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Sustainability 2017, 9(7), 1203;

Evaluating Retrieval Effectiveness by Sustainable Rank List

Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea
Sorrell College of Business, Troy University, Troy, AL 36082, USA
Department of Software Convergence Engineering, Kunsan National University, Gunsan-si, Jeollabuk-do 54150, Korea
Authors to whom correspondence should be addressed.
Received: 29 April 2017 / Revised: 30 June 2017 / Accepted: 5 July 2017 / Published: 8 July 2017
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The Internet of Things (IoT) and Big Data are among the most popular emerging fields of computer science today. IoT devices are creating an enormous amount of data daily on a different scale; hence, search engines must meet the requirements of rapid ingestion and processing followed by accurate and fast extraction. Researchers and students from the field of computer science query the search engines on these topics to reveal a wealth of IoT-related information. In this study, we evaluate the relative performance of two search engines: Bing and Yandex. This work proposes an automatic scheme that populates a sustainable optimal rank list of search results with higher precision for IoT-related topics. The proposed scheme rewrites the seed query with the help of attribute terms extracted from the page corpus. Additionally, we use newness and geo-sensitivity-based boosting and dampening of web pages for the re-ranking process. To evaluate the proposed scheme, we use an evaluation matrix based on discounted cumulative gain (DCG), normalized DCG (nDCG), and mean average precision (MAPn). The experimental results show that the proposed scheme achieves scores of [email protected] = 0.60, DCG5 = 4.43, and nDCG5 = 0.95 for general queries; DCG5 = 4.14 and nDCG5 = 0.93 for time-stamp queries; and DCG5 = 4.15 and nDCG5 = 0.96 for geographical location-based queries. These outcomes validate the usefulness of the suggested system in helping a user to access IoT-related information. View Full-Text
Keywords: Internet of Things; information retrieval; pseudo relevance feedback; search engines; bipartite graph; random walk Internet of Things; information retrieval; pseudo relevance feedback; search engines; bipartite graph; random walk

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ali, T.; Jhandir, Z.; Lee, I.; On, B.-W.; Choi, G.S. Evaluating Retrieval Effectiveness by Sustainable Rank List. Sustainability 2017, 9, 1203.

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