The problem of multiprogram scheduling on a single processor is studied from the viewpoint of the characteristics peculiar to the program functions that need guaranteed service. It is shown that an optimum fixed priority scheduler possesses an upper bound to processor utilization which may be as low as 70 percent for large task sets. It is also shown that full processor utilization can be achieved by dynamically assigning priorities on the basis of their current deadlines. A combination of these two scheduling techniques is also discussed.

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Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment

A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information in the real world. A special diagramatic technique is introduced as a tool for data base design. An example of data base design and description using the model and the diagramatic technique is given. Some implications on data integrity, information retrieval, and data manipulation are discussed.The entity-relationship model can be used as a basis for unification of different views of data: the network model, the relational model, and the entity set model. Semantic ambiguities in these models are analyzed. Possible ways to derive their views of data from the entity-relationship model are presented.

/pdf/the-entity-relationship-model-toward-a-unified-view-of-data-i5q0wnv07u.pdf

The entity-relationship model: toward a unified view of data

A data model, called the entity-relationship model, is proposed. This model incorporates some of the important semantic information about the real world. A special diagrammatic technique is introduced as a tool for database design. An example of database design and description using the model and the diagrammatic technique is given. Some implications for data integrity, information retrieval, and data manipulation are discussed.The entity-relationship model can be used as a basis for unification of different views of data: the network model, the relational model, and the entity set model. Semantic ambiguities in these models are analyzed. Possible ways to derive their views of data from the entity-relationship model are presented.

Research in IT must address the design tasks faced by practitioners. Real problems must be properly conceptualized and represented, appropriate techniques for their solution must be constructed, and solutions must be implemented and evaluated using appropriate criteria. If significant progress is to be made, IT research must also develop an understanding of how and why IT systems work or do not work. Such an understanding must tie together natural laws governing IT systems with natural laws governing the environments in which they operate. This paper presents a two dimensional framework for research in information technology. The first dimension is based on broad types of design and natural science research activities: build, evaluate, theorize, and justify. The second dimension is based on broad types of outputs produced by design research: representational constructs, models, methods, and instantiations. We argue that both design science and natural science activities are needed to insure that IT research is both relevant and effective.

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Design and natural science research on information technology

The aim of this research essay is to examine the structural nature of theory in Information Systems. Despite the importance of theory, questions relating to its form and structure are neglected in comparison with questions relating to epistemology. The essay addresses issues of causality, explanation, prediction, and generalization that underlie an understanding of theory. A taxonomy is proposed that classifies information systems theories with respect to the manner in which four central goals are addressed: analysis, explanation, prediction, and prescription. Five interrelated types of theory are distinguished: (1) theory for analyzing, (2) theory for explaining, (3) theory for predicting, (4) theory for explaining and predicting, and (5) theory for design and action. Examples illustrate the nature of each theory type. The applicability of the taxonomy is demonstrated by classifying a sample of journal articles. The paper contributes by showing that multiple views of theory exist and by exposing the assumptions underlying different viewpoints. In addition, it is suggested that the type of theory under development can influence the choice of an epistemological approach. Support is given for the legitimacy and value of each theory type. The building of integrated bodies of theory that encompass all theory types is advocated.

/pdf/the-nature-of-theory-in-information-systems-29dmwomevf.pdf

The nature of theory in information systems

Future users of large data banks must be protected from having to know how the data is organized in the machine (the internal representation). A prompting service which supplies such information is not a satisfactory solution. Activities of users at terminals and most application programs should remain unaffected when the internal representation of data is changed and even when some aspects of the external representation are changed. Changes in data representation will often be needed as a result of changes in query, update, and report traffic and natural growth in the types of stored information.Existing noninferential, formatted data systems provide users with tree-structured files or slightly more general network models of the data. In Section 1, inadequacies of these models are discussed. A model based on n-ary relations, a normal form for data base relations, and the concept of a universal data sublanguage are introduced. In Section 2, certain operations on relations (other than logical inference) are discussed and applied to the problems of redundancy and consistency in the user's model.

/pdf/a-relational-model-of-data-for-large-shared-data-banks-45czo3bg7b.pdf

A relational model of data for large shared data banks

In the near future, we can expect a great variety of languages to be proposed for interrogating and updating data bases. This paper attempts to provide a theoretical basis which may be used to determine how complete a selection capability is provided in a proposed data sublanguage independently of any host language in which the sublanguage may be embedded. A relational algebra and a relational calculus are defined. Then, an algorithm is presented for reducing an arbitrary relation-defining expression (based on the calculus) into a semantically equivalent expression of the relational algebra. Finally, some opinions are stated regarding the relative merits of calculusoriented versus algebra-oriented data sublanguages from the standpoint of optimal search and highly discriminating authorization schemes. RJ 987 Hl7041) March 6, 1972 Computer Sciences

http://www.geology.cz/personal/j/jan.sedlacek/codd2.pdf

Relational completeness of data base sublanguages

From the Preface (See Front Matter for full Preface)

An important adjunct to precision is a sound theoretical foundation. The relational model is solidly based on two parts of mathematics: firstorder predicate logic and the theory of relations. This book, however, does not dwell on the theoretical foundations, but rather on all the features of the relational model that I now perceive as important for database users, and therefore for DBMS vendors. My perceptions result from 20 years of practical experience in computing and data processing (chiefly, but not exclusively, with large-scale customers of IBM), followed by another 20 years of research.

I believe that this is the first book to deal exclusively with the relational approach. It does, however, include design principles in Chapters 21 and 22. It is also the first book on the relational model by the originator of that model. All the ideas in the relational model described in this book are mine, except in cases where I explicitly credit someone else.

In developing the relational model, I have tried to follow Einstein's advice, "Make it as simple as possible, but no simpler." I believe that in the last clause he was discouraging the pursuit of simplicity to the extent of distorting reality. So why does the book contain 30 chapters and two appendixes? To answer this question, it is necessary to look at the history of research and development of the relational model.

E. F. Codd

Papers

A relational model of data for large shared data banks

A relational model of data for large shared data banks

Relational completeness of data base sublanguages

The Relational Model for Database Management: Version 2

Further Normalization of the Data Base Relational Model