Title: Process-driven data analytics supported by a data warehouse model

Authors: Jorge Oliveira e Sá; Maribel Yasmina Santos

Addresses: ALGORITMI Research Centre, University of Minho, Portugal ' ALGORITMI Research Centre, University of Minho, Portugal

Abstract: Business process management and business intelligence initiatives are commonly seen as separated organisational projects, suffering from lack of coordination, leading to a poor alignment between strategic management and operational business processes execution. Information systems researchers and professionals have recognised that business processes are the key for identifying the user needs for developing the software that supports those requirements. This paper presents a process based approach for identifying an analytical data model using as input a set of interrelated business processes, modelled with business process model and notation (BPMN), and the corresponding persistent operational data model. This process-based approach extends the BPMN language allowing the integration of behavioural aspects and processes performance measures in the persistent operational data model. The proposed approach ensures the identification of an analytical data model for a data warehouse, integrating dimensions, facts, relationships and measures, providing useful data analytics perspectives of the data under analysis.

Keywords: analytical data model; business intelligence; business process model and notation; BPMN; operational data model; process performance indicators; PPIs.

DOI: 10.1504/IJBIDM.2017.086986

International Journal of Business Intelligence and Data Mining, 2017 Vol.12 No.4, pp.383 - 405

Received: 30 Jan 2017
Accepted: 03 Feb 2017
Published online: 03 Oct 2017 *

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