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

Bandara, Madhushi; Behnaz, Ali; Rabhi, Fethi A.; Demirörs, Onur

doi:10.1007/978-3-030-11641-5_43

Cited by 10 publications

(10 citation statements)

References 11 publications

(13 reference statements)

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“…This can be achieved within established software engineering frameworks, but they need to be specifically tai-lored to the particular characteristics of the DAS engineering life-cycle as presented in [6]. As the next stage of this research effort, authors are working on designing a requirement driven platform that provides support for end-to-end analytics process engineering, incorporating semantic concept types identified through this mapping study [51,56].…”

Section: Resultsmentioning

confidence: 99%

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Semantic modeling for engineering data analytics solutions

Bandara

Rabhi

2020

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Data Analytics Solution (DAS) engineering often involves multiple tasks from data exploration to result presentation which are applied in various contexts and on different datasets. Semantic modeling based on the open world assumption supports flexible modeling of linked knowledge. The objective of this paper is to review existing techniques that leverage semantic web technologies to tackle challenges such as heterogeneity and changing requirements in DAS engineering. We explore the application scope of those techniques, the different types of semantic concepts they use and the role these concepts play during DAS development process. To gather evidence for the study we performed a systematic mapping study by identifying and reviewing 82 papers that incorporate semantic models in engineering DASs. One of the paper's findings is that existing models can be classified within four types of knowledge spheres: domain knowledge, analytics knowledge, services and user intentions. Another finding is to show how this knowledge is used in literature to enhance different tasks within the analytics process. We conclude our study by discussing the limitations of the existing body of research, showcasing the potential of semantic modeling to enhance DASs and presenting the possibility of leveraging ontologies for effective end-to-end DAS engineering.

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Section: Resultsmentioning

confidence: 99%

“…List of Included Studies system for ontology-based data access, Semantic Web 2(1), 2011, pp. [43][44][45][46][47][48][49][50][51][52][53]S. Rudolph,P.…”

Section: Resultsmentioning

confidence: 99%

Semantic modeling for engineering data analytics solutions

Bandara

Rabhi

2020

Self Cite

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“…The data analytic ontology explained in Refs. [23,24] describes the data itself and the analysis methods, considering that each data has a set of parameters and criteria that determines the types, tasks, techniques for its further analysis.…”

Section: Oda Paradigmmentioning

confidence: 99%

Ontological model for the acoustic management in a smart environment

Santiago

Aguilar

2022

ACI

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Purpose The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management.Design/methodology/approach This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies.Findings This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC).Research limitations/implications Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype.Practical implications In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles.Originality/value The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.

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“…And that this information and related activities are a means of understanding the management of cognitive processes. The visual cognitive management through means that enhance (visual analysis, evaluation, correction) in models intended to improve knowledge management [14] and each of Loom [13] TUORIS [15] Shneiderman [16] focus on the possibilities of visual designations for interactive exploration to understand The large and huge data set through user interfaces contains high flexibility to accommodate user requirements [17]. Zhou et al [18] indicated the inclusion of information technologies to make strong and good knowledge management decisions without compromising quality.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of Secured Knowledge Management system using Information Technology Model

Al-Hashemy¹

2022

IJERAT

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Information Technology (IT) has contributed to the development of many areas, including the development of companies and in accordance with the requirements of the free market, so it has become necessary to contribute to the development of the knowledge management system because of its importance in the use and dissemination of knowledge through the IT model that focuses on all the requirements of the knowledge management system. And the factors affecting its development, this model is characterized by meeting the needs of the system and upgradable, as well as being comprehensive and integrated (with other systems). The model is designed in several programming languages such as (C#, PHP and MySQL) and according to business requirements in addition to its ease of use so that everyone can use it (employees, clients, partners), and that the model has the ability to identify obstacles and errors that cause non-development, In terms of communication, it uses all technical resources (interviews, video conferences, chat rooms, etc.) to contribute to enhancing teamwork and communication, enhancing expertise and processing data and relying on human expertise to promote innovation and knowledge acquisition to achieve the real objective of this study which is to develop a knowledge management system.

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

Cited by 10 publications

References 11 publications

Semantic modeling for engineering data analytics solutions

Semantic modeling for engineering data analytics solutions

Ontological model for the acoustic management in a smart environment

Development of Secured Knowledge Management system using Information Technology Model

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