Location-Based Instant Search

Abstract

Abstract. Location-based keyword search has become an important part of our daily life. Such a query asks for records satisfying both a spatial condition and a keyword condition. State-of-the-art techniques extend a spatial tree structure by adding keyword information. In this paper we study location-based instant search, where a system searches based on a partial query a user has typed in. We first develop a new indexing technique, called filtering-effective hybrid index (FEH), that judiciously uses two types of keyword filters based on their selectiveness to do powerful pruning. Then, we develop indexing and search techniques that store prefix information on the FEH index and efficiently answer partial queries. Our experiments show a high efficiency and scalability of these techniques.

An Efficient Algorithm for Location-Aware Query Autocompletion

“…All of them are based on a combination of spatial and textual indexes to process queries. They can be categorized into text-first [1], space-first [2], [3], and tightly-combined [4] methods, according to the ways in which the indexes are combined. For text-first methods, string descriptions of data objects are indexed in a trie, where objects as well as their locations can be retrieved on leaf nodes of the trie.…”

“…Filtering-Effective Hybrid Indexing (FEH) is a spacefirst method proposed by Ji et al [2] to answer range queries and kNN queries. The method builds an R-tree to index data objects by their locations.…”

“…For MT, we use the same region setting as for Tregion, and materialize score upper bounds for all trie nodes. We do not compare with the FEH method [2] as it has been shown to be significantly outperformed by PR-Tree and INSPIRE [3], [4]. We select three publicly available datasets: • FSQ is a dataset collected from Foursquare, which contains 1M worldwide points of interest.…”

Location-aware instant search

“…It is rather challenging to support location-aware instant search due to the requirement of a high interactive speed. Existing studies [13,21] devise hybrid index structures to address this challenge. However, [13] has very limited filtering power for short or frequent query prefixes.…”

“…Existing studies [13,21] devise hybrid index structures to address this challenge. However, [13] has very limited filtering power for short or frequent query prefixes. [21] cannot support multi-keyword queries and it also consumes a large amount of memory.…”