Generation of file processing programs based on JSP

This paper describes the generation of file processing programmes within the TUBA environment. Program structures are derived from data structures according to the JSP method. Expressions describing output data are specified in user-system dialogues. The program specifications are stored in the dictionary. Complete executable programs can be generated from these specifications

UML Class Diagram or Entity Relationship Diagram : An Object Relational Impedance Mismatch

It is now nearly 30 years since Peter Chen’s watershed paper “The Entity-Relationship Model –towards a Unified View of Data”. [1] The entity relationship model and variations and extensions to ithave been taught in colleges and universities for many years. In his original paper Peter Chen looked at converting his new ER model to the then existing data structure diagrams for the Network model. In recent years there has been a tendency to use a Unified Modelling Language (UML) class diagram forconceptual modeling for relational databases, and several popular course text books use UMLnotation to some degree [2] [3]. However Object and Relational technology are based on different paradigms. In the paper we argue that the UML class diagram is more of a logical model (implementation specific). ER Diagrams on theother hand, are at a conceptual level of database design dealing with the main items and their relationships and not with implementation specific detail. UML focuses on OOAD (Object Oriented Analysis and Design) and is navigational and program dependent whereas the relational model is set based and exhibits data independence. The ER model provides a well-established set of mapping rules for mapping to a relational model. In this paper we look specifically at the areas which can cause problems for the novice databasedesigner due to this conceptual mismatch of two different paradigms. Firstly, transferring the mapping of a weak entity from an Entity Relationship model to UML and secondly the representation of structural constraints between objects. We look at the mixture of notations which students mistakenly use when modeling. This is often the result of different notations being used on different courses throughout their degree. Several of the popular text books at the moment use either a variation of ER,UML, or both for teaching database modeling. At the moment if a student picks up a text book they could be faced with either; one of the many ER variations, UML, UML and a variation of ER both covered separately, or UML and ER merged together. We regard this problem as a conceptual impedance mismatch. This problem is documented in [21] who have produced a catalogue of impedance mismatch problems between object-relational and relational paradigms. We regard the problems of using UML class diagrams for relational database design as a conceptual impedance mismatch as the Entity Relationship model does not have the structures in the model to deal with Object Oriented concepts Keywords: EERD, UML Class Diagram, Relational Database Design, Structural Constraints, relational and object database impedance mismatch. The ER model was originally put forward by Chen [1] and subsequently extensions have been added to add further semantics to the original model; mainly the concepts of specialisation, generalisation and aggregation. In this paper we refer to an Entity-Relationship model (ER) as the basic model and an extended or enhanced entity-relationship model (EER) as a model which includes the extra concepts. The ER and EER models are also often used to aid communication between the designer and the user at the requirements analysis stage. In this paper when we use the term “conceptual model” we mean a model that is not implementation specific.ISBN: 978-84-616-3847-5 3594Peer reviewe

The Use of UML Class Diagrams To Teach Database Modelling and Database Design

It is now nearly 30 years since Peter Chen’s watershed paper “The Entity-Relationship Model – towards a Unified View of Data”. [1] The entity relationship model and variations and extensions to it have been taught in colleges and universities for many years. In his original paper Peter Chen looked at converting his new ER model to the then existing data structure diagrams for the Network model. In recent years there has been a tendency to use a Unified Modelling Language (UML) class diagram for conceptual modelling for relational databases, and several popular course text books use UML notation to some degree [2] [3]. This paper looks at the usefulness of using UML class diagrams for teaching database design in undergraduate courses. In this paper we look specifically at two concepts which can cause problems for the novice database designer. Firstly transferring the concept of a weak entity from an Entity Relationship model to UML and secondly the notation for structural constraints in different diagramming notations. We also look at the mixture of notations which students mistakenly use when modelling. This is often the result of different notations being used on different courses throughout their degree. Peter Chen wrote in his original paper “The entity-relationship model can be used as a tool in the structured design of databases using the network model” today we could write “the UML class diagram can be used as a tool in the structured design of databases using the relational model”. Or can we

Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure

Big data research has attracted great attention in science, technology, industry and society. It is developing with the evolving scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies. However, its nature and fundamental challenge have not been recognized, and its own methodology has not been formed. This paper explores and answers the following questions: What is big data? What are the basic methods for representing, managing and analyzing big data? What is the relationship between big data and knowledge? Can we find a mapping from big data into knowledge space? What kind of infrastructure is required to support not only big data management and analysis but also knowledge discovery, sharing and management? What is the relationship between big data and science paradigm? What is the nature and fundamental challenge of big data computing? A multi-dimensional perspective is presented toward a methodology of big data computing.Comment: 59 page

Software engineering methodologies and tools

Over the years many engineering disciplines have developed, including chemical, electronic, etc. Common to all engineering disciplines is the use of rigor, models, metrics, and predefined methodologies. Recently, a new engineering discipline has appeared on the scene, called software engineering. For over thirty years computer software has been developed and the track record has not been good. Software development projects often miss schedules, are over budget, do not give the user what is wanted, and produce defects. One estimate is there are one to three defects per 1000 lines of deployed code. More and more systems are requiring larger and more complex software for support. As this requirement grows, the software development problems grow exponentially. It is believed that software quality can be improved by applying engineering principles. Another compelling reason to bring the engineering disciplines to software development is productivity. It has been estimated that productivity of producing software has only increased one to two percent a year in the last thirty years. Ironically, the computer and its software have contributed significantly to the industry-wide productivity, but computer professionals have done a poor job of using the computer to do their job. Engineering disciplines and methodologies are now emerging supported by software tools that address the problems of software development. This paper addresses some of the current software engineering methodologies as a backdrop for the general evaluation of computer assisted software engineering (CASE) tools from actual installation of and experimentation with some specific tools

Modelling Tools for Life Cycle of Pedagogic Information System at Tu Kosice, Slovakia

The contribution presents a brief characteristic of structural and object models used for development and maintenance of information systems. It also describes utilisation of these models for generation of pedagogic information system IS STUDENT at the Technical University in Košice. It describes possibility of application of UML language (Unified modelling language) in life cycle of pedagogic information system. On practical examples it demonstrates the importance of modelling of such a system

To Map or Not to Map: Rethinking Crosswalk Agendas

In the two decades since their publication, the Functional Requirements of Bibliographic Records and succeeding standards such as the Library Reference Model have had a marked impact on discourse concerning descriptive theory and practice. The BIBFRAME model, which began as an effort to replace MARC as a linked data-capable modeling format, offers an alternate view of the bibliographic universe with three principal entities rather than four. Differences between BIBFRAME and LRM are based in competing intuitions on the nature of creative works, and at first the two approaches appear to compete for the same intellectual space. BIBFRAME offers us a less constrained model of bibliographic descriptions than the FRBR models, and if interoperability between BIBFRAME and WEMI-aligned standards like Resource Description and Access requires translation of RDA records both to and from BIBFRAME descriptions, then the latter’s flexibility poses problems for mapping between the models. Proposed solutions to those problems reveal as much about different modeling philosophies as they do about different views of creative works and their relationships to texts and copies. Linked data protocols are intended to support resources and scenarios that are far too diverse for either a single account of creative works or for a subsumption-based taxonomy of models. But a need for descriptions flexible enough to include them all does not require us to retreat from modeling commitments to either reductionism or operationalism. BIBFRAME can be seen as reaching for or pointing toward a descriptive domain that supports a complementary role to the IFLA standards