Estimating LOC for information systems from their conceptual data models

The development of an Electronic Health Record (EHR) system requires a well-defined data set and a robust data model to support complex clinical workflows, interoperability, and data quality. Many existing EHR implementations lack standardized and integrated data models, resulting in redundancy, semantic inconsistency, and limited support for secondary data use. This study aims to develop a comprehensive EHR data model based on a Minimum Data Set (MDS) that systematically represents medical service processes across emergency, outpatient, inpatient, and surgical care. The proposed approach involves the design of a conceptual, logical, and physical data model. Business processes were first analysed to identify relationships among clinical and administrative data classes, which were then transformed into a conceptual data model. The logical data model was developed through database normalization and schema definition, while the physical data model was constructed to represent the actual database structure for implementation. The results demonstrate a structured EHR data model comprising 21 interrelated data classes that reflect real-world medical service workflows. The conceptual data model establishes clear entity relationships, supporting data consistency and interoperability. The logical data model provides a normalized SQL-based schema that minimizes redundancy and enhances data integrity, while the physical data model enables efficient data storage and access. A prototype EHR system was implemented to evaluate the feasibility of the proposed data model, showing stable performance, usability, and alignment with functional requirements. This study concludes that a standardized EHR data model derived from an MDS can effectively support comprehensive clinical documentation, interoperability, and future scalability. The proposed model provides a strong foundation for developing national or institutional EHR systems and can serve as a reference for further EHR data standardization efforts.

A data base design aid, SYDADA, is introduced. Input to SYDADA is a requirements specification, expressed in a language called SYSDOC. A SYSDOC specification consists of two main parts: a conceptual data model and a transaction description. The conceptual data model is a high level, implementation independent data description. The transaction description is expressed in a very high level language which uses the conceptual data model. SYDADA analyzes the requirements specification by modelling the specified system. In the system model the conceptual data model is the data base, and the transactions specified can be executed using this data base. The most important result of the analysis is a set of statistics showing how, and how frequently, the various parts of the conceptual data model are used. We have shown how these statistics can be used as a basis for logical data base design.

Fuzzy set theory has been extensively applied to extend various database models and resulted in numerous contributions, mainly with respect to the popular relational model or to some related form of it. To satisfy the need of modeling complex objects with imprecision and uncertainty, recently many researches have been concentrated on fuzzy semantic (conceptual) data models. This paper reviews fuzzy conceptual data models proposed in the literature, where fuzzy ER/EER, IFO and UML data models are mainly discussed, and reviews the applications of fuzzy conceptual data models.

Defining and validating metrics for assessing the understandability of entity–relationship diagrams

Information systems have become the nerve center of current computer-based engineering applications, which hereby put the requirements on engineering information modeling. Databases are designed to support data storage, processing, and retrieval activities related to data management, and database systems are the key to implementing engineering information modeling. It should be noted that, however, the current mainstream databases are mainly used for business applications. Some new engineering requirements challenge today’s database technologies and promote their evolvement. Database modeling can be classified into two levels: conceptual data modeling and logical database modeling. In this chapter, we try to identify the requirements for engineering information modeling and then investigate the satisfactions of current database models to these requirements at two levels: conceptual data models and logical database models. In addition, the relationships among the conceptual data models and the logical database models for engineering information modeling are presented in the chapter viewed from database conceptual design.

In this short paper we will briefly introduce a data warehouse conceptual data model, which gracefully extends the standard entity-relationship (ER) conceptual data model with multi-dimensional aggregated entities. The conceptual data model has a clear model-theoretic semantics grounded on the extension of the standard ER semantics with the QMD logic-based multi-dimensional data model.

Computer applications in non-traditional areas have put requirements on conceptual data modeling. Some conceptual data models, being the tool of design databases, have been proposed. However, information in real-world applications is often vague or ambiguous. Currently, less research has been done in modeling imprecision and uncertainty in conceptual data models and the design of databases with imprecision and uncertainty. In this chapter, a different level of fuzziness based on fuzzy set and possibility distribution theory will be introduced into the IFO data model and the corresponding graphical representations will be given. The IFO data model is then extended to a fuzzy IFO data model, denoted IF2O. In particular, we provide the approach to mapping an IF2O model to a fuzzy relational database schema.

PurposeWeb applications are subject to continuous changes and rapid evolution triggered by increasing competition, especially in commercial domains such as electronic commerce. Unfortunately, usually they are implemented without producing any useful documentation for subsequent maintenance and evolution. Thereof, the maintenance of such systems becomes a challenging problem as the complexity of the web application grows. Reverse engineering has been heralded as one of the most promising technologies to support effective web application maintenance. This paper aims to present a reverse engineering approach that helps understanding existing undocumented web applications to be maintained or evolved.Design/methodology/approachThe proposed approach provides reverse engineering rules to generate a conceptual schema from a given domain ontology by using a set of transformation rules. The reverse engineering process consists of four phases: extracting useful information; identifying a set of ontological constructs representing the concepts of interest; enriching the identified set by additional constructs; and finally deriving a conceptual schema.FindingsThe advantage of using ontology for conceptual data modeling is the reusability of domain knowledge. As a result, the conceptual data model will be made faster, easier and with fewer errors than creating it in usual way. Designers can use the extracted conceptual schema to gain a better understanding of web applications and to assist in their maintenance.Originality/valueThe strong point of this approach is that it relies on a very rich semantic reference that is domain ontology. However, it is not possible to make a straightforward transformation of all elements from a domain ontology into a conceptual data model because ontology is semantically richer than data conceptual models.

In the past few years, object–oriented (O–O) conceptual data modeling has emerged as an alternative to the traditional technique of entity–relationship modeling. O–O modeling is based on the premise that the resulting models are easier to use and understand. However, most claims of O–O modeling superiority are not empirically verified. Previous studies in this area have focused on a database modeler’s ability to create conceptual data models from a written description, but the concept of understanding a completed data model by a database designer has not been investigated. Thus, this study explores a database designer’s ability to understand an O–O conceptual data model – the Object Modeling Technique (OMT) – compared to an E–R model – the extended–entity– relationship model (EER). The OMT and EER conceptual data modeling techniques are compared using three modeling performance criteria: (1) model understanding; (2) time to understand; and (3) perceived ease–of–use. Results of this study indicate that the only difference between the two techniques is in the time to understand – OMT is significantly faster for both simple and complex problems.

Conceptual data models can change during the information system development and teamwork phases, which require constantly monitoring with synonyms detection. This study elaborates on an approach for detecting synonyms in an entity-relationship model based on mapping with ontological elements. The use of a specific data model validator (DMV) tool enables formalization of the ontology and ER models, as well as their integration with the set of reasoning rules. The reasoning rules enable mapping between formalized elements of the ontology and ER model, and the extraction of synonyms. Formalized elements and reasoning rules are processed within Prolog for the extraction of synonyms. An empirical study conducted by using university student exams demonstrates usability of the proposed approach. The results show effectiveness in extraction of synonyms in all types of conceptual data model elements.

In this paper we define and formalize the model transformations that complete the methodological approach for the development of Object-Relational (OR) Databases (DB) proposed in MIDAS, a model driven methodology for the development of Web Information Systems. In this proposal the Platform Independent Model is the conceptual data model while the Platform Specific Model is the Object-Relational model that represents the OR DB schema. Since both of them will be represented in UML, we also summarize an UML profile for OR DB modeling. In this work we focus on the formalization of the mappings needed to get the OR DB schema from the conceptual data model. We have first specified them with natural language to later formalize them with graph transformation rules.

Computational analysis of the bonding, geometric, and topological relationships within proteins typically takes on the order of hours, mainly devoted to the writing of scripts and code to correctly parse the data. The Structured Query Language (SQL) built into modern database management systems eliminates the need for data parsing, effectively reducing the analysis time to seconds. To this end, we have formulated a conceptual data model (CDM) for proteins based on the IUPAC recommendations for biochemical nomenclature. This conceptual data model makes explicit the inherent bonding relationships between the atoms of a protein, as well as the geometric (bond angle and torsion angle) and topological (chirality) relationships between the bonds. The validity of the CDM has been tested with a reduced implementation using commercial database software. The ease in both populating the database with data from the Protein Data Bank and formulating/executing queries supports the correctness of the model. The ability to conduct truly interactive analyses of protein structure is essential to fully capitalize on the explosion in postgenomic protein structure data.

Effective software cost and size estimation is essential for planning and resource allocation in software development. Recent empirical work which attempts to validate, or which compares, software size and cost estimation models and tools is surveyed. Some observations are made concerning the rather poor results frequently obtained and the need for calibration of models for specific development environments.

Effective management of the software development process requires that management be able to estimate total development effort and cost. One of the fundamental problems associated with effort and cost estimation is the a priori estimation of software size. Function point analysis has emerged over the last decade as a popular tool for this task. Criticisms of the method that relate to the way in which function counts are calculated and the impact of the processing complexity adjustment on the function point count have arisen. SPQR/20 function points among others are claimed to overcome some of these criticisms. The SPQR/20 function point method is compared to traditional function point analysis as a measure of software size in an empirical study of MIS environments. In a study of 64 projects in one organization it was found that both methods would appear equally satisfactory. However consistent use of one method should occur since the individual counts differ considerably. >

Computer-based information systems have become the nerve center of current manufacturing systems. Engineering information modeling in databases is thus essential. However, information imprecision and uncertainty extensively arise in engineering design and manufacturing. So contemporary engineering applications have put a requirement on imprecise and uncertain information modeling. Viewed from database systems, engineering information modeling can be identified at two levels: conceptual data modeling and logical database modeling and correspondingly we have conceptual data models and logical database models, respectively. In this paper, we first investigate information imprecision and uncertainty in engineering applications. Then EXPRESS-G, which is a graphical modeling tool of EXPRESS for conceptual data modeling of engineering information, and nested relational databases are extended based on possibility distribution theory, respectively, in order to model imprecise and uncertain engineering information. The formal methods to mapping fuzzy EXPRESS-G schema to fuzzy relational schema are developed.