We propose a definition of a spatial database system as a database system that offers spatial data types in its data model and query language, and supports

Abstract: Ontologies are knowledge structures to explicate the contents, essential properties, and relationships between terms in a knowledge source. Many sources are now accessible with associated ontologies. Most prior work on use of ontologies relies on the construction of a single global ontology covering all sources. Such an approach is not scalable and maintainable especially when the sources change frequently. Wepropose a scalable and easily maintainable approach based on the interoperation of ontologies. To handle user queries crossing the boundaries of the underlying information systems, the interoperation between the ontologies should be precisely de ned. Our approach is to use rules that cross the semantic gap by creating an articulation or linkage between the systems. The rules are generated using a semi-automatic articulation tool with the help of a domain expert. To make the ontologies amenable for automatic composition based on the accumulated knowledge rules, we represent them using a graph-oriented model extended with a small algebraic operator set. ONION, a user-friendly toolkit, aids the experts in bridging the semantic gap in real-life settings. Our framework provides a sound foundation to simplify the work of do-This work was partially supported by a grant from the Air Force O ce of Scienti c Research (AFOSR).main experts, enables integration with public semantic dictionaries, like Wordnet, and will derive ODMG-compliant mediators automatically.

Modern search engines answer keyword-based queries extremely efficiently. The impressive speed is due to clever inverted index structures, caching, a domain-independent knowledge of strings, and thousands of machines. Several research efforts have attempted to generalize keyword search to keytree and keygraph searching, because trees and graphs have many applications in next-generation database systems. This paper surveys both algorithms and applications, giving some emphasis to our own work.

This paper presents structural recursion as the basis of the syntax and semantics of query languages for semistructured data and XML. We describe a simple and powerful query language based on pattern matching and show that it can be expressed using structural recursion, which is introduced as a top-down, recursive function, similar to the way XSL is defined on XML trees. On cyclic data, structural recursion can be defined in two equivalent ways: as a recursive function which evaluates the data top-down and remembers all its calls to avoid infinite loops, or as a bulk evaluation which processes the entire data in parallel using only traditional relational algebra operators. The latter makes it possible for optimization techniques in relational queries to be applied to structural recursion. We show that the composition of two structural recursion queries can be expressed as a single such query, and this is used as the basis of an optimization method for mediator systems. Several other fo...

As graph models are applied to more widely varying fields, researchers struggle with tools for exploring and analyzing these structures. We describe GUESS, a novel system for graph exploration that combines an interpreted language with a graphical front end that allows researchers to rapidly prototype and deploy new visualizations. GUESS also contains a novel, interactive interpreter that connects the language and interface in a way that facilities exploratory visualization tasks. Our language, Gython, is a domain-specific embedded language which provides all the advantages of Python with new, graph specific operators, primitives, and shortcuts. We highlight key aspects of the system in the context of a large user survey and specific, real-world, case studies ranging from social and knowledge networks to distributed computer network analysis.

Graph database models can be characterized as those where data structures for the schema and instances are modeled as graphs or generalizations of them, and data manipulation is expressed by graph-oriented operations and type constructors. These models flourished in the eighties and early nineties in parallel to object oriented models and their influence gradually faded with the emergence of other database models, particularly the geographical, spatial, semistructured and XML. Recently, the need to manage information with inherent graph-like nature has brought back the relevance of the area. In fact, a whole new wave of applications for graph databases emerged with the development of huge networks (e.g. Web, geographical systems, transportation, telephones), and families of networks generated due to the automation of the process of data gathering (e.g. social and biological networks). The main objective of this survey is to present in a single place the work that has been done in the area of graph database modeling, concentrating in data structures, query languages and integrity constraints.

The closely related research areas management of semistructured data and languages for querying the Web have recently attracted a lot of interest. We argue that languages supporting deduction and object-orientation (dood languages) are particularly suited in this context: Object-orientation provides a flexible common data model for combining information from heterogeneous sources and for handling partial information. Techniques for navigating in object-oriented databases can be applied to semistructured databases as well, since the latter may be viewed as (very simple) instances of the former. Deductive rules provide a powerful framework for expressing complex queries in a high-level, declarative programming style. We elaborate

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With the prevalence of graph data in a variety of domains, there is an increasing need for a language to query and manipulate graphs with heterogeneous attributes and structures. We propose a query language for graph databases that supports arbitrary attributes on nodes, edges, and graphs. In this language, graphs are the basic unit of information and each query manipulates one or more collections of graphs. To allow for flexible compositions of graph structures, we extend the notion of formal languages from strings to the graph domain. We present a graph algebra extended from the relational algebra in which the selection operator is generalized to graph pattern matching and a composition operator is introduced for rewriting matched graphs. Then, we investigate access methods of the selection operator. Pattern matching over large graphs is challenging due to the NP-completeness of subgraph isomorphism. We address this by a combination of techniques: use of neighborhood subgraphs and profiles, joint reduction of the search space, and optimization of the search order. Experimental results on real and synthetic large graphs demonstrate that our graph specific optimizations outperform an SQL-based implementation by orders of magnitude.

We propose a data model and query language that integrates an explicit modeling and querying of graphs smoothly into a standard database environment. For standard applications, some key features of object-oriented modeling are offered such as object classes organized into a hierarchy, object identity, and attributes referencing objects. Querying can be done in a familiar style with a derive statement that can be used like a select... from... where. On the other hand, the model allows for an explicit representation of graphs by partitioning object classes into simple classes, link classes, and path classes whose objects can be viewed as nodes, edges, and explicitly stored paths of a graph (which is the whole database instance). For querying graphs, the derive statement has an extended meaning in that it allows one to refer to subgraphs of the database graph. A powerful rewrite operation is offered for the manipulation of heterogeneous sequences of objects which often occur as a result of accessing the database graph. Additionally there are special graph operations like determining a shortest path or a subgraph and the model is extensible by such operations. Besides being attractive for standard applications, the model permits a natural representation and sophisticated querying of networks, in particular of spatially embedded networks like highways, public transport, etc.