Semantic Standards Quality Measured for Achieving Enterprise Interoperability: The Case of the SETU Standard for Flexible Staffing

Folmer, Erwin; Wu, Hao

doi:10.1002/9781118846995.ch23

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Interoperability also has other benefits in large systems that receive data from diverse sources, including data integration and processing. Moreover, data standardization, referred to as semantic interoperability, can help bring data into a commonly accepted format and definition that allows for data sharing, data integration, and collaborative research [124]. Bezuidenhout [125] investigated what infrastructures and resources for data standardization are needed to make data more accessible, interoperable, and reusable.…”

Section: B Data Collectionmentioning

confidence: 99%

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

et al. 2023

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Over the decades, Artificial Intelligence (AI) and machine learning has become a transformative solution in many sectors, services, and technology platforms in a wide range of applications, such as in smart healthcare, financial, political, and surveillance systems. In such applications, a large amount of data is generated about diverse aspects of our life. Although utilizing AI in real-world applications provides numerous opportunities for societies and industries, it raises concerns regarding data privacy. Data used in an AI system are cleaned, integrated, and processed throughout the AI life cycle. Each of these stages can introduce unique threats to individual's privacy and have an impact on ethical processing and protection of data. In this paper, we examine privacy risks in different phases of the AI life cycle and review the existing privacy-enhancing solutions. We introduce four different categories of privacy risk, including (i) risk of identification, (ii) risk of making an inaccurate decision, (iii) risk of non-transparency in AI systems, and (iv) risk of non-compliance with privacy regulations and best practices. We then examined the potential privacy risks in each AI life cycle phase, evaluated concerns, and reviewed privacy-enhancing technologies, requirements, and process solutions to countermeasure these risks. We also reviewed some of the existing privacy protection policies and the need for compliance with available privacy regulations in AI-based systems. The main contribution of this survey is examining privacy challenges and solutions, including technology, process, and privacy legislation in the entire AI life cycle. In each phase of the AI life cycle, open challenges have been identified.

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Section: B Data Collectionmentioning

confidence: 99%

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

et al. 2023

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“…Limited interoperability in practice issue: Recent work shows that even a highly successful standard with acclaimed positive benefits does not necessary lead to interoperability on technical/syntax level. This might be caused by a conceptual mismatch: Business people do not want plug and play e-business, 80% interoperability might be enough [15].…”

Section: Interorganizational Interoperabilitymentioning

confidence: 99%

Linked Data for Transaction Based Enterprise Interoperability

Folmer

Krukkert²

2015

Lecture Notes in Business Information Processing

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Interoperability is of major importance in B2B environments. Starting with EDI in the '80s, currently interoperability relies heavily on XMLbased standards. Although having great impact, still issues remain to be solved for improving B2B interoperability. These issues include lack of dynamics, cost of implementations, adoption and cross-industry exchange. Linked Data (part of the Semantic Web) technology, although originally not intended for the B2B domain, holds the promise of overcoming some of these issues. This paper explores the potential of linked data technology within a B2B context by introducing and studying six scenarios for combining from light to heavy weight 'traditional' standards with Linked Data technology. This research shows that using Linked Data technology has most potential for specifying semantics formally. This provides the 'best of both worlds' solution, in which legacy systems remain unaltered, and developers are supported in (semi) automated generation of transformation schema's to overcome different standards.

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“…Another often used framework for interoperability is ISO 11354, that defines the interoperability concerns: Business, Process, Service and Data (Dogac, Pattenden, & Zelm, 2010). However there are many other interoperability frameworks (Folmer & Verhoosel, 2011), including ones for specific industries. Also to add some complexity other terms like interconnectable, interworkable and interchangeable, are used sometimes as synonyms but more often with slightly different but not consistently used definitions (Kosanke, 2006;Stegwee & Rukanova, 2003).…”

Section: State Of the Artmentioning

confidence: 99%

Bomos

Folmer

2012

Handbook of Research on E-Business Standards and Protocols

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E-Business standards, or standards for interoperability, are developed outside the traditional standard development organizations, often within industry specific domain organizations. These organizations need some guidance in how to develop and manage standards for their specific domain in order to achieve long lasting standards that actually achieve interoperability between organizations. The Dutch government, together with the standards community, decided to publish a tool called BOMOS for giving guidance to the management and development of open standards. BOMOS is not profoundly grounded on scientific evidence, but it builds on the best practices already used in domain standardization. This chapter will present two highlights of BOMOS: the activity model for management of standardization, and a development approach for standards.

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Semantic Standards Quality Measured for Achieving Enterprise Interoperability: The Case of the SETU Standard for Flexible Staffing

Cited by 3 publications

References 13 publications

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

A Survey of Privacy Risks and Mitigation Strategies in the Artificial Intelligence Life Cycle

Linked Data for Transaction Based Enterprise Interoperability

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