Effectively Indexing the Multidimensional Uncertain Objects

Zhang, Ying; Zhang, Wenjie; Lin, Qianlu; Lin, Xuemin; Shen, Heng Tao

doi:10.1109/tkde.2013.21

Cited by 14 publications

(8 citation statements)

References 25 publications

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“…In the recent years, more and more studies have focused on the probability range query of moving objects in uncertain models, but most of them are concentrated on the Euclidean space [24][25][26][27][28][29]. Kuijpers and Othman [30] put forward the uncertain trajectory model on road network-the space-time prism.…”

Section: Index For Range Query Based On Processing Batchmentioning

confidence: 99%

A Hybrid Aggregate Index Method for Trajectory Data

Shi

Huang

Zheng

et al. 2019

Mathematical Problems in Engineering

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The aggregate query of moving objects on road network keeps being popular in the ITS research community. The existing methods often assume that the sampling frequency of the positioning devices like GPS or roadside radar is dense enough, making the result’s uncertainty negligible. However, such assumption is not always tenable, especially in the extreme occasions like wartime. Regarding this issue, a hybrid aggregate index framework is proposed in this paper, in order to perform aggregate queries on massive trajectories that are sampled sparsely. Firstly, this framework uses an offline batch processing component based on the UPBI-Sketch index to acquire each object’s most likely position between two continuous sampling instants. Next, it introduces the AMH+-Sketch index to processing the aggregate operation online, making sure each object is counted only once in the result. The experimental results show that the hybrid framework can ensure the query accuracy by adjusting the parameters L and U of AMH+-Sketch index and its space storage advantage becomes more and more obvious when the data scale is very large.

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Section: Index For Range Query Based On Processing Batchmentioning

confidence: 99%

A Hybrid Aggregate Index Method for Trajectory Data

Shi

Huang

Zheng

et al. 2019

Mathematical Problems in Engineering

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“…For example, in [38], the authors define a type of R *tree to maintain the objects, so they can filter some objects using the R * -tree, and the numerical integral is only calculated for a few objects. Similarly, in [45], the authors use a quadtree for the same purpose. However, in a volatile and highly-uncertain environment, where the objects are moving continuously and we cannot foresee the next movement for each object, it is difficult to maintain an index structure such an R * -tree or a quadtree.…”

Section: Probabilistic Inside Queries: Problem Statementmentioning

confidence: 99%

“…We will call to this type of queries probabilistic inside queries. This type of queries can be considered a specialization for the spatial domain in R 2 of fuzzy probabilistic range queries [38] or uncertain range queries [45]; the concept of probabilistic similarity join defined in [25] is also similar as well. While there have been many works on the processing of different types of queries in the presence of uncertainty [6,9,13,24,28,31,46], there is a lack of an in-depth formal study of this type of queries, along with an efficient processing approach, where uncertainty is also considered for the queried position (as it can be the position of moving object) and which does not assume the availability of indexes of moving objects.…”

Section: Introductionmentioning

confidence: 99%

Handling location uncertainty in probabilistic location-dependent queries

Bernad

Bobed

Ilarri

et al. 2017

Information Sciences

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Location-based services have motivated intensive research in the field of mobile computing, and particularly on location-dependent queries. Existing approaches usually assume that the location data are expressed at a fine geographic precision (physical coordinates such as GPS). However, many positioning mechanisms are subject to an inherent imprecision (e.g., the cell-id mechanism used in cellular networks can only determine the cell where a certain moving object is located). Moreover, even a GPS location can be subject to an error or be obfuscated for privacy reasons. Thus, moving objects can be considered to be associated to an uncertainty area where they can be located. In this paper, we analyze the problem introduced by the imprecision of the location data available in the data sources by modelling them using uncertainty areas. To do so, we propose to use a higher-level representation of locations which includes uncertainty, formalizing the concept of uncertainty location granule. This allows us to consider probabilistic location-dependent queries, among which we will focus on probabilistic inside (range) constraints. The adopted model allows us to develop a systematic and efficient approach for processing this kind of queries. An experimental evaluation shows that these probabilistic queries can be supported efficiently.

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“…Kanagal et al [6] proposed a structure for correlated probabilistic data based on junction trees, aka clique trees [20]. This subject has been extended to general metric spaces and multidimensions by Fabrizio et al [21] and Zhang et al [22]. Cheng et al [5] and Singh et al [10] developed other indexing methods based on traditional R-trees [23].…”

Section: Pdr Treementioning

confidence: 99%

Authentication of uncertain data based on k-means clustering

Ünver¹,

Gündem²

2016

Turk J Elec Eng & Comp Sci

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Probabilistic databases and database outsourcing are two recent and important applications of database systems. In this paper, we introduce authenticated query processing in outsourced probabilistic databases. We have proposed a novel authenticated data structure (ADS) called PH-tree, which is a combination of PDR-tree and MH-tree.We have also implemented k-means clustering as a preprocessor. We have compared our algorithm with an existing ADS called MR-tree and showed that PH-trees outperform MR-trees significantly.

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Effectively Indexing the Multidimensional Uncertain Objects

Cited by 14 publications

References 25 publications

A Hybrid Aggregate Index Method for Trajectory Data

A Hybrid Aggregate Index Method for Trajectory Data

Handling location uncertainty in probabilistic location-dependent queries

Authentication of uncertain data based on k-means clustering

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