NDLI: The Iris database system

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Volume 19, Issue 4, Dec. 1990

Volume 19, Issue 3, Sep. 1990

Volume 19, Issue 2, Jun. 1990

Practical selectivity estimation through adaptive sampling

Bayan: an Arabic text database management system

A graph-oriented object model for database end-user interfaces

OdeView: the graphical interface to Ode

The input/output complexity of transitive closure

IDLOG: extending the expressive power of deductive database languages

Hard problems for simple logic programs

Polynomial time designs toward both BCNF and efficient data manipulation

Efficient updates to independent schemes in the weak instance model

FastSort: a distributed single-input single-output external sort

Encapsulation of parallelism in the Volcano query processing system

Implementing recoverable requests using queues

Write-only disk caches

A new paradigm for parallel and distributed rule-processing

A framework for the parallel processing of Datalog queries

Concurrency control in multilevel-secure databases based on replicated architecture

Performance evaluation of semantics-based multilevel concurrency control protocols

Querying database knowledge

Extending logic programming

ACTA: a framework for specifying and reasoning about transaction structure and behavior

Organizing long-running activities with triggers and transactions

Reliable transaction management in a multidatabase system

Integrating object-oriented data modelling with a rule-based programming paradigm

Making deductive databases a practical technology: a step forward

Magic is relevant

Set-oriented production rules in relational database systems

A predicate matching algorithm for database rule systems

On rules, procedure, caching and views in data base systems

Query graphs, implementing trees, and freely-reorderable outerjoins

A performance evaluation of pointer-based joins

Randomized algorithms for optimizing large join queries

The R*-tree: an efficient and robust access method for points and rectangles

Linear clustering of objects with multiple attributes

A comparison of spatial query processing techniques for native and parameter spaces

The performance of a multiversion access method

Access support in object bases

Random sampling from hash files

Kaleidoscope: a cooperative menu-guided query interface

The G+/GraphLog Visual Query System

OdeView: a user-friendly graphical interface to Ode

The INA: a simple query language with only attribute names

Pasta-3: a graphical direct manipulation interface for knowledge base management systems

The Iris database system

Volume 19, Issue 1, March 1990

The Iris database system

Content Provider	ACM Digital Library
Author	Lyngback, Peter Mathur, Samir Wilkinson, Kevin Kent, Bill
Abstract	Iris is an object-oriented database management system being developed at Hewlett-Packard Laboratories [1], [3]. This videotape provides an overview of the Iris data model and a summary of our experiences in converting a computer-integrated manufacturing application to Iris. An abstract of the videotape follows.Iris is intended to meet the needs of new and emerging database applications such as office and engineering information systems, knowledge-based systems, manufacturing applications, and hardware and software design. These applications require a rich set of capabilities that are not supported by the current generation (i.e., relational) DBMSs.The Iris data model is an object and function model. It provides three basic constructs objects, types and functions. As with other object systems, Iris objects have a unique identifier and can only be accessed and manipulated through functions. Objects are classified by type. Objects that belong to the same type share common functions. Types are organized into a hierarchy with inherited functions. In Iris, functions are used to model properties of objects, relationships among objects and operations on objects. Thus, the behavior of an Iris object is completely specified through its participation in functions.Iris provides good separation among its three basic notions. This simplifies the data model making it easier to learn and easier to implement since there are fewer constructs than other object models. In addition, it facilitates Iris support for the following desirable features. Schema evolution: new types and functions may be added at any time. Object evolution: Iris objects may have multiple types and may acquire and lose types dynamically. Object participation in functions may be required or optional (e g, everyone has birthdate but not everyone has a phone number). Data independence: the implementation of a function is defined separately from its interface. Thus, the implementation of a function may change without affecting applications that use it. Functional extensibility: an Iris function may be implemented as a stored table, computed as an Iris expression, or computed as a subroutine in a general-purpose programming language. Thus, any computation can be expressed as an Iris function Schema and data uniformity: the metadata is modeled and manipulated using the primitives of the data model. Also, system functions (create type, delete object, etc) are invoked in the same manner as user functions. Thus, users need learn only one interface. Set processing: Iris supports set-at-a-time processing for efficient retrieval and update of collections of objects.To evaluate the usefulness of the Iris prototype, a project was undertaken to convert a large relational application to Iris [2]. The relational system contained nearly 200 relations and 2500 attributes. When transcribed to Iris, the schema size was reduced by over a third. There are two reasons for this large reduction. First, in the relational schema, many attributes were simply foreign keys required for joins. In the Iris schema, function inheritance through the type hierarchy eliminates the need for many of these foreign keys. A second reason for the schema reduction was that compound keys were replaced by object references. This permitted several attributes in a relation to be replaced by a single identifierIt was noted that application programs were easier to read and develop using the Iris schema. The Iris OSQL (Object SQL) language was a fairly natural interface for users familiar with SQL. The use of function composition and function inheritance and a large number of joins that, in the relational system, must be expressed by comparing keys. The function-orientation of Iris encouraged code sharing in that deriving and sharing new functions was simplified.Finally, since there are few tools and methodologies for using object-oriented database management systems, the ability of the Iris schema to easily evolve was valuable in iteratively refining the Iris schema. Also, the Iris Graphical Editor was a useful tool in graphically displaying the schema and browsing function definition and instances.
File Format	PDF
ISSN	01635808
DOI	10.1145/93605.98752
Journal	ACM SIGMOD Record (SGMD)
Volume Number	19
Issue Number	2
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	1999-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Content Type	Text
Resource Type	Article
Subject	Information Systems Software

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