We present the Lorel language, designed for querying semistructured data. Semistructured data is becoming more and more prevalent, e.g., in structured documents such as HTML and when performing simple integration of data from multiple sources. Traditional data models and query languages are inappropriate, since semistructured data often is irregular, some data is missing, similar concepts are represented using different types, heterogeneous sets are present, or object structure is not fully known. Lorel is a user-friendly language in the SQL/OQL style for querying such data effectively. For wide applicability, the simple object model underlying Lorel can be viewed as an extension of ODMG and the language as an extension of OQL. The main novelties of the Lorel language are: (i) extensive use of coercion to relieve the user from the strict typing of OQL, which is inappropriate for semistructured data

The amount of data of all kinds available electronically has increased dramatically in recent years. The data resides in different forms, ranging from unstructured data in file systems to highly structured in relational database systems. Data is accessible through a variety of interfaces including Web browsers, database query languages, application-specific interfaces, or data exchange formats. Some of this data is raw data, e.g. images or sound. Some of it has structure even if the structure is often implicit, and not as rigid or regular as that found in standard database systems. Sometimes the structure exists but has to be extracted from the data. Sometimes also it exists but we prefer to ignore it for certain purposes such as browsing. We call here semi-structured data this data that is (from a particular viewpoint) neither raw data nor strictly typed, i.e., not table-oriented as in a relational model or sorted-graph as in object databases...

TSIMMIS -- The Stanford-IBM Manager of Multiple Information Sources -- is a system for integrating information. It o ers a data model and a common query language that are designed to support the combining of information from many different sources. It also o ers tools for generating automatically the components that are needed to build systems for integrating information. In this paper we shall discuss the principal architectural features and their rationale.

In semistructured data, the information that is normally as-sociated with a schema is contained within the data, which is sometimes called “self-describing”. In some forms of semi-structured data there is no separate schema, in others it exists but only places loose constraints on the data. Semi-structured data has recently emerged as an important topic of study for a variety of reasons. First, there are data sources such as the Web, which we would like to treat as databases but which cannot be constrained by a schema. Second, it may be desirable to have an extremely flexible format for data exchange between disparate databases. Third, even when dealing with structured data, it may be helpful to view it. as semistructured for the purposes of browsing. This tu-torial will cover a number of issues surrounding such data: finding a concise formulation, building a sufficiently expres-sive language for querying and transformation, and opti-mizat,ion problems. 1 The motivation The topic of semistructured data (also called unstructured data) is relatively recent, and a tutorial on the topic may well be premature. It represents, if anything, the conver-gence of a number of lines of thinking about new ways to represent and query data that do not completely fit with conventional data models. The purpose of this tutorial is to to describe this motivation and to suggest areas in which further research may be fruitful. For a similar exposition, the reader is referred to Serge Abiteboul’s recent survey pa-per PI. The slides for this tutorial will be made available from a section of the Penn database home page

Abstract not found

Systems for managing and querying semistructured-data sources often store data in proprietary object repositories or in a tagged-text format. We describe a technique that can use relational database management systems to store and manage semistructured data. Our technique relies on a mapping between the semistructured data model and the relational data model, expressed in a query language called STORED. When a semistrcutured data instance is given, a STORED mapping can be generated automatically using data-mining techniques. We are interested in applying STORED to XML data, which is an instance of semistructured data. We show how a document-type-descriptor (DTD), when present, can be exploited to further improve performance.

Due to the development of the World Wide Web, the integration of heterogeneous data sources has become a major concern of the database community. Appropriate architectures and query languages have been proposed. Yet, the problem of data conversion which is essential for the development of mediators/wrappers architectures has remained largely unexplored. In this paper, we present the YAT system for data conversion. This system provides tools for the specification and the implementation of data conversions among heterogeneous data sources. It relies on a middleware model, a declarative language, a customization mechanism and a graphical interface. The model is based on named trees with ordered and labeled nodes. Like semistructured data models, it is simple enough to facilitate the representation of any data. Its main originality is that it allows to reason at various levels of representation. The YAT conversion language (called YATL) is declarative, rule-based and features enhanced patt...

Abstract not found

The evaluation of path expression queries on semistructured data in a distributed asynchronous environment is considered. The focus is on the use of local information expressed in the form of path constraints in the optimization of path expression queries. In particular, decidability and complexity results on the implication problem for path constraints are established.

Several languages, such as LOREL and UnQL, support querying of semi-structured data. Others, such as WebSQL and WebLog, query Web sites. All these languages model data as labeled graphs and use regular path expressions to express queries that traverse arbitrary paths in graphs. Naive execution of path expressions is inefficient, however, because it often requires exhaustive graph search. We describe two optimization techniques for queries with regular path expressions, which we call regular queries. Both rely on graph schemas, which specify partial knowledge of a graph's structure. Query pruning restricts search to a fragment of the graph; we give an efficient algorithm for rewriting any regular query into a pruned one. Query rewriting using state extents can entirely eliminate or substantially reduce graph traversal; it is reminiscent of optimizing relational queries using indices. There may be several ways to optimize a query using state extents; we give an exponential-time algorith...