Efficient type-ahead search on relational data

Li, Guoliang; Ji, Shengyue; Li, Chen; Feng, Jianhua

doi:10.1145/1559845.1559918

Cited by 71 publications

(55 citation statements)

References 27 publications

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“…Graph based approaches like EASE [21], [22], [27] and TASTIER [20] heavily utilize offline computation by indexing sub-graphs. These approaches are similar to generating and indexing forms offline, but operate at the data level by exploiting the notion of r-radius Steiner graphs [21].…”

Section: Hybrid Approach For Other Kws Systemsmentioning

confidence: 99%

Toward scalable keyword search over relational data

Baid

Rae

et al. 2010

Proc. VLDB Endow.

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Keyword search (KWS) over relational databases has recently received significant attention. Many solutions and many prototypes have been developed. This task requires addressing many issues, including robustness, accuracy, reliability, and privacy. An emerging issue, however, appears to be performance related: current KWS systems have unpredictable running times. In particular, for certain queries it takes too long to produce answers, and for others the system may even fail to return (e.g., after exhausting memory). In this paper we argue that as today's users have been "spoiled" by the performance of Internet search engines, KWS systems should return whatever answers they can produce quickly and then provide users with options for exploring any portion of the answer space not covered by these answers. Our basic idea is to produce answers that can be generated quickly as in today's KWS systems, then to show users query forms that characterize the unexplored portion of the answer space. Combining KWS systems with forms allows us to bypass the performance problems inherent to KWS without compromising query coverage. We provide a proof of concept for this proposed approach, and discuss the challenges encountered in building this hybrid system. Finally, we present experiments over real-world datasets to demonstrate the feasibility of the proposed solution.

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Section: Hybrid Approach For Other Kws Systemsmentioning

confidence: 99%

Toward scalable keyword search over relational data

Baid

Rae

et al. 2010

Proc. VLDB Endow.

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“…The studies in [13,6] investigated how to do error-tolerant interactive search. Li et al [15] studied type-ahead search on relational databases with multiple tables. These studies did not focus on how to answer location-based queries.…”

Section: Related Workmentioning

confidence: 99%

Location-Based Instant Search

2011

Lecture Notes in Computer Science

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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.

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“…As a less obvious scenario, consider autocompletion search in databases [22,24]. As the user types in a query, the system presents the k most highly scoring (i.e., the most popular) completions of the text entered so far, chosen from a lexicon of phrases.…”

Section: Introductionmentioning

confidence: 99%

Asymptotically Optimal Encodings of Range Data Structures for Selection and Top- k Queries

Grossi

Iacono

Navarro

et al. 2017

ACM Trans. Algorithms

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Given an array A[1, n] of elements with a total order, we consider the problem of building a data structure that solves two queries: (a) selection queries receive a range [i, j] and an integer k and return the position of the kth largest element in A[i, j]; (b) top-k queries receive [i, j] and k and return the positions of the k largest elements in A [i, j]. These problems can be solved in optimal time, O(1 + lg k/ lg lg n) and O(k), respectively, using linear-space data structures.We provide the first study of the encoding data structures for the above problems, where A cannot be accessed at query time. Several applications are interested in the relative order of the entries of A, and their positions, rather their actual values, and thus we do not need to keep A at query time. In those cases, encodings save storage space: we first show that any encoding answering such queries requires n lg k − O(n + k lg k) bits of space; then, we design encodings using O(n lg k) bits, that is, asymptotically optimal up to constant factors, while preserving optimal query time.

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Efficient type-ahead search on relational data

Cited by 71 publications

References 27 publications

Toward scalable keyword search over relational data

Toward scalable keyword search over relational data

Location-Based Instant Search

Asymptotically Optimal Encodings of Range Data Structures for Selection and Top- k Queries

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