Indexing Uncertain Data in General Metric Spaces

Angiulli, Fabrizio; Fassetti, Fabio

doi:10.1109/tkde.2011.93

Cited by 23 publications

(26 citation statements)

References 23 publications

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“…U P -Index. Recently, Angiulli et al [3] develop a pivot based indexing mechanism for uncertain data in general metric space. For a given pivot point p and an object U , the P DF summary of U is the histogram of the distance distribution regarding p and U .…”

Section: Related Workmentioning

confidence: 99%

“…The upper bound of Papp(U, rq) can be derived based on the reverse triangle inequality according to the histogram and the distance between the centre of rq and the pivot point p. Then an object can be pruned based on the upper bound derived. To enhance the pruning power, a set of pivot points are employed in [3]. The advantage of U P -Index is that it can support distance based range query in general metric space.…”

Section: Related Workmentioning

confidence: 99%

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Effectively indexing the multi-dimensional uncertain objects for range searching

Zhang

Lin

et al. 2012

Proceedings of the 15th International Conference on Extending Database Technology

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The range searching problem is fundamental in a wide spectrum of applications such as radio frequency identification (RFID) networks, location based services (LBS), and global position system (GPS). As the uncertainty is inherent in those applications, it is highly demanded to address the uncertainty in the range search since the traditional techniques cannot be applied due to the inherence difference between the uncertain data and traditional data. In the paper, we propose a novel indexing structure, named U -Quadtree, to organize the uncertain objects in a multi-dimensional space such that the range searching can be answered efficiently by applying filtering techniques. Particularly, based on some insights of the range search on uncertain data, we propose a cost model which carefully considers various factors that may impact the performance of the range searching. Then an effective and efficient index construction algorithm is proposed to build the optimal U -Quadtree regarding the cost model. Comprehensive experiments demonstrate that our technique outperforms the existing works for range searching on multi-dimensional uncertain objects.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Effectively indexing the multi-dimensional uncertain objects for range searching

Zhang

Lin

et al. 2012

Proceedings of the 15th International Conference on Extending Database Technology

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show abstract

“…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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“…Let D be a dataset with N = 4 and m = 3. Let S1, S2, S3 and S4 be the instantiated distance partitions: S1 = {[2, 2] : 0.33, [4,4] : 0.33, [6,6] : 0.33}, S2 = { [4,8] : 1}, S3 = {[1, 1] : 0.33, [5,5] : 0.33, [9,9] : 0.33} and S4 = {[1, 1] : 0.33, [3,3] : 0.33, [7,7] : 0.33}. The PNN probability estimates determined using the Eq.4 and Eq.…”

Section: Lemma 2 (Dependencies In Distance Partitions)mentioning

confidence: 99%

“…From our experiments, the optimal configuration is Holistic-PkNN-HS, also denoted in short by HolisticPkNN. Similarly to [5], we use the average of the series samples as unique pivot for each uncertain series. We note that the algorithms Baseline, Holistic-PkNN-Virtual and Holistic-PkNN are different algorithms that return the very same result set as defined by the formulation of top-k probable nearest neighbor queries defined in Section 2.…”

Section: Datasetsmentioning

confidence: 99%

Top-k nearest neighbor search in uncertain data series

2014

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Many real applications consume data that is intrinsically uncertain, noisy and error-prone. In this study, we investigate the problem of finding the top-k nearest neighbors in uncertain data series, which occur in several different domains. We formalize the top-k nearest neighbor problem for uncertain data series, and describe a model for uncertain data series that captures both uncertainty and correlation. This distinguishes our approach from prior work that compromises the accuracy of the model by assuming independence of the value distribution at neighboring time-stamps. We introduce the Holistic-PkNN algorithm, which uses novel metric bounds for uncertain series and an efficient refinement strategy to reduce the overall number of required probability estimates. We evaluate our proposal under a variety of settings using a combination of synthetic and 45 real datasets from diverse domains. The results demonstrate the significant advantages of the proposed approach.

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Indexing Uncertain Data in General Metric Spaces

Cited by 23 publications

References 23 publications

Effectively indexing the multi-dimensional uncertain objects for range searching

Effectively indexing the multi-dimensional uncertain objects for range searching

Authentication of uncertain data based on k-means clustering

Top-k nearest neighbor search in uncertain data series

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