{"title":"Task-Aware Data Selectivity in Pervasive Edge Computing Environments","authors":"Athanasios Koukosias;Christos Anagnostopoulos;Kostas Kolomvatsos","doi":"10.1109/TKDE.2024.3485531","DOIUrl":null,"url":null,"abstract":"Context-aware data selectivity in Edge Computing (EC) requires nodes to efficiently manage the data collected from Internet of Things (IoT) devices, e.g., sensors, for supporting real-time and data-driven pervasive analytics. Data selectivity at the network edge copes with the challenge of deciding which data should be kept at the edge for future analytics tasks under limited computational and storage resources. Our challenge is to efficiently learn the access patterns of data-driven tasks (analytics) and predict which data are \n<italic>relevant</i>\n, thus, being stored in nodes’ local datasets. Task patterns directly indicate which data need to be accessed and processed to support end-users’ applications. We introduce a task workload-aware mechanism which adopts one-class classification to learn and predict the relevant data requested by past tasks. The inherent uncertainty in learning task patterns, identifying inliers and eliminating outliers is handled by introducing a lightweight fuzzy inference estimator that dynamically adapts nodes’ local data filters ensuring accurate data relevance prediction. We analytically describe our mechanism and comprehensively evaluate and compare against baselines and approaches found in the literature showcasing its applicability in pervasive EC.","PeriodicalId":13496,"journal":{"name":"IEEE Transactions on Knowledge and Data Engineering","volume":"37 1","pages":"513-525"},"PeriodicalIF":8.9000,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Knowledge and Data Engineering","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10730754/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Context-aware data selectivity in Edge Computing (EC) requires nodes to efficiently manage the data collected from Internet of Things (IoT) devices, e.g., sensors, for supporting real-time and data-driven pervasive analytics. Data selectivity at the network edge copes with the challenge of deciding which data should be kept at the edge for future analytics tasks under limited computational and storage resources. Our challenge is to efficiently learn the access patterns of data-driven tasks (analytics) and predict which data are
relevant
, thus, being stored in nodes’ local datasets. Task patterns directly indicate which data need to be accessed and processed to support end-users’ applications. We introduce a task workload-aware mechanism which adopts one-class classification to learn and predict the relevant data requested by past tasks. The inherent uncertainty in learning task patterns, identifying inliers and eliminating outliers is handled by introducing a lightweight fuzzy inference estimator that dynamically adapts nodes’ local data filters ensuring accurate data relevance prediction. We analytically describe our mechanism and comprehensively evaluate and compare against baselines and approaches found in the literature showcasing its applicability in pervasive EC.
期刊介绍:
The IEEE Transactions on Knowledge and Data Engineering encompasses knowledge and data engineering aspects within computer science, artificial intelligence, electrical engineering, computer engineering, and related fields. It provides an interdisciplinary platform for disseminating new developments in knowledge and data engineering and explores the practicality of these concepts in both hardware and software. Specific areas covered include knowledge-based and expert systems, AI techniques for knowledge and data management, tools, and methodologies, distributed processing, real-time systems, architectures, data management practices, database design, query languages, security, fault tolerance, statistical databases, algorithms, performance evaluation, and applications.