Purpose-Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the information needs. The purpose of this paper is to present a literature review on recent works in BI. The two principal aims in this survey are to identify areas lacking in recent research, thereby offering potential opportunities for investigation. Design/methodology/approach-To simplify the study on BI literature, it is segregated into seven categories according to the usage. Each category of work is analyzed using parameters such as purpose, domain, problem identified, solution applied, benefit and outcome. Findings-The BI contribution in various domains, ongoing research in BI, the convergence of BI domains, problems and solutions, results of congregated domains, core problems and key solutions. It also outlines BI and its components composition, widely applied BI solutions such as algorithm-based, architecture-based and model-based solutions. Finally, it discusses BI implementation issues and outlines the security and privacy policies adopted in BI environment. Research limitations/implications-In this survey BI has been discussed in theoretical perspective whereas practical contribution has been given less attention. Originality/value-A comprehensive survey on BI which identifies areas lacking in recent research and providing potential opportunities for investigation.
Purpose
– Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.
Design/methodology/approach
– To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).
Findings
– This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.
Research limitations/implications
– This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.
Originality/value
– A comprehensive bankruptcy prediction model using BI with customized reporting.
Uncontrolled spread of pandemic COVID-19 in India and across the globe over several months, created an impact as never before any pandemic would have created. This certainly demands a technological intervention from all possibility to overcome the situation and lead a normal life as early as possible. AI/Machine learning responds to the situation, through inspecting different aspects of the pandemic. This paper analyses and studies those aspects, (I) Quarantine and statistical aspect: Quarantine potentially affected candidates (person who is in touch, travel history) through Data analytics/Machine learning. (II) Diagnosis and Treatment aspect: Early detection and fast treatment will save lives. Diagnosis using deep learning assists radiologist from saving their effort and time to a greater extent and arrives faster conclusion. (III) Prevention aspect: Monitoring and enforce social distancing through visual social distancing using deep learning and Computer vision.
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