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Toward Data Integration in the Era of Big Data
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
Houda EL Bouhissi, Archana Patel, Narayan C. Debnath
Let us analyze the following scenario: The Ministry of High education in Algeria manages more than 34 universities in the country and deploys big data architectures and some structures that operate with traditional databases. We suppose that the Ministry wants to integrate the activities of these universities toward operating centrally to offer better services to the students (Master application, etc.). Data integration involves the collection, storage, structuring, and combining of data to operate as a unified view. Data integration plays an important role in making it easier to exchange information and communicate across the enterprise, whether it is integrating core systems or integrating processes, administrative tasks, and databases. Data integration requires appropriate software tools that automatically gather and analyze real-time information from various online data sources. This is the place where researchers use ontologies because they represent knowledge in such a way that is understandable by both humans and machines. Ontologies describe the semantics of data and are widely used for semantic interoperability and integration.
Interoperability
Published in Vivek Kale, Digital Transformation of Enterprise Architecture, 2019
However, it is not always the case that collaborating systems have a common manner of codifying, understanding, and using the data that is exchanged. The difference can also be viewed in terms of three layers: Syntactic interoperability: When collaborating systems have a compatible way of structuring data during exchange; i.e., the manner in which the data is codified using a grammar or vocabulary, is mutually compatible.Semantic interoperability: When the collaborating systems understand the meaning of the syntactic elements; i.e., they share the same meaning of the data in relation to the entity or phenomena it represents in the real world. Semantic interoperability can only be achieved if systems are also syntactically interoperable.Pragmatic interoperability: When the collaborating systems understand the meaning of the message to cause the same effect intended by that system; i.e., the intended effect of the message is interpreted and acted similarly by the collaborating systems. Pragmatic interoperability can only be achieved if systems are also syntactically and semantically interoperable.
BIM solutions for construction lifecycle: A myth or a tangible future?
Published in Jan Karlshøj, Raimar Scherer, eWork and eBusiness in Architecture, Engineering and Construction, 2018
E. Papadonikolaki, M. Leon, A.M. Mahamadu
Interoperability is divided into organisational, semantic, syntactic and technical, according to the European Telecommunication Standards Institute (ETSI), while there is a strict hierarchy, which means that in order to achieve the organisational one, all the others have to be in place (Veer and Wiles, 2008). Technical interoperability “covers the technical issues of linking computer systems and services. It includes key aspects such as open interfaces, interconnection services, data integration and middleware, data presentation and exchange, accessibility and security services” (Kubicek et al., 2011). Syntactic interoperability is focused on data formats, and it supports the use of well-defined syntax and messages encoding. Semantic interoperability concerns the precision of the exchanged information for it to be understood in a meaningful manner by other applications that do not share the same developers. Finally, according to Kubicek et al. (2011) organisational interoperability focuses on the common descriptions of inter-organisational processes and can be achieved through common enterprise architectures and securing technical, syntactic and semantic interoperability.
Smart Condition Monitoring for Industry 4.0 Manufacturing Processes: An Ontology-Based Approach
Published in Cybernetics and Systems, 2019
Qiushi Cao, Franco Giustozzi, Cecilia Zanni-Merk, François de Bertrand de Beuvron, Christoph Reich
To develop such an ICMS, semantic interoperability among different system components and system users is a critical issue. Since the data collected by the ICMSs come from heterogeneous data sources, the “meaning” of these data varies according to different contexts and domains, thus making it difficult to be harmonized. To deal with this challenge, shared, rigorous and machine understandable vocabularies with robust structures are needed. In this context, semantic technologies, especially ontologies, appear as good candidates to cope with the semantic interoperability problem. An ontology is a formal representation of certain domain knowledge, which computationally captures and structures domain concepts and relationships (Usman et al. 2013). The use of ontologies can ensure the consistency of semantics, thus providing a shared understanding of knowledge among different participants within a domain.
Interoperability in cloud manufacturing: a case study on private cloud structure for SMEs
Published in International Journal of Computer Integrated Manufacturing, 2018
Xi Vincent Wang, Lihui Wang, Reinhold Gördes
As illustrated in Figure 1, Sheth (1999) identified three generations of interoperability in semantic level, syntax level and system level from the data’s perspective. Semantic interoperability focuses on the domain-specific semantic, which can be achieved based on the comprehensive use of metadata, i.e. semantics- and ontology-based approaches. Syntax interoperability emphasises on structured data types and formats, schematic, query languages and interfaces. It is essential to understand a variety of metadata and schematic heterogeneity. System interoperability concentrates on communications within and between computer systems. Limited aspects of syntax and structure are considered at this level. In Sheth’s research, it was also predicted that the information would be accessed in media-independent methods in multi-media views, which was proven correct and already realised and developed in CM research. Similarly, Bishr (1998) provided a more detailed classification of interoperability, i.e. semantics, data, database management system, file, hardware, protocol and system interoperability. In the data domain, the syntax is divided into data model, database and data file issues. Meanwhile, the system interoperability is also considered from a more detailed component perspective, which leads to hardware, protocol and system interoperability at three levels.
Enterprise modelling for interoperable and knowledge-based enterprises
Published in International Journal of Production Research, 2018
Georg Weichhart, Christian Stary, François Vernadat
With respect to semantic interoperability, the state of the art is to use ontologies. For instance, the Ontology of Enterprise Interoperability (OoEI) proposed by Naudet et al. (2010) supports enterprise modelling systems from an interoperability point of view. OoEI supports describing systemic relationships based on systems theory. The OoEI’s systemic core was extended with concepts from complex adaptive systems theory (OoEICAS) (Weichhart, Guédria, and Naudet 2016). While this approach builds on an ontology meta-model, it expresses and formalises the concepts using a Domain Specific Language (DSL) implemented in the functional programming language SCALA. This allows to model systems using agents in terms of agent interactions and agent behaviour, enabling emergent behaviour. This way the system dynamics may be described as agent behaviour as well as the network of agent interactions (Weichhart, Guédria, and Naudet 2016). The OoEICAS facilitates the integration of interoperability with knowledge management methods and approaches through this dynamic view and aspects (Weichhart and Stary 2015).