Hot Item Detection in Uncertain Data

Bernecker, Thomas; Kriegel, Hans‐Peter; Renz, Matthias; Zuefle, Andreas

doi:10.1007/978-3-642-01307-2_67

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…In contrast, user D is certainly inside the restaurant, while user C is certainly outside all three places. The probability P (U in l) that a user U is located inside a location l can be computed using techniques for range queries on uncertain data [6]. 2 Exemplary probabilities P (U in l) for all users U and all locations l are shown in the table of Figure 1.…”

Section: Problem Definitionmentioning

confidence: 99%

Geo-Social Co-location Mining

Weiler

Schmid

Mamoulis

et al. 2015

Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data

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Modern technology to capture geo-spatial information produces a huge flood of geo-spatial and geo-spatio-temporal data with a new user mentality of utilizing this technology to voluntarily share information. This location information, enriched with social information, is a new source to discover new and useful knowledge. This work introduces geo-social co-location mining, the problem of finding social groups that are frequently found at the same location. This problem has applications in social sciences, allowing to research interactions between social groups and permitting social-link prediction. It can be divided into two sub-problems. The first sub-problem of finding spatial co-location instances, requires to properly address the inherent uncertainty in geo-social network data, which is a consequence of generally very sparse check-in data, and thus very sparse trajectory information. For this purpose, we propose a probabilistic model to estimate the probability of a user to be located at a given location at a given time, creating the notion of probabilistic co-locations. The second sub-problem of mining the resulting probabilistic co-location instances requires efficient methods for large databases having a high degree of uncertainty. Our approach solves this problem by extending solutions for probabilistic frequent itemset mining. Our experimental evaluation performed on real (but anonymized) geo-social network data shows the high efficiency of our approach, and its ability to find new social interactions.

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Section: Problem Definitionmentioning

confidence: 99%

Geo-Social Co-location Mining

Weiler

Schmid

Mamoulis

et al. 2015

Second International ACM Workshop on Managing and Mining Enriched Geo-Spatial Data

Self Cite

View full text Add to dashboard Cite

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Similarity search and mining in uncertain databases

2010

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Managing, searching and mining uncertain data has achieved much attention in the database community recently due to new sensor technologies and new ways of collecting data. There is a number of challenges in terms of collecting, modelling, representing, querying, indexing and mining uncertain data. In its scope, the diversity of approaches addressing these topics is very high because the underlying assumptions of uncertainty are different across different papers. This tutorial provides a comprehensive and comparative overview of general techniques for the key topics in the fields of querying, indexing and mining uncertain data. In particular, it identifies the most generic types of probabilistic similarity queries and discusses general algorithmic methods to answer such queries efficiently. In addition, the tutorial sketches probabilistic methods for important data mining applications in the context of uncertain data with special emphasis on probabilistic clustering and probabilistic pattern mining. The intended audience of this tutorial ranges from novice researchers to advanced experts as well as practitioners from any application domain dealing with uncertain data retrieval and mining.

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Hot Item Detection in Uncertain Data

Cited by 2 publications

References 13 publications

Geo-Social Co-location Mining

Geo-Social Co-location Mining

Similarity search and mining in uncertain databases

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