Many Web sites support keyword search on their spatial data, such as business listings and photos. In these systems, inconsistencies and errors can exist in both queries and the data. To bridge the gap between queries and data, it is important to support approximate keyword search on spatial data. In this paper we study how to answer such queries efficiently. Wefocusonanaturalindexstructurethataugments a tree-based spatial index with capabilities for approximate keyword search. We systematically study how to efficiently combine these two types of indexes, and how to search the resultingindextofindanswers. Wedevelopthree algorithms for constructing the index, successively improving the time and space efficiency by exploiting the textual and spatial properties of the data. We experimentally demonstrate the efficiency of our techniques on real, large datasets. Categories and Subject Descriptors H.2.8 [Database Management]: Database Applications—

Abstract. In recent years, many websites have started providing keywordsearch services on maps. In these systems, users may experience difficulties finding the entities they are looking for if they do not know their exact spelling, such as the name of a restaurant. In this paper, we present a solution to support fuzzy keyword search on spatial data. We combine a spatial index structure with inverted indexes on grams to efficiently answer fuzzy queries on maps. We show two system prototypes to demonstrate the practicality of our solution. 1

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. 1

For GIS data situated on a road network, shortest path search is a basic operation. In practice, however, users are often interested at routing when certain constraints on the textual information have been also incorporated. This work complements the standard shortest path search with multiple keywords and an approximate string similarity function, where the goal is to find the shortest path that passes through at least one matching object per keyword; we dub this problem the multi-approximate-keyword routing (makr) query. We present both exact and approximate solutions. When the number κ of query keywords is small (e.g., κ ≤ 6), the exact solution works efficiently. However, when κ increases, it becomes increasingly expensive (especially on large GIS data). In this case, our approximate methods achieve superb query efficiency, excellent scalability, and high approximation quality, as indicated in our extensive experiments on large, real datasets (up to 2 million points on road networks with hundreds of thousands of nodes and edges). We also prove that one approximate method has a κ-approximation in the worst case.