Continuous skyline queries in distributed environment

Interests in skyline query processing have significantly increased since it can be used in many applications, such as multi-criteria decision making, data mining, and user preference queries. In this paper, we propose continuous skyline queries over dynamic data set. Since the dynamic data set constantly changes as time passes, continuous skyline queries processing over dynamic data set becomes ever more complicated compared with static data set. We propose a multiple layer grids scheme for efficiently processing continuous skyline queries over skewed data set. The proposed scheme divides the work space into multiple layer grids and creates the skyline influence regions of each layer grid based on the existing data set. The continuous skyline queries are handled only when the dynamic data points update within the skyline influence regions of each layer grid. Since the multiple layer grids are divided dynamically, it is feasible even for the highly skewed data set. Experiments based on various synthetic data sets and real data sets show that our proposed scheme outperforms the existing schemes.

The application of skyline queries on outsourced databases significantly aids online analysis, yet efficiently handling encrypted queries remains a formidable obstacle. Moreover, query outcomes are vulnerable to potential malicious cloud services. To circumvent these limitations, this work presents the <u>H</u>onest-<u>M</u>ajority and <u>M</u>aliciously <u>S</u>kyline <u>Q</u>uery scheme (HMMSQ), which facilitates efficient skyline queries while safeguarding the privacy of datasets, queries, and skylines, as well as detecting malevolent activities. The core of HMMSQ is an optimized skyline diagram constructed by a novel skyline region-splitting algorithm for accurate skyline queries. Furthermore, it mitigates the frequency of dataset accesses by leveraging a multi-path R-tree for secure skyline retrieval. Notably, the majority of malicious behavior detection is focused on the servers, thereby minimizing user authentication overhead. The complexity and security are thoroughly analyzed, and experimental evaluations on various datasets demonstrate its efficiency and practicality in terms of computational cost and communication overhead. Remarkably, HMMSQ outperforms existing methods in query latency, achieving up to an order of magnitude improvement.

An efficient skyline query algorithm in the distributed environment

Recently, some approaches of finding probabilistic skylines on uncertain data have been proposed. In these approaches, a data object is composed of instances, each associated with a probability. The probabilistic skyline is then defined as a set of non-dominated objects with probabilities exceeding or equaling a given threshold. In many applications, data are generated as a form of continuous data streams. Accordingly, we make the first attempt to study a problem of continuously returning probabilistic skylines over uncertain data streams in this paper. Moreover, the sliding window model over data streams is considered here. To avoid recomputing the probability of being not dominated for each uncertain object according to the instances contained in the current window, our main idea is to estimate the bounds of these probabilities for early determining which objects can be pruned or returned as results. We first propose a basic algorithm adapted from an existing approach of answering skyline queries on static and certain data, which updates these bounds by repeatedly processing instances of each object. Then, we design a novel data structure to keep dominance relation between some instances for rapidly tightening these bounds, and propose a progressive algorithm based on this new structure. Moreover, these two algorithms are also adapted to solve the problem of continuously maintaining top-k probabilistic skylines. Finally, a set of experiments are performed to evaluate these algorithms, and the experiment results reveal that the progressive algorithm much outperforms the basic one, directly demonstrating the effectiveness of our newly designed structure.

With the rapid development of network data communication technology, the complexity of the network environment makes the data in the data stream have uncertain characteristics, and a large number of data streams are generated in many fields. Therefore, it is necessary to find an efficient and accurate processing method to process a large amount of data in a computer network. This paper takes the skyline probability calculation and the actual efficiency of the unknown object index structure in the skyline query processing technology on the uncertain data stream as the research object. Based on the skyline query technology, we study an efficient skyline query processing method on the unknown network traffic based on the model SUMG. The method includes two algorithms: dynamic modeling algorithm DMG and skyline query algorithm GST. The DMG algorithm samples the data in the sliding window of the uncertain data stream and establishes a model to convert the data stream into the parameter stream in the uncertain object probability density function, the GST algorithm establishes the R-tree index structure, which is in order to reduce the amount of calculation, it uses the parameter flow of the model. In both methods, the set of local skyline results is first obtained at the distributed node, and the skyline query is performed again on the union of the local skyline results to obtain the global skyline result set. The experimental results show that compared with the skyline query method BNL for unknown network traffic without an index structure, the SUMG method can not only effectively model the link-type unknown object to assist the skyline query, but also effectively prune the uncertain data object and improve the skyline. The distributed skyline query method can cope with the skyline query task on the distributed data stream.

Nowadays, big data is coming to the force in a lot of applications. Processing a skyline query on big data in more than linear time is by far too expensive and often even linear time may be too slow. It is obviously not possible to compute an exact solution to a skyline query in sublinear time, since an exact solution may itself have linear size. Fortunately, in many situations, a fast approximate solution is more useful than a slower exact solution. This paper proposes two sampling-based approximate algorithms for processing skyline queries. The first algorithm obtains a fixed size sample and computes the approximate skyline on it. The error of the algorithm is not only relatively small in most cases, but also is almost unaffected by the input size. The second algorithm returns an [Formula: see text]-approximation for the exact skyline efficiently. The running time of the algorithm has nothing to do with the input size in practical, achieving the goal of sublinearity on big data. Experiments verify the error analysis of the first algorithm, and show that the second is much faster than the existing skyline algorithms.

Reverse skyline query is an extension of the classical skyline query, widely used in the decision support in e-business. The vast burst of big data in e-business challenges the classical algorithms for such queries. This paper provides a novel definition of decision set and a decision set based reverse skyline query method called DRS on the double-layer R tree indexing in a map-reduce manner. Theoretical proofs are provided for the correctness and complexity of the DRS algorithm. Experiments made using several large data sets are presented and analyzed to illustrate the applicability and the outperformance of DRS over the state-of-the-art reverse skyline query methods.

Filtering uninteresting data is important to utilize “big data”. Skyline query is popular technique to filter uninteresting data, in which it selects a set of objects that are not dominated by another from a given large database. However, a skyline query often retrieves too many objects to analyze intensively especially for high-dimensional dataset. To solve the problem, k-dominant skyline queries have been introduced. The size of databases sometimes become too large to compute in a centralized environment. Conventional algorithms for computing k-dominant skyline queries are not well suited for parallel and distributed environments, such as the MapReduce framework. In this paper, we consider an efficient parallel algorithm to process k-dominant skyline query in MapReduce framework. Extensive experiments demonstrate the scalability of proposed algorithm for synthetic big datasets under different settings of data distribution, dimensionality, and cardinality.

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.

The distribution of personal protective equipment (PPE) plays a vital role in meeting the needs of PPE in an area. This study aims to measure the priority of PPE recipient regions in West Java Province using a skyline query algorithm, namely Sort Filter Skyline (SFS). In this study, the SFS algorithm is modified to optimize the dominance measurement section. Regions that do not have hospitals will not be prioritized for PPE recipients. The preferences used in this study are maximum and minimum. The maximum preference rule is used for the number of ODP, PDP, positive and dead cases, while the minimum preference rule is used for the cured and distance attributes. The application of SFS for calculating priority regions has been successfully carried out by developing two models, namely MS1 using unmodified SFS and MS2 using modified SFS by adding a selection process for regions with no hospitals. The MS1 produces 21 skyline objects (55.55 %), while MS2 15 (66.66 %) skyline objects. The MS2 is faster than that of MS1 because fewer objects are being tested. The MS1 takes 0.0222 seconds, while MS2 only 0.0193 seconds.

The skyline query is an efficient data analysis tool for multi-criteria decision making that has received significant attention in the database community. As multi-core architectures have gone mainstream, we present a new parallel skyline query algorithm that can be applied to multi-core and multiprocessor systems, to progressively return skyline points as they are identified efficiently. In this paper, we proposed a parallel skyline algorithm which can eliminate redundant computations and improve parallelism of the skyline query. Experimental results show that our algorithm successfully exploits the features of multiple cores to improve the performance of skyline computation for large high-dimensional datasets.

Skyline queries are useful in decision making applications. Skyline queries in highly mobile distributed environments have attracted many attentions recently due to the development of mobile internet device. The properties of distributed computing make skyline queries more complicated especially in any subspace. Conventional skyline algorithms do not support subspace skyline queries in distributed environment. In this paper, we focus on how to perform distributed skyline queries in any subspace according to user preference. So we propose a system model in a mobile and distributed environment. An efficient parallel algorithm for processing the Subspace Skyline Query (SSQ) using MapReduce is applied to the system model. This algorithm can report skyline points in any subspace. Meanwhile, a pruning strategy is also proposed in order to reduce the network communication and minimize the response time. We conduct experiments on real and synthetic data. Experimental results indicate that our SSQ algorithm is much more efficient. Furthermore, the pruning strategy can further improve the performance of the algorithm.

Although quantification queries are important for querying sets and databases, nevertheless, they haven't yet been directly supported by the MapReduce paradigm. Universal quantification queries are considered a powerful and important type of queries that appear in many applications. Today with the continuous increase in the size of the data has driven the need for new processing environments to access, process, store, and maintain huge amounts of valuable data. Thus, using clusters of commodity machines turned to be an optimal solution for several big data problems. In this paper, we present a number of algorithms for processing universal quantification queries on large datasets using the popular MapReduce framework. In addition, we present experimental results that show the speed-up and scale-out properties of our proposed algorithms.

Skyline queries are well-known for their intuitive query formalization and easy to understand semantics for selecting the most interesting data objects from large data sets. They naturally fill the gap between set-based queries using strict predicates and only few personalization options and rank-aware database retrieval, offering a high degree of personalization at the cost of very complex query formalization. Thus, skyline queries enjoyed popularity in the database personalization research community. Unfortunately, the simplicity and elegance of the query paradigm come at high costs: skyline queries often suffer from a problem usually known as “curse of dimensionality”. With the increasing number of query attributes, the size of skyline result sets grows exponentially and the results are thus hardly useful or manageable by users. This problem severely hinders the practical application of the skyline paradigm. In this paper, the concept of trade-offs skylines is proposed as a natural extension to the skyline paradigm which is specifically designed as a remedy for the curse of dimensionality.

Wireless sensor network amalgamates sensing, computing and communication technologies. Because of the energy limitation of sensor nodes, how to manage the data collected by the sensor nodes energy-efficiently becomes the focus of the research recently. As the main mean of multi-decision and data mining, skyline query plays a more and more important role in sensing applications. However, as the increase of data dimensionality, skyline query results extend greatly, which not only obstacles the decision, but also costs most of the energy of the nodes. In this article, k-dominant skyline query is researched deeply to deal with the above problem. An energy-efficient $k$-dominant skyline query algorithm (EKS) is proposed to calculate the $k$-dominant skyline of the wireless sensor network. The experimental results show that EKS could reduce the communication cost of the sensor network,while it calculates the k-dominant skyline, therefore, prolong the life-span of it.