A Methodology for Quality-Based Selection of Internet Data Sources in Maritime Domain

Stróżyna, Milena; Eiden, Gerd; Filipiak, Dominik; Małyszko, Jacek; Węcel, Krzysztof

doi:10.1007/978-3-319-39426-8_2

Cited by 2 publications

(3 citation statements)

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“…Mission and objectives of the institution provided an initial set of requirements for definition of quality criteria for data collection. As our implemented VAS, BEEx, provided the capacity to adapt models in run time, we could iteratively adapt and extend quality criteria, allowing us to focus on stakeholder requirements and to disregard application system-specific requirements as necessitated in other VAS [59].…”

Section: Data Collectionmentioning

confidence: 99%

“…Depending on the focus of the database, the information provided ranges from a sole company name or a superficial categorization to more detailed information about each entity such as information about the headquarter location, the current CEO or a general description of the field of activity. In general, these databases offer access to their data either open source-meaning the data is freely accessible and reusable without an additional authorization step, open source with enforced registration (without charge), or as proprietary data sources, which require registration, authorization, and charge fees [59].…”

Section: Identification Of Internet Data Sourcesmentioning

confidence: 99%

“…Starting from the publicly funded institution we had identified as main user and beneficiary of the ecosystem visualizations, we collected requirements from this stakeholder within two interviews. Additional quality measures were extracted from existing research [59][60][61][62]. For our initial data collection, the identified Internet sources were evaluated using the following criteria: (a) data access-we focused on openly available Internet data sources; (b) platform focus-the data source should at least contain data that is relevant for a mobility business ecosystem within the scope of the smart city project; (c) geographic focus-the content of the source should contain data that is relevant for the local mobility ecosystem; (d) data scope-defining what kind of data is covered regarding (d1) entities-with attributes such as name, legal type, headquarter, CEO, description, and (d2) relations-with attributes such as type of relation, involved partners, date (in case of a funding); (e) data extraction-how easy can the relevant data be extracted from the source, and (f) data validity-can the source be trusted.…”

Section: Internet Data Source Assessment and Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Modeling and Visualizing Smart City Mobility Business Ecosystems: Insights from a Case Study

et al. 2018

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Smart mobility is a central issue in the recent discourse about urban development policy towards smart cities. The design of innovative and sustainable mobility infrastructures as well as public policies require cooperation and innovations between various stakeholders—businesses as well as policy makers—of the business ecosystems that emerge around smart city initiatives. This poses a challenge for deploying instruments and approaches for the proactive management of such business ecosystems. In this article, we report on findings from a smart city initiative we have used as a case study to inform the development, implementation, and prototypical deployment of a visual analytic system (VAS). As results of our design science research we present an agile framework to collaboratively collect, aggregate and map data about the ecosystem. The VAS and the agile framework are intended to inform and stimulate knowledge flows between ecosystem stakeholders in order to reflect on viable business and policy strategies. Agile processes and roles to collaboratively manage and adapt business ecosystem models and visualizations are defined. We further introduce basic categories for identifying, assessing and selecting Internet data sources that provide the data for ecosystem models and we detail the ecosystem data and view models developed in our case study. Our model represents a first explication of categories for visualizing business ecosystem models in a smart city mobility context.

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Section: Data Collectionmentioning

confidence: 99%

Section: Identification Of Internet Data Sourcesmentioning

confidence: 99%