Aggregated Boolean Query Processing for Document Retrieval in Edge Computing

Abstract

Search engines use significant hardware and energy resources to process billions of user queries per day, where Boolean query processing for document retrieval is an essential ingredient. Considering the huge number of users and large scale of the network, traditional query processing mechanisms may not be applicable since they mostly depend on a centralized retrieval method. To remedy this issue, this paper proposes a processing technique for aggregated Boolean queries in the context of edge computing, where each sub-region of the network corresponds to an edge network regulated by an edge server, and the Boolean queries are evaluated in a distributed fashion on the edge servers. This decentralized query processing technique has demonstrated its efficiency and applicability for the document retrieval problem. Experimental results on two real-world datasets show that this technique achieves high query performance and outperforms the traditional centralized methods by 2–3 times.

Managing and Retrieving Bilingual Documents Using Artificial Intelligence-Based Ontological Framework

“…For instance, the metadata of a document can be extracted using this approach [22][23][24][25][26][27]. A text extractor can be built using the AI technique for the automated extraction of key terms from a document [28][29][30][31][32][33][34]. An ontology-based dynamic information extraction framework identifies a wide range of document resources published in the scientific community and extracts the whole structural information [35][36][37][38][39][40][41].…”

“…On the one hand, there is an availability of a wide range of IR systems. On the other hand, there is a lack of domainspeci c ontologies or IR systems to serve an organization [26][27][28][29][30]. In the Kingdom of Saudi Arabia (KSA), most organizations o er a sophisticated application for employees and stakeholders to share the information and valuable documents.…”