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Delay‐bounded skyline computing for large‐scale real‐time online data analytics
Author(s) -
Wang Qian,
Yu Chao,
Zhang Yiming,
Li Huiba,
Zhong Ping
Publication year - 2017
Publication title -
concurrency and computation: practice and experience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.309
H-Index - 67
eISSN - 1532-0634
pISSN - 1532-0626
DOI - 10.1002/cpe.4085
Subject(s) - skyline , computer science , cloud computing , analytics , big data , focus (optics) , data science , set (abstract data type) , the internet , scale (ratio) , data mining , world wide web , physics , quantum mechanics , optics , programming language , operating system
Summary The proliferation of Internet applications, cloud systems, and mobile social networks results in unprecedented data set scale and high data generation rate. For us to be able to extract any meaningful information, it is important to achieve real‐time online data analytics. Skyline queries are important in many online data applications such as real‐time Web mining, multipreference analysis, and decision making. Most existing studies mainly focus on centralized systems, and distributed skyline query processing is still an emerging and challenging topic. In this paper, we propose SkyStorm, a delay‐bounded parallel skyline computing approach for large‐scale real‐time data analytics by dividing the search into multiple rounds and limiting the search in each round within a budget‐restricted range. The effectiveness of our proposals is demonstrated through analysis and simulations.