Semantic Model Based Approach for Knowledge Intensive Processes

Bandara, Madhushi; Rabhi, Fethi A.; Meymandpour, Rouzbeh

doi:10.1007/978-3-030-00623-5_15

Cited by 5 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Next step of our research is to design an effective interface that enables users to communicate with requirements templates, ontologies and instance repository. We are experimenting with a web-based application that drives dialog-based communication with the user to finalize the requirements definition [4]. Further, we are looking at tools that capture and catalog business models through ontologies and how they can be extended to support our system design.…”

Section: Discussionmentioning

confidence: 99%

From Requirements to Data Analytics Process: An Ontology-Based Approach

Bandara

Behnaz

Rabhi

et al. 2019

Business Process Management Workshops

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

From Requirements to Data Analytics Process: An Ontology-Based Approach

Bandara

Behnaz

Rabhi

et al. 2019

Business Process Management Workshops

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because of that, organisations can easily connect the Knowledge Base of the DASE architecture to the Jalapeno tool and conduct Platform Modelling. The Digital Interaction (DI) Framework within the Jalapeno tool is specially designed to support rapid design and development of software applications that reflect knowledgeintensive and service-oriented processes in an organisation [5]. This framework is a dynamic composition of concrete services, a set of interactions and underlying information concepts which can be easily converted into executionlevel workflow code.…”

Section: Use Of Capsifi Jalapeno As the Platform Design Interface And...mentioning

confidence: 99%

“…This implementation was conducted manually for the two case studies using the R Shiny framework. Previous work [5] suggests that the Digital Interaction Framework in Jalapeno tool has sufficient information captured about each artifact of the data analytics platform to be automatically converted into an executable platform in a model-driven fashion. Yet further evaluation is necessary to validate the feasibility and workload associated with such an automatic model-driven implementation.…”

Section: Limitationsmentioning

confidence: 99%

A knowledge-driven approach for designing data analytics platforms

2022

Self Cite

View full text Add to dashboard Cite

Big data analytics technologies are rapidly expanding across all industry sectors as organisations try to make analytics an integral part of their everyday decision-making. Although there are many software tools and libraries to assist analysts and software engineers in developing solutions, organisations are looking for flexible analytics platforms that can address their specific objectives and requirements. To minimise costs, such platforms also need to co-exist with existing IT infrastructures and reuse knowledge and resources already accumulated within the organisation. To address such needs, this paper proposes the Data Analytics Solution Engineering (DASE) framework—a knowledge-driven approach supported by semantic web technologies for requirements engineering, design and development of new data analytics platforms. It includes a meta-model that captures data analytics platform requirements via a Knowledge Base, a set of guidelines that organisations can follow in engineering data analytics platforms and a reference architecture that demonstrates how to use these guidelines. We evaluate the DASE framework through two case studies and demonstrate how it can facilitate knowledge-based and requirements-driven data analytics platform engineering. The resulting data analytics platforms are observed to be user friendly, easy to maintain and flexible in handling changes to requirements. This work contributes to the body of knowledge in knowledge-driven requirements engineering, and data analytics platform engineering by providing a meta-model and a reference architecture that can be tailored to different analytics application domains.

show abstract

“…Detailed evaluation of the capabilities and limitations of the Digital Interactions framework can be found in our work published in Bandara et. al [6]…”

mentioning

confidence: 99%

A Digital Interaction Framework for Managing Knowledge Intensive Business Processes

Bandara

Rabhi

Meymandpour³

et al. 2019

Service Research and Innovation

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Semantic Model Based Approach for Knowledge Intensive Processes

Cited by 5 publications

References 13 publications

From Requirements to Data Analytics Process: An Ontology-Based Approach

From Requirements to Data Analytics Process: An Ontology-Based Approach

A knowledge-driven approach for designing data analytics platforms

A Digital Interaction Framework for Managing Knowledge Intensive Business Processes

Contact Info

Product

Resources

About