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Citation - Scopus: 1
A Digital Interaction Framework for Managing Knowledge Intensive Business Processes
(Springer, 2019) Bandara, Madhushi; Rabhi, Fethi A.; Meymandpour, Rouzbeh; Demirörs, Onur
Many business processes present in modern enterprises are loosely defined, highly interactive, involve frequent human interventions and coupled with a multitude of abstract entities defined within an enterprise architecture. Further, they demand agility and responsiveness to address the frequently changing business requirements. Traditional business process modelling and knowledge management technologies are not adequate to represent and support those processes. In this paper, we propose a framework for modelling such processes in a service-oriented fashion, extending an ontology-based enterprise architecture modelling platform. Finally, we discuss how our solution can be used as a stepping stone to cater for the management and execution of knowledge-intensive business processes in a broader context. © 2019, Springer Nature Switzerland AG.
Citation - Scopus: 7
From Requirements to Data Analytics Process: An Ontology-Based Approach
(Springer International Publishing AG, 2019) Bandara, Madhushi; Behnaz, Ali; Rabhi, Fethi A.; Demirors, Onur
Comprehensively describing data analytics requirements is becoming an integral part of developing enterprise information systems. It is a challenging task for analysts to completely elicit all requirements shared by the organization's decision makers. With a multitude of data available from e-commerce sites, social media and data warehouses selecting the correct set of data and suitable techniques for an analysis itself is difficult and time-consuming. The reason is that analysts have to comprehend multiple dimensions such as existing analytics techniques, background knowledge in the domain of interest and the quality of available data. In this paper, we propose to use semantic models to represent different spheres of knowledge related to data analytics space and use them to assist in analytics requirements definition. By following this approach users can create a sound analytics requirements specification, linked with concepts from the operation domain, available data, analytics techniques and their implementations. Such requirements specifications can be used to drive the creation and management of analytics solutions, well aligned with organizational objectives. We demonstrate the capabilities of the proposed method by applying on a data analytics project for house price prediction.
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.

Computer Engineering / Bilgisayar Mühendisliği

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