Collaborative business intelligence virtual assistant
- URL: http://arxiv.org/abs/2312.12778v1
- Date: Wed, 20 Dec 2023 05:34:12 GMT
- Title: Collaborative business intelligence virtual assistant
- Authors: Olga Cherednichenko and Fahad Muhammad
- Abstract summary: This study focuses on the applications of data mining within distributed virtual teams through the interaction of users and a CBI Virtual Assistant.
The proposed virtual assistant for CBI endeavors to enhance data exploration accessibility for a wider range of users and streamline the time and effort required for data analysis.
- Score: 1.9953434933575993
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The present-day business landscape necessitates novel methodologies that
integrate intelligent technologies and tools capable of swiftly providing
precise and dependable information for decision-making purposes. Contemporary
society is characterized by vast amounts of accumulated data across various
domains, which hold considerable potential for informing and guiding
decision-making processes. However, these data are typically collected and
stored by disparate and unrelated software systems, stored in diverse formats,
and offer varying levels of accessibility and security. To address the
challenges associated with processing such large volumes of data, organizations
often rely on data analysts. Nonetheless, a significant hurdle in harnessing
the benefits of accumulated data lies in the lack of direct communication
between technical specialists, decision-makers, and business process analysts.
To overcome this issue, the application of collaborative business intelligence
(CBI) emerges as a viable solution. This research focuses on the applications
of data mining and aims to model CBI processes within distributed virtual teams
through the interaction of users and a CBI Virtual Assistant. The proposed
virtual assistant for CBI endeavors to enhance data exploration accessibility
for a wider range of users and streamline the time and effort required for data
analysis. The key contributions of this study encompass: 1) a reference model
representing collaborative BI, inspired by linguistic theory; 2) an approach
that enables the transformation of user queries into executable commands,
thereby facilitating their utilization within data exploration software; and 3)
the primary workflow of a conversational agent designed for data analytics.
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