Courses

• Systemintegration av IT-baserade affärssystem (SYSTINT)
• Skalbar och ansvarsfull AI i organisationer (RESPAI)
• Big Data with NoSQL Databases (BIGDATA)
• Databasmetodik (DB)
• Kompletteringskurs i data- och systemvetenskap (SUPCOM)
• IT i organisationer (ITO)
• Projektarbete inom affärssystem (PROAFF)
• Introduktion till Tjänstebaserade affärssystem (SAFFK)

Thesis and course projects

• Grafanalys
• Designa AI-lösningar
• Datadriven analys
• Robotic Process Automation (RPA)
• Innehållshanteringssystem
• Molnbaserade system
• Utveckling av webbtjänster och integrerade applikationer
• Design och utveckling av systemintegrationslösningar

I urval från Stockholms universitets publikationsdatabas

• A Systematic Literature Review about Idea Mining

  2021. Workneh Yilma Ayele, Gustaf Juell-Skielse. Advances in Information and Communication, 744-762

  Konferens

  Idea generation is the core activity of innovation. Digital data sources, which are sources of innovation, such as patents, publications, social media, websites, etc., are increasingly growing at unprecedented volume. Manual idea generation is time-consuming and is affected by the subjectivity of the individuals involved. Therefore, the use machine-driven data analytics techniques to analyze data to generate ideas and support idea generation by serving users is useful. The objective of this study is to study state-of the-art machine-driven analytics for idea generation and data sources, hence the result of this study will generally serve as a guideline for choosing techniques and data sources. A systematic literature review is conducted to identify relevant scholarly literature from IEEE, Scopus, Web of Science and Google Scholar. We selected a total of 71 articles and analyzed them thematically. The results of this study indicate that idea generation through machine-driven analytics applies text mining, information retrieval (IR), artificial intelligence (AI), deep learning, machine learning, statistical techniques, natural language processing (NLP), NLP-based morphological analysis, network analysis, and bibliometric to support idea generation. The results include a list of techniques and procedures in idea generation through machine-driven idea analytics. Additionally, characterization and heuristics used in idea generation are summarized. For the future, tools designed to generate ideas could be explored. 

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• A Process Model for Generating and Evaluating Ideas

  2020. Workneh Y. Ayele, Gustaf Juell-Skielse. Electronic Government and the Information Systems Perspective, 189-203

  Konferens

  The significance and possibilities of idea generation and evaluation are increasing due to the increasing demands for digital innovation and the abundance of textual data. Textual data such as social media, publications, patents, documents, etc. are used to generate ideas, yet manual analysis is affected by bias and subjectivity. Machine learning and visual analytics tools could be used to support idea generation and evaluation, referred to as idea mining, to unlock the potential of voluminous textual data. Idea mining is applied to support the extraction of useful information from textual data. However, existing literature merely focuses on the outcome and overlooks structuring and standardizing the process itself. In this paper, to support idea mining, we designed a model following design science research, which overlaps with the Cross-Industry-Standard-Process for Data Mining (CRISP-DM) process and adapts well-established models for technology scouting. The first layer presents and business layer, where tasks performed by technology scouts, incubators, accelerators, consultants, and contest managers are detailed. The second layer presents the technical layer where tasks performed by data scientists, data engineers, and similar experts are detailed overlapping with CRISP-DM. For future research, we suggest an ex-post evaluation and customization of the model to other techniques of idea mining.

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• Adapting CRISP-DM for Idea Mining

  2020. Workneh Yilma Ayele. International Journal of Advanced Computer Sciences and Applications 11 (6), 20-32

  Artikel

  Data mining project managers can benefit from using standard data mining process models. The benefits of using standard process models for data mining, such as the de facto and the most popular, Cross-Industry-Standard-Process model for Data Mining (CRISP-DM) are reduced cost and time. Also, standard models facilitate knowledge transfer, reuse of best practices, and minimize knowledge requirements. On the other hand, to unlock the potential of ever-growing textual data such as publications, patents, social media data, and documents of various forms, digital innovation is increasingly needed. Furthermore, the introduction of cutting-edge machine learning tools and techniques enable the elicitation of ideas. The processing of unstructured textual data to generate new and useful ideas is referred to as idea mining. Existing literature about idea mining merely overlooks the utilization of standard data mining process models. Therefore, the purpose of this paper is to propose a reusable model to generate ideas, CRISP-DM, for Idea Mining (CRISP-IM). The design and development of the CRISP-IM are done following the design science approach. The CRISP-IM facilitates idea generation, through the use of Dynamic Topic Modeling (DTM), unsupervised machine learning, and subsequent statistical analysis on a dataset of scholarly articles. The adapted CRISP-IM can be used to guide the process of identifying trends using scholarly literature datasets or temporally organized patent or any other textual dataset of any domain to elicit ideas. The ex-post evaluation of the CRISP-IM is left for future study.

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• Eliciting Evolving Topics, Trends and Foresight about Self-driving Cars Using Dynamic Topic Modeling

  2020. Workneh Y. Ayele, Gustaf Juell-Skielse. Advances in Information and Communication, 488-509

  Konferens

  Self-driving technology is part of smart city ecosystems, and it touches a broader research domain. There are advantages associated with using this technology, such as improved quality of life, reduced pollution, and reduced fuel cost to name a few. However, there are emerging concerns, such as the impact of this technology on transportation systems, safety, trust, affordability, control, etc. Furthermore, self-driving cars depend on highly complex algorithms. The purpose of this research is to identify research agendas and innovative ideas using unsupervised machine learning, dynamic topic modeling, and to identify the evolution of topics and emerging trends. The identified trends can be used to guide academia, innovation intermediaries, R&D centers, and the auto industry in eliciting and evaluating ideas. The research agendas and innovative ideas identified are related to intelligent transportation, computer vision, control and safety, sensor design and use, machine learning and algorithms, navigation, and human-driver interaction. The result of this study shows that trending terms are safety, trust, transportation system (traffic, modeling traffic, parking, roads, power utilization, the buzzword smart, shared resources), design for the disabled, steering and control, requirement handling, machine learning, LIDAR (Light Detection And Ranging) sensor, real-time 3D image processing, navigation, and others. 

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• Identifying Emerging Trends and Temporal Patterns About Self-driving Cars in Scientific Literature

  2020. Workneh Y. Ayele, Imran Akram. Advances in Computer Vision, 355-372

  Konferens

  Self-driving is an emerging technology which has several benefits such as improved quality of life, crash reductions, and fuel efficiency. There are however concerns regarding the utilization of self-driving technology such as affordability, safety, control, and liabilities. There is an increased effort in research centers, academia, and the industry to advance every sphere of science and technology yet it is getting harder to find innovative ideas. However, there is untapped potential to analyze the increasing research results using visual analytics, scientometrics, and machine learning. In this paper, we used scientific literature database, Scopus to collect relevant dataset and applied a visual analytics tool, CiteSpace, to conduct co-citation clustering, term burst detection, time series analysis to identify emerging trends, and analysis of global impacts and collaboration. Also, we applied unsupervised topic modeling, Latent Dirichlet Allocation (LDA) to identify hidden topics for gaining more insight about topics regarding self-driving technology. The results show emerging trends relevant to self-driving technology and global and regional collaboration between countries. Moreover, the result form the LDA shows that standard topic modeling reveals hidden topics without trend information. We believe that the result of this study indicates key technological areas and research domains which are the hot spots of the technology. For the future, we plan to include dynamic topic modeling to identify trends.

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• Unveiling Topics from Scientific Literature on the Subject of Self-driving Cars using Latent Dirichlet Allocation

  2018. Workneh Y. Ayele, Gustaf Juell-Skielse. 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), 1113-1119

  Konferens

  Self-driving cars are becoming popular topics in academia. Consumers of self-driving cars and vehicles have different concerns, for example, safety and security, to name a few. Also, the public sector has interests in self-driving cars such as amending policies to enable the management of self-driving vehicles in cities, urban planning, traffic management and, etc. In this paper, more than 2700 corpus are extracted from literature from several subject areas to identify latent (hidden) topics of self-driving cars. Latent Dirichlet Allocation (LDA) is used for topic identification. The result of this study shows that topics identified are valid research areas such as urban planning, driver car (computer) interaction, self-driving control and system design, ethics in self-driving cars, safety and risk assessment, training dataset quality and machine learning in self-driving cars are among the topics identified. Furthermore, the network visualization of association graph of terms shows that the most frequently discussed concepts reveal that control of self-driving cars is based on algorithms, data, design, method, and model. The methods used in this study and the results can be used as decision tools, if carefully applied, in diverse disciplines that are disrupted by the introduction of self-driving cars. For future study, we plan to extend this study with a larger dataset and other data mining techniques.

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• Unveiling DRD

  2018. Workneh Y. Ayele (et al.). Systems, Signs & Actions 11 (1), 25-53

  Artikel

  The growing open data market opens possibilities for the development of viable digital artifacts that facilitate the creation of social and business values. Contests are becoming popular means to facilitate the development of digital artifacts utilizing open data. The increasing popularity of contests gives rise to a need for measuring contest performance. However, the available measurement model for digital innovation contests, the DICM-model, was designed based on a single case study and there is a need for a methodological approach that can accommodate for contests’ variations in scope. Therefore, we use design science to construct a nine-step method, the DRD method, to design and refine DICM-models. The DRD-method is designed using goal- and quality oriented approaches. It extends innovation measurement to the application domain of digital innovation contests and provides an improvement of innovation measurement as it offers a new solution for a known problem. The DRD-method provides comprehensive support to practice for designing and refining DICM-models and supports reflection and organizational learning across several contests. For future study, we suggest an ex-post evaluation of the method inconjunction with real contests and systematic efforts to generalize the method within as well as beyond the context of the contest. Finally, we propose to further investigate the potential of topdown and goal oriented approaches to measure open and iterative forms of innovation.

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• From Contest to Market Entry

  2015. Anders Hjalmarsson (et al.). 23rd European Conference on Information Systems, ECIS 2015

  Konferens

  Open data services have emerged as a research field. One important area of investigation within this field is exploration into how sustainable open data markets are created. Contests have become a popular method to propel and catalyse open data service development providing services to such markets. Recent research has identified numerous innovation barriers hampering development adjacent to the contest in developers’ effort to transform contest contributions to viable digital services based on open data. Little is however known about what innovation barriers over time constrain the post-contest process to transform initial innovations to finalized open data services ready for market entry. This paper presents a longitudinal survey of innovation barriers constraining teams performing open data service development after an innovation contest. The survey provides insights into 1) 24 innovation barriers constraining development, 2) a comparison of barrier importance based on team progress, and 3) a conceptualisation of phases structuring the process from contests to market entry, stipulating different innovation barriers impact open data service development. The results contribute to the understanding of how sustainable open data markets emerge and serve as a starting point for investigating how different stakeholders can manage innovation barriers constraining open data service development.

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• Social Media Analytics and Internet of Things: Survey

  2017. Workneh Yilma Ayele, Gustaf Juell-Skielse. Proceedings of the 1st International Conference on Internet of Things and Machine Learning

  Konferens

  Due to the emergence of social media, there is a paradigm shift in the area of information production, processing and consumption. Hence, investigation in the utilization of open social media data is a relevant research topic. The openness of data, social media data, enables innovation and societal value creation. Social media analytics is an evolving research domain with interdisciplinary methods that are common in data mining such as text mining, social network analysis, trend analysis, and sentiment analysis. Also, social media analytics deals with development and evaluation of frameworks and informatics tools to process noisy and unstructured social media data. On the other hand, Internet of Things (IoT) enables the utilization of digital artifacts with well-established solutions and allows things to be connected regardless of location and time. However, a literature review about social media analytics and IoT integration is missing. In this paper, we conducted a systematic literature review of social media analytics and IoT integration. The literature review indicates that there are fewer research works done in the area of social media analytics and IoT compared to Data Mining and IoT. This paper facilitates discussion and elicits research potentials in social media analytics and IoT integration.

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• Evaluating Open Data Innovation

  2015. Workneh Yilma Ayele (et al.). PACIS 2015 proceedings

  Konferens

  Digital innovation contests emerge as important intermediaries in open data markets. However the understanding of how contests affect innovation value chains is low and there is a lack of innovation measurement frameworks to support the management of digital innovation contests. Therefore, in this paper we apply design science to design a measurement model for digital innovation contests from the organizer’s perspective that adds to the available knowledge of innovation measurement. We use a recent case of digital innovation contests to motivate the model and discuss its implications on the innovation value chain. The measurement model contributes with new knowledge in the area of open data innovation and provides support for practice in managing innovation through digital innovation contests. For future research we intend to enhance the model to also measure the effects on innovation ecosystems, to operationalize the measures and to evaluate the model in several digital innovation contests as well as to include the perspective of the participants.

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