Fifth special issue on knowledge discovery and business intelligence

Cortez, Paulo; Santos, Manuel Filipe

doi:10.1111/exsy.12628

Cited by 4 publications

(3 citation statements)

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“…The study emphasizes the strategic usage of AI in creating business value, addressing the challenges in practical implementation and the need for expertise in AI applications. Cortez and Bifet (2020) discuss the shift in AI systems from being expert-driven to more datadriven, focusing on the development of computerized systems that use AI techniques to solve specific real-world domain application tasks. Their work fosters the interaction between Knowledge Discovery (KD) and Business Intelligence (BI), highlighting the role of AI in extracting useful knowledge from raw data.…”

Section: Synthesis Of Literature On Ai-driven Business Intelligencementioning

confidence: 99%

“…This section delves into practical applications and real-world examples of AI-driven BI, drawing insights from recent scholarly works. AI in Knowledge Discovery and Business Intelligence: Cortez and Bifet (2020) highlight the significant role of AI in enhancing knowledge discovery and BI. Their work emphasizes the evolution of BI from traditional data analysis to sophisticated AI-driven processes.…”

Section: Practical Applications and Real-world Examples In Ai-driven ...mentioning

confidence: 99%

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Business Intelligence Transformation Through Ai and Data Analytics

Emmanuel Osamuyimen Eboigbe,

Oluwatoyin Ajoke Farayola,

Funmilola Olatundun Olatoye

et al. 2023

Eng. sci. technol. j.

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This paper delves into the transformative role of Artificial Intelligence (AI) and Data Analytics in the realm of Business Intelligence (BI), marking a significant shift in the landscape of business decision-making and strategic planning. The study's purpose was to comprehensively explore the evolution of BI, underscored by the integration of AI and advanced data analytics, and to project the future trajectory of these technologies within the business context. Adopting a systematic literature review as its methodology, the study meticulously analyzed a wide array of scholarly articles and industry reports. This approach facilitated a deep understanding of the historical development of BI, the current synergy between AI, Data Analytics, and BI, and the emerging trends shaping their future. The inclusion and exclusion criteria for sources were rigorously applied to ensure the relevance and quality of the information gathered. The findings of the study highlighted a paradigm shift from traditional data processing methods to AI-driven predictive analytics, significantly enhancing the efficiency, accuracy, and predictive capabilities of BI tools. This evolution has redefined business operations, offering unprecedented insights and fostering more informed decision-making processes. Conclusively, the study posits that the integration of AI and Data Analytics into BI is a fundamental, rather than a transient, shift in business operations. It recommends further exploration into the ethical implications of AI in BI, the development of user-friendly AI tools for non-technical users, and an examination of the long-term impacts of AI-driven BI across various industries. The study's classical and engaging tone aims to captivate and inform a diverse audience, from academic researchers to industry practitioners. Keywords: Artificial Intelligence, Business Intelligence, Data Analytics, Predictive Analytics.

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Section: Synthesis Of Literature On Ai-driven Business Intelligencementioning

confidence: 99%

Section: Practical Applications and Real-world Examples In Ai-driven ...mentioning

confidence: 99%

Business Intelligence Transformation Through Ai and Data Analytics

Emmanuel Osamuyimen Eboigbe,

Oluwatoyin Ajoke Farayola,

Funmilola Olatundun Olatoye

et al. 2023

Eng. sci. technol. j.

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“…In 2011, a special KDBI issue was published in Wiley's Expert Systems journal (EXSY), including extended versions of the best track papers (Cortez & Santos, 2013). Since then, all KDBI tracks have produced an EXSY special issue (e.g., (Cortez & Bifet, 2020)). This ‘Seventh special issue on Knowledge Discovery and Business Intelligence’ includes extended versions of selected papers presented at the eighth KDBI thematic track of EPIA 2022, held on Lisbon, Portugal.…”

Section: Introductionmentioning

confidence: 99%

Editorial: Seventh special issue on Knowledge Discovery and Business Intelligence

Cortez,

Bifet

2023

Expert Systems

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Currently, there is a growing interest in the field of artificial intelligence (AI) (Russell & Norvig, 2022). Indeed, there are several successful AI applications that are impacting in diverse real-world domains, such as Industry 4.0 (Silva et al., 2021), recommendation systems (e.g., Amazon, Netflix) (Deepjyoti & Mala, 2022) and virtual assistants (e.g., Siri, Alexa, ChatGPT) (Grudin, 2023). AI encompasses several approaches to provide machine intelligence, including: expert systems (ES) and decision support systems (DSS) (Arnott & Pervan, 2014); machine learning (ML) and deep learning (DL) (Alpaydin, 2021); and metaheuristics or modern optimization (Cortez, 2021). ES were a popular AI tool in the 1970s and 1980s, assuming an explicit knowledge representation (e.g., via symbolic rules) that was extracted from decision-makers and then processed by inference systems in order to support real-world tasks (Cortez et al., 2018). After the 1990s, following the growth of big data and computational power, there was a shift towards data-driven AI (Darwiche, 2018). This shift gave the rise to several data processing terms that often overlap and that aim to extract value from raw data, such as: Knowledge Discovery (KD) and Data Mining (DM) (Fayyad et al., 1996); Business Intelligence (BI) and Business Analytics (BA) (Arnott & Pervan, 2014); and Big Data and Data Science (Provost & Fawcett, 2013). The first Knowledge Discovery and Business Intelligence (KDBI) track of the EPIA conference on Artificial Intelligence was held in 2009, in Aveiro, Portugal. The aim of the track was to promote a research interaction between the KD and BI areas. Following the first edition success, the KDBI track become regularly associated with the EPIA conference, resulting in nine editions from 2009 to 2023. In 2011, a special KDBI issue was published in Wiley's Expert Systems journal (EXSY), including extended versions of the best track papers (Cortez & Santos, 2013). Since then, all KDBI tracks have produced an EXSY special issue (e.g., (Cortez & Bifet, 2020)). This 'Seventh special issue on Knowledge Discovery and Business Intelligence' includes extended versions of selected papers presented at the eighth KDBI thematic track of EPIA 2022, held on Lisbon,Portugal. The track received 11 paper submissions, and 9 of these papers were presented at the KDBI track of EPIA 2022. This EXSY special issue includes four extended papers that were accepted after two rounds of reviews.The special issue accepted papers that describe novel KDBI methods, systems and challenging real-world applications. For instance, the repair process of digital devices requires time and costs. A critical repairing issue is the proper selection of spare parts. Furthermore, analysing coastal upwelling patterns is essential for studying ocean dynamics and climate models. It is also highly relevant to support the management of coastal resources. Moreover, BI systems often require the development of an extract-transform-load (ETL) system, which can benefit from assuming a service-...

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