Advanced Conjunctive Boolean Streaming Spatial Keyword Processing

• Published in: 2022 23rd IEEE International Conference on Mobile Data Management (MDM) pp. 41 - 43
• Main Author: Patil, Mayur
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.06.2022
• Subjects: advanced boolean spatial keyword; geo textual streams; Indexing; negative keywords; Query processing; Search problems; Social networking (online)
• ISSN: 2375-0324
• DOI: 10.1109/MDM55031.2022.00027

Due to the meteoric rise in the use of location-aware systems (for ex. social media) that generate streaming spatio-textual data in a fast-paced and highly dynamic nature, faster query processing is paramount for applications that demand performance. In this paper, we present methods to efficiently process multi-keyword spatial queries with advanced conjunctive boolean constraints which include a negative keyword list on streaming data. Modern applications require quick system responses for complex queries with spatial and multiple keyword filters. Processing such large amounts of geo-tagged stream data with various keyword filters is time-consuming and inefficient with current methods. To solve this problem we build techniques for indexing, updating, and query processing that proactively prunes the search space called the PNLR-Tree based on an R-Tree. Specifically, given positive and negative keyword lists along with spatial constraints we develop techniques for enabling advanced boolean constraints like 'But-Not', 'XOR', and others on queries such as range query, top-k, kNN, and their extensions over a sliding window. Our experiments on real datasets using various real and synthetic query workloads verify that our method has increased performance over alternate techniques.