Seed-Driven Geo-Social Data Extraction

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Abstrakt

Geo-social data has been an attractive source for a variety of problems such as mining mobility patterns, link prediction, location recommendation, and influence maximization. However, new geo-social data is increasingly unavailable and suffers several limitations. In this paper, we aim to remedy the problem of effective data extraction from geo-social data sources. We first identify the limitations of extracting geo-social data. To overcome the limitations, we propose a novel seed-driven approach that uses the points of one source as the seed to feed as queries for the others. We additionally handle differences between, and dynamics within the sources by proposing three variants for optimizing search radius. Furthermore, we provide an optimization based on recursive clustering to minimize the number of requests and an adaptive procedure to learn the specific data distribution of each source. Our comprehensive experiments with six popular sources show that our seed-driven approach yields 14.3 times more data overall, while our request-optimized algorithm retrieves up to 95% of the data with less than 16% of the requests. Thus, our proposed seed-driven approach set new standards for effective and efficient extraction of geo-social data.
OriginalsprogEngelsk
TitelProceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019 : SSTD
Antal sider10
ForlagAssociation for Computing Machinery
Publikationsdato19 aug. 2019
Sider11-20
ISBN (Trykt)978-1-4503-6280-1
ISBN (Elektronisk)978-1-4503-6280-1
DOI
StatusUdgivet - 19 aug. 2019
BegivenhedInternational Symposium on Spatial and Temporal Databases - Wien, Østrig
Varighed: 19 aug. 201921 aug. 2019
Konferencens nummer: 16th
http://sstd2019.org/

Konference

KonferenceInternational Symposium on Spatial and Temporal Databases
Nummer16th
LandØstrig
ByWien
Periode19/08/201921/08/2019
Internetadresse

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Citationsformater

Isaj, S., & Pedersen, T. B. (2019). Seed-Driven Geo-Social Data Extraction. I Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD 2019: SSTD (s. 11-20). Association for Computing Machinery. https://doi.org/10.1145/3340964.3340973