Arseneau, Yoann S. M.2023-03-012023-03-012019https://unbscholar.lib.unb.ca/handle/1882/14295The proliferation of location-enabled devices and the increasing use of social media platforms is producing a deluge of multi-dimensional data. Novel index structures are needed to efficiently process massive amounts of geotagged data, and to promptly answer queries with textual, spatial, and temporal components. Existing approaches to spatio-textual data processing either use separate spatial and textual indices, or a combined index that integrates an inverted index with a tree data structure, such as an R-tree or Quadtree. These approaches, however, do not integrate temporal, spatial, and textual data together. We propose a novel integrated index called Spatio-temporal Textual Interleaved Trie (STILT), which unifies spatial, textual, and temporal components within a single structure. STILT is a multi-dimensional binary-trie-based index that interleaves text, location, and time data in a space-efficient manner. It supports dynamic and parallel indexing as well as concurrent searching. With extensive evaluation we demonstrate that STILT is significantly faster than the state-of-the-art approaches in terms of index construction time and query latency.text/xmlxii, 55 pageselectronicen-CAhttp://purl.org/coar/access_right/c_abf2master thesis2023-03-01Nickerson, BradfordRay, SuprioComputer Science