China
Africa
Explore chapters and articles related to this topic
Understanding Data Sources
Published in Praveen Kumar, Jay Alameda, Peter Bajcsy, Mike Folk, Momcilo Markus, Hydroinformatics: Data Integrative Approaches in Computation, Analysis, and Modeling, 2005
Perhaps, to extract and analyze the data from multiple storage media and databases poses a much bigger challenge for researchers than to generate the data [10]. In order to extract and analyze information from distributed databases, distributed heterogeneous data must be gathered, characterized, and cleaned. These processing steps might be very time-consuming because they might require multiple scans of large distributed databases to ensure the data quality defined by application domain experts and computer scientists. From a semantic integration viewpoint, there are quite often challenges due to the heterogeneous and distributed nature of data. The preprocessing steps might require the data to be transformed(e.g., the Normalized Difference Vegetation Index [NDVI] computation), linked with distributed annotation or metadata files (e.g., geographic locations and attribute descriptions), or more exactly specified using auxiliary programs running on a remote server (e.g., making subsets and identifying a spatial match or any temporal changes).
Data Science with Semantic Technologies: Application to Information Systems Development
Published in Journal of Computer Information Systems, 2023
From architectural point of view, semantic computing can be seen as composed of:62,69Semantic analysis: where content, such as pixels and words, is analyzed with the goal of converting it to meanings (semantics).70Semantic Integration: where content and semantics from various sources are integrated within a unified model; it also encompasses languages and methodologies required for developing semantic applications.51Semantic Services: where content and semantics are used to solve problems by some applications that are made available to other applications as services.Service Integration: where different services are integrated in order to provide more efficient service.Semantic Interface: where user intentions may be represented in a natural form.71
Leveraging Open E-Logistic Standards to Achieve Ambidexterity in Supply Chain
Published in Journal of Computer Information Systems, 2020
Xiaodie Pu, Zhengxu Wang, Felix Tung Sun Chan
IOS have been increasingly used by firms as the digital enablers of cross-boundary collaboration.11,16,35 It is suggested that, to effectively manage interfirm relationships and leverage external resources, integration and flexibility must be developed as two key IOS capabilities.36 IOS integration is the capability of a firm’s information systems resources to “work as a functional whole in conjunction with”11 (p. 324) the IOS of other partners. High integration level is supported by tight IOS coupling,12 which requires supply chain partners to resolve inconsistences at both syntactic and semantic boundaries.16,37 Syntactic integration ensures that data are based on common language and consistent presentation format, which is the foundation for information access and exchange among supply chain partners.38 At a higher abstraction layer, semantic integration harmonizes the meaning of data across different sources and channels, which ensures that exchanged information can be interpreted by different partners in the same manner.39 By ensuring syntactic and semantic consistencies, IOS integration permits communication and interoperation among the IOS applications and platforms of different partners,38 which allows companies to transmit rich-content data, align business processes, and coordinate supply chain activities.12