The management of geometric objects is a prime example of an application where efficiency is the bottleneck; this bottleneck cannot be eliminated without using suitable access structures. The most popular approach for handling complex spatial objects in spatial access methods is to use their minimum bounding boxes as a geometric key. Obviously, the rough approximation by bounding boxes provides a fast but inaccurate filter for the set of answers to a query. In order to speed up the query processing by a better approximation quality, we investigate six different types of approximations. Depending on the complexity of the objects and the type of queries, the approximations 5-corner, ellipse and rotated bounding box clearly outperform the bounding box. An important ingredient of our approach is to organize these approximations in efficient spatial access

Observing that networks are ubiquitous in applications for spatial databases, we define a new data model and query language that especially supports graph structures. This model integrates concepts of functional data modeling with order-sorted algebra. Besides object and data type hierar-chies graphs are available as an explicit modeling tool, and graph operations are part of the query lan-guage. Graphs have three classes of components, namely nodes, edges, and explicit paths. These are at the same time object types within the object type hierarchy and can be used like any other type. Explicit paths are useful because “real world ” objects often correspond to paths in a network. Further-more, a dynamic generalization concept is introduced to handle heterogeneous collections of objects in a query. In connection with spatial data types this leads to powerful modeling and querying capa-bilities for spatial databases, in particular for spatially embedded networks such as highways, rivers, public transport, and so forth. We use multi-level order-sorted algebra as a formal framework for the specification of our model. Roughly spoken, the first level algebra defines types and operations of the query language whereas the second level algebra defines kinds (collections of types) and type con-structors as functions between kinds and so provides the types that can be used at the first level.

Index structures for multiple sets can be classified into those that group entries according to their key values and those that group entries according to their set membership. The former are particularly suited for exact match queries on all indexed sets, the latter especially support range queries on a small number of all indexed sets. The goal is to thoroughly evaluate the performance of both grouping strategies. There exist two reasons for adding a new index structure to the evaluation: (1) The performance potentials of set grouping index structures are not yet fully exploited. (2) Up to now, the database administrator has to choose between key grouping and set grouping index structures, supporting either exact match or range queries. What is needed is a more flexible index structure that can be tuned to a given query mix containing both, exact match and range queries. These two reasons led us to the development of the CG-tree. The focus of the paper is on intr...

Abstract. In this paper, we propose a new technique for multidimensional query processing which can be widely applied in database systems. Our new technique, called tree striping, generalizes the well-known inverted lists and multidimensional indexing approaches. A theoretical analysis of our generalized technique shows that both, inverted lists and multidimensional indexing approaches, are far from being optimal. A consequence of our analysis is that the use of a set of multidimensional indexes provides considerable improvements over one d-dimensional index (multidimensional indexing) or d one-dimensional indexes (inverted lists). The basic idea of tree striping is to use the optimal number k of lower-dimensional indexes determined by our theoretical analysis for efficient query processing. We confirm our theoretical results by an experimental evaluation on large amounts of real and synthetic data. The results show a speed-up of up to 310 % over the multidimensional indexing approach and a speed-up factor of up to 123 (12,300%) over the inverted-lists approach. 1.

The role of indexing in query optimization is well-understood in the database community. Indexes enable efficient access to a subset of a database. There have been many studies on indexing within the context of relational database management systems (RDBMS). The most popular indexing techniques are based on B-trees [1]. However, these index structures do not apply for images in Multimedia Database Systems (MMDBS). Some novel index structures to retrieve images have been proposed. In this report 1 , we discuss some issues concerning these advanced indexing techniques, and report the results. 1 This research has been supported by a grant from the Canadian Institute for Telecommunication Research (CITR), a Federal Network of Centre of Excellence funded by the Government of Canada. Contents 1 Introduction 4 2 Image representations 5 2.1 Texture features : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : : 6 2.2 Color features : : : : : : : : : : : : : : : : : :...

Recent content-based similar shape retrieval techniques are mostly based on either the global invariant statistics derived from the entire shape or the primitive features computed from a shape's local regions. Global features are easy to compute but do not accurately reflect a real shape, hence, have a limited discrimination ability. On the contrary, primitive local features are computationally intensive but offer precise shape descriptions. We observed that the effectiveness and efficiency of the similar shape retrieval can be enhanced by integrating global and local features embedded in the images. Using this approach, the retrieval is a two-stage process with the global features serve as an initial filtering mechanism to eliminate the significantly dissimilar images. The remaining images are then considered for the subsequent local feature based similarity matching. The two-stage retrieval mechanisms have been implemented in a metaserver. Extensive experiments have been co...

: A multidimensional access method offering significant performance increases by intelligently partitioning the query space is applied to relational database management systems (RDBMS). We introduce a formal model for multidimensional partitioned relations and discuss several typical query patterns. The model identifies the significance of multidimensional range queries and sort operations. The discussion of current access methods gives rise to the need for a multidimensional partitioning of relations. A detailed analysis of space partitioning focussing especially on Z-ordering illustrates the principle benefits of multidimensional indexes. After describing the UB-Tree and its standard algorithms for insertion, deletion, point queries, and range queries, we introduce the spiral algorithm for nearest neighbor queries with UB-Trees and the Tetris algorithm for efficient access to a table in arbitrary sort order. We then describe the complexity of the involved algorithms and give solutio...

In a moving objects database (MOD) the dataset and the workload change frequently. As the locations of objects change in space and time, the data distribution also changes and the answer for a same query over the same region may vary widely over time. As a result, traditional static indexes are not able to perform well and it is critical to develop selftuning indexes that can be reconfigured automatically based on the state of the system. Towards this goal we propose the ST 2 B-tree, a Self-Tunable Spatio-Temporal B +-Tree index for MODs, which is amenable to tuning. Frequent updates to its subtrees allows rebuilding (tuning) a subtree using a different set of reference points and different grid size without significant overhead. We also present an online tuning framework for the ST 2 B-tree, where the tuning is conducted online and automatically without human intervention, also not interfering with regular functions of the MOD. Our extensive experiments show that the self-tuning process minimizes the effectiveness degradation of the index caused by workload changes at the cost of virtually no overhead.

In this paper, we introduce the concept of extended feature objects for similarity retrieval. Conventional approaches for similarity search in databases map each object in the database to a point in some high-dimensional feature space and define similarity as some distance measure in this space. For many similarity search problems, this feature-based approach is not sufficient. When retrieving partially similar polygons, for example, the search cannot be restricted to edge sequences, since similar polygon sections may start and end anywhere on the edges of the polygons. In general, inherently continuous problems such as the partial similarity search cannot be solved by using point objects in feature space. In our solution, we therefore introduce extended feature objects consisting of an infinite set of feature points. For an efficient storage and retrieval of the extended feature objects, we determine the minimal bounding boxes of the feature objects in multidimensional s...

In this paper, we present ideas how visualization technology can be used to improve the difficult process of querying very large databases. With our VisDB system, we try to provide visual support not only for the query specification process, but also for evaluating query results and, thereafter, refining the query accordingly. The main