Knowledge-Driven Framework for Designing Visual Analytics Applications

• Main Authors: Bandara, Madhushi, Rabhi, Fethi A.
• Format: Conference Proceeding
• Language: English
• Subjects: analytics process; Data analysis; data processing; knowledge-driven; Machine learning; Machine learning algorithms; Organizations; Task analysis; Technological innovation; Visual analytics
• ISSN: 2375-0138
• DOI: 10.1109/IV51561.2020.00089

Machine learning and data analysis are becoming an essential part of the decision-making process in modern organizations. Even though new and improved analytics algorithms are developed frequently, organizations are struggling to develop analytics applications that can stay up-to-date with changing business requirements and technology innovations. The rapid development of ad-hoc programs to conduct machine learning tasks at hand has resulted in creating more expenses and efforts in the long term, a phenomenon referred to as technical debt in literature. This paper addresses the technical debt associated with data analytics applications by proposing a knowledge repository that captures analytics-related knowledge, which can be developed and maintained separately from the organization's IT infrastructure and used to design analytics applications with visual interfaces. This way, organizations can develop dynamic and adaptable analytics applications with easy-to-follow front-ends and can accommodate new data sources or machine learning models. We evaluate the proposed approach by conducting a case study that develops an application for the acquisition and management of high-frequency financial market data.