Skyframe: a framework for skyline query processing in peer-to-peer systems

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.

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.

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

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

Skyline queries have been extensively incorporated in various contemporary database applications. The list includes but is not limited to multi-criteria decision-making systems, decision support systems, and recommendation systems. Due to its great benefits and wide application range, many skyline algorithms have already been proposed in numerous data settings. Nonetheless, most researchers presume the completion of data meaning that all data item values are available. Since this assumption cannot be sustained in a large number of real-world database applications, the existing algorithms are rather inadequate to be directly applied on a database with incomplete data. In such cases, processing skyline queries on incomplete data incur exhaustive pairwise comparisons between data items, which may lead to loss of the transitivity property of the skyline technique. Losing the transitivity property may in turn give rise to the problem of cyclic dominance. In order to address these issues, we propose a new skyline algorithm called Sorting-based Cluster Skyline Algorithm (SCSA) that combines the sorting and partitioning techniques and simplifies the skyline computation on an incomplete dataset. These two techniques help boost the skyline process and avoid many unnecessary pairwise comparisons between data items to prune the dominated data items. The comprehensive experiments carried out on both synthetic and real-life datasets demonstrate the effectiveness and versatility of our approach as compared to the currently used approaches.

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 help users make intelligent decisions over complex data, where different and often conflicting criteria are considered. A challenging problem is to support skyline queries in distributed environments, where data is scattered over independent sources. The query response time of skyline processing over distributed data depends on the amount of transferred data and the query processing cost at each server. In this paper, we propose AGiDS, a framework for efficient skyline processing over distributed data. Our approach reduces significantly the amount of transferred data, by using a grid-based data summary that captures the data distribution on each server. AGiDS consists of two phases to compute the result: in the first phase the querying server gathers the grid-based summary, whereas in the second phase a skyline request is sent only to the servers that may contribute to the skyline result set asking only for the points of non-dominated regions. We provide an experimental evaluation showing that our approach performs efficiently and outperforms existing techniques.

(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.

In this paper, we present Pampoo, a novel distributed framework for efficient query processing in P2P systems. We propose a new locality preserving data structure Skip-trie as its substrate. Skip-trie incorporates the advantages of skip graph with features of traditional trie. Thus, Pampoo can efficiently support various types of queries such as range queries and k nearest neighbor queries. We study the time cost of search and update operations on Skip-trie structure under our Pampoo framework. We further briefly present a repairing strategy to boost the robustness of Pampoo system. Extensive experiments are conducted to verify the effectiveness and efficiency of our approach.

Among the open problems in P2P systems, support for nontrivial search predicates, standardized query languages, distributed query processing, query load balancing, and quality of query results have been identified as some of the most relevant issues. This paper describes how range queries as an important nontrivial search predicate can be supported in a structured overlay network that provides O(log n) search complexity on top of a trie abstraction. We provide analytical results that show that the proposed approach is efficient, supports arbitrary granularity of ranges, and demonstrate that its algorithmic complexity in terms of messages is independent of the size of the queried ranges and only depends on the size of the result set. In contrast to other systems which provide evaluation results only through simulations, we validate the theoretical analysis of the algorithms with large-scale experiments on the PlanetLab infrastructure using a fully-fledged implementation of our approach.

The Survey on Skyline Query Processing for Data-Specific Applications

Skyline queries play an important role in applications such as multicriteria decision making and user preference systems. Recently, more attention has been paid to the problem of efficient skyline computation in the P2P systems. Due to the high distribution of the P2P networks, the skyline computation incurs too many intermediate results transferred between peers, which consumes mass of the network bandwidth. Additionally, a large number of peers are involved in the skyline computation, which introduces both heavy communication cost and computational overhead. In this paper, we propose a novel Distributed Caching Mechanism (DCM) to efficiently improve the performance of the skyline calculation in the structured P2P networks, using a Distributed Caching Index (DCI) scheme and an advanced cache utilization strategy. The DO scheme is employed to efficiently locate the cache that can properly answer a future skyline query. Exploring the property of emended skyline, we can optimize the utilization of the cached results for answering future skyline queries. Extensive evaluations on both synthetic and real dataset show that our approach can significantly reduce both bandwidth consumption and communication cost, and greatly shorten the response time.

Particle Swarm Optimization (PSO) is a stochastic optimization approach that originated from early attempts to simulate the behavior of birds looking for food. Estimation of distributions algorithms (EDAs) are a class of evolutionary algorithms that build and maintain a probabilistic model capturing the search space characteristics and continuously use this model to generate new individuals. In this work, we propose a new PSO and EDA hybrid algorithm that uses the particles' distribution in the search space in order to adjust the search space bounds, hence, restricting the particles movement as well as their allowable maximum velocity. The algorithms is augmented with a mechanism to overcome premature convergence and escape local minima. The algorithm is compared to the standard PSO algorithm using a suite of well-known benchmark optimization functions. Experimental results show that the proposed algorithm has a promising performance.

Skyline query processing and the more general preference queries become reality in current database systems. Preference queries select those tuples from a database that are optimal with respect to a set of designated preference attributes. In a Skyline query these preferences only refer to minimum and maximum, whereas the more general approach of preference queries allow a more granular specification of user wishes as well as the specification of the relative importance of individual preferences. The incorporation of preferences into practical relational database engines necessitates an efficient and effective selectivity estimation module: A better understanding of the preference selectivity is useful for better design of algorithms and necessary to extend a database query optimizer's cost model to accommodate preference queries. This paper presents a survey on selectivity and cardinality estimation for arbitrary preference queries. The paper presents current approaches and discusses their advantages and disadvantages, such that one could decide which model should be used in a database engine to estimate optimization costs.

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.