this study, we have investigated how to evaluate the information from humans to improve the model itself. We used evaluation research and collected the research data with the help of focus group interviews and questionnaires. The result of the study suggests a way to determine the trustworthiness of answers from humans and how to understand if these answers indicate a change to the underlying expert model. The introduction of divergence, and candidate areas, made it possible to evaluate the trustworthiness and changes to the expert model. These were deemed valuable by practitioners.

  • 9.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Svahnberg, Mikael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Towards Executable Business Rules2017Other (Refereed)
    Abstract [en]

    Context:  In today's implementations of business support systems, business rules are configured in different places of the system, and in different formats. This makes it hard to have a common view of what is defined, and to execute the same logic in different parts of systems. It is desired to have a common governance structure and a standardized way of handling the business rules.

    Objective: To investigate if it is possible to support visual and logical verification of business rules and to generate executable business rules.

    Method: Together with practitioners we conducted an experiment.

    Results: We have implemented a machine learning pipe-line which supports visual and logical verification of business rules, and the generation of executable business rules. From a machine learning perspective, we have added the possibility for the ID3 algorithm to use continuous features.

    Conclusion: The experiment shows that it is possible to support visual and logical verification of business rules, and to generate executable business rules with the help of a machine learning pipe-line.

  • 10.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Svahnberg, Mikael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Uncover and Assess Rule Adherence Based on Decisions2018In: Lecture Notes in Business Information Processing / [ed] Shishkov B., Springer Verlag , 2018, Vol. 319, p. 249-259Conference paper (Refereed)
    Abstract [en]

    Context: Decisions taken by medical practitioners may be based on explicit and implicit rules. By uncovering these rules, a medical practitioner may have the possibility to explain its decisions in a better way, both to itself and to the person which the decision is affecting. Objective: We investigate if it is possible for a machine learning pipe-line to uncover rules used by medical practitioners, when they decide if a patient could be operated or not. The uncovered rules should have a linguistic meaning. Method: We are evaluating two different algorithms, one of them is developed by us and named “The membership detection algorithm”. The evaluation is done with the help of real-world data provided by a hospital. Results: The membership detection algorithm has significantly better relevance measure, compared to the second algorithm. Conclusion: A machine learning pipe-line, based on our algorithm, makes it possibility to give the medical practitioners an understanding, or to question, how decisions have been taken. With the help of the uncovered fuzzy decision algorithm it is possible to test suggested changes to the feature limits. © Springer International Publishing AG, part of Springer Nature 2018.

  • 11.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Svahnberg, Mikael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Uncovering Implicit Rules in Medicine DiagnosisManuscript (preprint) (Other academic)
    Abstract [en]

    Context:  Decisions taken by experts may be based on explicit and implicit rules. By uncovering the implicit rules the expert may have the possibility to explain its decisions in a better way, both for itself and the person which the decision is affecting. In the area of medicine, laws are enforcing the expert to be able to explain its decision when a patient is complaining about a decision. Another vital aspect is the ability of the expert to explain to the patient why a certain decision is taken, and the risks associated with the decision.

    Objective: To investigate if it is possible for a machine learning pipe-line to find implicit rules used by experts, when they decide if a patient could be operated or not.

    Method: We conduct an analysis of a data set, containing information about patients and the decision if an operation should be performed or not.

    Results: We have implemented a machine learning pipe-line which supports detection of implicit rules in a data set. The detection of the implicit rules are supported by an algorithm which implements an agglomerative merging of feature values. We have improved the original algorithm by showing the boarders of the feature values of a discretization bin.

    Conclusion: The analysis of the data set shows it is possible to find implicit rules used by the experts with the help of an agglomerative merging of feature values.

  • 12.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wilson, Magnus
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Encouraging Business Flexibility by Improved Context Descriptions2017In: Proceedings of the Seventh International Symposium on Business Modeling and Software Design / [ed] Boris Shishkov, SciTePress, 2017, Vol. 1, p. 225-228Conference paper (Refereed)
    Abstract [en]

    Business-driven software architectures are emerging and gaining importance for many industries. As softwareintensive solutions continue to be more complex and operate in rapidly changing environments, there is a pressure for increased business flexibility realized by more efficient software architecture mechanisms to keep up with the necessary speed of change. We investigate how improved context descriptions could be implemented in software components, and support important software development practices like business modeling and requirement engineering. This paper proposes context descriptions as an architectural support for improving the connection between business flexibility and software components. We provide initial results regarding software architectural mechanisms which can support context descriptions as well as the context description’s support for business-driven software architecture, and the business flexibility demanded by the business ecosystems.

  • 13.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wilson, Magnus
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Svahnberg, Mikael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Supporting Continuous Changes to Business Intents2017In: International journal of software engineering and knowledge engineering, ISSN 0218-1940, Vol. 27, no 8, p. 1167-1198Article in journal (Refereed)
    Abstract [en]

    Context: Software supporting an enterprise’s business, also known as a business support system, needs to support the correlation of activities between actors as well as influence the activities based on knowledge about the value networks in which the enterprise acts. This requires the use of policies and rules to guide or enforce the execution of strategies or tactics within an enterprise as well as in collaborations between enterprises. With the help of policies and rules, an enterprise is able to capture an actor’s intent in its business support system, and act according to this intent on behalf of the actor. Since the value networks an enterprise is part of will change over time the business intents’ life cycle states might change. Achieving the changes in an effective and efficient way requires knowledge about the affected intents and the correlation between intents.

    Objective: The aim of the study is to identify how a business support system can support continuous changes to business intents. The first step is to find a theoretical model which serves as a foundation for intent-driven systems.

    Method: We conducted a case study using a focus group approach with employees from Ericsson. This case study was influenced by the spiral case study process.

    Results: The study resulted in a model supporting continuous definition and execution of an enterprise. The model is divided into three layers; Define, Execute, and a com- mon governance view layer. This makes it possible to support continuous definition and execution of business intents and to identify the actors needed to support the business intents’ life cycles. This model is supported by a meta-model for capturing information into viewpoints.

    Conclusion: The research question is addressed by suggesting a solution supporting con- tinuous definition and execution of an enterprise as a model of value architecture compo- nents and business functions. The results will affect how Ericsson will build the business studio for their next generation business support systems.

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  • 14.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Svahnberg, Mikael
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Systematic literature review on intent-driven systems2020In: IET Software, ISSN 1751-8806, E-ISSN 1751-8814, Vol. 14, no 4, p. 345-357Article, review/survey (Refereed)
    Abstract [en]

    Context: The aim of intent-driven systems is to capture stakeholders’ intents and transform these into a form that enables computer processing of the intents. Only then are different computer- based agents able to negotiate with each other on behalf of their respective stakeholders and their intents, and suggest a mutually beneficial agreement. This requires a separation of concerns between the parts of the system used to execute the stakeholder business, and the parts which are used to design the business based on stakeholder intents.

    Objective: The aim is to find out which methods/techniques as well as enabling aspects, useful for an intent-driven system, that are covered by research literature.

    Method: As a part of a design science study, a Systematic Literature Review is conducted.

    Results: The existence of methods/techniques which can be used as building blocks to construct intent-driven systems exist in the literature. How these methods/techniques can interact with the aspects needed to enabling flexible realizations of intent-driven systems is not evident in the existing literature.

    Conclusion: The synthesis shows a need for further research regarding semantic interchange of information, actor interaction in intent-driven systems, and the governance of intent-driven systems.

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  • 15.
    Silvander, Johan
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wälitalo, Lisa
    Blekinge Institute of Technology, Faculty of Engineering, Department of Strategic Sustainable Development.
    Knowledge creation through a teaching and learning spiral2016Conference paper (Refereed)
    Abstract [en]

    Context: We have experienced the use of a domain specific language sometimes makes it difficult to present domain knowledge to a group or an individual that has limited or different knowledge about the specific domain, and where the presenter and the audience do not have sufficient insight into each other's contexts. In order to create an environment w here knowledge transfer can exists it is vital to understand how the roles are shifting during the interaction between the participants. In an educational environment Teaching and Learning Activities (TLA) could, in ideal situations, be invented during the design of the curriculum. This might not be the case when interacting with practitioners or students from diverse fields. This situation requires a method to find TLAs for the specific situation. For the domain knowledge to be useful for learners it has to be connected to the context/domain where the learners are active. In this paper we combine a spiral learning process with constructive alignment, which resulted in a teaching and learning spiral process. The outcome of the teach - ing and learning spiral process is to provide the knowledge of using the introduced domain knowledge in a context/domain where the learners are active.

    Objective: The aim with this work is to present guidelines that will contribute to a more effective knowledge creation process in heterogeneous groups, both in an educational environment and in interaction with different groups of practitioners in society.

    Method: We conducted a case study using observations and surveys.

    Results: The results from our case study support a positive effect on the learning outcomes when adopting this methodology. The learning outcome is to gain deeper understanding of the introduced domain knowledge and being able to dis - cuss how the new domain knowledge can be integrated to the learners own context.

    Conclusions: We have formulated guidelines for how to use the teaching and learning spiral process in an effective and efficient way.

  • 16.
    Wilson, Magnus
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Wnuk, Krzysztof
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Silvander, Johan
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Gorschek, Tony
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    A Literature Review on the Effectiveness and Efficiency of Business Modeling2018In: e-Informatica Software Engineering Journal, ISSN 1897-7979, E-ISSN 2084-4840, Vol. 12, no 1, p. 265-302Article, review/survey (Refereed)
    Abstract [en]

    Background: Achieving and maintaining a strategic competitive advantage through business and technology innovation via continually improving effectiveness and efficiency of the operations are the critical survival factors for software-intensive product development companies. These companies invest in business modeling and tool support for integrating business models into their product development, but remain uncertain, if such investments generate desired results. Aim: This study explores the effects of business modeling on effectiveness and efficiency for companies developing software-intensive products. Method: We conducted a systematic literature review using the snowballing methodology, followed by thematic and narrative analysis. 57 papers were selected for analysis and synthesis, after screening 16 320 papers from multiple research fields. Results: We analyzed the literature based on purpose, benefit, challenge, effectiveness, and efficiency with software and software-intensive products as the unit of analysis. The alignment between strategy and execution is the primary challenge, and we found no evidence that business modeling increases effectiveness and efficiency for a company. Any outcome variations may simply be a result of fluctuating contextual or environmental factors rather than the application of a specific business modeling method. Therefore, we argue that governance is the fundamental challenge needed for business modeling, as it must efficiently support simultaneous experimentation with products and business models while turning experiences into knowledge. Conclusion: We propose a conceptual governance model for exploring the effectiveness and efficiency of business modeling to occupy the missing link between business strategy, processes and software tools. We also recommend managers to introduce a systematic approach for experimentation and organizational learning, collaboration, and value co-creation.

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