Towards an Efficient Processing of Outranking-Based Preference Queries

POIs (points of interest) as well as users’ check-in information and their ratings on POIs have been widely studies in systems providing location-based services. We note that users usually have their own preferences for POI categories and their own network of friends. Therefore, we aim to provide for a group of users a new kind of POI-finding query that considers not only POI preferences of each user but also other aspects of location-based social networks such as users’ locations and POI ratings. We name such a new query as Spatial Group Preference (SGP) query. For a group of users, an SGP query returns top-k POIs that are much likely to satisfy the needs of users. Specially, we propose a new evaluation model that considers user preferences for user preferences for POI categories. Based on this model, we develop basic algorithms based on R-tree to evaluate SGP queries. Further, we design a new index structure called CR-tree to accelerate the query performance. We prove that CR-tree has better pruning efficiency than the traditional R-tree. We conduct experiments on a simulation dataset as well as two real datasets with respect to various configurations. The results suggest the efficiency of our proposal.

Many location-based applications are enabled by handling numerous moving queries over mobile objects. Efficient processing of such queries mainly relies on effective probing, i.e., polling the objects to obtain their current locations (required for processing the queries). With effective probing, one can monitor the current location of the objects with sufficient accuracy for the existing queries, by striking a balance between communication cost of probing and accuracy of the knowledge about current location of the objects. In this paper, we focus on location-based applications that reduce to processing a large set of proximity monitoring queries simultaneously, where each query continuously monitors if a pair of objects are within a certain predefined distance. Accordingly, we propose an effective object probing solution for efficient processing of proximity monitoring queries. In particular, with our proposed solution for the first time we formulate optimal probing as a batch processing problem and propose a method to prioritize probing the objects such that the total number of probes required to answer all queries is minimized. Our extensive experiments demonstrate the efficiency of our proposed solution for a wide range of applications involving up to hundreds of millions of queries.

The management of complex spatial objects in applications, such as geography and cartography, imposes stringent new requirements on spatial database systems, in particular on efficient query processing. As shown before, the performance of spatial query processing can be improved by decomposing complex spatial objects into simple components. Up to now, only decomposition techniques generating a linear number of very simple components, e.g. triangles or trapezoids, have been considered. In this paper, we will investigate the natural trade-off between the complexity of the components and the redundancy, i.e. the number of components, with respect to its effect on efficient query processing. In particular, we present two new decomposition methods generating a better balance between the complexity and the number of components than previously known techniques. We compare these new decomposition methods to the traditional undecomposed representation as well as to the well-known decomposition into convex polygons with respect to their performance in spatial query processing. This comparison points out that for a wide range of query selectivity the new decomposition techniques clearly outperform both the undecomposed representation and the convex decomposition method. More important than the absolute gain in performance by a factor of up to an order of magnitude is the robust performance of our new decomposition techniques over the whole range of query selectivity.

Query Processing is a key determinant in the overall performance of distributed databases. It requires processing of data at their respective sites and transmission of the same between them. These together constitute a distributed query processing strategy (DQP). DQP aims to arrive at an efficient query processing strategy for a given query. This strategy involves generation of efficient query plans for a distributed query. In case of distributed relational queries, the number of possible query plans grows exponentially with an increase in the number of relations accessed by the query. This number increases further when the relations, accessed by the query, have replicas at different sites. Such a large search space renders it infeasible to find optimal query plans. This paper presents a query plan generation algorithm that attempts to generate optimal query plans, for a given query, using genetic algorithm. The query plans so generated involve fewer sites, thus leading to efficient query processing. Further, experimental results show that the proposed algorithm converges quickly towards optimal query plans for an observed crossover and mutation probability.

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.

With the increasing in demand on location-based aware services and RFIDs, efficient processing of continuous queries over moving object streams becomes important. In this paper, we propose an efficient in-memory processing of continuous queries on themoving object streams.We model moving objects using function of time and use it in the prediction of usefulness of objects with respect to the continuous queries. To effectively utilize the limited memory, we derive several replacement policies to discard objects that are of no potential interest to the queries and design efficient algorithms with light data structures. Experimental studies are conducted and the results show that our proposed method is both memory and query efficient.

We define continual queries as a useful tool for monitoring of updated information. Continual queries are standing queries that monitor the source data and notify the users whenever new data matches the query. In addition to periodic refresh, continual queries include Epsilon Transaction concepts to allow users to specify query refresh based on the magnitude of updates. To support efficient processing of continual queries, we propose a differential re-evaluation algorithm (DRA), which exploits the structure and information contained in both the query expressions and the database update operations. The DRA design can be seen as a synthesis of previous research on differential files, incremental view maintenance, and active databases.

Finding all the occurrences of a twig pattern in an XML database is a core operation for efficient evaluation of XML queries. Some researches have proposed part solutions to process XML twig queries with AND,OR, or NOT predicates. However, very Little work has handled a twig query with three predicates comprehensively. In this paper, we propose a novel path-partitioned encoding scheme, and present a powerful XPattern extended from tree pattern with logic predicates. We also develop a holistic twig join algorithm, called MPTwig, which is designed for efficient matching an XML twig pattern with compound and nested predicates. We show that MPTwig based on path-partitioned encoding scheme guarantee the I/O and CPU optimality. Finally, experimental results on a representative data set indicate that the proposed algorithm performs significantly.

Due to the growing popularity of indoor location-based services, indoor data management has received significant research attention in the past few years. However, we observe that the existing indexing and query processing techniques for the indoor space do not fully exploit the properties of the indoor space. Consequently, they provide below par performance which makes them unsuitable for large indoor venues with high query workloads. In this demonstration, we present IndoorViz, a new indoor spatial data management system that integrates three novel index structures proposed in [4] and [6] with well designed query processing algorithms and 3D visualization functions. The IndoorViz is able to support indoor spatial object indexing, efficient query processing and interactive 3D display.

Preference queries are considered as a major necessity tool in today’s database management system (DBMS). Adopting preference queries in the database application systems enable users to determine more than one objective in the submitted query which result into more accurate results
\ncompared to the traditional queries. Preference queries prefer one data item (tuple) p over the other data item (tuple) q if and only if p is better than q in all dimensions (attributes) and not worse than q in at least one dimension (attribute). Several preference evaluation techniques for preference queries have been proposed which aimed at finding the “best” results that meet the user preferences. These include but not limited to top-k, skyline, ranked skylines, k-representative dominance, k-dominance,top-k dominating, and k-frequency. This paper attempts to survey and analyze the following preference
\nevaluation techniques of query processing in database systems: top-k, skyline, top-k dominating, k-dominance, and k-frequency by highlighting the strengths and the weaknesses of each technique.

Multi-dimensional data structures are applied in many real index applications, i.e. data mining, indexing multimedia data, indexing of text documents and so on. Many index structures and algorithms have been proposed. There are two major approaches to multi-dimensional indexing: data structures to indexing metric and vector spaces. R-trees, R*-trees and (B)UB-trees are representatives of the vector data structures. These data structures provide efficient processing of many types of queries, i.e. point queries, range queries and so on. As far as the vector data structures are concerned, the range query retrieves all points in defined hyper box in an n-dimensional space. The narrow range query is an important type of the range query. Its processing is inefficient in vector data structures. Moreover, the efficiency decreases as the dimension of the indexed space increases. We depict an application of the signature for more efficient processing of narrow range queries. The approach puts the signature into the multi-dimensional data structures like R-tree or UB-tree but original functionalities are preserved, i.e. the range query algorithm for general range query. The novel data structure is called the signature data structure, e.g., Signature R-tree or Signature UB-tree.

The domain of statistical and scientific databases is targeted here, and the class of aggregate queries which are very often encountered in this domain is considered. Such a query is aimed at retrieving some aggregate characteristics of the raw data. The tree-based statistics access method (TBSAM), which provides support for the efficient processing of aggregate queries, is presented. It is related to the B/sup +/-tree and also processes the B/sup +/-tree's efficient update properties. Complementing TBSAM is the provision of a grouped update algorithm for minimizing expensive indexed database updates. >

An image data warehousing extends a conventional data warehousing to also manipulate images represented by feature vectors and attributes for similarity search. A challenge that arises is the efficient processing of analytical queries extended with a similarity search predicate. These queries have a high computational cost since they require the processing of costly star join operations and distance calculations in the same setting. We consider applications that manage huge volumes of data, where the use of parallel and distributed data processing frameworks is needed. In this article, we introduce two methods to efficiently solve this challenge in Spark. BrOmnImg is based on the integration of the broadcast join and the Omni techniques for the processing of the star join operation and the distance calculations, respectively. BrOmnImgCF extends BrOmnImg by using the conventional predicate to further reduce the number of distance calculations. Compared with the closest method available in the literature, BrOmnImg reduced the time spent on query processing by up to about 65%. Compared with BrOmnImg, BrOmnImgCF improved the performance by up to about 54%.

Crowd-sourcing is a powerful solution for finding correct answers to expensive and unanswered queries in databases, including those with uncertain and incomplete data. Attempts to use crowd-sourcing to exploit human abilities to process these expensive queries using human workers have helped to provide accurate results by utilising the available data in the crowd. Crowd-sourcing database systems (CSDBs) combine the knowledge of the crowd with a relational database by using some variant of a relational database with minor changes. This paper surveys the leading studies conducted in the area of query processing with regard to both traditional and preference queries in CSDBs. The focus of this work is on highlighting the strengths and the weakness of each approach. A detailed discussion of current and future trends research associated with query processing in the area of CSDBs is also presented.

In many advanced applications, data are described by multiple high-dimensional features. Moreover, different queries may weight these features differently; some may not even specify all the features. In this paper, we propose our solution to support efficient query processing in these applications. We devise a novel representation that compactly captures f features into two components. The first component is a 2D vector that reflects a distance range (minimum and maximum values) of the f features with respect to a reference point (the center of the space) in a metric space and the second component is a bit signature, with two bits per dimension, obtained by analyzing each feature's descending energy histogram. This representation enables two levels of filtering: the first component prunes away points that do not share similar distance ranges, while the bit signature filters away points based on the dimensions of the relevant features. Moreover, the representation facilitates the use of a single index structure to further speed up processing. We employ the classical B/sup +/-tree for this purpose. We also propose a KNN search algorithm that exploits the access orders of critical dimensions of highly selective features and partial distances to prune the search space more effectively. Our extensive experiments on both real-life and synthetic data sets show that the proposed solution offers significant performance advantages over sequential scan and retrieval methods using single and multiple VA-files.