[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124356-en":3,"doc-seo-124356-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124356,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Efficient Skyline Query Processing in Incomplete Graph Databases Using Machine Learning Techniques","Skyline queries support multi-criteria decision-making by returning non-dominated points, but efficient processing becomes difficult on incomplete, large-scale graph databases where missing values and high dimensionality increase computational load. Traditional skyline methods often do not scale or handle data imperfections reliably. This study designs and implements an optimization framework using machine learning techniques—domination score ranking, dimension-based filtering, K-Means clustering, and quicksort—to reduce the search space and redundant comparisons. Experiments on real graph datasets show improved skyline time and accuracy via fewer pairwise comparisons and better scalability for large graphs.","# Efficient Skyline Query Processing in Incomplete GraphDatabases Using Machine Learning Techniques\n\nUbair Noor,Raini Binti Hassan,Dini Oktarina Dwi Handayani  \nDepartment of Computer Science,International Islamic University Malaysia,Kuala Lumpur,53100,Malaysia  \n*Corresponding author:hrai@iium.edu.my  \n(Received:12ᵗth June 2025;Accepted:2ndJuly,2025;Published on-line:30ᵗJuly,2025)  \nAbstract—Skyline queries play a critical role in multi-criteria decision-making systems by retrieving non-dominated data points from large datasets.In recent years,therapid growth of graph-structured data acrossvarious domains has introduced challenges in efficiently processing skyline queries over incomplete andlarge-scale graph databases.Processing skyline queries in such massive,incomplete graphs iscomputationally intensive due to missing values and high-dimensional data.Traditional techniques often failto scale or effectively handle data imperfections.There is a pressing need for a scalable,intelligentframework that can manage missing data,reduce computational overhead,and improve skyline queryefficiency.This study adopts the Design Science Research Methodology(DSRM)to design and implementan optimisation framework that integrates machine learning techniques,including domination scoreranking,dimension-based filtering,K-Means clustering andquicksort.These methods collectively reduce thesearch space and redundant comparisons.Experimental evaluation on real graph datasets demonstratessignificant improvements in skyline computation time and accuracy,with clear reductions in pairwisecomparisons and improved processing efficiency on large-scale graphs.By leveraging machine learningtechniques for sorting,filtering and clustering,the approach reduces computational complexity andenhances scalability.These results show promising directions for applying intelligent query optimization inbig data environments.  \nKeywords—Skyline queries,Incomplete graph database,Machine learning,Graph database  \nwith affordable prices.If some hotels are ratings or priceinformation,they still might be valuable candidatesdepending on the available data.Traditional skylinealgorithms often leave out these incomplete entries,whichpotentially eliminates useful information from the results.  \n## I.  INTRODUCTION\n\nThese Skyline queries are used in database systems toretrieve non-dominated tuples data points that are notdominated by any other nodes [1].In graph databases,thismeans identifying nodes that are optimal based onattributes such as distance,cost or relevance,makingskyline queries particularly useful in applications likerecommendation systems,e-commerce,road networks andurban planning.  \nProcessing skyline queries efficiently over incompletegraph databases thus requires innovative techniques whichcan reduce the computational cost,handle missing valueswithout compromising the accuracy of results and adapt tohigh-dimensional and constantly changing data.This studyaims to tackle these challenges by proposing a methodwhich integrates machine learning techniques particularlyclustering to enhance skyline query performance.Machinelearning can help infer patterns from incomplete data,cluster similar nodes to narrow the search space anddynamically adapt to query updates,thus making skylineprocessing more accurate and scalable.  \nA big challenge happens when graph databases containincomplete data [2][3][4].These missing values fail thetransitivity of dominance relationships,which isfoundational to skyline computations.This can lead to cycliccomparisons and ambiguous dominance,significantlyincrease the complexity of processing queries.Despite thewidespread use of skyline queries in practice,limitedresearch has addressed how to efficiently compute skylineswhen dealing with incompleteness in graph-based datasets.  \nTo address the limitations of existing approaches,thefollowing objectives and contributions of the study areproposed:  \nGraphs in real-world applications are often dynamic an","cbCaibBUuthYodp7","https://ap.wps.com/l/cbCaibBUuthYodp7","pdf",6767076,1,16,"English","en",105,"# Abstract\n# I. Introduction","[{\"question\":\"What challenges arise when processing skyline queries over incomplete graph databases?\",\"answer\":\"Missing values break dominance transitivity and can cause cyclic or ambiguous dominance relationships, increasing query processing complexity and computation cost.\"},{\"question\":\"What machine learning techniques does the study use to improve skyline query processing?\",\"answer\":\"The framework integrates domination score ranking, dimension-based filtering, K-Means clustering, and quicksort to shrink the search space and reduce redundant comparisons.\"},{\"question\":\"How does the proposed approach improve performance on large-scale graphs?\",\"answer\":\"Experimental results on real datasets show significant reductions in skyline computation time and improved accuracy, mainly by lowering pairwise comparisons and enhancing scalability.\"}]","Efficient Skyline Query Processing in Incomplete Graph Databases Using Machine Learning Techniques | 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challenges arise when processing skyline queries over incomplete graph databases?","Question",{"text":75,"@type":76},"Missing values break dominance transitivity and can cause cyclic or ambiguous dominance relationships, increasing query processing complexity and computation cost.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning techniques does the study use to improve skyline query processing?",{"text":80,"@type":76},"The framework integrates domination score ranking, dimension-based filtering, K-Means clustering, and quicksort to shrink the search space and reduce redundant comparisons.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the proposed approach improve performance on large-scale graphs?",{"text":84,"@type":76},"Experimental results on real datasets show significant reductions in skyline computation time and improved accuracy, mainly by lowering pairwise comparisons and enhancing 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