Logical Data Model Research Articles

The logical data model

We propose an object-oriented data model that generalizes the relational, hierarchical, and network models. A database scheme in this model is a directed graph, whose leaves represent data and whose internal nodes represent connections among the data. Instances are constructed from objects, which have separate names and values. We define a logic for the model, and describe a nonprocedural query language that is based on the logic. We also describe an algebraic query language and show that it is equivalent to the logical language.

On the complexity of queries in the logical data model

We investigate the complexity of query processing in the logical data model (LDM). We use two measures: data complexity, which is complexity with respect to the size of the data, and expression complexity, which is complexity with respect to the size of the expressions denoting the queries. Our investigation shows that while the operations of product and union are essentially first-order operations, the power set operation is inherently a higher-order operation and is exponentially expensive. We define a hierarchy of queries based on the depth of nesting of power set operations and show that this hierarchy corresponds to a natural hierarchy of Turing machines that run in multiply exponential time.

TCK: A clinical genetics data collection system

This work examines the database design and user interface design for a clinical genetics data collection system known as TCK. A specific design goal is automatic generation of the CORN reports. Emphasis in this paper is on how the logical data model resulting from the database design, and the user interface work together to enforce the enterprise results pertaining to data. Data screens are shown, sample queries are explained and the data mapping to the CORN reports presented.

Relating structural failures to subsurface conditions using GIS

This paper discusses the use of GIS to disclose patterns of building structural failures, structural systems and the underlying soil types on a regional basis. The approach proposes the integration of a topologically specified logical data model of regional subsurface conditions in three dimensions with a model that describes the relevant attributes of the foundation/structural systems of buildings using the same geographical referencing systems as a layer of a GIS system. Attributes of structural failures associated with individual buildings are included in the database. With subsurface conditions on one hand and foundation/structural systems data on the other, it becomes possible to compare soil/structure combinations associated with specific types of failure and to give insights into the proper choice of structural systems in the region.

Modelling an information system using the MERISE method for agricultural research: The example of a database for a study on performances in dairy cows

The MERISE method for designing the information system for a research project is described, with emphasis on the data modelling steps. The description is based on an example concerning the design of a relational database, to be used in an exploratory study of the interrelationships between production and health performances of dairy cows over their productive life. The collection of requirements, the construction of a conceptual data model, its conversion into a logical data model suited to a relational structure, and the influence of processing trends on data models, are thus illustrated. It is suggested that using standard guidelines for designing an information system is an aid to research, and the useful role of data conceptual models is pointed out.

On end-user computing productivity: Results of controlled experiments

Two important classes of end-users are: command level users, and end-user programmers. For each of these classes, controlled experiments have been conducted to measure their productivity. For command level users working with databases, our results indicate that they perform better when the logical data model used is consistent with their own views of reality. For end-user programmers writing programs in high-level procedural programming language, the quality of programs is generally low and no program development method clearly stands out in terms of quality; though writing programs directly without any preplanning is expeditious.

Abstract data types for the logical modeling of complex data

In this paper we propose a logical data model for complex data. Our proposal extends the relational model by using abstract data types for domains specification and an extended relational algebra is also introduced. The introduction of the parameterized type Geometry( S), where S is a ground set of elements, allows the representation of complex aggregated data. As an example, we discuss how our model supports the definition of geographical DBMSs. Moreover, to show the generality of our approach, we sketch how the model can be used in the framework of statistical applications.

Handbook of Relational Database Design

Introduction. Why a Handbook of Relational Database Design? Introduction to Logical Data Modeling. Relational Concepts and SQL. Introduction to Relational Database Design. Build Skeletal User Views. Add Keys to User Views. Add Detail to User Views. Validate User Views through Normalization. Determine Additional Attribute Business Rules. Integrate User Views. Translate the Logical Data Structure. Translate the Logical Data Integrity. Tune by Establishing Storage-Related Access Mechanisms. Tune by Adding Indexes. Tune by Introducing Controlled Data Redundancy. Tune by Redefining the Relational Database Structure. Special Design Challenges. Appendix. Bibliography. Index of Design Rules. Index.

Data analysis and learning: an experimental study of data modeling tools

The purpose of the current study was to seek insight into the ease of learning logical data modeling among novices. The two tools examined in the three learning experiments were the Logical Data Structure (LDS), which is based on the entity-relationship concept, and the Relational Data Model (RDM). For a series of trials, naive analysts were asked to generate data models using one of the tools. Feedback regarding the correct model was provided after each trial. The results suggest that when given structured tasks, novices are able to learn rather quickly how to produce small, good quality models using either of the tools. Comparatively, the LDS tool promoted significantly more top-down directed analysis and resulted in more accurate data models than the RDM tool. The significant differences are explained in terms of the visual appearance of the notation associated with the tools. The results have implications for data modeling training and for the development of data modeling tools.

The Changing Role of Management in Information Systems Development

That management involvement in information systems (IS) development is correlated with success in IS implementation has long been accepted (Poll, William J. 1985. Avenues for top management involvement in successful MSI development. MIS Quart. 9(1, March) 43–49.). Initially, management found it difficult to become involved much beyond the front end of IS development because of the extreme difference between the methods and languages of the management and data processing (DP) worlds. One of the most important developments that have enabled management to participate more fully in IS development has been the information analysis methodologies. These have enabled the data and processes of business systems to be analyzed in purely logical terms, producing a logical data model independent of any DP terminology or hardware considerations.

A DBMS prototype to support extended NF2 relations: an integrated view on flat tables and hierarchies

Recently, extensions for relational database management systems (DBMS) have been proposed to support also hierarchical structures (complex objects). These extensions have been mainly implemented on top of an existing DBMS. Such an approach leads to many disadvantages not only from the conceptual point of view but also from performance aspects. Thus paper reports on a 3-year effort to design and prototype a DBMS to support a generalized relational data model, called extended NF 2 ( N on F irst N ormal F orm) data model which treats flat relations, lists, and hierarchical structures in a uniform way. The logical data model, a language for this model, and alternatives for storage structures to implement generalized relations are presented and discussed.

On the expressive power of the logical data model

On the expressive power of the logical data model

The information management system IMS/VS, Part III: Batch processing facilities

The batch processing facilities of the IMS/VS Data Base System permit users to write application programs in COBOL, PL/I, or System/370 assembler language; to define these programs to the system; and to execute them through batch executions of the system. Application programs may issue calls to the system to retrieve, modify, add, and delete data base data. For programming simplicity, application programs view data bases as sets of hierarchic records, and transformations between this view and the underlying data are carried out automatically. A system log and a set of utility programs are provided to facilitate the recovery of data bases from the effects of incorrect programs, and to facilitate the restarting of long-running programs, following a system outage. IMS/VS was one of the first generalized data base management systems to provide hierarchic data structures. Other early systems providing such structures were the FACT <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> system for the Honeywell H800, the Generalized Information System (GIS), <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and to a limited extent the formatted file systems. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> The original motive for this type of structure was to eliminate redundancy inherent in flat files, when the entity being modeled has variably-occurring attributes or subordinate entities. (Engineering documents, a common entity in the early IMS milieu, have this property.) Since then, the hierarchic data structure has become a proper logical view or model of data, i.e., it is a natural way of looking at certain collections of data. IMS is frequently cited as the exemplar of the hierarchic model. The lesson from this experience and other similar experiences seems to be that if a data model is to be successful, it must simultaneously solve the data view problem and the implementation-performance problem.