A Framework for Context-Aware Query Processing

Abstract

Using natural language processing (NLP) and artificial intelligence techniques to handle database queries can make query results more useful and accurate, especially in context-aware applications, where their query results depend on the user and the surrounding environment settings. The existing research work [1] can be extended by adding context-aware services to the Location Based Services (LBSS) server of each cell. A new framework has been proposed for handling context-aware queries based on analysis of a user's query text, and a General Query Format (GQF) has been suggested to exchange queries between different database systems (Relational, NoSQL). A prototype has been implemented by using Python 3.6.10 (Anaconda package) and NLTK 3.5 library to analyze the dependency between text words of a user's query and convert the query text into various formats. The main contributions of this paper are summarized as follows: Selecting a cell location-based service server to provide context-aware services to users in its cell. Suggesting to unify query formats among database systems. Using natural language techniques to analyze the user's query text written in the user's natural language to determine which database will be accessed, and convert it into a standard query format that can be executed by database systems, and a technique for handling the context-aware quires.