Laura M. Haas

Distributed deadlock detection

Distributed deadlock models are presented for resource and communication deadlocks. Simple distributed algorithms for detection of these deadlocks are given. We show that all true deadlocks are detected and that no false deadlocks are reported. In our algorithms, no process maintains global information; all messages have an identical short length. The algorithms can be applied in distributed database and other message communication systems.

The Clio project: managing heterogeneity

Clio is a system for managing and facilitating the complex tasks of heterogeneous data transformation and integration. In Clio, we have collected together a powerful set of data management techniques that have proven invaluable in tackling these difficult problems. In this paper, we present the underlying themes of our approach and present a brief case study.

Clio grows up: from research prototype to industrial tool

Clio, the IBM Research system for expressing declarative schema mappings, has progressed in the past few years from a research prototype into a technology that is behind some of IBMs mapping technology. Clio provides a declarative way of specifying schema mappings between either XML or relational schemas. Mappings are compiled into an abstract query graph representation that captures the transformation semantics of the mappings. The query graph can then be serialized into different query languages, depending on the kind of schemas and systems involved in the mapping. Clio currently produces XQuery, XSLT, SQL, and SQL/XML queries. In this paper, we revisit the architecture and algorithms behind Clio. We then discuss some implementation issues, optimizations needed for scalability, and general lessons learned in the road towards creating an industrial-strength tool.

DiscoveryLink: a system for integrated access to life sciences data sources

Vast amounts of life sciences data reside today in specialized data sources, with specialized query processing capabilities. Data from one source often must be combined with data from other sources to give users the information they desire. There are database middleware systems that extract data from multiple sources in response to a single query. IBMs DiscoveryLink is one such system, targeted to applications from the life sciences industry. DiscoveryLink provides users with a virtual database to which they can pose arbitrarily complex queries, even though the actual data needed to answer the query may originate from several different sources, and none of those sources, by itself, is capable of answering the query. We describe the DiscoveryLink offering, focusing on two key elements, the wrapper architecture and the query optimizer, and illustrate how it can be used to integrate the access to life sciences data from heterogeneous data sources.

Starburst mid-flight: as the dust clears (database project)

The purpose of the Starburst project is to improve the design of relational database management systems and enhance their performance, while building an extensible system to better support nontraditional applications and to serve as a testbed for future improvements in database technology. The design and implementation of the Starburst system to date are considered. Some key design decisions and how they affect the goal of improved structure and performance are examined. How well the goal of extensibility has been met is examined: what aspects of the system are extensible, how extensions can be done, and how easy it is to add extensions. Some actual extensions to the system, including the experiences of the first real customizers, are discussed. >

Extensible query processing in starburst

Todays DBMSs are unable to support the increasing demands of the various applications that would like to use a DBMS. Each kind of application poses new requirements for the DBMS. The Starburst project at IBMs Almaden Research Center aims to extend relational DBMS technology to bridge this gap between applications and the DBMS. While providing a full function relational system to enable sharing across applications, Starburst will also allow (sophisticated) programmers to add many kinds of extensions to the base systems capabilities, including language extensions (e.g., new datatypes and operations), data management extensions (e.g., new access and storage methods) and internal processing extensions (e.g., new join methods and new query transformations). To support these features, the database query language processor must be very powerful and highly extensible. Starbursts language processor features a powerful query language, rule-based optimization and query rewrite, and an execution system based on an extended relational algebra. In this paper, we describe the design of Starbursts query language processor and discuss the ways in which the language processor can be extended to achieve Starbursts goals.

Information integration in the enterprise

A guide to the tools and core technologies for merging information from disparate sources.

Data-driven understanding and refinement of schema mappings

At the heart of many data-intensive applications is the problem of quickly and accurately transforming data into a new form. Database researchers have long advocated the use of declarative queries for this process. Yet tools for creating, managing and understanding the complex queries necessary for data transformation are still too primitive to permit widespread adoption of this approach. We present a new framework that uses data examples as the basis for understanding and refining declarative schema mappings. We identify a small set of intuitive operators for manipulating examples. These operators permit a user to follow and refine an example by walking through a data source. We show that our operators are powerful enough both to identify a large class of schema mappings and to distinguish effectively between alternative schema mappings. These operators permit a user to quickly and intuitively build and refine complex data transformation queries that map one data source into another.

Clio: Schema Mapping Creation and Data Exchange

The Clio project provides tools that vastly simplify information integration. Information integration requires data conversions to bring data in different representations into a common form. Key contributions of Clio are the definition of non-procedural schema mappings to describe the relationship between data in heterogeneous schemas, a new paradigm in which we view the mapping creation process as one of query discovery, and algorithms for automatically generating queries for data transformation from the mappings. Clio provides algorithms to address the needs of two major information integration problems, namely, data integration and data exchange . In this chapter, we present our algorithms for both schema mapping creation via query discovery, and for query generation for data exchange. These algorithms can be used in pure relational, pure XML, nested relational, or mixed relational and nested contexts.

A snapshot differential refresh algorithm

This article presents an algorithm to refresh the contents of database snapshots. A database snapshot is a read-only table whose contents are extracted from other tables in the database. The snapshot contents can be periodically refreshed to reflect the current state of the database. Snapshots are useful in many applications as a cost effective substitute for replicated data in a distributed database system. When the snapshot contents are a simple restriction and projection of a single base table, differential refresh techniques can reduce the message and update costs of the snapshot refresh operation. The algorithm presented annotates the base table to detect the changes which must be applied to the snapshot table during snapshot refresh. The cost of maintaining the base table annotations is minimal and the amount of data transmitted during snapshot refresh is close to optimal in most circumstances.