Skyline-join query processing in distributed databases

Bai, Mei; Xin, Junchang; Wang, Guoren; Zimmermann, Roger; Wang, Xite

doi:10.1007/s11704-015-4534-y

Cited by 6 publications

(7 citation statements)

References 11 publications

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“…The point p in Observation 1 is called an anchor point [ 31 , 32 , 33 ]. Given

in vertically partitioned distributed databases, there is an algorithm that can conduct skyline filtering after finding an anchor point [ 32 ].…”

Section: Skyline Join Query Processing In Wsnsmentioning

confidence: 99%

“…Another useful variant of skyline queries is the skyline join query [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. In the example of Figure 1 , the attributes distance and price are called skyline attributes.…”

Section: Introductionmentioning

confidence: 99%

“…Much work was conducted on the processing of skyline join queries in traditional centralized and distributed databases [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. However, to our best knowledge, processing skyline join queries in WSNs was not addressed.…”

Section: Introductionmentioning

confidence: 99%

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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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“…The point p in Observation 1 is called an anchor point [ 31 , 32 , 33 ]. Given

in vertically partitioned distributed databases, there is an algorithm that can conduct skyline filtering after finding an anchor point [ 32 ].…”

Section: Skyline Join Query Processing In Wsnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…For the skyline query problem of multiple data streams, Jinchao Zhang et al [21] proposed the Naive Parallel Sliding Window Join (NP-SWJ) and Incremental Parallel Sliding Window Join (IP-SWJ) algorithms. Mei Bai et al [9] proposed the distributed skyline-join query algorithm (DSJQ) to process skyline-join queries in distributed databases. However, none of these tasks involves incomplete databases.…”

Section: A Skyline-join Query Processingmentioning

confidence: 99%

“…Skyline-join query can return the results from multiple datasets. The issue of skyline-join query has been extensively studied, and the representative works include [9,[20][21][22][23]. Figure 1 illustrates an example of computing the skyline-join over two datasets.…”

Section: Introductionmentioning

confidence: 99%

CrowdSJ: Skyline-Join Query Processing of Incomplete Datasets With Crowdsourcing

Ding

Zhang

et al. 2021

IEEE Access

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Skyline query is very useful in decision-making systems, WSN and so on. As a variation of skyline query, skyline-join query can return the results from multiple datasets. However, incomplete datasets are a frequent phenomenon due to the widespread use of automated information extraction and aggregation. Existing methods for dealing with incomplete data, such as probability, data padding can solve the problem, but cannot effectively reflect the real situation and are lack of integrality. Therefore, in this paper, in order to reflect the situation more accuracy and more user-centric, we research the problem of skyline-join query over incomplete datasets with crowdsourcing, named CrowdSJ. The crowdsourcingbased skyline-join query processing problem over incomplete datasets is divided into two situations. One is the skyline-join query only involves the unknown crowdsourcing attribute and the join attribute, named Partial Skyline-Join with Crowdsourcing (PSJCrowd). The other one is the skyline-join query involves all the attributes, named All Skyline-Join with Crowdsourcing (ASJCrowd). For PSJCrowd, first, we filter the known dataset. Then, we present the level-preference-tree-index, and propose the partial skyline-join with crowdsourcing algorithm. For ASJCrowd, first, we filter the known dataset too. Second, we build a levelpreference-tree-index based on the known attributes of the incomplete dataset. Third, we propose the skyline-join with crowdsourcing on single dataset algorithm, CrowdSJ-single, to filter the dataset containing unknown attributes. Then, we build a global level-preference-tree-index based on the known attributes of the incomplete dataset and the complete dataset. We propose the skyline-join with crowdsourcing on multiple datasets algorithm, CrowdSJ-multiple. We filter the linked tuples based on the global level-preference-tree-index and the results of each round of crowdsourcing. Numerous experiments on synthetic and real datasets demonstrate that our algorithms are efficient and effective.

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SCP: Skyline Computation Planner for Distributed, Update Intensive Environment

Kulkarni¹,

Momin²

2017

Information and Communication Technology for Intelligent Systems (ICTIS 2017) - Volume 1

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Skyline-join query processing in distributed databases

Cited by 6 publications

References 11 publications

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

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

CrowdSJ: Skyline-Join Query Processing of Incomplete Datasets With Crowdsourcing

SCP: Skyline Computation Planner for Distributed, Update Intensive Environment

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