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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Aug 2018
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Avg review time: 82 days


Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  4/2014 - 16

A Popularity-Aware Semantic Overlay for Efficient Peer-to-Peer Search

LEE, C. See more information about LEE, C. on SCOPUS See more information about LEE, C. on IEEExplore See more information about LEE, C. on Web of Science, CHOI, J. See more information about  CHOI, J. on SCOPUS See more information about  CHOI, J. on SCOPUS See more information about CHOI, J. on Web of Science, KIM, E. See more information about KIM, E. on SCOPUS See more information about KIM, E. on SCOPUS See more information about KIM, E. on Web of Science
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (784 KB) | Citation | Downloads: 249 | Views: 1,584

Author keywords
computer networks, distributed computing, distributed information systems, keyword search, peer to peer computing

References keywords
peer(14), search(8), networks(6), systems(5), social(4), semantic(4), science(4), notes(4), lecture(4), information(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2014-11-30
Volume 14, Issue 4, Year 2014, On page(s): 105 - 108
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2014.04016
Web of Science Accession Number: 000348772500016
SCOPUS ID: 84921628988

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This paper presents a novel semantic overlay scheme that connects peers based on the similarity of their contents. Semantic closeness among overlay peers can effectively be determined via the exchanges of their content summary using Bloom filters. The overlay link quality is further improved by carefully selecting semantic neighbors according to their potential to contribute to content-based searches. The basic idea behind the semantic neighbor selection is that highly replicated documents should not excessively dominate the overlay topology, overshadowing rare to modestly-replicated items whose query efficiency is often more critical for overall search performance. The efficacy of the proposed semantic overlay is validated through our simulation study which demonstrates superior overlay link quality and query routing performance.

References | Cited By  «-- Click to see who has cited this paper

[1] J. Risson and T. Moors, "Survey of research towards robust peer-to-peer networks: search methods," Computer Networks, vol. 50, no. 17, pp. 3485-3521, Dec. 2006.
[CrossRef] [Web of Science Times Cited 122] [SCOPUS Times Cited 176]

[2] M. Bender et al., "Peer-to-peer information search: semantic, social, or spiritual?", IEEE Data Engineering Bulletin, vol. 30, no. 2, pp. 51-60, Jun. 2007.

[3] S. B. Handurukande, A. -M. Kermarrec, F. L. Fessant, and L. Massoulie, "Exploiting semantic clustering in the eDonkey P2P network," in Proc. 11th ACM SIGOPS European Workshop, Leuven, Belgium, 2004, pp. 1-6.

[4] S. Voulgaris, A. -M. Kermarrec, L. Massoulie, and M. van Steen, "Exploiting semantic proximity in peer-to-peer content searching," in Proc. 10th International Workshop Future Trends in Distributed Computing Systems,Suzhou, China, 2004, pp. 238-243.

[5] S. Voulgaris and M. van Steen, "Epidemic-style management of semantic overlays for content-based searching," Lecture Notes in Computer Science, vol. 3648, pp. 1143-1152, 2005.

[6] J. P. Callan, Z. Lu, and W. B. Croft, "Searching distributed collections with inference networks," in Proc. 18th Annu. International ACM SIGIR Conf. Research and Development in Information Retrieval, Seattle, WA, USA, 1995, pp.21-28.

[7] J. A. Pouwelse et al., "Tribler: a social-based peer-to-peer system," Concurrency and Computation: Practice & Experience, vol. 20, no. 2, pp. 127-138, Feb. 2008.
[CrossRef] [Web of Science Times Cited 117] [SCOPUS Times Cited 185]

[8] F. Saihan and V. Issarny, "Scalable service discovery for MANET," in Proc. 3rd International Conf. Pervasive Computing and Communications, Kauai Island, HI, USA, 2005, pp. 235-244.

[9] M. Bender, S. Michel, P. Triantafillou, G. Weikum, and C. Zimmer, "Improving collection selection with overlap awareness in P2P search engines," in Proc. 28th Annu. International ACM SIGIR Conf. Research and Development in Information Retrieval, Salvador, Brazil, 2005, pp. 67-74.

[10] G. Koloniari, Y. Petrakis, and E. Pitoura, "Content-based overlay networks for XML peers based on multi-level Bloom filters," Lecture Notes in Computer Science, vol. 2944, pp. 232-247, 2004.

[11] B. T. Loo, R. Huebsch, I. Stoica, and J. Hellerstein, "The case for a hybrid P2P search infrastructure," in Proc. 3rd International Workshop Peer-to-Peer Systems, San Diego, CA, USA, 2004, pp. 141-150.

[12] A. Broder and M. Mitzenmacher, "Network applications of Bloom filters: a survey," Internet Mathematics, vol. 1, no. 4, pp. 485-509, Oct. 2004.
[CrossRef] [SCOPUS Times Cited 963]

[13] B. H. Bloom, "Space/time trade-offs in hash coding with allowable errors," Communications of the ACM, vol. 13, no. 7, pp. 422-426, Jul. 1970.
[CrossRef] [SCOPUS Times Cited 3629]

[14] G. Sakaryan, M. Wulff, and H. Unger, "Search methods in P2P networks: a survey," Lecture Notes in Computer Science, vol. 3473, pp. 59-68, 2006.
[CrossRef] [SCOPUS Times Cited 2]

[15] Y. Aytas, H. Ferhatosmanoglu, and O. Ulusoy, "Link recommendation in P2P social networks," in Proc. 1st International Workshop Online Social Systems, Istanbul, Turkey, Aug. 2012.

[16] R. Zhang and Y. C. Hu, "Assisted peer-to-peer search with partial indexing," IEEE Trans. Parallel and Distributed Systems, vol. 18, no. 8, pp. 1146-1158, Aug. 2007.
[CrossRef] [Web of Science Times Cited 33] [SCOPUS Times Cited 50]

[17] F. Draidi, E. Pacitti, and B. Kemme, "P2Prec: a P2P recommendation system for large-scale data sharing," Lecture Notes in Computer Science, vol. 6790, pp. 87-116, 2011.
[CrossRef] [SCOPUS Times Cited 6]

[18] K. Sripanidkulchai, B. M. Maggs, and H. Zhang, "Efficient content location using interest-based locality in peer-to-peer systems," in Proc. IEEE INFOCOM, San Francisco, CA, USA, 2003, p. 2166-2176.

[19] Y. Zhang, G. Shen, and Y. Yu, "LiPS: efficient P2P search scheme with novel link prediction techniques," in Proc. IEEE International Conf. Communications, Glasgow, Scotland, 2007, pp. 1875-1880.

[20] H. Han, J. He, and C. Zuo, "A hybrid P2P overlay network for high efficient search," in Proc. 2nd IEEE International Conf. Information and Financial Engineering, Chongqing, China, 2010, pp. 241-245.

References Weight

Web of Science® Citations for all references: 272 TCR
SCOPUS® Citations for all references: 5,011 TCR

Web of Science® Average Citations per reference: 13 ACR
SCOPUS® Average Citations per reference: 239 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2018-10-13 09:28 in 66 seconds.

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