International Journal of Data Science and Analytics（国际数据科学与分析杂志）影响因子_期刊投稿发表

期刊简介

Data Science has been established as an important emergent scientific field and paradigm driving research evolution in such disciplines as statistics, computing science and intelligence science, and practical transformation in such domains as science, engineering, the public sector, business, social science, and lifestyle. The field encompasses the larger areas of artificial intelligence, data analytics, machine learning, pattern recognition, natural language understanding, and big data manipulation. It also tackles related new scientific challenges, ranging from data capture, creation, storage, retrieval, sharing, analysis, optimization, and visualization, to integrative analysis across heterogeneous and interdependent complex resources for better decision-making, collaboration, and, ultimately, value creation.The International Journal of Data Science and Analytics (JDSA) brings together thought leaders, researchers, industry practitioners, and potential users of data science and analytics, to develop the field, discuss new trends and opportunities, exchange ideas and practices, and promote transdisciplinary and cross-domain collaborations. The journal is composed of three streams: Regular, to communicate original and reproducible theoretical and experimental findings on data science and analytics; Applications, to report the significant data science applications to real-life situations; and Trends, to report expert opinion and comprehensive surveys and reviews of relevant areas and topics in data science and analytics.Topics of relevance include all aspects of the trends, scientific foundations, techniques, and applications of data science and analytics, with a primary focus on:statistical and mathematical foundations for data science and analytics;understanding and analytics of complex data, human, domain, network, organizational, social, behavior, and system characteristics, complexities and intelligences;creation and extraction, processing, representation and modelling, learning and discovery, fusion and integration, presentation and visualization of complex data, behavior, knowledge and intelligence;data analytics, pattern recognition, knowledge discovery, machine learning, deep analytics and deep learning, and intelligent processing of various data (including transaction, text, image, video, graph and network), behaviors and systems;active, real-time, personalized, actionable and automated analytics, learning, computation, optimization, presentation and recommendation; big data architecture, infrastructure, computing, matching, indexing, query processing, mapping, search, retrieval, interoperability, exchange, and recommendation;in-memory, distributed, parallel, scalable and high-performance computing, analytics and optimization for big data;review, surveys, trends, prospects and opportunities of data science research, innovation and applications;data science applications, intelligent devices and services in scientific, business, governmental, cultural, behavioral, social and economic, health and medical, human, natural and artificial (including online/Web, cloud, IoT, mobile and social media) domains; andethics, quality, privacy, safety and security, trust, and risk of data science and analytics

数据科学已被确立为重要的新兴科学领域，范式推动了统计学，计算科学和情报科学等学科的研究发展，以及科学，工程，公共部门，商业，社会科学和生活方式。该领域涵盖了人工智能，数据分析，机器学习，模式识别，自然语言理解和大数据操作等更大领域。它还应对相关的新科学挑战，从数据捕获，创建，存储，检索，共享，分析，优化和可视化，到跨异构和相互依赖的复杂资源的集成分析，以实现更好的决策，协作，并最终，价值创造。《国际数据科学与分析杂志》 (JDSA) 汇集了思想领袖，研究人员，行业从业者以及数据科学与分析的潜在用户，以开发该领域，讨论新趋势和机遇，交流思想和实践，并促进跨学科和跨领域的合作。该期刊由三个流组成: 定期，传达有关数据科学和分析的原始和可复制的理论和实验发现; 应用程序，将重要的数据科学应用报告到现实生活中; 和趋势，报告专家意见以及数据科学和分析相关领域和主题的综合调查和审查。相关主题包括数据科学和分析的趋势、科学基础、技术和应用的所有方面，主要关注: 数据科学和分析的统计和数学基础；理解和分析复杂数据、人类、领域、网络、组织、社会、行为和系统特征、复杂性和智能; 创建和提取、处理、表示和建模、学习和发现、融合和集成、复杂数据、行为、知识和智能的呈现和可视化；数据分析、模式识别、知识发现、机器学习、深度分析和深度学习，以及各种数据 (包括交易、文本、图像、视频、图形和网络) 、行为和系统的智能处理; 主动、实时、个性化、可操作和自动化的分析、学习、计算、优化、展示和推荐; 大数据架构、基础架构、计算、匹配、索引、查询处理、映射、搜索、检索、互操作、交换和推荐; 内存、分布式、并行、可扩展和高性能大数据计算、分析和优化; 审查、调查、数据科学研究、创新和应用的趋势、前景和机遇; 科学、商业、政府、文化、行为、社会和经济、健康和医疗、人类、自然和人工 (包括在线/网络、云、物联网、移动和社交媒体) 领域; 数据科学和分析的质量，质量，隐私，安全与保障，信任以及风险

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