Skyline Query Processing Algorithm Research Articles

A parallel computation of skyline using multiple regression analysis-based filtering on MapReduce

In the last decade, skyline query processing has become widely important because of its usefulness in decision making applications. Since the size of the datasets used for skyline query processing are huge, algorithms for MapReduce-based skyline query processing have been widely studied. However, existing algorithms suffer from low-filtering efficiency for local skyline computation, and unrealistically assume both uniform data distributions and dimensional independence. In this paper, we propose a parallel skyline query processing algorithm for MapReduce using multiple regression analysis. The goal of our algorithm is to efficiently find a set of skylines from a large dataset by reducing the number of candidates prior to the skyline computation. To develop the skyline computation algorithm on anti-correlated datasets, we computed a data filtering threshold line based on a multiple regression analysis of the sampled dataset. To guarantee the accuracy of the skyline result, we considered both a filtering threshold line and a grid-based cell dominance condition. Thus, only relevant data could be computed in the real skyline computation step. For local skyline computation, we utilized an angle-based partitioning of data space that effectively eliminates non-promising points in partitions. For the global skyline computation, we used the dominance relationship among grid-based partitions to prune out unnecessary skyline points. Performance analyses showed that our parallel skyline query processing algorithm outperformed existing algorithms, under various settings.

Connected Key Node Set‐Based Skyline Query Processing over Wireless Sensor Networks

(2. Zhejiang Fashion Institute of Technology, Ningbo 315021, China) Abstract — Skyline query has been applied widely in sensor networks. We propose a connected key node set- based skyline Efficient skyline query processing (EffiSky) algorithm to minimize communication traffic for resources- limited sensor networks. In the EffiSky algorithm, we dis- cover a Connected key node set (CKNS) used to trans- mit and collect queries and results among the sensor nodes, which can reduce the average communication cost of the networks significantly. We set up a two-level fil- tering scheme that prunes many useless dominated tu- ples. Both the theoretical analysis and experiment results demonstrate that EffiSky excels the existing work in terms of network traffic, scalability in network expansion, node density, and dimension change.

A continuous reverse skyline query processing method in moving objects environments

Many studies on reverse skyline query processing have been done for various services. The existing reverse skyline query processing methods are based on dynamic skylines. There are no reverse skyline query processing algorithms based on metric spaces for location-based services. The existing methods for processing a reverse skyline query have the limitation of service domains and require the high costs of computation to provide various location-based services. In this paper, we propose a new reverse skyline query processing method that efficiently processes a query over the moving objects. In addition, the proposed method processes a continuous reverse skyline query efficiently. In order to show the superiority of the proposed method, we compare it with the previous reverse skyline query processing method in various environments. As a result, the proposed method achieves better performance than the existing method.

FilterBased Probabilistic Skyline Query Processing Algorithm in Wireless Sensor Network

FilterBased Probabilistic Skyline Query Processing Algorithm in Wireless Sensor Network

Histogram Estimation for Optimal Filter Skyline Query Processing in Wireless Sensor Networks

The skyline query processing technique plays an increasingly important role for multicriteria decision making applications in wireless sensor networks. The technique of saving energy to prolong the lifetime of sensor nodes is one of the dominating challenges to resource-constrained wireless sensor networks. In this paper, we propose an energy-efficient skyline query processing algorithm, called the histogram filter based algorithm (HFA), to efficiently retrieve skyline results from a sensor network. First, we use historical data at the base station to construct histograms for further estimating the probability density distributions of the sensor data. Second, the dominance probability of each tuple is computed based on the histograms, and the optimal tuple which has the largest possibility of dominance/filtering capability is obtained using in-network aggregation approach. After that, the base station broadcasts the optimized tuple as the global filter to each sensor node. Then, the tuples which do not satisfy the skyline query semantics are discarded to avoid unnecessary data transmissions. An extensive experimental study demonstrates that the proposed HFA algorithm performs more efficiently than existing algorithms on reducing data transmissions during skyline query processing, which saves the energy and prolongs the lifetime of wireless sensor networks.

Continuous Probabilistic Subspace Skyline Query Processing Using Grid Projections

As an important type of multidimensional preference query, the skyline query can find a superset of optimal results when there is no given linear function to combine values for all attributes of interest. Its processing has been extensively investigated in the past. While most skyline query processing algorithms are designed based on the assumption that query processing is done for all attributes in a static dataset with deterministic attribute values, some advanced work has been done recently to remove part of such a strong assumption in order to process skyline queries for real-life applications, namely, to deal with data with multi-valued attributes (known as data uncertainty), to support skyline queries in a subspace which is a subset of attributes selected by the user, and to support continuous queries on streaming data. Naturally, there are many application scenarios where these three complex issues must be considered together. In this paper, we tackle the problem of probabilistic subspace skyline query processing over sliding windows on uncertain data streams. That is, to retrieve all objects from the most recent window of streaming data in a user-selected subspace with a skyline probability no smaller than a given threshold. Based on the subtle relationship between the full space and an arbitrary subspace, a novel approach using a regular grid indexing structure is developed for this problem. An extensive empirical study under various settings is conducted to show the effectiveness and efficiency of our PSS algorithm.

Continuous Skyline Query on Sensor Network

Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query but supports various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages.

Skyline Query Processing in Sensor Network Based on Data Centric Storage

Data centric storages for sensor networks have been proposed to efficiently process multi-dimensional range queries as well as exact matches. Usually, a sensor network does not process only one type of the query, but processes various types of queries such as range queries, exact matches and skyline queries. Therefore, a sensor network based on a data centric storage for range queries and exact matches should process skyline queries efficiently. However, existing algorithms for skyline queries have not considered the features of data centric storages. Some of the data centric storages store similar data in sensor nodes that are placed on geographically similar locations. Consequently, all data are ordered in a sensor network. In this paper, we propose a new skyline query processing algorithm that exploits the above features of data centric storages.

Approximate Skyline Query Processing Algorithm in Wireless Sensor Networks

Due to the limitation of wireless sensor networks in energy resources and the fact that part of Skyline query results can satisfy the users in many applications, this paper proposes an energy efficient approximate Skyline query processing algorithm to save the energy maximally according to the different requirements of applications. The proposed algorithm can compute an approximate Skyline result set only by making partial sensor nodes transmitting their sensing data back. And it is energy efficient because each sensor node transmits its sensing data back or not only depending on the information of itself, without the comparison with other data. Accordingly, communication cost is greatly reduced and network energy is greatly saved. Extensive experiments in simulation environment indicate that the proposed algorithm can process the approximate Skyline queries in wireless sensor networks energy efficiently according to different requirements of applications.

Energy Efficient Skyline Query Processing in Wireless Sensor Networks

In sensor networks, many studies have been proposed to process in-network aggregation efficiently. Unlike general aggregation queries, skyline query processing compares multi-dimensional data for the result. Therefore, it is very difficult to process the skyline queries in sensor networks. It is important to filter unnecessary data for energy-efficient skyline query processing. Existing approaches get rid of unnecessary data transmission by deploying filters to whole sensors. However, network lifetime is reduced due to energy consumption for transmitting filters. In this paper, we propose a lazy filtering-based in-network skyline query processing algorithm to reduce energy consumption by transmitting filters. Our algorithm creates the skyline filter table (SFT) in the data gathering process which sends data from sensor nodes to the base station and filters out unnecessary data transmissions using it. The experimental results show that our algorithm reduces false positive by 53% and improves network lifetime by 44% on average over the existing method.

Continuous Skyline Queries for Moving Objects

The literature on skyline algorithms has so far dealt mainly with queries of static query points over static data sets. With the increasing number of mobile service applications and users, however, the need for continuous skyline query processing has become more pressing. A continuous skyline query involves not only static dimensions, but also the dynamic one. In this paper, we examine the spatiotemporal coherence of the problem and propose a continuous skyline query processing strategy for moving query points. First, we distinguish the data points that are permanently in the skyline and use them to derive a search bound. Second, we investigate the connection between the spatial positions of data points and their dominance relationship, which provides an indication of where to find changes in the skyline and how to maintain the skyline continuously. Based on the analysis, we propose a kinetic-based data structure and an efficient skyline query processing algorithm. We concisely analyze the space and time costs of the proposed method and conduct an extensive experiment to evaluate the method. To the best of our knowledge, this is the first work on continuous skyline query processing