A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit <a rel="external noopener" href="http://www.dbs.informatik.uni-muenchen.de/Publikationen/Papers/DASFAA14-GeoSocial.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
Geo-Social Skyline Queries
[chapter]
<span title="">2014</span>
<i title="Springer International Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a>
</i>
Preserved Fulltext
By leveraging the capabilities of modern GPS-equipped mobile devices providing social-networking services, the interest in developing advanced services that combine location-based services with social networking services is growing drastically. Based on geo-social networks that couple personal location information with personal social context information, such services are facilitated by geo-social queries that extract useful information combining social relationships and current locations of
<span class="external-identifiers">
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-05813-9_6">doi:10.1007/978-3-319-05813-9_6</a>
<a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v5k7exbzvbbopmpyzdttzcoyo4">fatcat:v5k7exbzvbbopmpyzdttzcoyo4</a>
</span>
more »
... e users. In this paper, we tackle the problem of geo-social skyline queries, a problem that has not been addressed so far. Given a set of persons D connected in a social network SN with information about their current location, a geo-social skyline query reports for a given user U ∈ D and a given location P (not necessarily the location of the user) the pareto-optimal set of persons who are close to P and closely connected to U in SN. We measure the social connectivity between users using the widely adoted, but very expensive Random Walk with Restart method (RWR) to obtain the social distance between users in the social network. We propose an efficient solution by showing how the RWR-distance can be bounded efficiently and effectively in order to identify true hits and true drops early. Our experimental evaluation shows that our presented pruning techniques allow to vastly reduce the number of objects for which a more exact social distance has to be computed, by using our proposed bounds only.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808161252/http://www.dbs.informatik.uni-muenchen.de/Publikationen/Papers/DASFAA14-GeoSocial.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext">
<button class="ui simple right pointing dropdown compact black labeled icon button serp-button">
<i class="icon ia-icon"></i>
Web Archive
[PDF]
<div class="menu fulltext-thumbnail">
<img src="https://blobs.fatcat.wiki/thumbnail/pdf/0e/0d/0e0da6202d7da69dfe55ec67a8e16b914ab1d684.180px.jpg" alt="fulltext thumbnail" loading="lazy">
</div>
</button>
</a>
<a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-05813-9_6">
<button class="ui left aligned compact blue labeled icon button serp-button">
<i class="external alternate icon"></i>
springer.com
</button>
</a>