A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (I...A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.展开更多
The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of ...The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.展开更多
A semantic analysis approach is proposed, by which semantic relationships between concepts are identified and defined, and then mapped or transformed to OWL （web ontology language） ontology. The most common abstract...A semantic analysis approach is proposed, by which semantic relationships between concepts are identified and defined, and then mapped or transformed to OWL （web ontology language） ontology. The most common abstractions （namely, inclusion, aggregation and association） and their implication in ontology are discussed; then the OWL implementation for three abstractions are analyzed and illustrated. Taxonomies, constraints on properties for each class, and the relations between taxonomies in OWL ontology are established after all the semantic relationships are identified and described. This research is the basis for the development of the ontology conceptual model （OCM） and the mapping from OCM to OWL ontology.展开更多
文摘A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.
基金supported by the National Science Foundation of China(No.51575264)the Jiangsu Province Science Foundation for Excellent Youths under Grant BK20121011the Fundamental Research Funds for the Central Universities(No.NS2015050)
文摘The process inference cannot be achieved effectively by the traditional expert system,while the ontology and semantic technology could provide better solution to the knowledge acquisition and intelligent inference of expert system.The application mode of ontology and semantic technology on the process parameters recommendation are mainly investigated.Firstly,the content about ontology,semantic web rule language(SWRL)rules and the relative inference engine are introduced.Then,the inference method about process based on ontology technology and the SWRL rule is proposed.The construction method of process ontology base and the writing criterion of SWRL rule are described later.Finally,the results of inference are obtained.The mode raised could offer the reference to the construction of process knowledge base as well as the expert system's reusable process rule library.
基金The Natural Science Foundation of Hubei Province(No.2004ABA040).
文摘A semantic analysis approach is proposed, by which semantic relationships between concepts are identified and defined, and then mapped or transformed to OWL （web ontology language） ontology. The most common abstractions （namely, inclusion, aggregation and association） and their implication in ontology are discussed; then the OWL implementation for three abstractions are analyzed and illustrated. Taxonomies, constraints on properties for each class, and the relations between taxonomies in OWL ontology are established after all the semantic relationships are identified and described. This research is the basis for the development of the ontology conceptual model （OCM） and the mapping from OCM to OWL ontology.
