Efficient query processing in geographic web search engines

Chen, Yen‐Yu; Suel, Torsten; Alexander, Mathew

doi:10.1145/1142473.1142505

Cited by 228 publications

(196 citation statements)

References 32 publications

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“…Satellite imagery in particular represents another situation, in which redundant information is also considered, as very close pixels present very little differences although weighting in the processing, storage and visualisation time of the data. Therefore compression algorithms and proficient clustering tools are needed in order to extract more precise and complete set of geographic information (Bedard, 2014;Chen et al, 2006;Worboys and Duckham, 2004).…”

Section: Spatial Big Datamentioning

confidence: 99%

A methodology for dealing with spatial big data

Schoier

Borruso

2017

IJBIDM

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Spatial data mining (SDM) refers to the mining of knowledge from spatial data. Recently, location-based services have enabled the gathering of a significant amount of geo-referenced data, i.e., of spatial big data (SBD). Spatial datasets often exceed the ability of current computing systems to manage these data with reasonable effort; therefore, data-intensive computing and data mining techniques are useful tools for conducting an analysis. In this paper, we present an approach to the clustering of high-dimensional data that allows a flexible approach to the statistical modelling of phenomena characterised by unobserved heterogeneity. Numerous clustering algorithms have been developed for large databases; density-based algorithms particularly treat a huge amount of data in large spatial databases. We present the Modified Density-Based Spatial Clustering of Applications with Noise (MDBSCAN) algorithm and compare it to the classical k-means approach. Both applications use synthetic datasets and a dataset of satellite images.

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Section: Spatial Big Datamentioning

confidence: 99%

A methodology for dealing with spatial big data

Schoier

Borruso

2017

IJBIDM

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“…Information about the previous queries and the results that were selected by the user is used also in [40] and are integrated into a ranking model. Other contextual factors, such as spatiotemporal and environmental aspects have also been used to rerank the search results, as in the works of [3] and [7].…”

Section: Related Workmentioning

confidence: 99%

Accessibility-based reranking in multimedia search engines

Kalamaras

Dimitriou

Drosou

et al. 2016

Multimed Tools Appl

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Traditional multimedia search engines retrieve results based mostly on the query submitted by the user, or using a log of previous searches to provide personalized results, while not considering the accessibility of the results for users with vision or other types of impairments. In this paper, a novel approach is presented which incorporates the accessibility of images for users with various vision impairments, such as color blindness, cataract and glaucoma, in order to rerank the results of an image search engine. The accessibility of individual images is measured through the use of vision simulation filters. Multi-objective optimization techniques utilizing the image accessibility scores are used to handle users with multiple vision impairments, while the impairment profile of a specific user is used to select one from the Pareto-optimal solutions. The proposed approach has been tested with two image datasets, using both simulated and real impaired users, and the results verify its applicability. Although the proposed method has been used for vision accessibility-based reranking, it can also be extended for other types of personalization context.

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“…Early studies utilize a keyword index (inverted lists) and a spatial index (such as Rtree [10] or R*-tree [4]) separately [22,7,18]. These proposed methods answer a query by using the keyword and spatial indexes separately.…”

Section: Related Workmentioning

confidence: 99%

“…If the query interval is found in the O-filter, we verify the records from its list without accessing this subtree. Otherwise, we lookup the ool$ [7,7] Logical representation of consistent prefixes Physical representation Fig. 6.…”

Section: Representing Prefix Filters Efficientlymentioning

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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Efficient query processing in geographic web search engines

Cited by 228 publications

References 32 publications

A methodology for dealing with spatial big data

A methodology for dealing with spatial big data

Accessibility-based reranking in multimedia search engines

Location-Based Instant Search

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