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 the ODMG data model and the Lorel language as an extension of OQL. The main novelties of the Lorel language are: (i) the extensive use of coercion to relieve the user from the strict typing of OQL, which is inappropriate for semistructured data; and (ii) powerful path expressions, which permit a flexible form of declarative navigational access and are particularly suitable when the details of the structure are not known to the user. Lorel also includes a declarative update language. Lorel is implemented as the query language of the Lore prototype database management system at Stanford. Information about Lore can be found at http://www-db.stanford.edu/lore. In addition to presenting the Lorel language in full, this paper briefly describes the Lore system and query processor. We also briefly discuss a second implementation of Lorel on top of a conventional object-oriented database management system, the O2 system.

The Lorel Query Language for Semistructured Data

Virtually all proposals for querying XML include a class of query we term “containment queries”. It is also clear that in the foreseeable future, a substantial amount of XML data will be stored in relational database systems. This raises the question of how to support these containment queries. The inverted list technology that underlies much of Information Retrieval is well-suited to these queries, but should we implement this technology (a) in a separate loosely-coupled IR engine, or (b) using the native tables and query execution machinery of the RDBMS? With option (b), more than twenty years of work on RDBMS query optimization, query execution, scalability, and concurrency control and recovery immediately extend to the queries and structures that implement these new operations. But all this will be irrelevant if the performance of option (b) lags that of (a) by too much. In this paper, we explore some performance implications of both options using native implementations in two commercial relational database systems and in a special purpose inverted list engine. Our performance study shows that while RDBMSs are generally poorly suited for such queries, under conditions they can outperform an inverted list engine. Our analysis further identifies two significant causes that differentiate the performance of the IR and RDBMS implementations: the join algorithms employed and the hardware cache utilization. Our results suggest that contrary to most expectations, with some modifications, a native implementations in an RDBMS can support this class of query much more efficiently.

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On supporting containment queries in relational database management systems

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.

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Lore: a database management system for semistructured data

that consist in grammars annotated with database programs. To query documents, we introduce an extension of OQL, the ODMG standard query language for object databases. Our extension (named OQL-doc) allows us to query documents without a precise knowledge of their structure using in particular generalized path expressions and pattern matching. This allows us to introduce in a declarative language (in the style of SQL or OQL), navigational and information retrieval styles of accessing data. Query processing in the context of documents and path expressions leads to challenging implementation issues. We extend an object algebra with new operators to deal with generalized path expressions. We then consider two essential complementary optimization techniques. We show that almost standard database optimization techniques can be used to answer queries without having to load the entire document into the database. We also consider the interaction of full-text indexes (e.g., inverted files) with standard database collection indexes (e.g., B-trees) that provide important speed-up.

Querying Documents in Object Databases

We introduce NATIX, an efficient, native repository for storing, retrieving and managing tree-structured large objects, preferably XML documents. In contrast to traditionallarge object (LOB) managers, we do not split at arbitrary byte positions but take the semantics of the underlying tree structure of XML documents into account. Our parameterizable split algorithm dynamically maintains physical records of size smaller than a page which contain sets of connected tree nodes. This not only improves efficiency by clustering subtrees but also facilitates their compact representation. Existing approaches to store XML documents either use flat files or map every single tree node onto a separate physical record. The increased flexibility of our approach results in higher efficiency. Performance measurements validate this claim.

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Efficient Storage of XML Data

We describe the integration of a structured-text retrieval system (TextMachine) into an object-oriented database system (OpOur approach is a light-weight one, using the external function capability of the database system to encapsulate the text retrieval system as an external information source. Yet, we are able to provide a tight integration in the query language and processing; the user can access the text retrieval system using a standard database query language. The effcient and effective retrieval of structured text performed by the text retrieval system is seamlessly combined with the rich modeling and general-purpose querying capabilities of the database system, resulting in an integrated system with querying power beyond those of the underlying systems. The integrated system also provides uniform access to textual data in the text retrieval system and structured data in the database system, thereby achieving information fusion. We discuss the design and implementation of our prototype system, and address issues such as the proper framework for external integration, the modeling of complex categorization and structure hierarchies of documents (under automatic document schema impand techniques to reduce the performance overhead of accessing an external source.

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Tak W. Yan

Papers

Integrating a Structured-Text Retrieval System with an Object-Oriented Database System