An efficient skyline query algorithm in the distributed environment

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.

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.

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.

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.

With the expanding number of communications from different mobile applications that acquire location information, the demand for continuous skyline queries has increased. In addition, the extremely fast increase in the data volume and mobile applications that deal with such volume of data such as check-ins recommendation, information services and applications of road networks; have both driven the need to adapt new processing environments to deal with huge amounts of data. In this paper, we present a number of efficient algorithms for processing continuous skyline queries on large datasets using MapReduce framework. The main idea of our proposed algorithms is to compute the skyline query only once at the starting position; then update on the result at the movement of the query point rather than computing the skyline at every time from scratch. In addition, experimental results are conducted which demonstrate the accuracy, performance and efficiency of the proposed algorithms.

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.

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.

The skyline query has been received much attention as an important operator in database systems for multi-preference analysis and decision making. Most of the previous works have focused on processing skyline queries on centralized data sets. However, the related data of real applications are practically scattered at several different servers. The skyline query computation in distributed environment is needed to gather a large number of data from the connected servers. The existing methods for a skyline query in distributed environment have two problems: (i) They have slow processing time for a skyline query. (ii) Most of the transferred data among servers in the network are unnecessary. In this paper, we propose a multi-layer grid method for efficiently processing skyline queries in distributed environments (MGDS). The proposed method minimizes the unnecessary transferred data using the grid mechanism. Experiments based on various data sets show that our proposed method outperforms the existing methods.KeywordsSkyline querydistributed skyline querygrid methoddistributed data

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.

Skyline computation for frequent queries in update intensive environment

Effective and efficient skyline query processing over attribute-order-preserving-free encrypted data in cloud-enabled databases

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.

(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.

Lecturers are required to carry out the Tri Dharma, one of which is research. In the research process a lecturer sometimes encounters several obstacles. Some of the factors that become obstacles include incentives related to publication costs and the unavailability of appropriate journal references. Several ways to overcome obstacles in the publication process have been carried out, but there are still problems with lecturers being confused about publishing their scientific work in a journal. This obstacle is due to the absence of a journal selection recommendation system. This study recommends journals based on the scope of the journal, the number of editions of the journal, and the cost of publication of the journal. The recommendation process is carried out using a skyline query algorithm, namely Block-nested loop (BNL). BNL works by comparing domination between objects on an attribute. The object, in this case the journal, is recommended if the journal dominates several attributes or at least one attribute. This study also developed a visualization of journal recommendations in the form of a web-based application. This visualization is expected to facilitate the journal recommendation process to users, so that it can help find journals that are in accordance with the papers to be published in a journal.

<title>Abstract</title> Diabetes Mellitus (DM) is a chronic metabolic disease with increasing prevalence, and it remains a major health problem worldwide. This study investigated the anti-diabetic potential of Indonesian traditional herbal plants ( <italic>Curcuma amada</italic> Roxb., <italic>Curcuma longa</italic> , and <italic>Allium cepa</italic> L. var. <italic>aggregatum</italic> ) using a network pharmacology approach combined with molecular docking. Active compounds were collected from IJAH Analytics and KNApSAcK, screened using oral bioavailability and drug-likeness criteria, and then mapped to predicted protein targets using SwissTargetPrediction. Diabetes-related targets were obtained from OMIM, UniProt, MalaCards, and GeneCards, and overlapping targets were selected for network analysis. A protein--protein interaction network was built using STRING v12.0, followed by centrality analysis (degree, betweenness, closeness, and eigenvector). The Skyline query algorithm was applied to prioritize key proteins, and the top five proteins were selected as receptors for docking. Molecular docking was performed using AutoDock Vina against nine candidate ligands. The docking results showed that Tropeoside B1 consistently produced the strongest and most frequent binding across the selected receptors, followed by Progesterone and Peonidin-3-Arabinoside. Overall, this workflow helps narrow down promising herbal compounds and protein targets, providing candidates for further experimental validation.