Next Article in Journal
Land Use/Land Cover Change Modeling and the Prediction of Subsequent Changes in Ecosystem Service Values in a Coastal Area of China, the Su-Xi-Chang Region
Previous Article in Journal
Evaluation of the Agronomic Impacts on Yield-Scaled N2O Emission from Wheat and Maize Fields in China
Previous Article in Special Issue
Velocity Obstacle Based 3D Collision Avoidance Scheme for Low-Cost Micro UAVs
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Sustainability 2017, 9(7), 1203; doi:10.3390/su9071203

Evaluating Retrieval Effectiveness by Sustainable Rank List

1
Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Korea
2
Sorrell College of Business, Troy University, Troy, AL 36082, USA
3
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
View Full-Text   |   Download PDF [2236 KB, uploaded 8 July 2017]   |  

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 (MAPn). The experimental results show that the proposed scheme achieves scores of MAP@5 = 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
Figures

Figure 1

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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

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

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sustainability EISSN 2071-1050 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top