Evaluating retrieval effectiveness by sustainable rank list

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.