Range Query Processing Research Articles

Main Memory Evaluation of Monitoring Queries Over Moving Objects

In this paper we evaluate several in-memory algorithms for efficient and scalable processing of continuous range queries over collections of moving objects. Constant updates to the index are avoided by query indexing. No constraints are imposed on the speed or path of moving objects or fraction of objects that move at any moment in time. We present a detailed analysis of a grid approach which shows the best results for both skewed and uniform data. A sorting based optimization is developed for significantly improving the cache hit-rate. Experimental evaluation establishes that indexing queries using the grid index yields orders of magnitude better performance than other index structures such as R*-trees.

An efficient algorithm for hyperspherical range query processing in high-dimensional data space

Recently, similarity search on high-dimensional feature vectors has become an important search paradigm for various multimedia retrieval applications. The technique used is to map the data items as points into a high-dimensional feature space. The feature space is usually indexed using a multi-dimensional index structure. Similarity search then corresponds to the hyperspherical range query that returns all objects within a threshold level of similarity to the query object [1]. The hyperspherical range query for similarity search can be defined as follows:

SEE: a Spatial Exploration Environment based on a direct-manipulation paradigm

The need to provide effective tools for analyzing and querying spatial data is becoming increasingly important with the explosion of data in applications such as geographic information systems, image databases, CAD, and remote sensing. The SEE (Spatial Exploration Environment) is the first effort at applying direct-manipulation visual information seeking (VIS) techniques to spatial data analysis by visually querying as well as browsing spatial data and reviewing the visual results for trend analysis. The SEE system incorporates a visual query language (SVIQUEL) that allows users to specify the relative spatial position (both topology and direction) between objects using direct manipulation. The quantitative SVIQVEL sliders (S-sliders) are complemented by the qualitative active-picture-for-querying (APIQ) interface that allows the user to specify qualitative relative position queries. APIQ provides qualitative visual representations of the quantitative query specified by the S-sliders. This increases the utility of the system for spatial browsing and spatial trend discovery with no particular query in mind. The SVIQUEL queries are processed using a k-Bucket index structure specifically tuned for incremental processing of the multidimensional range queries that represent the class of queries that can be expressed by SVIQUEL. We have also designed a tightly integrated map visualization that helps to preserve the spatial context and a bar visualization that provides a qualitative abstraction of aggregates.

An efficient bitmap encoding scheme for selection queries

Bitmap indexes are useful in processing complex queries in decision support systems, and they have been implemented in several commercial database systems. A key design parameter for bitmap indexes is the encoding scheme , which determines the bits that are set to 1 in each bitmap in an index. While the relative performance of the two existing bitmap encoding schemes for simple selection queries of the form “ v 1 ≤ A ≤ v 2 ” is known (specifically, one of the encoding schemes is better for processing equality queries; i.e., v 1 = v 2 , while the other is better for processing range queries; i.e., v 1 < v 2 ), it remains an open question whether these two encoding schemes are indeed optimal for their respective query classes in the sense that there is no other encoding scheme with better space-time tradeoff. In this paper, we establish a number of optimality results for the existing encoding schemes; in particular, we prove that neither of the two known schemes is optimal for the class of two-sided range queries. We also propose a new encoding scheme and prove that it is optimal for that class. Finally, we present an experimental study comparing the performance of the new encoding scheme with that of the existing ones as well as four hybrid encoding schemes for both simple selection queries and the more general class of membership queries of the form “ A ∈ { v 1 , v 2 , .…, v k }”. These results demonstrate that the new encoding scheme has an overall better space-time performance than existing schemes.

Updating and Querying Databases that Track Mobile Units

In this paper, we consider databases representing information about moving objects (e.g., vehicles), particularly their location. We address the problems of updating and querying such databases. Specifically, the update problem is to determine when the location of a moving object in the database (namely its database location) should be updated. We answer this question by proposing an information cost model that captures uncertainty, deviation, and communication. Then we analyze dead-reckoning policies, namely policies that update the database location whenever the distance between the actual location and the database location exceeds a given threshold, x. Dead-reckoning is the prevalent approach in military applications, and our cost model enables us to determine the threshold x. We propose several dead-reckoning policies and we compare their performance by simulation. Then we consider the problem of processing range queries in the database. An example of a range query is ‘retrieve the objects that are currently inside a given polygon P′. We propose a probabilistic approach to solve the problem. Namely, the DBMS will answer such a query with a set of objects, each of which is associated with a probability that the object is inside P.

The pyramid-technique

In this paper, we propose the Pyramid-Technique, a new indexing method for high-dimensional data spaces. The Pyramid-Technique is highly adapted to range query processing using the maximum metric L max . In contrast to all other index structures, the performance of the Pyramid-Technique does not deteriorate when processing range queries on data of higher dimensionality. The Pyramid-Technique is based on a special partitioning strategy which is optimized for high-dimensional data. The basic idea is to divide the data space first into 2d pyramids sharing the center point of the space as a top. In a second step, the single pyramids are cut into slices parallel to the basis of the pyramid. These slices from the data pages. Furthermore, we show that this partition provides a mapping from the given d-dimensional space to a 1-dimensional space. Therefore, we are able to use a B+-tree to manage the transformed data. As an analytical evaluation of our technique for hypercube range queries and uniform data distribution shows, the Pyramid-Technique clearly outperforms index structures using other partitioning strategies. To demonstrate the practical relevance of our technique, we experimentally compared the Pyramid-Technique with the X-tree, the Hilbert R-tree, and the Linear Scan. The results of our experiments using both, synthetic and real data, demonstrate that the Pyramid-Technique outperforms the X-tree and the Hilbert R-tree by a factor of up to 14 (number of page accesses) and up to 2500 (total elapsed time) for range queries.

On the difficulty of range searching

We consider the general problem of (2-dimensional) range reporting allowing arbitrarily convex queries. We show that using a traditional approach, even when incorporating techniques like those used in fusion trees, a polylogarithmic query time cannot be achieved unless more than linear space is used. Our arguments are based on a new non-trivial lower bound in a model of computation which, in contrast to the pointer machine model, allows for the use of arrays and bit manipulation. The crucial property of our model, Layered partitions, is that it can be used to describe all known algorithms for processing range queries, as well as many other data structures used to represent multi-dimensional data. We show that Ω( logn logT(n) ) partitions must be used to allow queries in O( T( n) + k) time, for n total and k reported elements, and for any growing function T( n).

Retrieving similar pictures from iconic databases using G-tree

G-tree, combining the features of both grids and B-trees, is a disk-based spatial data structure for organizing multidimensional data. This paper presents an efficient access method for retrieving similar pictures from iconic databases using G-tree. The proposed method preserves the merits of G-tree, and hence indexing the pictures in the database, processing range queries for similarity retrieval, inserting/deleting a picture into/from the database, and effective memory utilization can be achieved in practical applications. In order to improve the acceptability of similarity retrieval as compared to previous works, we define a similarity function by the usage of query ranges, and each range corresponds to one object in the query. This similarity function reflects both the direction and the distance knowledge among objects in the pictures. Besides, the result of similarity retrieval can be further refined by simply adjusting the query ranges or increasing the threshold value of the predefined similarity function.

Range query processing in multidisk systems

In order to reduce the disk access time, a database can be stored on several simultaneously accessible disks. In this paper, we are concerned with the dynamicd-attribute database allocation problem for range queries. An allocation method, called coordinate modulo allocation method, is proposed to allocate data in ad-attribute database among disks so that the maximum disk accessing concurrency can be achieved for range queries. Our analysis and experiments show that the method achieves the optimum or near-optimum parallelism for range queries. The paper offers the conditions under which the method is optimal. The worst case bounds of the performance of the method are also given. In addition, the parallel algorithm of processing range queries is described at the end of the paper. The method has been used in the statistic and scientific database management system which is being designed by us.

Associative searching in multiple storage units

A file maintenance model, called the multiple random access storage units model, is introduced. Storage units can be accessed simultaneously, and the parallel processing of an associative query is achieved by distributing data evenly among the storage units. Maximum parallelism is obtained when data satisfying an associative query are evenly distributed for every possible query. An allocation scheme called M -cycle allocation is proposed to maintain large files of data on multiple random access storage units. The allocation scheme provides an efficient and straightforward indexing over multidimensional key spaces and supports the parallel processing of orthogonal range queries. Our analysis shows that M -cycle allocation achieves the near-optimum parallelism for processing the orthogonal range queries. Moreover, there is no duplication of records and no increase in insertion/deletion cost.

On the number of range queries in k-space

A range query on a set of points in a k-dimensions coordinate space asks for all points lying within a hyper-rectangle specified by ranges of permissible values for each of the coordinates. In this paper we regard as identical any two range queries which return the same set of points. We then investigate the number of range queries possible on a set—given a set of N points in k-space, what is the maximum number of distinct subsets that may be specified by giving bounding hyper-rectangles? The bounds we find for this number (as a function of N and k) are substantial improvements over previous results, and tighten a lower bound on the computational time required to process range queries. We also report some results concerning the expected number of range queries on random distributions of points.