Theoretical and practical data science and analytics: challenges and solutions

IF 3.4 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

International Journal of Data Science and Analytics Pub Date : 2023-10-01 DOI:10.1007/s41060-023-00465-x

Carson K. Leung, Gabriella Pasi, Li Wang

引用次数: 0

Abstract

Big data have become a core technology for providing innovative solutions in numerical applications and services in many fields. Embedded in these big data is valuable information and knowledge. This calls for data science and analytics, which has emerged as an important paradigm for driving the new economy and domains (e.g., Internet of Things, social and mobile networks, cloud computing), reforming classic disciplines (e.g., telecommunications, biology, health and social science), as well as upgrading core business and economic activity. In this article, we focus on both theoretical and practical data science and analytics. We summarize and highlight some of its challenges and solutions, which are covered in the eight articles in the current Special Issue on "theoretical and practical data science and analytics."

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理论与实践数据科学与分析:挑战与解决方案

大数据已成为为数值应用提供创新解决方案和服务的核心技术。这些大数据中蕴含着宝贵的信息和知识。这就需要数据科学和分析，它已经成为推动新经济和新领域(例如，物联网、社交和移动网络、云计算)、改革经典学科(例如，电信、生物学、卫生和社会科学)以及升级核心业务和经济活动的重要范例。在本文中，我们将重点介绍数据科学和分析的理论和实践。我们总结并强调了其中的一些挑战和解决方案，这些内容在本期“理论和实践数据科学与分析”特刊的八篇文章中有所介绍。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Data Science and Analytics Multiple-

CiteScore

6.40

自引率

8.30%

发文量

期刊介绍： 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