David S. Reiner

Tools and transformations—rigorous and otherwise—for practical database design

We describe the tools and theory of a comprehensive system for database design, and show how they work together to support multiple conceptual and logical design processes. The Database Design and Evaluation Workbench (DDEW) system uses a rigorous, information-content-preserving approach to schema transformation, but combines it with heuristics, guess work, and user interactions. The main contribution lies in illustrating how theory was adapted to a practical system, and how the consistency and power of a design system can be increased by use of theory. First, we explain why a design system needs multiple data models, and how implementation over a unified underlying model reduces redundancy and inconsistency. Second, we present a core set of small but fundamental algorithms that reaarange a schema without changing its information content. From these reusable components, we easily built larger tools and transformations that were still formally justified. Third, we describe heuristic tools that attempt to improve a schema, often by adding missing information. In these tools, unreliable techniques such as normalization and relationship inference are bolstered by system-guided user interactions to remove errors. We present a rigorous criterion for identifying unnecessary relationships, and discuss an interactive view integrator. Last, we examine the relevance of database theory to building these practically motivated tools and contrast the paradigms of system builders with those of theoreticians.

An architecture for query optimization

We describe an optimizer for relational queries to databases stored as flat files and Codasyl networks. We include sophisticated manipulations on a broad range of direct access structures (DASs). To achieve this with minimum additional code, we allow operations like sort, scan, and join to apply to DASs, and categorize indexes and other DASs in terms of the operations which can be performed on them. Our storage model, based on indivisible units of access and a small set of associated physical operators, provides a uniform interface to both relational and Codasyl storage mechanisms. The optimizer derives a sequence of internal data structures at successively more detailed levels. For a given query, a graph representing an overview of alternative joins is constructed, and then used to derive a physical graph which considers the physical attributes (location and sort order) of the data objects involved. Using cost predictions and other heuristics, the optimizer prunes the physical graph to produce a final access strategy tree. This layered approach and reliance on primitive operators make explicit (and permit changes to) the universe of possible strategies for the query at hand, and ease extension of the optimizer to new storage structures.

Parallel database processing on the KSR1 computer

The Kendall Square Research high performance computer (KSR1) provides a spectrum of parallel database processing techniques to achieve scalability and performance in a shared memory environment. The techniques include running multiple transactions in parallel, decomposing queries into parallel subqueries, running multiple instances of the DBMS and partitioning data over disks. These techniques enable on-line transactions to be run in parallel at high throughput rates and decision-support queries to be parallelized and executed very rapidly. This paper focuses upon two of the parallel database processing techniques used on the KSR1—the Kendall Square Query Decomposer and the Oracle Parallel Server. The Query Decomposer intercepts costly decision support queries and decomposes them into subqueries which are executed in parallel. Parallel Server enables multiple ORACLE instances to run simultaneously on the same database.

Querying Relational Views of Networks

An organization that uses a relational Database Management System (DBMS) also may have a substantial investment in data created under a network (Codasyl) DBMS. We describe an architecture for supporting relational queries to data stored under both network and relational models.

Database design: methodologies, tools, and environments (panel session)

Desrqning database applications 1s becoming more and more Important, statlstlcs zndlcate that ovcc 30% of applications that will be built tn 1385 will use a database management system, qnd thus database design is becomlny: a common prnctlce In software development. This presentation focuses on the current state of the art of lethodoloqies, models, tools, and deslo,n environments which can assrst the deqLgn process, and 1 ndlcates some cmerginp research topics.

The Lotus DataLens approach to heterogeneous database connectivity

DataLens is a specification for a programming interface that enables applications such as 1-2-3 to access external data sources through the client-server model. Developed at Lotus, DataLens was designed to support access from both decision support and transaction-oriented applications to a broad spectrum of backend servers and data sources. Differences in access protocols are transparent to both the application and its end user. The specification permits DataLens applications to leverage advanced database server features if desired, without requiring that all data sources supply these features. This paper covers the DataLens architecture, how it copes with differences in server capabilities, and how it compares with other approaches to heterogeneous data access.<<ETX>>