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 problem of answering queries using views is to find efficient methods of answering a query using a set of previously defined materialized views over the database, rather than accessing the database relations. The problem has recently received significant attention because of its relevance to a wide variety of data management problems. In query optimization, finding a rewriting of a query using a set of materialized views can yield a more efficient query execution plan. To support the separation of the logical and physical views of data, a storage schema can be described using views over the logical schema. As a result, finding a query execution plan that accesses the storage amounts to solving the problem of answering queries using views. Finally, the problem arises in data integration systems, where data sources can be described as precomputed views over a mediated schema. This article surveys the state of the art on the problem of answering queries using views, and synthesizes the disparate works into a coherent framework. We describe the different applications of the problem, the algorithms proposed to solve it and the relevant theoretical results.

An important application of XML is the interchange of electronic data (EDI) between multiple data sources on the Web. As XML data proliferates on the Web, applications will need to integrate and aggregate data from multiple source and clean and transform data to facilitate exchange. Data extraction, conversion, transformation, and integration are all well-understood database problems, and their solutions rely on a query language. We present a query language for XML, called XML-QL, which we argue is suitable for performing the above tasks. XML-QL is a declarative, "relational complete" query language and is simple enough that it can be optimized. XML-QL can extract data from existing XML documents and construct new XML documents. Keywords: XML, query languages, electronic-data interchange (EDI) 1. Introduction The goal of XML is to provide many of SGML's benefits not available in HTML and to provide them in a language that is easier to learn and use than complete SGML. These benefits...

Lore (for Lightweight Object Repository) is a DBMS designed specifically for managing semistructured information. Implementing Lore has required rethinking all aspects of a DBMS, including storage management, indexing, query processing and optimization, and user interfaces. This paper provides an overview of these aspects of the Lore system, as well as other novel features such as dynamic structural summaries and seamless access to data from external sources.

In recent years there has been an increased interest in managing data which does not conform to traditional data models, like the relational or object oriented model. The reasons for this non-conformance are diverse. One one hand, data may not conform to such models at the physical level: it may be stored in data exchange formats, fetched from the Internet, or stored as structured les. One the other hand, it may not conform at the logical level: data may have missing attributes, some attributes may be of di erent types in di erent data items, there may be heterogeneous collections, or the data may be simply specified by a schema which is too complex or changes too often to be described easily as a traditional schema. The term semistructured data has been used to refer to such data. The data model proposed for this kind of data consists of an edge-labeled graph, in which nodes correspond to objects and edges to attributes or values. Figure 1 illustrates a semistructured database providing information about a city. Relational databases are traditionally queried with associative queries, retrieving tuples based on the value of some attributes. To answer such queries efciently, database management systems support indexes for translating attribute values into tuple ids (e.g. B-trees or hash tables). In object-oriented databases, path queries replace the simpler associative queries. Several data structures have been proposed for answering path queries e ciently: e.g., access support relations 14] and path indexes 4]. In the case of semistructured data, queries are even more complex, because they may contain generalized path expressions 1, 7, 8, 16]. The additional exibility is needed in order to traverse data whose structure is irregular, or partially unknown to the user.

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

Query containment under constraints is the problem of checking whether for every database satisfying a given set of constraints, the result of one query is a subset of the result of another query. Recent research points out that this is a central problem in several database applications, and we address it within a setting where constraints are specified in the form of special inclusion dependencies over complex expressions, built by using intersection and difference of relations, special forms of quantification, regular expressions over binary relations, and cardinality constraints. These types of constraints capture a great variety of data models, including the relational, the entity-relational, and the object-oriented model. We study the problem of checking whether q is contained in q 0 with respect to the constraints specified in a schema S, where q and q 0 are nonrecursive Datalog programs whose atoms are complex expressions. We present the following results on query containme...

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We develop a new schema for unstructured data. Traditional schemas resemble the type systems of programming languages. For unstructured data, however, the underlying type may be much less constrained and hence an alternative way of expressing constraints on the data is needed. Here, we propose that both data and schema be represented as edge-labeled graphs. We develop notions of conformance between a graph database and a graph schema and show that there is a natural and e ciently computable ordering on graph schemas. We then examine certain subclasses of schemas and show that schemas are closed under query applications. Finally, we discuss how they may be used in query decomposition and optimization. 1

The Internet presents numerous sources of useful information---telephone directories, product catalogs, stock quotes, event listings, etc. Recently, many systems have been built that automatically gather and manipulate such information on a user's behalf. However, these resources are usually formatted for use by people (e.g., the relevant content is embedded in HTML pages), so extracting their content is difficult. Most systems use customized wrapper procedures to perform this extraction task. Unfortunately, writing wrappers is tedious and error-prone. As an alternative, we advocate wrapper induction, a technique for automatically constructing wrappers. In this article, we describe six wrapper classes, and use a combination of empirical and analytical techniques to evaluate the computational tradeoffs among them. We first consider expressiveness: how well the classes can handle actual Internet resources, and the extent to which wrappers in one class can mimic those in another. We then...