基于高斯模型的不确定数据流Skyline查询方法

由于数据的动态性及不确定性等特征,使得不确定数据流上Skyline查询研究面临挑战.不确定对象一般采用多元概率密度函数(PDF)表示,现有的不确定数据流Skyline查询方法均采用离散型随机变量建模.然而不确定数据流中的对象可能是连续变化的,离散模型对连续性随机变量难以适用.针对连续PDF建模的不确定数据流Skyline查询进行了研究,提出了基于高斯模型的不确定数据流Skyline查询方法(SGMU),该方法包含2个过程:1)动态高斯建模算法(DGM):对滑动窗口采样并建立高斯模型,将原始的数据流转化为不确定对象PDF的参数流;2)提出了基于高斯树的查询算法(GTS)以建立空间索引结构和执行Skyline查询.实验结果表明,SGMU算法不仅能够对连续型不确定对象进行有效建模以辅助Skyline查询,而且能够有效地减少查询对象个数,提高Skyline查询效率.     

Efficient monitoring of skyline queries over distributed data streams

Data management and data mining over distributed data streams have received considerable attention within the database community recently. This paper is the first work to address skyline queries over distributed data streams, where streams derive from multiple horizontally split data sources. Skyline query returns a set of interesting objects which are not dominated by any other objects within the base dataset. Previous work is concentrated on skyline computations over static data or centralized data streams. We present an efficient and an effective algorithm called BOCS to handle this issue under a more challenging environment of distributed streams. BOCS consists of an efficient centralized algorithm GridSky and an associated communication protocol. Based on the strategy of progressive refinement in BOCS, the skyline is incrementally computed by two phases. In the first phase, local skylines on remote sites are maintained by GridSky. At each time, only skyline increments on remote sites are sent to the coordinator. In the second phase, a global skyline is obtained by integrating remote increments with the latest global skyline. A theoretical analysis shows that BOCS is communication-optimal among all algorithms which use a share-nothing strategy. Extensive experiments demonstrate that our proposals are efficient, scalable, and stable.     

Efficient processing of top-k dominating queries in distributed environments

Due to the recent massive data generation, preference queries are becoming an increasingly important for users because such queries retrieve only a small number of preferable data objects from a huge multi-dimensional dataset. A top-k dominating query, which retrieves the k data objects dominating the highest number of data objects in a given dataset, is particularly important in supporting multi-criteria decision making because this query can find interesting data objects in an intuitive way exploiting the advantages of top-k and skyline queries. Although efficient algorithms for top-k dominating queries have been studied over centralized databases, there are no studies which deal with top-k dominating queries in distributed environments. The recent data management is becoming increasingly distributed, so it is necessary to support processing of top-k dominating queries in distributed environments. In this paper, we address, for the first time, the challenging problem of processing top-k dominating queries in distributed networks and propose a method for efficient top-k dominating data retrieval, which avoids redundant communication cost and latency. Furthermore, we also propose an approximate version of our proposed method, which further reduces communication cost. Extensive experiments on both synthetic and real data have demonstrated the efficiency and effectiveness of our proposed methods.     

Efficient group-by reverse skyline computation

The reverse skyline query is very useful in many decision making applications. Given a multi-dimensional dataset P and a query point q, the reverse skyline query returns all the points in P whose dynamic skyline contains q. Although the reverse skyline retrieval has been well-studied in the literature, there is, to the best of our knowledge, no prior work on one of the most intuitive and practical types of reverse skyline queries, namely, group-by reverse skyline (GRS) query, which retrieves the reverse skyline for each group in a specified dataset. We formalize the GRS query including monochromatic and bichromatic versions, and identify its properties, and then propose a set of efficient algorithms for computing the group-by reverse skyline. Extensive experimental evaluation using both real and synthetic datasets demonstrates the performance of our proposed algorithms in terms of effectiveness and efficiency under a variety of experimental settings.     

Efficient Distributed Skyline Queries for Mobile Applications

In this paper, we consider skyline queries in a mobile and distributed environment, where data objects are distributed in some sites (database servers) which are interconnected through a high-speed wired network, and queries are issued by mobile units (laptop, cell phone, etc.) which access the data objects of database servers by wireless channels. The inherent properties of mobile computing environment such as mobility, limited wireless bandwidth, frequent disconnection, make skyline queries more complicated. We show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach, called efficient distributed skyline based on mobile computing (EDS-MC). In EDS-MC, a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication, network delay and query response time. We conduct extensive experiments in a simulated mobile database system, and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.     

Continuous range-based skyline queries in road networks

With the ever-growing popularity of smartphone devices in recent years, skyline queries over spatial Web objects in road networks have received increasing attention. In the literature, various techniques have been developed to tackle skyline queries that take both spatial and non-spatial attributes into consideration. However, the existing solutions only focus on solving point-based queries, where the query location is a spatial point. We observe that in many real-life applications, the user location is often represented by a spatial range. Thus, in this paper, we study a new problem of range-based skyline queries (CRSQs) in road networks. Two efficient algorithms named landmark-based (LBA) and index-based (IBA) algorithms are proposed. We also present incremental versions of LBA and IBA to handle continuous range-based skyline queries over moving objects. Extensive experiments using real road network datasets demonstrate the effectiveness and efficiency of our proposed algorithms.     

Sliding window top-k dominating query processing over distributed data streams

Preference query processing is important for a wide range of applications involving distributed databases, such as network monitoring, web-based systems, and market analysis. In such applications, data objects are generated frequently and massively, which presents an important and challenging problem of continuous query processing over distributed data stream environments. A top-k dominating query, which has been receiving much research attention recently, returns the k data objects that dominate the highest number of data objects in a given dataset, and due to its dominance-based ranking function, we can easily obtain superior data objects. An emerging requirement in distributed stream environments is an efficient technique for continuously monitoring top-k dominating data objects. Despite of this fact, no study has addressed this problem. In this paper, therefore, we address the problem of continuous top-k dominating query processing over distributed data stream environments. We present two algorithms that monitor the exact top-k dominating data and efficiently eliminate unqualified data objects for the result, which reduces both communication and computation costs. In addition to these algorithms, we present an approximate algorithm that further reduces both communication and computation costs. Extensive experiments on both synthetic and real data have demonstrated the efficiency and scalability of our algorithms.     

Continuous monitoring of range spatial keyword query over moving objects

In this paper, we propose an efficient solution for processing continuous range spatial keyword queries over moving spatio-textual objects (namely, CRSK-mo queries). Major challenges in efficient processing of CRSK-mo queries are as follows: (i) the query range is determined based on both spatial proximity and textual similarity; thus a straightforward spatial proximity based pruning of the search space is not applicable as any object far from a query location with a high textual similarity score can still be the answer (and vice versa), (ii) frequent location updates may invalidate a query result, and thus require frequent re-computing of the result set for any object updates. To address these challenges, the key idea of our approach is to exploit the spatial and textual upper bounds between queries and objects to form safe zones (at the client-side) and buffer regions (at the server-side), and then use these bounds to quickly prune objects and queries through smart in-memory data structures. We conduct extensive experiments with a synthetic dataset that verify the effectiveness and efficiency of our proposed algorithm.     

Approximate Continuous Top-<Emphasis Type="Italic">k</Emphasis> Query over Sliding Window

Continuous top-k query over sliding window is a fundamental problem in database, which retrieves k objects with the highest scores when the window slides. Existing studies mainly adopt exact algorithms to tackle this type of queries, whose key idea is to maintain a subset of objects in the window, and try to retrieve answers from it. However, all the existing algorithms are sensitive to query parameters and data distribution. In addition, they suffer from expensive overhead for incremental maintenance, and thus cannot satisfy real-time requirement. In this paper, we define a novel query named (ε, δ)-approximate continuous top-k query, which returns approximate answers for top-k query. In order to efficiently support this query, we propose an efficient framework, named PABF (Probabilistic Approximate Based Framework), to support approximate top-k query over sliding window. We firstly maintain a self-adaptive pruning value, which could filter out newly arrived objects who have a probability less than 1 ? δ of being a query result. For those objects that are not filtered, we combine them together, if the score difference among them is less than a threshold. To efficiently maintain these combined results, the framework PABF also proposes a multi-phase merging algorithm. Theoretical analysis indicates that even in the worst case, we require only logarithmic complexity for maintaining each candidate.     

Efficient mining of skyline objects in subspaces over data streams

Given a set of k-dimensional objects, the skyline query finds the objects that are not dominated by others. In practice, different users may be interested in different dimensions of the data, and issue queries on any subset of k dimensions in stream environments. This paper focuses on supporting concurrent and unpredictable subspace skyline queries over data streams. Simply to compute and store the skyline objects of every subspace in stream environments will incur expensive update cost. To balance the query cost and update cost, we only maintain the full space skyline in this paper. We first propose an efficient maintenance algorithm and several novel pruning techniques. Then, an efficient and scalable two-phase algorithm is proposed to process the skyline queries in different subspaces based on the full space skyline. Furthermore, we present the theoretical analyses and extensive experiments that demonstrate our method is both efficient and effective.     

Exploring correlation for fast skyline computation

Scaling skyline queries over high-dimensional datasets remains to be challenging due to the fact that most existing algorithms assume dimensional independence when establishing the worst-case complexity by discarding correlation distribution. In this paper, we present HashSkyline, a systematic and correlation-aware approach for scaling skyline queries over high-dimensional datasets with three novel features: First, it offers a fast hash-based method to prune non-skyline points by utilizing data correlation characteristics and speed up the overall skyline evaluation for correlated datasets. Second, we develop \(HashSkyline_{GPU}\), which can dramatically reduce the response time for anti-correlated and independent datasets by capitalizing on the parallel processing power of GPUs. Third, the HashSkyline approach uses the pivot cell-based mechanism combined with the correlation threshold to determine the correlation distribution characteristics for a given dataset, enabling adaptive configuration of HashSkyline for skyline query evaluation by auto-switching of \(HashSkyline_{CPU}\) and \(HashSkyline_{GPU}\). We evaluate the validity of HashSkyline using both synthetic datasets and real datasets. Our experiments show that HashSkyline consumes significantly less pre-processing cost and achieves significantly higher overall query performance, compared to existing state-of-the-art algorithms.     

XML 数据流上Top-K 关键字查询处理

利用关键字可以在模式未知的情况下对XML数据进行查询.在当前的XML数据流上的关键字查询处理中,打分函数往往不能都满足各种用户不同的需求.提出了一种基于skyline的XML数据流上的Top-K关键字查询.对于这种查询,不需要考虑影响结果与查询相关性的复杂因素,只需利用skyline挑选与查询最相关的结果.提出了两种XML数据流上的有效的基于skyline的Top-K关键查询处理算法,包括对单查询和多查询的处理算法.通过扩展实验对两种算法的有效性和可扩展性进行了验证.经过实验验证,所提出的查询处理算法的效率几乎不受关键字个数、查询结果数量、查询数量等参数的影响,运行时间和文档大小大致呈线性关系.     

Ranking continuous nearest neighbors for uncertain trajectories

This article addresses the problem of performing Nearest Neighbor (NN) queries on uncertain trajectories. The answer to an NN query for certain trajectories is time parameterized due to the continuous nature of the motion. As a consequence of uncertainty, there may be several objects that have a non-zero probability of being a nearest neighbor to a given querying object, and the continuous nature further complicates the semantics of the answer. We capture the impact that the uncertainty of the trajectories has on the semantics of the answer to continuous NN queries and we propose a tree structure for representing the answers, along with efficient algorithms to compute them. We also address the issue of performing NN queries when the motion of the objects is restricted to road networks. Finally, we formally define and show how to efficiently execute several variants of continuous NN queries. Our experiments demonstrate that the proposed algorithms yield significant performance improvements when compared with the corresponding naïve approaches.     

An intelligent,uncertainty driven aggregation scheme for streams of ordered sets

Data streams management has attracted the attention of many researchers during the recent years. The reason is that numerous devices generate huge amounts of data demanding an efficient processing scheme for delivering high quality applications. Data are reported through streams and stored into a number of partitions. Separation techniques facilitate the parallel management of data while intelligent methods are necessary to manage these multiple instances of data. Progressive analytics over huge amounts of data could be adopted to deliver partial responses and, possibly, to save time in the execution of applications. An interesting research domain is the efficient management of queries over multiple partitions. Usually, such queries demand responses in the form of ordered sets of objects (e.g., top-k queries). These ordered sets include objects in a ranked order and require novel mechanisms for deriving responses based on partial results. In this paper, we study a setting of multiple data partitions and propose an intelligent, uncertainty driven decision making mechanism that aims to respond to streams of queries. Our mechanism delivers an ordered set of objects over a number of partial ordered subsets retrieved by each partition of data. We envision that a number of query processors are placed in front of each partition and report progressive analytics to a Query Controller (QC). The QC receives queries, assigns the task to the underlying processors and decides the right time to deliver the final ordered set to the application. We propose an aggregation model for deriving the final ordered set of objects and a Fuzzy Logic (FL) inference process. We present a Type-2 FL system that decides when the QC should stop aggregating partial subsets and return the final response to the application. We report on the performance of the proposed mechanism through the execution of a large set of experiments. Our results deal with the throughput of the QC, the quality of the final ordered set of objects and the time required for delivering the final response.     

Probabilistic nearest neighbor query processing on distributed uncertain data

A nearest neighbor (NN) query, which returns the most similar object to a user-specified query object, plays an important role in a wide range of applications and hence has received considerable attention. In many such applications, e.g., sensor data collection and location-based services, objects are inherently uncertain. Furthermore, due to the ever increasing generation of massive datasets, the importance of distributed databases, which deal with such data objects, has been growing. One emerging challenge is to efficiently process probabilistic NN queries over distributed uncertain databases. The straightforward approach, that each local site forwards its own database to the central server, is communication-expensive, so we have to minimize communication cost for the NN object retrieval. In this paper, we focus on two important queries, namely top-k probable NN queries and probabilistic star queries, and propose efficient algorithms to process them over distributed uncertain databases. Extensive experiments on both real and synthetic data have demonstrated that our algorithms significantly reduce communication cost.     

Nearest and reverse nearest neighbor queries for moving objects

With the continued proliferation of wireless communications and advances in positioning technologies, algorithms for efficiently answering queries about large populations of moving objects are gaining interest. This paper proposes algorithms for k nearest and reverse k nearest neighbor queries on the current and anticipated future positions of points moving continuously in the plane. The former type of query returns k objects nearest to a query object for each time point during a time interval, while the latter returns the objects that have a specified query object as one of their k closest neighbors, again for each time point during a time interval. In addition, algorithms for so-called persistent and continuous variants of these queries are provided. The algorithms are based on the indexing of object positions represented as linear functions of time. The results of empirical performance experiments are reported.     

Uncertain top-<Emphasis Type="Italic">k</Emphasis> query processing in distributed environments

The top-k query on uncertain data set has been a very hot topic these years, and there have been many studies on uncertain top-k queries. Unfortunately, most of the existing algorithms only consider centralized processing environments, and they are not suitable for the large-scale data. In this paper, it is the first attempt to process probabilistic threshold top-k queries (an important uncertain top-k query, PT-k for short) in a distributed environment. We propose 3 efficient algorithms. The serial distributed approach adopts a new method, which only requires a few amount of calculations, to serially process PT-k queries in distributed environments. The global sorting first algorithm for PT-k query processing (GSP) is designed for improving the computation speed. In GSP, a distributed sorting operation is performed, and then we compute the candidates for PT-k queries in parallel. The query results can be computed by using a novel incremental method which can reduce the number of calculations. The local filtering first algorithm for PT-k query processing is designed for reducing the network overhead. Specifically, several filtering strategies are proposed to filter out redundant data locally, and then the incremental method in GSP is used to process the PT-k queries. Finally, the effectiveness of our proposed algorithms is verified through a series of experiments.     

A threshold-based algorithm for continuous monitoring of k nearest neighbors

Assume a set of moving objects and a central server that monitors their positions over time, while processing continuous nearest neighbor queries from geographically distributed clients. In order to always report up-to-date results, the server could constantly obtain the most recent position of all objects. However, this naive solution requires the transmission of a large number of rapid data streams corresponding to location updates. Intuitively, current information is necessary only for objects that may influence some query result (i.e., they may be included in the nearest neighbor set of some client). Motivated by this observation, we present a threshold-based algorithm for the continuous monitoring of nearest neighbors that minimizes the communication overhead between the server and the data objects. The proposed method can be used with multiple, static, or moving queries, for any distance definition, and does not require additional knowledge (e.g., velocity vectors) besides object locations.     

Efficient k-dominant skyline query over incomplete data using MapReduce

Skyline queries are extensively incorporated in various real-life applications by filtering uninteresting data objects. Sometimes, a skyline query may return so many results because it cannot control the retrieval conditions especially for highdimensional datasets. As an extension of skyline query, the kdominant skyline query reduces the control of the dimension by controlling the value of the parameter kto achieve the purpose of reducing the retrieval objects. In addition, with the continuous promotion of Bigdata applications, the data we acquired may not have the entire content that people wanted for some practically reasons of delivery failure, no power of battery, accidental loss, so that the data might be incomplete with missing values in some attributes. Obviously, the k-dominant skyline query algorithms of incomplete data depend on the user definition in some degree and the results cannot be shared. Meanwhile, the existing algorithms are unsuitable for directly used to the incomplete big data. Based on the above situations, this paper mainly studies k-dominant skyline query problem over incomplete dataset and combines this problem with the distributed structure like MapReduce environment. First, we propose an index structure over incomplete data, named incomplete data index based on dominate hierarchical tree (ID-DHT). Applying the bucket strategy, the incomplete data is divided into different buckets according to the dimensions of missing attributes. Second, we also put forward query algorithm for incomplete data in MapReduce environment, named MapReduce incomplete data based on dominant hierarchical tree algorithm (MR-ID-DHTA). The data in the bucket is allocated to the subspace according to the dominant condition by Map function. Reduce function controls the data according to the key value and returns the k-dominant skyline query result. The effective experiments demonstrate the validity and usability of our index structure and the algorithm.     

Processing SPARQL queries over distributed RDF graphs

We propose techniques for processing SPARQL queries over a large RDF graph in a distributed environment. We adopt a “partial evaluation and assembly” framework. Answering a SPARQL query Q is equivalent to finding subgraph matches of the query graph Q over RDF graph G. Based on properties of subgraph matching over a distributed graph, we introduce local partial match as partial answers in each fragment of RDF graph G. For assembly, we propose two methods: centralized and distributed assembly. We analyze our algorithms from both theoretically and experimentally. Extensive experiments over both real and benchmark RDF repositories of billions of triples confirm that our method is superior to the state-of-the-art methods in both the system’s performance and scalability.