A Statistical Learning Ontology for Managing Analytics Knowledge

Behnaz, Ali; Bandara, Madhushi; Rabhi, Fethi A.; Peat, Maurice

doi:10.1007/978-3-030-19037-8_12

Cited by 3 publications

(1 citation statement)

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“…At the core of the Data Layer is the Statistical Learning Ontology (SLO) [5] we designed to capture knowledge related to the data analytics space such as variables, prediction models and their relationships. The main components of the SLO are shown in Fig.…”

Section: -Data Layermentioning

confidence: 99%

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

Bandara

Behnaz

Rabhi

et al. 2019

Business Process Management Workshops

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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.

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Section: -Data Layermentioning

confidence: 99%