雜志介紹

Journal Of Data And Information Science雜志介紹

《Journal Of Data And Information Science》是一本以English為主的開(kāi)放獲取國(guó)際優(yōu)秀期刊，中文名稱數(shù)據(jù)與信息科學(xué)雜志，本刊主要出版、報(bào)道管理學(xué)-INFORMATION SCIENCE & LIBRARY SCIENCE領(lǐng)域的研究動(dòng)態(tài)以及在該領(lǐng)域取得的各方面的經(jīng)驗(yàn)和科研成果，介紹該領(lǐng)域有關(guān)本專業(yè)的最新進(jìn)展，探討行業(yè)發(fā)展的思路和方法，以促進(jìn)學(xué)術(shù)信息交流，提高行業(yè)發(fā)展。該刊已被國(guó)際權(quán)威數(shù)據(jù)庫(kù)SCIE收錄，為該領(lǐng)域相關(guān)學(xué)科的發(fā)展起到了良好的推動(dòng)作用，也得到了本專業(yè)人員的廣泛認(rèn)可。該刊最新影響因子為1.5，最新CiteScore 指數(shù)為3.5。

英文介紹

Journal Of Data And Information Science雜志英文介紹

JDIS devotes itself to the study and application of the theories, methods, techniques, services, infrastructural facilities using big data to support knowledge discovery for decision & policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. The special effort is on the knowledge discovery to detect and predict structures, trends, behaviors, relations, evolutions and disruptions in research, innovation, business, politics, security, media and communications, and social development, where the big data may include metadata or full content data, text or non-textural data, structured or non-structural data, domain specific or cross-domain data, and dynamic or interactive data.

The main areas of interest are:

(1) New theories, methods, and techniques of big data based data mining, knowledge discovery, and informatics, including but not limited to scientometrics, communication analysis, social network analysis, tech & industry analysis, competitive intelligence, knowledge mapping, evidence based policy analysis, and predictive analysis.

(2) New methods, architectures, and facilities to develop or improve knowledge infrastructure capable to support knowledge organization and sophisticated analytics, including but not limited to ontology construction, knowledge organization, semantic linked data, knowledge integration and fusion, semantic retrieval, domain specific knowledge infrastructure, and semantic sciences.

(3) New mechanisms, methods, and tools to embed knowledge analytics and knowledge discovery into actual operation, service, or managerial processes, including but not limited to knowledge assisted scientific discovery, data mining driven intelligent workflows in learning, communications, and management.

Specific topic areas may include:

Knowledge organization

Knowledge discovery and data mining

Knowledge integration and fusion

Semantic Web metrics

Scientometrics

Analytic and diagnostic informetrics

Competitive intelligence

Predictive analysis

Social network analysis and metrics

Semantic and interactively analytic retrieval

Evidence-based policy analysis

Intelligent knowledge production

Knowledge-driven workflow management and decision-making

Knowledge-driven collaboration and its management

Domain knowledge infrastructure with knowledge fusion and analytics

Development of data and information services