Search (3 results, page 1 of 1)

  • × author_ss:"Batra, D."
  • × language_ss:"e"
  • × year_i:[1990 TO 2000}
  1. Batra, D.; Marakas, G.M.: Conceptual data modelling in theory and practice (1995) 0.02 
    0.019006012 = product of:
  0.07602405 = sum of:
    0.07602405 = weight(_text_:data in 4109) [ClassicSimilarity], result of:
      0.07602405 = score(doc=4109,freq=18.0), product of:
        0.120893985 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03823278 = queryNorm
        0.6288489 = fieldWeight in 4109, product of:
          4.2426405 = tf(freq=18.0), with freq of:
            18.0 = termFreq=18.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=4109)
  0.25 = coord(1/4)
    Abstract
    Conceptual data modelling (CDM) refers to the phase of the information systems development process that involves the abstraction and representation of the real world data pertinent to an organization. When CDM is properly and rigorously performed, the delivered system is expected to be functionally richer, less error-prone, more fully attuned to meet user needs, mor able to adjust to changing user requirements and less expensive. However, there is little evidence that conceptual data modelling of the enterprise is actually conducted. There is a feeling that the 'corporate reality' is much different. In many organizations, CDM is never employed. In others, it is applied in a haphazard, project to project basis, thus leading to considerable redundancy. The academic community has mainly focused on proposing semantic data models but has not demonstrated a rigorous basis for conceptual data modelling. Specially, the community has failed to show how a conceptual data model can map to an accurate logical data model. Discusses and compares the perspectives of academic and practitioner communities regarding the application of conceptual data modelling
  2. Batra, D.; Davis, J.G.: Conceptual data modelling in database design : similarities and differences between expert and novice designers (1992) 0.01 
    0.010973128 = product of:
  0.04389251 = sum of:
    0.04389251 = weight(_text_:data in 3005) [ClassicSimilarity], result of:
      0.04389251 = score(doc=3005,freq=6.0), product of:
        0.120893985 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03823278 = queryNorm
        0.3630661 = fieldWeight in 3005, product of:
          2.4494898 = tf(freq=6.0), with freq of:
            6.0 = termFreq=6.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.046875 = fieldNorm(doc=3005)
  0.25 = coord(1/4)
    Abstract
    Exploration of the similarities and differences between experts and novices engaged in a conceptual data modelling task, a critical part of overall database design, using data gathered in the form of think-aloud protocols. It was found that the experts focussed on generating a holistic understanding of the problem before developing the conceptual model. They were able to categorize problem descriptions into standard abstractions. The novices tended to have more errors in their solutions due to their inability to integrate the various parts of the problem description and map them into appropriate knowledge structures. The study also found that the expert and novice behaviour was similar in terms of modelling facets, like entity, identifier, descriptor and binary relationship, but quite different in the modelling of unary relationship and category. These findings are discussed
  3. Batra, D.; Zanakis, S.H.: ¬A conceptual database design approach based on rules and heuristics (1994) 0.01 
    0.0073912274 = product of:
  0.02956491 = sum of:
    0.02956491 = weight(_text_:data in 786) [ClassicSimilarity], result of:
      0.02956491 = score(doc=786,freq=2.0), product of:
        0.120893985 = queryWeight, product of:
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.03823278 = queryNorm
        0.24455236 = fieldWeight in 786, product of:
          1.4142135 = tf(freq=2.0), with freq of:
            2.0 = termFreq=2.0
          3.1620505 = idf(docFreq=5088, maxDocs=44218)
          0.0546875 = fieldNorm(doc=786)
  0.25 = coord(1/4)
    Abstract
    Conceptual and logical database design are complex tasks for non-expert designers. The popular data models for conceptual and logical database design are the entity-relationship and the relational model, respectively. Logical design methodologies for relational databases have relied on mathematically rigorous approaches which are impractical, or textbook approaches which do not provide the rich constructs to capture real applications. Designers have to use intuition to develop rules and heuristics. Proposes a realistic and detailed approach for conceptual design using the ER model for relational databases, based on 3 rules that specify the order in which various types of relationships must be modelled, 3 rules that pertain to detection of derived relationships, and 3 heuristics based on observation of constructs in real applications. The approach is illustrated by many examples

Authors