Analyzing the Query Performance over a Distributed Network of Data Aggregators

α
Dr. P. Prabhakar
Dr. P. Prabhakar
σ
S. Nageswara Rao
S. Nageswara Rao
α Jawaharlal Nehru Technological University Anantapur Jawaharlal Nehru Technological University Anantapur

Send Message

To: Author

Analyzing the Query Performance over a Distributed Network of Data Aggregators

Article Fingerprint

ReserarchID

CSTNWSA6256

Analyzing the Query Performance over a Distributed Network of Data Aggregators Banner

AI TAKEAWAY

Connecting with the Eternal Ground
  • English
  • Afrikaans
  • Albanian
  • Amharic
  • Arabic
  • Armenian
  • Azerbaijani
  • Basque
  • Belarusian
  • Bengali
  • Bosnian
  • Bulgarian
  • Catalan
  • Cebuano
  • Chichewa
  • Chinese (Simplified)
  • Chinese (Traditional)
  • Corsican
  • Croatian
  • Czech
  • Danish
  • Dutch
  • Esperanto
  • Estonian
  • Filipino
  • Finnish
  • French
  • Frisian
  • Galician
  • Georgian
  • German
  • Greek
  • Gujarati
  • Haitian Creole
  • Hausa
  • Hawaiian
  • Hebrew
  • Hindi
  • Hmong
  • Hungarian
  • Icelandic
  • Igbo
  • Indonesian
  • Irish
  • Italian
  • Japanese
  • Javanese
  • Kannada
  • Kazakh
  • Khmer
  • Korean
  • Kurdish (Kurmanji)
  • Kyrgyz
  • Lao
  • Latin
  • Latvian
  • Lithuanian
  • Luxembourgish
  • Macedonian
  • Malagasy
  • Malay
  • Malayalam
  • Maltese
  • Maori
  • Marathi
  • Mongolian
  • Myanmar (Burmese)
  • Nepali
  • Norwegian
  • Pashto
  • Persian
  • Polish
  • Portuguese
  • Punjabi
  • Romanian
  • Russian
  • Samoan
  • Scots Gaelic
  • Serbian
  • Sesotho
  • Shona
  • Sindhi
  • Sinhala
  • Slovak
  • Slovenian
  • Somali
  • Spanish
  • Sundanese
  • Swahili
  • Swedish
  • Tajik
  • Tamil
  • Telugu
  • Thai
  • Turkish
  • Ukrainian
  • Urdu
  • Uzbek
  • Vietnamese
  • Welsh
  • Xhosa
  • Yiddish
  • Yoruba
  • Zulu

Abstract

Typically a user desires to obtain the value of some aggregation function over distributed data items. We present a low-cost, scalable technique to answer continuous aggregation queries using a network of aggregators of dynamic data items. In such a network of data aggregators, each data aggregator serves a set of data items at specific coherencies. Our technique involves decomposing a client query into sub-queries and executing sub-queries on judiciously chosen data aggregators with their individual sub-query incoherency bounds. We provide a technique for getting the optimal set of sub-queries with their incoherency bounds, which satisfies client query’s coherency requirement with least number of refresh messages sent from aggregators to the client. For estimating the number of refresh messages, we build a query cost model which can be used to estimate the number of messages required to satisfy the client specified incoherency bound. Performance results using real-world traces show that our cost based query planning leads to queries being executed using less than one third the number of messages required by existing schemes.

References

16 Cites in Article
  1. A Davis,J Parikh,W Weihl (2004). Edgecomputing.
  2. D Vander Meer,A Datta,K Dutta,H Thomas,K Ramamritham (2004). Proxy-Based Acceleration of Dynamically Generated Content on the World Wide Web.
  3. J Dilley,B Maggs,J Parikh,H Prokop,R Sitaraman,B Weihl (2002). Globally Distributed Content Delivery.
  4. S Shah,K Ramamritham,P Shenoy Maintaining Coherency of Dynamic Data in Cooperating Repositories.
  5. Yanguo Peng (null). VeriRange: A Verifiable Range Query Model on Encrypted Geographic Data for loT Environment_supp1-3294589.pdf.
  6. C Olston,J Jiang,J Widom (2003). Adaptive Filter for Continuous Queries over Distributed Data Streams.
  7. D Hochbaum (1982). Approximation algorithms for the set covering and vertex cover problems.
  8. Zongming Fei (2001). A Novel Approach to Managing Consistency in Content Distribution.
  9. Rajeev Gupta,Ashish Puri,Krithi Ramamritham (2005). Executing incoherency bounded continuous queries at web data aggregators.
  10. Pearson Product moment correlation coefficient.
  11. S Agrawal,K Ramamritham,S Shah (null). Construction of a Coherency Preserving Dynamic Data Dissemination Network.
  12. D Redkha (2006). Big Data Cluster Processing Through Optimized Speculative Execution.
  13. Sampath Rangarajan,Sarit Mukherjee,Pablo Rodriguez (2003). User Specific Request Redirection in a Content Delivery Network.
  14. Rajeev Gupta,Krithi Ramamritham (2007). Optimized query planning of continuous aggregation queries in dynamic data dissemination networks.
  15. R Gupta,K Ramamritham (2011). Query Planning for Continuous Aggregation Queries over a Network of Data Aggregators.
  16. S Shah,K Ramamritham,C Ravishankar (2005). Client Assignment in Content Dissemination Networks for Dynamic Data.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Dr. P. Prabhakar. 2012. \u201cAnalyzing the Query Performance over a Distributed Network of Data Aggregators\u201d. Global Journal of Computer Science and Technology - E: Network, Web & Security GJCST-E Volume 12 (GJCST Volume 12 Issue E14): .

Download Citation

Issue Cover
GJCST Volume 12 Issue E14
Pg. 13- 20
Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

Version of record

v1.2

Issue date

October 6, 2012

Language
en
Experiance in AR

Explore published articles in an immersive Augmented Reality environment. Our platform converts research papers into interactive 3D books, allowing readers to view and interact with content using AR and VR compatible devices.

Read in 3D

Your published article is automatically converted into a realistic 3D book. Flip through pages and read research papers in a more engaging and interactive format.

Article Matrices
Total Views: 10398
Total Downloads: 2647
2026 Trends
Related Research

Published Article

Typically a user desires to obtain the value of some aggregation function over distributed data items. We present a low-cost, scalable technique to answer continuous aggregation queries using a network of aggregators of dynamic data items. In such a network of data aggregators, each data aggregator serves a set of data items at specific coherencies. Our technique involves decomposing a client query into sub-queries and executing sub-queries on judiciously chosen data aggregators with their individual sub-query incoherency bounds. We provide a technique for getting the optimal set of sub-queries with their incoherency bounds, which satisfies client query’s coherency requirement with least number of refresh messages sent from aggregators to the client. For estimating the number of refresh messages, we build a query cost model which can be used to estimate the number of messages required to satisfy the client specified incoherency bound. Performance results using real-world traces show that our cost based query planning leads to queries being executed using less than one third the number of messages required by existing schemes.

Our website is actively being updated, and changes may occur frequently. Please clear your browser cache if needed. For feedback or error reporting, please email [email protected]

Request Access

Please fill out the form below to request access to this research paper. Your request will be reviewed by the editorial or author team.
X

Quote and Order Details

Contact Person

Invoice Address

Notes or Comments

This is the heading

Lorem ipsum dolor sit amet, consectetur adipiscing elit. Ut elit tellus, luctus nec ullamcorper mattis, pulvinar dapibus leo.

High-quality academic research articles on global topics and journals.

Analyzing the Query Performance over a Distributed Network of Data Aggregators

Dr. P. Prabhakar
Dr. P. Prabhakar Jawaharlal Nehru Technological University Anantapur
S. Nageswara Rao
S. Nageswara Rao

Research Journals