Approximate convex skyline: A partitioned layer-based index for efficient processing top-k queries

Ihm, Sun-Young; Lee, Ki-Eun; Nasridinov, Aziz; Heo, Jun-Seok

doi:10.1016/j.knosys.2014.01.022

Cited by 14 publications

(11 citation statements)

References 12 publications

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“…Unlike AppCSE [12] that uses k-means method to partition the skyline, we use projection partitioning method. The main problem with k-means method is that it consumes much time when database is high-dimensional which enlarges the convex hull construction time.…”

Section: Partition Stepmentioning

confidence: 99%

“…These approaches find the top-k answers by accessing just the first few layers. ONION [3], HL-index [4,5], AppRI [14], DG [15], PL-index [6] and AppCSE [12] are well-known methods of this approach. ONION and HL-index use convex hull [1] to construct layers.…”

Section: Introductionmentioning

confidence: 99%

“…That is, convex skyline is constructed by computing the convex hull over the skyline. Thus, the layer size of the convex skyline is smaller than those of the skyline, and the index construction time is shorter than those of the convex hull [12]. PL-index and AppCSE achieve high accuracy of query answer at the expense of enlarged index construction time.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, PL-index has a relatively smaller layer size compared to those that use skyline, and the index construction time is relatively shorter than those that use convex hull. The approximate convex skyline enhanced (AppCSE) [12] was proposed to further improve the performance of PL-index. Unlike PL-index, it first constructs skyline over the entire set of tuples in database, and partitions the resulting skyline into subregions.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Partition Stepmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…Second, the view-based methods pre-compute the results of multiple queries and store these results as a view. Third, disk-based methods build an index using disk [3].…”

Section: Introductionmentioning

confidence: 99%

Tuning Different Types of Complex Queries Using the Appropriate Indexes in Parallel/Distributed Database Systems

Chakraoui¹

2016

Geomate

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ABSTRACT:In this paper, we discuss the most powerful techniques of tuning parallel/distributed databases. As in engineering, database tuning becomes an inescapable part of big projects since the conception phase of research projects. The needs of companies including big data have increased to databases optimization. Systems that not take into account the optimization rules become heavy after five years of their production; these reasons were of a paramount of importance to prepare this paper. Indexing is the most suitable way to optimize database systems, further one of the top ways of optimizing index is the application of parallelization. In this paper, we will discuss parallelization and we will practice it with different complex queries and sub-queries using different types of indexes; then we will compare the results gotten from each index. To top it all, the most suitable interference between the major types of index: B*Tree index, Bitmap index, composite parallel index, local parallel index and global parallel index.

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A two-phase data space partitioning for efficient skyline computation

Nasridinov

Choi

2017

Cluster Comput

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Approximate convex skyline: A partitioned layer-based index for efficient processing top-k queries

Cited by 14 publications

References 12 publications

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

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

Tuning Different Types of Complex Queries Using the Appropriate Indexes in Parallel/Distributed Database Systems

A two-phase data space partitioning for efficient skyline computation

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