Special Issue: Big Scientific Data and Machine Learning in Science and Engineering.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Big Data Pub Date : 2024-08-01 Epub Date: 2024-07-31 DOI:10.1089/big.2024.59218.kpa
Farhad Pourkamali-Anaraki
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特刊:科学与工程中的大科学数据和机器学习。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
期刊最新文献
DMHANT: DropMessage Hypergraph Attention Network for Information Propagation Prediction. Maximizing Influence in Social Networks Using Combined Local Features and Deep Learning-Based Node Embedding. A Weighted GraphSAGE-Based Context-Aware Approach for Big Data Access Control. Attribute-Based Adaptive Homomorphic Encryption for Big Data Security. Hybrid Deep Learning Approach for Traffic Speed Prediction.
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