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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-...
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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