1,404 research outputs found
Multi-Source Spatial Entity Linkage
Besides the traditional cartographic data sources, spatial information can
also be derived from location-based sources. However, even though different
location-based sources refer to the same physical world, each one has only
partial coverage of the spatial entities, describe them with different
attributes, and sometimes provide contradicting information. Hence, we
introduce the spatial entity linkage problem, which finds which pairs of
spatial entities belong to the same physical spatial entity. Our proposed
solution (QuadSky) starts with a time-efficient spatial blocking technique
(QuadFlex), compares pairwise the spatial entities in the same block, ranks the
pairs using Pareto optimality with the SkyRank algorithm, and finally,
classifies the pairs with our novel SkyEx-* family of algorithms that yield
0.85 precision and 0.85 recall for a manually labeled dataset of 1,500 pairs
and 0.87 precision and 0.6 recall for a semi-manually labeled dataset of
777,452 pairs. Moreover, we provide a theoretical guarantee and formalize the
SkyEx-FES algorithm that explores only 27% of the skylines without any loss in
F-measure. Furthermore, our fully unsupervised algorithm SkyEx-D approximates
the optimal result with an F-measure loss of just 0.01. Finally, QuadSky
provides the best trade-off between precision and recall, and the best
F-measure compared to the existing baselines and clustering techniques, and
approximates the results of supervised learning solutions
Ranked Spatial-keyword Search over Web-accessible Geotagged Data: State of the Art
Search engines, such as Google and Yahoo!, provide efficient retrieval and ranking of web pages based on queries consisting of a set of given keywords. Recent studies show that 20% of all Web queries also have location constraints, i.e., also refer to the location of a geotagged web page. An increasing number of applications support location based keyword search, including Google Maps, Bing Maps, Yahoo! Local, and Yelp. Such applications depict points of interest on the map and combine their location with the keywords provided by the associated document(s). The posed queries consist of two conditions: a set of keywords and a spatial location. The goal is to find points of interest with these keywords close to the location. We refer to such a query as spatial-keyword query. Moreover, mobile devices nowadays are enhanced with built-in GPS receivers, which permits applications (such as search engines or yellow page services) to acquire the location of the user implicitly, and provide location-based services. For instance, Google Mobile App provides a simple search service for smartphones where the location of the user is automatically captured and employed to retrieve results relevant to her current location. As an example, a search for ”pizza” results in a list of pizza restaurants nearby the user. Given the popularity of spatial-keyword queries and their wide applicability in practical scenarios, it is critical to (i) establish mechanisms for efficient processing of spatial-keyword queries, and (ii) support more expressive query formulation by means of novel 1 query types. Although studies on both keyword search and spatial queries do exist, the problem of combining the search capabilities of both simultaneously has received little attention
Location- and keyword-based querying of geo-textual data: a survey
With the broad adoption of mobile devices, notably smartphones, keyword-based search for content has seen increasing use by mobile users, who are often interested in content related to their geographical location. We have also witnessed a proliferation of geo-textual content that encompasses both textual and geographical information. Examples include geo-tagged microblog posts, yellow pages, and web pages related to entities with physical locations. Over the past decade, substantial research has been conducted on integrating location into keyword-based querying of geo-textual content in settings where the underlying data is assumed to be either relatively static or is assumed to stream into a system that maintains a set of continuous queries. This paper offers a survey of both the research problems studied and the solutions proposed in these two settings. As such, it aims to offer the reader a first understanding of key concepts and techniques, and it serves as an “index” for researchers who are interested in exploring the concepts and techniques underlying proposed solutions to the querying of geo-textual data.Agency for Science, Technology and Research (A*STAR)Ministry of Education (MOE)Nanyang Technological UniversityThis research was supported in part by MOE Tier-2 Grant MOE2019-T2-2-181, MOE Tier-1 Grant RG114/19, an NTU ACE Grant, and the Singtel Cognitive and Artificial Intelligence Lab for Enterprises (SCALE@NTU), which is a collaboration between Singapore Telecommunications Limited (Singtel) and Nanyang Technological University (NTU) that is funded by the Singapore Government through the Industry Alignment Fund Industry Collaboration Projects Grant, and by the Innovation Fund Denmark centre, DIREC
QDR-Tree: An Efcient Index Scheme for Complex Spatial Keyword Query
With the popularity of mobile devices and the development of geo-positioning
technology, location-based services (LBS) attract much attention and top-k
spatial keyword queries become increasingly complex. It is common to see that
clients issue a query to find a restaurant serving pizza and steak, low in
price and noise level particularly. However, most of prior works focused only
on the spatial keyword while ignoring these independent numerical attributes.
In this paper we demonstrate, for the first time, the Attributes-Aware Spatial
Keyword Query (ASKQ), and devise a two-layer hybrid index structure called
Quad-cluster Dual-filtering R-Tree (QDR-Tree). In the keyword cluster layer, a
Quad-Cluster Tree (QC-Tree) is built based on the hierarchical clustering
algorithm using kernel k-means to classify keywords. In the spatial layer, for
each leaf node of the QC-Tree, we attach a Dual-Filtering R-Tree (DR-Tree) with
two filtering algorithms, namely, keyword bitmap-based and attributes
skyline-based filtering. Accordingly, efficient query processing algorithms are
proposed. Through theoretical analysis, we have verified the optimization both
in processing time and space consumption. Finally, massive experiments with
real-data demonstrate the efficiency and effectiveness of QDR-Tree
A Location Analytics Method for the Utilisation of Geotagged Photos in Travel Marketing Decision-Making
Location analytics offers statistical analysis of any geo- or spatial data concerning user location. Such analytics can produce useful insights into the attractions of interest to travellers or visitation patterns of a demographic group. Based on these insights, strategic decision-making by travel marketing agents, such as travel package design, may be improved. In this paper, we develop and evaluate an original method of location analytics to analyse travellers' social media data for improving managerial decision support. The method proposes an architectural framework that combines emerging pattern data mining techniques with image processing to identify and process appropriate data content. The design artefact is evaluated through a focus group and a detailed case study of Australian outbound travellers. The proposed method is generic, and can be applied to other specific locations or demographics to provide analytical outcomes useful for strategic decision support
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