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Trie and LOUDS hybrid model for efficient e-commerce processing in cloud environment.

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Title: Trie and LOUDS hybrid model for efficient e-commerce processing in cloud environment.
Authors: Jia, Lianyin1,2 (AUTHOR) lianyinjia@kust.edu.cn, Li, Sisi1 (AUTHOR) 2389256767@qq.com, Zhang, Yuna1 (AUTHOR) 1464382963@qq.com, Chen, Yinong3 (AUTHOR) yinong@asu.edu, Yuan, Xiaohui4 (AUTHOR) xiaohui.yuan@unt.edu, Ding, Jiaman1 (AUTHOR) jiamanding@kust.edu.cn
Source: Simulation Modelling Practice & Theory. Jul2024, Vol. 134, pN.PAG-N.PAG. 1p.
Subjects: Electronic commerce, Data modeling
Abstract: Set superset query is widely used in e-commerce processing and many other domains, particularly in cloud computing environments. Indexing is an efficient way to model e-commerce data. Many existing indexes, however, primarily focus on enhancing either query performance or space efficiency, often neglecting the need to strike a balance between these two factors. We have observed that upper nodes closer to the root of a tree are frequently accessed, while lower nodes near the leaves tend to entail expensive storage costs. To address this issue, we introduce TLI model, a trie and level-ordered unary degree sequence (LOUDS) hybrid model. The upper part of TLI is a trie, which is optimized for superior query performance. The lower part of TLI uses the LOUDS structure. TLI strikes a good balance between query performance and space utilization. To seamlessly integrate these two parts, we have developed efficient connecting strategies. Our simulation results on localhost demonstrate that TLI outperforms its competitors in terms of both space and time efficiency. Remarkably, it enhances query performance by up to 1.89 times, with a modest 6.72% increase in space overhead compared to LOUDS-based alternatives. [ABSTRACT FROM AUTHOR]
Copyright of Simulation Modelling Practice & Theory is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
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Header DbId: egs
DbLabel: Engineering Source
An: 177515247
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Trie and LOUDS hybrid model for efficient e-commerce processing in cloud environment.
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  Data: <searchLink fieldCode="AR" term="%22Jia%2C+Lianyin%22">Jia, Lianyin</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> lianyinjia@kust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Li%2C+Sisi%22">Li, Sisi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 2389256767@qq.com</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yuna%22">Zhang, Yuna</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 1464382963@qq.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yinong%22">Chen, Yinong</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> yinong@asu.edu</i><br /><searchLink fieldCode="AR" term="%22Yuan%2C+Xiaohui%22">Yuan, Xiaohui</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> xiaohui.yuan@unt.edu</i><br /><searchLink fieldCode="AR" term="%22Ding%2C+Jiaman%22">Ding, Jiaman</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> jiamanding@kust.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Simulation+Modelling+Practice+%26+Theory%22">Simulation Modelling Practice & Theory</searchLink>. Jul2024, Vol. 134, pN.PAG-N.PAG. 1p.
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  Data: <searchLink fieldCode="DE" term="%22Electronic+commerce%22">Electronic commerce</searchLink><br /><searchLink fieldCode="DE" term="%22Data+modeling%22">Data modeling</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Set superset query is widely used in e-commerce processing and many other domains, particularly in cloud computing environments. Indexing is an efficient way to model e-commerce data. Many existing indexes, however, primarily focus on enhancing either query performance or space efficiency, often neglecting the need to strike a balance between these two factors. We have observed that upper nodes closer to the root of a tree are frequently accessed, while lower nodes near the leaves tend to entail expensive storage costs. To address this issue, we introduce TLI model, a trie and level-ordered unary degree sequence (LOUDS) hybrid model. The upper part of TLI is a trie, which is optimized for superior query performance. The lower part of TLI uses the LOUDS structure. TLI strikes a good balance between query performance and space utilization. To seamlessly integrate these two parts, we have developed efficient connecting strategies. Our simulation results on localhost demonstrate that TLI outperforms its competitors in terms of both space and time efficiency. Remarkably, it enhances query performance by up to 1.89 times, with a modest 6.72% increase in space overhead compared to LOUDS-based alternatives. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Simulation Modelling Practice & Theory is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.simpat.2024.102960
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Data modeling
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              M: 07
              Text: Jul2024
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              Y: 2024
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