Ontology-based data semantic management and application in IoT- and cloud-enabled smart homes

The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning

Enterprise application reuse: Semantic discovery of business grid services

Web services have emerged as a prominent paradigm for the development of distributed software systems as they provide the potential for software to be modularized in a way that functionality can be described, discovered and deployed in a platform independent manner over a network (e.g., intranets, extranets and the Internet). This paper examines an extension of this paradigm to encompass ‘Grid Services’, which enables software capabilities to be recast with an operational focus and support a heterogeneous mix of business software and data, termed a Business Grid - "the grid of semantic services". The current industrial representation of services is predominantly syntactic however, lacking the fundamental semantic underpinnings required to fulfill the goals of any semantically-oriented Grid. Consequently, the use of semantic technology in support of business software heterogeneity is investigated as a likely tool to support a diverse and distributed software inventory and user. Service discovery architecture is therefore developed that is (a) distributed in form, (2) supports distributed service knowledge and (3) automatically extends service knowledge (as greater descriptive precision is inferred from the operating application system). This discovery engine is used to execute several real-word scenarios in order to develop and test a framework for engineering such grid service knowledge. The examples presented comprise software components taken from a group of Investment Banking systems. Resulting from the research is a framework for engineering servic

THE ISSUE OF SEMANTIC MODELING OF THE LEARNING ORGANIZATIONAL MEMORY FOR E-LEARNING

The development of open and long-distance learning – within universities but also withingeographically distributed enterprises –has led to the development of researches focusing on modeling onsemantic bases the learning organizational memory of an e-learning type. This paper reviews the literaturein the field, focusing on defining a generic template of semantic modeling of the content of the learningorganizational memory of the e-learning type, by proposing a study case of semantic representation oflearning objects applied to the economic-financial analysis. The research is both theoretic and applied-deductive in character, starting from a general background regarding learning in general and reachingparticularity by providing an ontology specific to the economic-financial analysis.learning organizational memory, learning object, ontology, metadata, indexing, e-learning,modeling standards, economical and financial analysis.

OntoEng: A design method for ontology engineering in information systems

This paper addresses the design problem relating to ontology engineering in the discipline of information systems. Ontology engineering is a realm that covers issues related to ontology development and use throughout its life span. Nowadays, ontology as a new innovation promises to improve the design, semantic integration, and utilization of information systems. Ontologies are the backbone of knowledge-based systems. In addition, they establish sharable and reusable common understanding of specific domains amongst people, information systems, and software agents. Notwithstanding, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. On the basis of the gathered experience during the development of V4 Telecoms Business Model Ontology as well as the conducted integration of the related literature from the design science paradigm, this paper introduces OntoEng and its application as a novel systematic design method for ontology engineering

Applied Epistemology and Understanding in Information Studies

Introduction. Applied epistemology allows information studies to benefit from developments in philosophy. In information studies, epistemic concepts are rarely considered in detail. This paper offers a review of several epistemic concepts, focusing on understanding, as a call for further work in applied epistemology in information studies. Method. A hermeneutic literature review was conducted on epistemic concepts in information studies and philosophy. Relevant research was retrieved and reviewed iteratively as the research area was refined. Analysis. A conceptual analysis was conducted to determine the nature and relationships of the concepts surveyed, with an eye toward synthesizing conceptualizations of understanding and opening future research directions. Results. The epistemic aim of understanding is emerging as a key research frontier for information studies. Two modes of understanding (hermeneutic and epistemological) were brought into a common framework. Conclusions. Research on understanding in information studies will further naturalistic information research and provide coherence to several strands of philosophic thought

Computational challenges of systems biology

Progress in the study of biological systems such as the heart, brain, and liver will require computer scientists to work closely with life scientists and mathematicians. Computer science will play a key role in shaping the new discipline of systems biology and addressing the significant computational challenges it poses