The problem of indexing multidimensional objects is considered. First, a classification of existing methods is given along with a discussion of the major issues involved in multidimensional data indexing. Second, a variation to Guttman’s R-trees (R +-trees) that avoids overlapping rectangles in intermediate nodes of the tree is introduced. Algorithms for searching, updating, initial packing and reorganization of the structure are discussed in detail. Finally, we provide analytical results indicating that R +-trees achieve up to 50 % savings in disk accesses compared to an R-tree when searching files of thousands of rectangles. 1

The problem of indexing multidimensional objects is considered. First, a classification of existing methods is given along with a discussion of the major issues involved in multidimensional data indexing. Second, a variation to Guttman's R-trees (R -trees) that avoids overlapping rectangles in intermediate nodes of the tree is introduced. Algorithms for searching, updating, initial packing and reorganization of the structure are discussed in detail. Finally, we provide analytical results indicating that R -trees achieve up to 50% savings in disk accesses compared to an R-tree when searching files of thousands of rectangles. 1 Also with University of Maryland Systems Research Center. 2 Also with University of Maryland Institute for Advanced Computer Studies (UMIACS). This research was sponsored partialy by the National Science Foundation under Grant CDR-85-00108. 1. Introduction It has been recognized in the past that existing Database Management Systems (DBMSs) do not ...

In this paper we propose the use of fractals and especially the Hilbert curve, in order to design good distance-preserving mappings. Such mappings improve the performance of secondary-key- and spatial- access methods, where multi-dimensional points have to be stored on an 1-dimensional medium (e.g., disk). Good clustering reduces the number of disk accesses on retrieval, improving the response time. Our experiments on range queries and nearest neighbor queries showed that the proposed Hilbert curve achieves better clustering than older methods ("bit-shuffling", or Peano curve), for every situation we tried.

Abstract–In the future we envision systems that will provide video information delivery services to customers on a very large scale. These systems must provide customers with mechanisms to select programs of their choice from live broadcasts. Customers should also be provided with easy means of browsing and accessing pre-recorded digital data (e.g., distributed digital multimedia libraries), and downloading data from other information sources. To be viable for such large information sets, these systems must understand customer preferences and tailor the available information to the customer’s needs. To support this vision, a number of issues must be addressed and obstacles overcome. Intuitive interfaces, powerful query formulation and evaluation techniques, comprehensive data models, and flexible presentation functionalities must be developed. To realize these components, an effective query evaluation engine with the capabilities of query resolution in different content-specific formats (e.g., by graphics, by image, by sound) and in different domain-specific models (e.g., database of movies, database of newsclips) should be present. Additionally, the digital video database will require an efficient indexing system for easy access to the stored information. In this paper we discuss existing research trends in this

Ordered, labeled trees are trees in which each node has a label and the left-to-right order of its children (if it has any) is fixed. Such trees have many applications in vision, pattern recognition, molecular biology, programming compilation and natural language processing. Many of the applications involve comparing trees or retrieving/extracting information from a repository of trees. Examples include classification of unknown patterns, analysis of newly sequenced RNA structures, semantic taxonomy for dictionary definitions, generation of interpreters for nonprocedural programming languages, and automatic error recovery and correction for programming languages. Previous systems use exact matching (or generalized regular expression matching) for tree comparison. This paper presents a system, called Approximate-Tree-By-Example (ATBE), which allows inexact matching of trees. The ATBE system interacts with the user through a simple, but powerful query language; graphical devices a...

We introduce a semantic data model to capture the hierarchical, spatial, temporal, and evolutionary semantics of images in pictorial databases. This model mimics the user's conceptual view of the image content, providing the framework and guidelines for preprocessing to extract image features. Based on the model constructs, a spatial evolutionary query language (SEQL), which provides direct image object manipulation capabilities, is presented. With semantic information captured in the model, spatial evolutionary queries are answered efficiently. Using an object-oriented platform, a prototype medical-image management system was implemented at UCLA to demonstrate the feasibility of the proposed approach.

Abstract. Spatial database management involves two main categories of data: vector and raster data. The former has received a lot of in-depth investigation; the latter still lacks a sound framework. Current DBMSs either regard raster data as pure byte sequences where the DBMS has no knowledge about the underlying semantics, or they do not complement array structures with storage mechanisms suitable for huge arrays, or they are designed as specialized systems with sophisticated imaging functionality, but no general database capabilities (e.g., a query language). Many types of array data will require database support in the future, notably 2-D images, audio data and general signal-time series (I-D), animations (3-D), static or time-variant voxel fields (3-D and 4-D), and the ISO/IEC PIKS (Programmer's Imaging Kernel System) BasicImage type (5-D). In this article, we propose a comprehensive support of multidimensional discrete data (MDD) in databases, including operations on arrays of arbitrary size over arbitrary data types. A set of requirements is developed, a small set of language constructs is proposed (based on a formal algebraic semantics), and a novel MDD architecture is outlined to provide the basis for efficient MDD query evaluation.

Abstract – In the past few years considerable demand for user oriented multimedia information systems has developed. These systems must provide a rich set of functionality so that new, complex, and interesting applications can be addressed. This places considerable importance on the management of diverse data types including text, images, audio and video. These requirements generate the need for a new generation of distributed heterogeneous multimedia database systems. In this paper we identify a set of functional requirements for a multimedia server considering database management, object synchronization and integration, and multimedia query processing. A generalization of the requirements to a distributed system is presented, and some of our current research and developing activities are discussed.

In this paper, a method is discussed to store and retrieve images efficiently from an image database on the basis of the data structure called E() representation. The E() representation is a spatial knowledge representation preserving the spatial information between objects embedded in symbolic images as an iconic index for the purpose of efficient image retrieval. The image retrieval method is invariant under, at least, the affine transformation (i.e. translation, rotation and scale) and is able to deal with substantial object occlusion. A metric is defined to express similarity between symbolic images. Initial experiments carried out for two applications show that the image retrieval method is very efficient and robust to similarity retrieval in image databases. Together with the inherent high parallelism, it makes the method a promising image retrieval method. keywords: image database, image indexing, similarity retrieval, spatial relations, E() representation, metric, spatial quer...

The objectives of the Knowledge-Based Multimedia Medical Distributed Database System (KMeD) are to: query medical multimedia distributed databases by both image content and alphanumeric content; model the temporal, spatial, and evolutionary nature of medical objects; formulate queries using conceptual and imprecise medical terms and support cooperative processing; develop a domain-independent, high-level query language and a medical domain user interface to support KMeD functionality; and provide analysis and presentation methods for visualization of knowledge and data models. Using rules derived from application and domain knowledge, approximate and conceptual queries may be answered. These concepts are validated in a testbed linked with radiology image databases. The joint research between the UCLA Computer Science Department and the School of Medicine assures that the prototype system is of direct interest to medical research and practice. The results of this research are extensible...