Efficient Dual-Resolution Layer Indexing for Top-k Queries

Lee, Jongwuk; Cho, Hyunsouk; Hwang, Seung-won

doi:10.1109/icde.2012.73

Cited by 12 publications

(15 citation statements)

References 30 publications

(60 reference statements)

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“…It is proved that the number of retrieved records from the record set to answer top-k query is directly related to the cardinality of skyline points in the record set. Lee et al [16] propose a dual-resolution layer, which complements the advantages of the existing layer-based approaches. The authors first build coarse-level layers using the skylines and then divide each coarse-level layer into multiple fine-level sub-layers using the convex skylines.…”

Section: Index-based and View-based Methodsmentioning

confidence: 97%

TKAP: Efficiently processing top-k query on massive data by adaptive pruning

Han

Liu

et al. 2015

Knowl Inf Syst

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In many applications, top-k query is an important operation to return a set of interesting points in a potentially huge data space. The existing algorithms, either maintaining too many candidates, or requiring assistant structures built on the specific attribute subset, or returning results with probabilistic guarantee, cannot process top-k query on massive data efficiently. This paper proposes a sorted-list-based TKAP algorithm, which utilizes some data structures of low space overhead, to efficiently compute top-k results on massive data. In round-robin retrieval on sorted lists, TKAP performs adaptive pruning operation and maintains the required candidates until the stop condition is satisfied. The adaptive pruning operation can be adjusted by the information obtained in round-robin retrieval to achieve a better pruning effect. The adaptive pruning rule is developed in this paper, along with its theoretical analysis. The extensive experimental results, conducted on synthetic and real-life data sets, show the significant advantage of TKAP over the existing algorithms.

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Section: Index-based and View-based Methodsmentioning

confidence: 97%

TKAP: Efficiently processing top-k query on massive data by adaptive pruning

Han

Liu

et al. 2015

Knowl Inf Syst

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“…The use here of duality is fairly common (e.g., [7,12,20]) as is the emphasis on (layers of) lower envelopes (e.g., [5,25]). Transforming points into dual space in two dimensions often leads to the employment of plane sweep algorithms [9] and the availability of many results on arrangements of lines.…”

Section: Related Workmentioning

confidence: 99%

Computing k-regret minimizing sets

et al. 2014

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Regret minimizing sets are a recent approach to representing a dataset D by a small subset R of size r of representative data points. The set R is chosen such that executing any top-1 query on R rather than D is minimally perceptible to any user. However, such a subset R may not exist, even for modest sizes, r. In this paper, we introduce the relaxation to k-regret minimizing sets, whereby a top-1 query on R returns a result imperceptibly close to the top-k on D.We show that, in general, with or without the relaxation, this problem is NP-hard. For the specific case of two dimensions, we give an efficient dynamic programming, plane sweep algorithm based on geometric duality to find an optimal solution. For arbitrary dimension, we give an empirically effective, greedy, randomized algorithm based on linear programming. With these algorithms, we can find subsets R of much smaller size that better summarize D, using small values of k larger than 1.

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“…On the other hand, we can get a few top results immediately after the query was issued using the top-k query technique. A top-k query returns k tuples with the highest or the lowest scores from a relation [4,5,8,9]. The following is an example of top-k query in healthcare application.…”

Section: Introductionmentioning

confidence: 99%

“…That is, it constructs the first layer with skyline over the entire set of tuples in database, and then, constructs the second layer with a new skyline over the set of remaining tuples, and so on. In DG, the tuples of adjacent layers are linked together based on dominance relationships which enables selective access to layers [8,9]. Although, AppRI and DG can decrease index construction time compared to the methods that use convex hull, however, index construction time in high-dimensional databases still remains poor.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive convex skyline: a threshold-based project partitioned layer-based index for efficient-processing top-k queries in entrepreneurship applications

2016

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Many entrepreneurship applications use data as the core concept of their business to better understand the needs of their customers. However, as the size of databases used by these entrepreneurship applications grows and as more users access data through various interactive interfaces, obtaining the result for a top-k query may take long time if the query matches millions of the tuples in the database. Traditionally, layer-based indexing methods are representative for processing top-k queries efficiently. These methods form tuples into a list of layers where the ith layer holds the tuples that can be the top-i answer. Layer-based indexing methods enable us to obtain top-k answers by accessing at most k layers. Most of these methods achieve high accuracy of query answer at the expense of enlarged index construction time. However, we can adjust between accuracy and index construction time to achieve an optimal performance. Thus in this paper, we propose a method, called the adaptive convex skyline (AdaptCS) for efficient-processing top-k queries in entrepreneurship applications. AdaptCS first prunes the data with a virtual threshold point and finds skyline points over the pruned data. Here, by adjusting virtual threshold we are able to 123Adaptive convex skyline: a threshold-based project partitioned… 4263 achieve optimal performance. Then, AdaptCS divides the skyline into m subregions with projection partitioning method and constructs the convex hull m times for each subregion with virtual objects. Lastly, AdaptCS combines the objects obtained by computing the convex hull. The experimental results show that the proposed method outperforms the existing methods.

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Efficient Dual-Resolution Layer Indexing for Top-k Queries

Cited by 12 publications

References 30 publications

TKAP: Efficiently processing top-k query on massive data by adaptive pruning

TKAP: Efficiently processing top-k query on massive data by adaptive pruning

Computing k-regret minimizing sets

Adaptive convex skyline: a threshold-based project partitioned layer-based index for efficient-processing top-k queries in entrepreneurship applications

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