An efficient approach for big data processing using spatial Boolean queries

The web is being used more and more by users of mobile devices. In addition, it is increasingly possible to track the user's location, which provides immense opportunities in geospatial data and its management. Due to the use of location information in services for each mobile device, a large size of spatial data makes it difficult to process spatial queries efficiently and, therefore, we need a lightweight and scalable approach to process large amounts of stored data in distributed file systems. For the most part, all SNSs (social network services) focus on connecting the user account with their location information, such as check-in services, which helps them collect information about user activities and ratings. Of location, but also increases the load of data on their servers. . In this article we propose an indexing technique in combination with efficient processing of Boolean top-k spatial queries where location data is compressed to save space and the Boolean query helps filter results so that unrelated data is not processed, what helps to save space and faster processing of queries.