Knowledge discovery computing for management

Manaligod, Hector John T.; Diño, Michael Joseph S.; Jo, Sun-Moon; Park, Roy C.

doi:10.1007/s10799-020-00315-3

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Actually, knowledge discovery aims to extract interesting and potentially useful information from massive collections of data (Wu et al , 2009) and ultimately derive understandable patterns in data (Marucci et al , 2020; Feyyad, 1996; Vercellis, 2009) that the human ability is usually not able to recognize (Kasemsap, 2018; Manaligod et al , 2020; Tao et al , 2020). Knowledge discovery embraces multiple domains and communities including computational learning theory, machine learning and data mining (Grobelnik and Mladenić, 2005).…”

Section: Theory Backgroundmentioning

confidence: 99%

“…Knowledge discovery embraces multiple domains and communities including computational learning theory, machine learning and data mining (Grobelnik and Mladenić, 2005). Knowledge discovery algorithms and systems have been researched and applied in various areas, including the public governance (Manaligod et al , 2020).…”

Section: Theory Backgroundmentioning

confidence: 99%

“…As for knowledge visualization, it enables people to create, assess, reference or transfer insights, experiences, opinions and predictions (Eppler and Burkhard, 2007). It uses various forms to communicate knowledge, which help domain experts to interpret data correctly and implement an effective decision-making (Manaligod et al , 2020; Addam et al , 2016).…”

Section: Theory Backgroundmentioning

confidence: 99%

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Leveraging knowledge discovery and knowledge visualization to define the “inner areas”: an application to an Italian province

Moretto

Elia

Ghiani

2022

JKM

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Purpose Starting from a critical analysis of the main criteria currently used to identify marginal areas, this paper aims to propose a new classification model of such territories by leveraging knowledge discovery approaches and knowledge visualization techniques, which represent a fundamental pillar in the knowledge-based urban development process. Design/methodology/approach The methodology adopted in this study relies on the design science research, which includes five steps: problem identification, objective definition, solution design and development, demonstration and evaluation. Findings Results demonstrate how to exploit knowledge discovery and visualization to obtain multiple mappings of inner areas, in the aim to identify good practices and optimize resources to set up more effective territorial development strategies and plans. The proposed approach overcomes the traditional way adopted to map inner areas that uses a single indicator (i.e. the distance between a municipality and the nearest pole where it is possible to access to education, health and transportation services) and leverages seven groups of indicators that represent the distinguishing features of territories (territorial capital, social costs, citizenship, geo-demography, economy, innovation and sustainable development). Research limitations/implications The proposed model could be enriched by new variables, whose value can be collected by official sources and stakeholders engaged to provide both structured and unstructured data. Also, another enhancement could be the development of a cross-algorithms comparison that may reveal useful to suggest which algorithm can better suit the needs of policy makers or practitioners. Practical implications This study sets the ground for proposing a decision support tool that policy makers can use to classify in a new way the inner areas, thus overcoming the current approach and leveraging the distinguishing features of territories. Originality/value This study shows how the availability of distributed knowledge sources, the modern knowledge management techniques and the emerging digital technologies can provide new opportunities for the governance of a city or territory, thus revitalizing the domain of knowledge-based urban development.

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