A unified approach for computing top-k pairs in multidimensional space

Cheema, Muhammad Aamir; Lin, Xuemin; Wang, Haixun; Wang, Jianmin; Zhang, Wenjie

doi:10.1109/icde.2011.5767903

Cited by 10 publications

(6 citation statements)

References 23 publications

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“…In [39] the exclusive closest pairs problem is introduced (which is a spatial assignment problem) and several solutions that solve it in main memory are proposed, exploiting the space partitioning. In [40] a unified approach that supports a broad class of top-K pairs queries (i.e. K-closest pairs queries, K-furthest pairs queries, etc.)…”

Section: Other Related Distance-based Join Queriesmentioning

confidence: 99%

“…Analogously, run setP is set to true when q ≤ p (then the current active run will get ref erence points from the P S.P , starting from p, and the comparison points will come from the QS.P , starting from the previous point of q). Note that, since active runs always alternate between the data sets, in Algorithm 2, there is no need for an Else block to follow the If block of lines 11-26 (the execution of code in lines 11-26 should be followed by execution of code in lines [27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42].…”

Section: Reverse Run Plane-sweep Algorithm For Kcpqsmentioning

confidence: 99%

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New plane-sweep algorithms for distance-based join queries in spatial databases

et al. 2016

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Efficient and effective processing of the distance-based join query (DJQ) is of great importance in spatial databases due to the wide area of applications that may address such queries (mapping, urban planning, transportation planning, resource management, etc.). The most representative and studied DJQs are the K Closest Pairs Query (KCPQ) and εDistance Join Query (εDJQ). These spatial queries involve two spatial data sets and a distance function to measure the degree of closeness, along with a given number of pairs in the final result (K) or a distance threshold (ε). In this paper, we propose four new plane-sweep-based algorithms for KCPQs and their extensions for εDJQs in the context of spatial databases, without the use of an index for any of the two disk-resident data sets (since, building and using indexes is not always in favor of processing performance). They employ a combination of plane-sweep algorithms and space partitioning techniques to join the data sets. Finally, we present results of an extensive experimental study, that compares the efficiency and effectiveness of the proposed algorithms for KCPQs and εDJQs. This performance study, conducted on medium and big spatial data sets (real and synthetic) validates that the proposed plane-sweepbased algorithms are very promising in terms of both efficient and effective measures, when neither inputs are indexed. Moreover, the best of the new algorithms is experimentally compared to the best algorithm that is based on the R-tree (a widely accepted access method), for KCPQs and εDJQs, using the same data sets. This comparison shows that the new algorithms outperform R-tree based algorithms, in most cases.

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Section: Other Related Distance-based Join Queriesmentioning

confidence: 99%

Section: Reverse Run Plane-sweep Algorithm For Kcpqsmentioning

confidence: 99%

New plane-sweep algorithms for distance-based join queries in spatial databases

et al. 2016

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“…State-of-the-art. Sorted-list aggregation [21] and best-first search [19], [20] are the state-of-the-art solutions in finding 4 In our framework, we always set w smaller than .…”

Section: B Filter-and-refinement (Far)mentioning

confidence: 99%

“…However, the cost of the heap operations may already outweigh its benefit in motif discovery due to the problem dimensionality. For instance, [21] only evaluates their method up to 6 dimensions. For clarity, we demonstrate the performance of these two methods in Table II on TAO dataset.…”

Section: Mbmentioning

confidence: 99%

Quick-motif: An efficient and scalable framework for exact motif discovery

Yiu

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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Discovering motifs in sequence databases has been receiving abundant attentions from both database and data mining communities, where the motif is the most correlated pair of subsequences in a sequence object. Motif discovery is expensive for emerging applications which may have very long sequences (e.g., million observations per sequence) or the queries arrive rapidly (e.g., per 10 seconds). Prior works cannot offer fast correlation computations and prune subsequence pairs at the same time, as these two techniques require different orderings on examining subsequence pairs. In this work, we propose a novel framework named Quick-Motif which adopts a two-level approach to enable batch pruning at the outer level and enable fast correlation calculation at the inner level. We further propose two optimization techniques for the outer and the inner level. In our experimental study, our method is up to 3 orders of magnitude faster than the state-of-the-art methods.

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“…Conventional top-k similarity joins, also called closest pair queries, have been extensively studied over conventional (point) spatial databases [3], [6], [9]. The most recent technique proposes to build an index on the fly [3].…”

Section: Using the Partitioning Technique When S Is Not Sorted The Tmentioning

confidence: 99%

Top-k Similarity Join over Multi-valued Objects

Zhang

Liang

et al. 2012

Database Systems for Advanced Applications

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Abstract. The top-k similarity joins have been extensively studied and used in a wide spectrum of applications such as information retrieval, decision making, spatial data analysis and data mining. Given two sets of objects U and V, a top-k similarity join returns k pairs of most similar objects from U ×V. In the conventional model of top-k similarity join processing, an object is usually regarded as a point in a multi-dimensional space and the similarity between two objects is usually measured by distance metrics such as Euclidean distance. However, in many applications an object may be described by multiple values (instances) and the conventional model is not applicable since it does not address the distributions of object instances. In this paper, we study top-k similarity join queries over multi-valued objects. We apply quantile based distance to explore the relative instance distribution among the multiple instances of objects. Efficient and effective techniques to process top-k similarity joins over multi-valued objects are developed following a filtering-refinement framework. Novel distance, statistic and weight based pruning techniques are proposed. Comprehensive experiments on both real and synthetic datasets demonstrate the efficiency and effectiveness of our techniques.

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A unified approach for computing top-k pairs in multidimensional space

Cited by 10 publications

References 23 publications

New plane-sweep algorithms for distance-based join queries in spatial databases

New plane-sweep algorithms for distance-based join queries in spatial databases

Quick-motif: An efficient and scalable framework for exact motif discovery

Top-k Similarity Join over Multi-valued Objects

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