Computer Engineering / Bilgisayar Mühendisliği

Permanent URI for this collectionhttps://hdl.handle.net/11147/10

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Author: search.filters.author.Rabhi, FethiPublication Index: search.filters.publicationIndex.Scopus

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  • Article
    Citation - WoS: 6
    Citation - Scopus: 9
    Process Ontology Development Using Natural Language Processing: a Multiple Case Study
    (Emerald Group Publishing, 2019) Gürbüz, Özge; Rabhi, Fethi; Demirörs, Onur
    Purpose: Integrating ontologies with process modeling has gained increasing attention in recent years since it enhances data representations and makes it easier to query, store and reuse knowledge at the semantic level. The authors focused on a process and ontology integration approach by extracting the activities, roles and other concepts related to the process models from organizational sources using natural language processing techniques. As part of this study, a process ontology population (PrOnPo) methodology and tool is developed, which uses natural language parsers for extracting and interpreting the sentences and populating an event-driven process chain ontology in a fully automated or semi-automated (user assisted) manner. The purpose of this paper is to present applications of PrOnPo tool in different domains. Design/methodology/approach: A multiple case study is conducted by selecting five different domains with different types of guidelines. Process ontologies are developed using the PrOnPo tool in a semi-automated and fully automated fashion and manually. The resulting ontologies are compared and evaluated in terms of time-effort and recall-precision metrics. Findings: From five different domains, the results give an average of 70 percent recall and 80 percent precision for fully automated usage of the PrOnPo tool, showing that it is applicable and generalizable. In terms of efficiency, the effort spent for process ontology development is decreased from 250 person-minutes to 57 person-minutes (semi-automated). Originality/value: The PrOnPo tool is the first one to automatically generate integrated process ontologies and process models from guidelines written in natural language. © 2018, Emerald Publishing Limited.
  • Conference Object
    Citation - Scopus: 13
    Big Data Analytics Has Little To Do With Analytics
    (Springer, 2018) Rabhi, Fethi; Bandara, Madhushi; Namvar, Anahita; Demirörs, Onur
    As big data analytics is adapted across multitude of domains and applications there is a need for new platforms and architectures that support analytic solution engineering as a lean and iterative process. In this paper we discuss how different software development processes can be adapted to data analytic process engineering, incorporating service oriented architecture, scientific workflows, model driven engineering and semantic technology. Based on the experience obtained through ADAGE framework [1] and the findings of the survey on how semantic modeling is used for data analytic solution engineering [6], we propose two research directions - big data analytic development lifecycle and data analytic knowledge management for lean and flexible data analytic platforms.
  • Conference Object
    Citation - Scopus: 6
    A Comprehensive Evaluation of Agile Maturity Self-Assessment Surveys
    (Springer Verlag, 2018) Yürüm, Ozan Raşit; Demirörs, Onur; Rabhi, Fethi
    Agile methodologies are adapted by growing number of software organizations. Agile maturity (also called agility) assessment is a way to ascertain the degree of this adoption and determine a course of action to improve agile maturity. There are a number of agile maturity assessment surveys in order to assess team or organization agility and many of them require no guidance. However, the usability of these surveys are not widely studied. The purpose of this study is to determine available agile maturity self-assessment surveys and evaluate their strengths and weaknesses for agile maturity assessment. An extensive case study is conducted to measure the sufficiency of 22 available agile maturity self-assessment surveys according to the seven expected features: comprehensiveness, fitness for purpose, discriminativeness, objectivity, conciseness, generalizability, and suitability for multiple assessment. Our case study results show that they do not satisfy all of the expected features fully but are helpful in some degree based on the purpose of usage.
  • Conference Object
    Citation - Scopus: 14
    Systematic Mapping Study on Process Mining in Agile Software Development
    (Springer Verlag, 2018) Erdem, Sezen; Demirörs, Onur; Rabhi, Fethi
    Process mining is a process management technique that allows for the analysis of business processes based on the event logs and its aim is to discover, monitor and improve executed processes by extracting knowledge from event logs readily available in information systems. The popularity of agile software development methods has been increasing in the software development field over the last two decades and many software organizations develop software using agile methods. Process mining can provide complementary tools to Agile organizations for process management. Process mining can be used to discover agile processes followed by agile teams to establish the baselines and to determine the fidelity or they can be used to obtain feedback to improve agility. Despite the potential benefit of using process mining for agile software development, there is a lack of research that systematically analyzes the usage of process mining in agile software development. This paper presents a systematic mapping study on usage of process mining in agile software development approaches. The aim is to find out the usage areas of process mining in agile software development, explore commonly used algorithms, data sources, data collection mechanisms, analysis techniques and tools. The study has shown us that process mining is used in Agile software development especially for the purpose of process discovery from task tracking applications. We also observed that source code repositories are main data sources for process mining, a diversity of algorithms are used for analysis of collected data and ProM is the most widely used analysis tool for process mining.