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In this paper we study database query languages and their use for programming nontraditional applications, such as engineering and artificial intelligence applications. In such environments, database programs are used to code applications that work over large data sets it- siding in databases. Optimizing such programs then becomes a necessity. We look at various optimization techniques and suggest transformations to be used for Improving the performance of database programs. These transformations result in new equivalent database programs with better space and lime performance. Several of these techniques apply to classical query languages, although the primary focus of this paper is on extended query languages which include an iteration operator. [ABSTRACT FROM AUTHOR]

This work introduces decentralized query processing techniques based on MIDAS, a novel distributed multidimensional index. In particular, MIDAS implements a distributed k-d tree, where leaves correspond to peers, and internal nodes dictate message routing. MIDAS requires that peers maintain little network information, and features mechanisms that support fault tolerance and load balancing. The proposed algorithms process point and range queries over the multidimensional indexed space in only O(log n) hops in expectance, where n is the network size. For nearest neighbor queries, two processing alternatives are discussed. The first, termed eager processing, has low latency (expected value of O(log n) hops) but may involve a large number of peers. The second, termed iterative processing, has higher latency (expected value of O(log n) hops) but involves far fewer peers. A detailed experimental evaluation demonstrates that our query processing techniques outperform existing methods for settings involving real spatial data as well as in the case of high dimensional synthetic data. [ABSTRACT FROM AUTHOR]

Abstract: Sharing structured data in a P2P network is a challenging problem, especially in the absence of a mediated schema. The standard practice of answering a consecutively rewritten query along the propagation path often results in significant loss of information. On the opposite, the use of mediated schemas requires human interaction and global agreement, both during creation and maintenance. In this paper we present GrouPeer, an adaptive, automated approach to both issues in the context of unstructured P2P database overlays. By allowing peers to individually choose which rewritten version of a query to answer and evaluate the received answers, information-rich sources left hidden otherwise are discovered. Gradually, the overlay is restructured as semantically similar peers are clustered together. Experimental results show that our technique produces very accurate answers and builds clusters that are very close to the optimal ones by contacting a very small number of nodes in the overlay. [Copyright &y& Elsevier]

Abstract: Sharing of structured data in decentralized environments is a challenging problem, especially in the absence of a global schema. Social network structures map network links to semantic relations between participants in order to assist in efficient resource discovery and information exchange. In this work, we propose a scheme that automates the process of creating schema synopses from semantic clusters of peers which own autonomous relational databases. The resulting mediated schemas can be used as global interfaces for relevant queries. Active nodes are able to initiate the group schema creation process, which produces a mediated schema representative of nodes with similar semantics. Group schemas are then propagated in the overlay and used as a single interface for relevant queries. This increases both the quality and the quantity of the retrieved answers and allows for fast discovery of interest groups by joining peers. As our experimental evaluations show, this method increases both the quality and the quantity of the retrieved answers and allows for faster discovery of semantic groups by joining peers. [Copyright &y& Elsevier]

In this paper, we introduce a family of expressive models for qualitative spatial reasoning with directions. The proposed family is based on the cognitive plausible cone-based model. We formally define the directional relations that can be expressed in each model of the family. Then, we use our formal framework to study two interesting problems: computing the inverse of a directional relation and composing two directional relations. For the composition operator, in particular, we concentrate on two commonly used definitions, namely, consistency-based and existential composition. Our formal framework allows us to prove that our solutions are correct. The presented solutions are handled in a uniform manner and apply to all of the models of the family. [ABSTRACT FROM AUTHOR]

Commercially available database systems do not meet the information and processing needs of design and manufacturing environments. A new generation of systems-engineering Information systems-must be built to meet these needs. In this paper, we address the architectural and computational aspects of such systems and propose solutions. We argue that a mainframe-workstation architecture is needed to provide distributed functionality while ensuring high availability and low communication overhead, that explicit control of meta-knowledge is needed to support extendibility and evolution, that large rule bases are needed to make the knowledge of the systems active, and that incremental computation models are needed to achieve the required performance of such engineering information systems. [ABSTRACT FROM AUTHOR]

Pictorial databases require efficient and direct spatial search based on the analog form of spatial objects and relationships instead of search based on some cumbersome alphanumeric encodings of the pictures. This paper briefly describes PSQL, a query language which allows pictorial domains to be presented to the user in their analog form and allows him to do direct manipulation on the objects found on those domains. Direct spatial search and computation on the pictures is done using efficient data structures, R- and R+-trees (multi-dimensional B-trees), which are excellent devices for searching spatial objects and relationships found on pictures. [ABSTRACT FROM AUTHOR]

Qualitative spatial reasoning forms an important part of the commonsense reasoning required for building intelligent Geographical Information Systems (GIS). Previous research has come up with models to capture cardinal direction relations for typical GIS data. In this paper, we target the problem of efficiently computing the cardinal direction relations between regions that are composed of sets of polygons and present two algorithms for this task. The first of the proposed algorithms is purely qualitative and computes, in linear time, the cardinal direction relations between the input regions. The second has a quantitative aspect and computes, also in linear time, the cardinal direction relations with percentages between the input regions. Our experimental evaluation indicates that the proposed algorithms outperform existing methodologies. The algorithms have been implemented and embedded in an actual system, CARDIRECT, that allows the user to 1) specify and annotate regions of interest in an image or a map, 2) compute cardinal direction relations between them, and 3) pose queries in order to retrieve combinations of interesting regions. [ABSTRACT FROM AUTHOR]