Skyline recomputation in Big Data

Bourahla, Chouaib; Maamri, Ramdane; Brahimi, Said

doi:10.1016/j.is.2022.102164

Cited by 4 publications

(1 citation statement)

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“…A heterogeneous architecture that employs both CPU and GPU for skyline computation on streaming data is investigated in [ 9 ]. A technique of efficient skyline recomputation after data updates in a big data environment is developed in [ 10 ]. The WSNs were also one of the major target environments of research, because the skyline query and its variants are very useful in monitoring applications [ 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

In-Network Processing of Skyline Join Queries in Wireless Sensor Networks Using Synopses of Skyline Attribute Value Ranges

Kang

2023

Sensors

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We investigate the in-network processing of a skyline join query in wireless sensor networks (WSNs). While much research was conducted on processing skyline queries in WSNs, skyline join queries were dealt with only in traditional centralized or distributed database environments. However, such techniques cannot be applied to WSNs. Carrying out join filtering, as well as skyline filtering using them in WSNs, is infeasible due to limited memory in senor nodes and to excessive energy consumption in wireless communications. In this paper, we propose a protocol to process a skyline join query in WSNs energy efficiently with only a small amount of memory in each sensor node. It uses a synopsis of skyline attribute value ranges, which is a very compact data structure. The range synopsis is used both in the search of anchor points for skyline filtering and in 2-way semijoins for join filtering. We describe the structure of a range synopsis and present our protocol. To optimize our protocol, we solve some optimization problems. Through implementation and a set of detailed simulations, we show the effectiveness of our protocol. The range synopsis is confirmed to be compact enough for our protocol to work well with the limited memory and energy in each sensor node. For the correlated and random distributions, our protocol significantly outperforms other possible protocols, confirming the effectiveness of an in-network skyline as well as the join filtering capabilities of our protocol.

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Section: Introductionmentioning

confidence: 99%

In-Network Processing of Skyline Join Queries in Wireless Sensor Networks Using Synopses of Skyline Attribute Value Ranges

Kang

2023

Sensors

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Computing Prominent Skyline on Massive Data

Wan,

Han,

Wang

2024

Data Sci. Eng.

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In many practical applications, skyline query is an important operation to return the pareto optimal tuples, which provides a candidate set for the optimum. On massive data, skyline often reports too many results, the users will be overwhelmed and be difficult to find the desired information easily. This paper devises P-skyline to reduce the size of the returned results. Given the approximation factor, P-skyline only generates the prominent skyline results by the definition of p-dominance. To the best of our knowledge, this paper is the first work to study P-skyline problem. This paper first proposes a baseline algorithm, which requires one full table scan to compute the results. It is found that baseline algorithm incurs a relatively high execution cost on massive data. Then, PSTP algorithm is proposed, which consists of two stages: candidate acquisition and refinement. On the presorted table, PSTP utilizes selective retrieval and selective checking to process P-skyline with much lower I/O cost and computation cost. The extensive experimental results, conducted on synthetic and real-life data sets, show that PSTP can compute P-skyline on massive data efficiently.

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