Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data

Reverse nearest neighbor (RNN) search is very crucial in many real applications. In particular, given a database and a query object, an RNN query retrieves all the data objects in the database that have the query object as their nearest neighbors. Often, due to limitation of measurement devices, environmental disturbance, or characteristics of applications (for example, monitoring moving objects), data obtained from the real world are uncertain (imprecise). Therefore, previous approaches proposed for answering an RNN query over exact (precise) database cannot be directly applied to the uncertain scenario. In this paper, we re-define the RNN query in the context of uncertain databases, namely probabilistic reverse nearest neighbor (PRNN) query, which obtains data objects with probabilities of being RNNs greater than or equal to a user-specified threshold. Since the retrieval of a PRNN query requires accessing all the objects in the database, which is quite costly, we also propose an effective pruning method, called geometric pruning (GP), that significantly reduces the PRNN search space yet without introducing any false dismissals. Furthermore, we present an efficient PRNN query procedure that seamlessly integrates our pruning method. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed GP-based PRNN query processing approach, under various experimental settings.     

Finding the least influenced set in uncertain databases

Due to the inherent existence of uncertainty in many real-world applications, in this paper, we investigate an important query in uncertain databases, namely probabilistic least influenced set (PLIS) query, which retrieves all the uncertain objects in an uncertain database such that they are the least affected by a given query object with high probabilities. Such a PLIS query is useful in applications such as business planning. We propose and tackle both monochromatic and bichromatic versions (i.e. M-PLIS and B-PLIS, respectively) of the PLIS query. In order to efficiently answer PLIS queries, we present three pruning methods, MINMAX, Regional, and Candidate pruning, which can effectively reduce the PLIS search space. The proposed pruning methods can be seamlessly integrated into efficient query procedures. Moreover, we also study important variants of PLIS query with uncertain query object (i.e. UQ-PLIS). Furthermore, we formulate and tackle the PLIS problem on uncertain moving objects (i.e. UMOD-PLIS). Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches under various settings.     

Shooting top-<Emphasis Type="Italic">k</Emphasis> stars in uncertain databases

Query processing in the uncertain database has played an important role in many real-world applications due to the wide existence of uncertain data. Although many previous techniques can correctly handle precise data, they are not directly applicable to the uncertain scenario. In this article, we investigate and propose a novel query, namely probabilistic top-k star (PTkS) query, which aims to retrieve k objects in an uncertain database that are “closest” to a static/dynamic query point, considering both distance and probability aspects. In order to efficiently answer PTkS queries with a static/moving query point, we propose effective pruning methods to reduce the PTkS search space, which can be seamlessly integrated into an efficient query procedure. Finally, extensive experiments have demonstrated the efficiency and effectiveness of our proposed PTkS approaches on both real and synthetic data sets, under various parameter settings.     

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.     

Efficient Processing of Metric Skyline Queries

Skyline query is of great importance in many applications, such as multi-criteria decision making and business planning. In particular, a skyline point is a data object in the database whose attribute vector is not dominated by that of any other objects. Previous methods to retrieve skyline points usually assume static data objects in the database (i.e. their attribute vectors are fixed), whereas several recent work focus on skyline queries with dynamic attributes. In this paper, we propose a novel variant of skyline queries, namely metric skyline, whose dynamic attributes are defined in the metric space (i.e. not limited to the Euclidean space). We illustrate an efficient and effective pruning mechanism to answer metric skyline queries through a metric index. Most importantly, we formalize the query performance of the metric skyline query in terms of the pruning power, by a cost model, in light of which we construct an optimized metric index aiming to maximize the pruning power of metric skyline queries. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed pruning techniques as well as the constructed index in answering metric skyline queries.     

Ranked Query Processing in Uncertain Databases

Recently, many new applications, such as sensor data monitoring and mobile device tracking, raise up the issue of uncertain data management. Compared to "certain” data, the data in the uncertain database are not exact points, which, instead, often reside within a region. In this paper, we study the ranked queries over uncertain data. In fact, ranked queries have been studied extensively in traditional database literature due to their popularity in many applications, such as decision making, recommendation raising, and data mining tasks. Many proposals have been made in order to improve the efficiency in answering ranked queries. However, the existing approaches are all based on the assumption that the underlying data are exact (or certain). Due to the intrinsic differences between uncertain and certain data, these methods are designed only for ranked queries in certain databases and cannot be applied to uncertain case directly. Motivated by this, we propose novel solutions to speed up the probabilistic ranked query (PRank) with monotonic preference functions over the uncertain database. Specifically, we introduce two effective pruning methods, spatial and probabilistic pruning, to help reduce the PRank search space. A special case of PRank with linear preference functions is also studied. Then, we seamlessly integrate these pruning heuristics into the PRank query procedure. Furthermore, we propose and tackle the PRank query processing over the join of two distinct uncertain databases. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches in answering PRank queries, in terms of both wall clock time and the number of candidates to be refined.     

不确定数据上范围受限的最近邻查询算法

不确定数据的查询处理是数据库领域近年来的热点研究课题.提出一种不确定数据上的范围受限的最近邻查询.给定不确定数据集D={o1,o2,…,on},范围约束R是一个简单多边形,q为一固定的查询点,范围受限的最近邻查询返回的是在数据集D中,既满足范围约束R,又能成为查询点q的最近邻的对象集合.为处理该查询,提出了范围受限的最近邻核心集的概念和范围受限的最近邻核心集的查找算法.并提出一种计算范围受限的最近邻候选集的优化方法,降低了查询代价.最后通过实验验证了该算法的有效性.     

Continuous monitoring of skylines over uncertain data streams

Uncertain data are inevitable in many applications due to various factors such as the limitations of measuring equipment and delays in data updates. Although modeling and querying uncertain data have recently attracted considerable attention from the database community, there are still many critical issues to be resolved with respect to conducting advanced analysis on uncertain data. In this paper, we study the execution of the probabilistic skyline query over uncertain data streams. We propose a novel sliding window skyline model where an uncertain tuple may take the probability to be in the skyline at a certain timestamp t. Formally, a Wp-Skyline(p, t) contains all the tuples whose probabilities of becoming skylines are at least p at timestamp t. However, in the stream environment, computing a probabilistic skyline on a large number of uncertain tuples within the sliding window is a daunting task in practice. In order to efficiently calculate Wp-Skyline, we propose an efficient and effective approach, namely the candidate list approach, which maintains lists of candidates that might become skylines in future sliding windows. We also propose algorithms that continuously monitor the newly incoming and expired data to maintain the skyline candidate set incrementally. To further reduce the computation cost of deciding whether or not a candidate tuple belongs to the skyline, we propose an enhanced refinement strategy that is based on a multi-dimensional indexing structure combined with a grouping-and-conquer strategy. To validate the effectiveness of our proposed approach, we conduct extensive experiments on both real and synthetic data sets and make comparisons with basic techniques.     

Spatial inverse query processing

Traditional spatial queries return, for a given query object q, all database objects that satisfy a given predicate, such as epsilon range and k-nearest neighbors. This paper defines and studies inverse spatial queries, which, given a subset of database objects Q and a query predicate, return all objects which, if used as query objects with the predicate, contain Q in their result. We first show a straightforward solution for answering inverse spatial queries for any query predicate. Then, we propose a filter-and-refinement framework that can be used to improve efficiency. We show how to apply this framework on a variety of inverse queries, using appropriate space pruning strategies. In particular, we propose solutions for inverse epsilon range queries, inverse k-nearest neighbor queries, and inverse skyline queries. Furthermore, we show how to relax the definition of inverse queries in order to ensure non-empty result sets. Our experiments show that our framework is significantly more efficient than naive approaches.     

Enhancing accuracy and expressive power of range query answers over incomplete spatial databases via a novel reasoning approach

Modern spatial database applications built on top of distributed and heterogeneous spatial information sources such as conventional spatial databases underlying Geographical Information Systems (GIS), spatial data files and spatial information acquired or inferred from the Web, suffer from data integration and topological consistency problems. This more-and-more conveys in incomplete information, which makes answering range queries over incomplete spatial databases a leading research challenge in spatial database systems research. A significant instance of this setting is represented by the application scenario in which the geometrical information on a sub-set of spatial database objects is incomplete whereas the spatial database still stores topological relations among these objects (e.g., containment relations). Focusing on the spatial database application scenario above, in this paper we propose and experimentally assess a novel technique for efficiently answering range queries over incomplete spatial databases via integrating geometrical information and topological reasoning. We also propose I-SQE (Spatial Query Engine for Incomplete Information), an innovative query engine implementing this technique. Our proposed technique results not only effective but also efficient against both synthetic and real-life spatial data sets, and it finally allows us to enhance the quality and the expressive power of retrieved answers by meaningfully taking advantages from the amenity of representing spatial database objects via both the geometrical and the topological level.     

Vague continuous K-nearest neighbor queries over moving objects with uncertain velocity in road networks

Recent research has focused on Continuous K Nearest Neighbor (CKNN) queries in road networks, where the queries and the data objects are moving. Most existing approaches assume the fixed velocity of moving objects. The release of fixed moving velocity makes the query process slowly due to the significant repetitive query cost. In this paper, we study CKNN queries over moving objects with uncertain velocity in road networks. A Distance Interval Model (DIM) is designed to calculate the minimal and maximal road network distances between moving objects and query point. Furthermore, we propose a novel Possibility-based Vague KNN (PVKNN) algorithm to process the query efficiently, which determines the CKNN query results with possibility within each division time subinterval of given time interval by applying the vague set theory. In the PVKNN algorithm, the query efficiency can be improved significantly with the pruning, distilling and possibility-ranking phases. With these phases, the objects candidates are scaled down and the given time interval is divided into subintervals to reduce the repetitive query cost. In addition, an index structure TPR^uv-Tree is designed to efficiently index moving objects with uncertain velocity in road network by involving edge connection and moving objects information. Experiments with simulation and comparison show that significant improvement in the performance of efficiency can be achieved with our proposed algorithms.     

Uncertain Distance-Based Range Queries over Uncertain Moving Objects

Distance-based range search is crucial in many real applications. In particular, given a database and a query issuer, a distance-based range search retrieves all the objects in the database whose distances from the query issuer are less than or equal to a given threshold. Often, due to the accuracy of positioning devices, updating protocols or characteristics of applications (for example, location privacy protection), data obtained from real world are imprecise or uncertain. Therefore, existing approaches over exact databases cannot be directly applied to the uncertain scenario. In this paper, we redefine the distance-based range query in the context of uncertain databases, namely the probabilistic uncertain distance-based range (PUDR) queries, which obtain objects with confidence guarantees. We categorize the topological relationships between uncertain objects and uncertain search ranges into six cases and present the probability evaluation in each case. It is verified by experiments that our approach outperform Monte-Carlo method utilized in most existing work in precision and time cost for uniform uncertainty distribution. This approach approximates the probabilities of objects following other practical uncertainty distribution, such as Gaussian distribution with acceptable errors. Since the retrieval of a PUDR query requires accessing all the objects in the databases, which is quite costly, we propose spatial pruning and probabilistic pruning techniques to reduce the search space. Two metrics, false positive rate and false negative rate are introduced to measure the qualities of query results. An extensive empirical study has been conducted to demonstrate the efficiency and effectiveness of our proposed algorithms under various experimental settings.     

Parallelizing skyline queries over uncertain data streams with sliding window partitioning and grid index

Skyline query processing over uncertain data streams has attracted considerable attention in database community recently, due to its importance in helping users make intelligent decisions over complex data in many real applications. Although lots of recent efforts have been conducted to the skyline computation over data streams in a centralized environment typically with one processor, they cannot be well adapted to the skyline queries over complex uncertain streaming data, due to the computational complexity of the query and the limited processing capability. Furthermore, none of the existing studies on parallel skyline computation can effectively address the skyline query problem over uncertain data streams, as they are all developed to address the problem of parallel skyline queries over static certain data sets. In this paper, we formally define the parallel query problem over uncertain data streams with the sliding window streaming model. Particularly, for the first time, we propose an effective framework, named distributed parallel framework to address the problem based on the sliding window partitioning. Furthermore, we propose an efficient approach (parallel streaming skyline) to further optimize the parallel skyline computation with an optimized streaming item mapping strategy and the grid index. Extensive experiments with real deployment over synthetic and real data are conducted to demonstrate the effectiveness and efficiency of the proposed techniques.     

Efficient fuzzy ranking queries in uncertain databases

Recently, uncertain data have received dramatic attention along with technical advances on geographical tracking, sensor network and RFID etc. Also, ranking queries over uncertain data has become a research focus of uncertain data management. With dramatically growing applications of fuzzy set theory, lots of queries involving fuzzy conditions appear nowadays. These fuzzy conditions are widely applied for querying over uncertain data. For instance, in the weather monitoring system, weather data are inherent uncertainty due to some measurement errors. Weather data depicting heavy rain are desired, where ??heavy?? is ambiguous in the fuzzy query. However, fuzzy queries cannot ensure returning expected results from uncertain databases. In this paper, we study a novel kind of ranking queries, Fuzzy Ranking queries (FRanking queries) which extend the traditional notion of ranking queries. FRanking queries are able to handle fuzzy queries submitted by users and return k results which are the most likely to satisfy fuzzy queries in uncertain databases. Due to fuzzy query conditions, the ranks of tuples cannot be evaluated by existing ranking functions. We propose Fuzzy Ranking Function to calculate tuples?? ranks in uncertain databases for both attribute-level and tuple-level uncertainty models. Our ranking function take both the uncertainty and fuzzy semantics into account. FRanking queries are formally defined based on Fuzzy Ranking Function. In the processing of answering FRanking queries, we present a pruning method which safely prunes unnecessary tuples to reduce the search space. To further improve the efficiency, we design an efficient algorithm, namely Incremental Membership Algorithm (IMA) which efficiently answers FRanking queries by evaluating the ranks of incremental tuples under each threshold for the fuzzy set. We demonstrate the effectiveness and efficiency of our methods through the theoretical analysis and experiments with synthetic and real datasets.     

Probabilistic inverse ranking queries in uncertain databases

Query processing in the uncertain database has become increasingly important due to the wide existence of uncertain data in many real applications. Different from handling precise data, the uncertain query processing needs to consider the data uncertainty and answer queries with confidence guarantees. In this paper, we formulate and tackle an important query, namely probabilistic inverse ranking (PIR) query, which retrieves possible ranks of a given query object in an uncertain database with confidence above a probability threshold. We present effective pruning methods to reduce the PIR search space, which can be seamlessly integrated into an efficient query procedure. Moreover, we tackle the problem of PIR query processing in high dimensional spaces, which reduces high dimensional uncertain data to a lower dimensional space. Furthermore, we study three interesting and useful aggregate PIR queries, that is, MAX, top-m, and AVG? PIRs. Moreover, we also study an important query type, PIR with uncertain query object (namely UQ-PIR), and design specific rules to facilitate the pruning. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approaches over both real and synthetic data sets, under various experimental settings.     

Ranking queries on uncertain data

Uncertain data is inherent in a few important applications. It is far from trivial to extend ranking queries (also known as top-k queries), a popular type of queries on certain data, to uncertain data. In this paper, we cast ranking queries on uncertain data using three parameters: rank threshold k, probability threshold p, and answer set size threshold l. Systematically, we identify four types of ranking queries on uncertain data. First, a probability threshold top-k query computes the uncertain records taking a probability of at least p to be in the top-k list. Second, a top-(k, l) query returns the top-l uncertain records whose probabilities of being ranked among top-k are the largest. Third, the p-rank of an uncertain record is the smallest number k such that the record takes a probability of at least p to be ranked in the top-k list. A rank threshold top-k query retrieves the records whose p-ranks are at most k. Last, a top-(p, l) query returns the top-l uncertain records with the smallest p-ranks. To answer such ranking queries, we present an efficient exact algorithm, a fast sampling algorithm, and a Poisson approximation-based algorithm. To answer top-(k, l) queries and top-(p, l) queries, we propose PRist+, a compact index. An efficient index construction algorithm and efficacious query answering methods are developed for PRist+. An empirical study using real and synthetic data sets verifies the effectiveness of the probabilistic ranking queries and the efficiency of our methods.     

Probabilistic Group Nearest Neighbor Queries in Uncertain Databases

The importance of query processing over uncertain data has recently arisen due to its wide usage in many real-world applications. In the context of uncertain databases, previous works have studied many query types such as nearest neighbor query, range query, top-k query, skyline query, and similarity join. In this paper, we focus on another important query, namely, probabilistic group nearest neighbor (PGNN) query, in the uncertain database, which also has many applications. Specifically, given a set, Q, of query points, a PGNN query retrieves data objects that minimize the aggregate distance (e.g., sum, min, and max) to query set Q. Due to the inherent uncertainty of data objects, previous techniques to answer group nearest neighbor (GNN) query cannot be directly applied to our PGNN problem. Motivated by this, we propose effective pruning methods, namely, spatial pruning and probabilistic pruning, to reduce the PGNN search space, which can be seamlessly integrated into our PGNN query procedure. Extensive experiments have demonstrated the efficiency and effectiveness of our proposed approach, in terms of the wall clock time and the speed-up ratio against linear scan.     

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 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.     

A highly optimized algorithm for continuous intersection join queries over moving objects

Given two sets of moving objects with nonzero extents, the continuous intersection join query reports every pair of intersecting objects, one from each of the two moving object sets, for every timestamp. This type of queries is important for a number of applications, e.g., in the multi-billion dollar computer game industry, massively multiplayer online games like World of Warcraft need to monitor the intersection among players’ attack ranges and render players’ interaction in real time. The computational cost of a straightforward algorithm or an algorithm adapted from another query type is prohibitive, and answering the query in real time poses a great challenge. Those algorithms compute the query answer for either too long or too short a time interval, which results in either a very large computation cost per answer update or too frequent answer updates, respectively. This observation motivates us to optimize the query processing in the time dimension. In this study, we achieve this optimization by introducing the new concept of time-constrained (TC) processing. Further, TC processing enables a set of effective improvement techniques on traditional intersection join algorithms. Finally, we provide a method to find the optimal value for an important parameter required in our technique, the maximum update interval. As a result, we achieve a highly optimized algorithm for processing continuous intersection join queries on moving objects. With a thorough experimental study, we show that our algorithm outperforms the best adapted existing solution by several orders of magnitude. We also validate the accuracy of our cost model and its effectiveness in optimizing the performance.