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

数据与信息科学杂志致力于研究和应用大数据理论、方法、技术、服务、基础设施，为决策和政策制定提供知识发现支持。基本重点是基于大数据、以分析为中心、以知识发现为驱动、以决策为支撑。特别致力于知识发现，以检测和预测研究、创新、商业、政治、安全、媒体和通信以及社会发展中的结构、趋势、行为、关系、演变和中断，其中大数据可能包括元数据或完整内容数据、文本或非文本数据、结构化或非结构化数据、领域特定或跨领域数据以及动态或交互式数据。

主要感兴趣的领域是：

（1）基于大数据的数据挖掘、知识发现和信息学的新理论、方法和技术，包括但不限于科学计量学、通信分析、社交网络分析、技术和行业分析、竞争情报、知识图谱、基于证据的政策分析和预测分析。

（2）开发或改进能够支持知识组织和复杂分析的知识基础设施的新方法、架构和设施，包括但不限于本体构建、知识组织、语义链接数据、知识集成和融合、语义检索、领域特定知识基础设施和语义科学。

(3) 将知识分析和知识发现嵌入实际运营、服务或管理过程的新机制、方法和工具，包括但不限于知识辅助的科学发现、数据挖掘驱动的学习、通信和管理中的智能工作流。

具体主题领域可能包括：

知识组织

知识发现和数据挖掘

知识集成与融合

语义网度量

科学计量学

分析和诊断信息计量学

竞争情报

预测分析

社交网络分析和度量

语义和交互式分析检索

基于证据的政策分析

智能知识生产

知识驱动的工作流管理和决策

知识驱动的协作及其管理

具有知识融合和分析的领域知识基础设施

数据和信息服务的开发

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