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Evaluating Retrieval Effectiveness by Sustainable Rank List

Author

Listed:
  • Tenvir Ali

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea)

  • Zeeshan Jhandir

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea)

  • Ingyu Lee

    (Sorrell College of Business, Troy University, Troy, AL 36082, USA)

  • Byung-Won On

    (Department of Software Convergence Engineering, Kunsan National University, Gunsan-si, Jeollabuk-do 54150, Korea)

  • Gyu Sang Choi

    (Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea)

Abstract

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 (MAP n ). The experimental results show that the proposed scheme achieves scores of MAP@5 = 0.60, DCG 5 = 4.43, and nDCG 5 = 0.95 for general queries; DCG 5 = 4.14 and nDCG 5 = 0.93 for time-stamp queries; and DCG 5 = 4.15 and nDCG 5 = 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.

Suggested Citation

  • Tenvir Ali & Zeeshan Jhandir & Ingyu Lee & Byung-Won On & Gyu Sang Choi, 2017. "Evaluating Retrieval Effectiveness by Sustainable Rank List," Sustainability, MDPI, vol. 9(7), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:7:p:1203-:d:104062
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    References listed on IDEAS

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    1. Tefko Saracevic, 2007. "Relevance: A review of the literature and a framework for thinking on the notion in information science. Part III: Behavior and effects of relevance," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(13), pages 2126-2144, November.
    2. Dirk Lewandowski, 2015. "Evaluating the retrieval effectiveness of web search engines using a representative query sample," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(9), pages 1763-1775, September.
    3. Stephen P. Harter, 1996. "Variations in relevance assessments and the measurement of retrieval effectiveness," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 47(1), pages 37-49, January.
    4. Arif Mehmood & Gyu Sang Choi & Otto F. Feigenblatt & Han Woo Park, 2016. "Proving ground for social network analysis in the emerging research area “Internet of Things” (IoT)," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(1), pages 185-201, October.
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