Editorial

IntroductionThe theme of this special section, "Low-Consumption Computing Technologies for Big Data Analytics (LCCT-BigDataAna)", focuses on the critical challenge of balancing limited computing resources with the exponential growth of data.As big data analytics becomes increasingly essential, small and medium-sized enterprises, along with budgetconstrained research institutions struggle to afford high-performance computing solutions [1] .Consequently, the development of cost-effective and efficient methods for big data processing has become a pivotal research focus, attracting significant attention from both academia and industry.LCCT-BigDataAna covers hardware and software optimizations for improving computational efficiency, including data preprocessing, storage management, and novel learning and modeling technologies.However, raw big data often contains noise, missing values, and outliers, which complicates large-scale big data analytics [2] .Additionally, the widespread use of decentralized and cross-regional data storage [3] necessitates the development of advanced analytical methods capable of extracting reliable insights from incomplete yet statistically representative data subsets.These challenges drive substantial improvements over traditional big data computation approaches.Despite the increasing focus on big data analytics, there are still major limitations and challenges for lowconsumption computing technologies.Some key areas of research are training memory-free learning models under the Spark computing framework, managing computing tasks for cross-regional stored big data, leveraging advanced statistical methods to obtain multi-sample computation results, and formalizing the relationship between big data analytics and digital economic activities [4] .Consequently, there is a significant opportunity to explore the latest advancements in LCCT-BigDataAna [5] .To dissiminate the latest developments, current challenges, and highlight potential applications of low-consumption computing technologies for big data analytics, we are delighted and honored to present this special section in Tsinghua Science and Technology.