Modeling the temporal complexities of symptoms.

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.

In the field of databases, time management at different levels of granularity has been an issue for several years, for instance when dealing with clinical information from different databases using different time units, dealing with natural language expressions, or when dealing with temporal uncertainty. A temporal data model is proposed to manage the temporal aspect of data, presented at various and mixed levels of granularity. The concept of temporal assertions shapes the entire temporal information. The model provides a temporal dimension to the data by using intervals that can be specified at different granularities. The model supports a three-valued logic, where True, False and Undefined are the truth values. The temporal data model allows to manage some degrees of uncertainty when establishing temporal relationships between intervals or between temporal assertions, expressed at different granularities. The logical connectives and quantifiers can manage each of the three truth-values. We applied the temporal data model by implementing an object-oriented database system for managing follow-up clinical data from patients who underwent percutaneous transluminal coronary angioplasty.

Informal and graphical modeling techniques enable developers to construct abstract representations of systems. Object-oriented modeling techniques further facilitate the development process. The Unified Modeling Language (UML), an object-oriented modeling approach, could be broad enough in scope to represent a variety of domains and gain widespread use. Currently, UML comprises several different notations with no formal semantics attached to the individual diagrams. Therefore, it is not possible to apply rigorous automated analysis or to execute a UML model in order to test its behavior, short of writing code and performing exhaustive testing. We introduce a general framework for formalizing a subset of UML diagrams in terms of different formal languages based on a homomorphic mapping between metamodels describing UML and the formal language. This framework enables the construction of a consistent set of rules for transforming UML models into specifications in the formal language. The resulting specifications derived from UML diagrams enable either execution through simulation or analysis through model checking, using existing tools. This paper describes the use of this framework for formalizing UML to model and analyze embedded systems. A prototype system for generating the formal specifications and results from an industrial case study are also described.

A bi-level object-oriented data model together with a user query language called OFQL is designed which can support applications like GIS (geographic information systems). The data model is divided into two layers, the higher-level data model and the lower-level data model. The higher-level data model or the geographic object data model primarily consists of the geographic objects and a set of semantic spatial functions through which the topological relationships of the geographic objects are defined or derived. The lower-level data model or the geometric object data model has geometric objects which are the actual spatial representations of the geographic objects in the higher-level data model. It also has a set of geometric functions that retrieve, manipulate, and compute for geometric objects. The general architecture of a GIS system using this data modeling approach consists of two modules: the query processor and the function implementor. With the OFQL user-interface, the user is able to pose queries of a geographic nature without knowing the details of the spatial representation and computation of the geographic objects.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

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.

Developing a three-dimensional (3D) data model for Geographic Information Systems (GIS) is an essential and complex issue. 3D modelling in GIS is becoming ever more important for the development of cyber cities and digital earth, which have recently become feasible. A competent 3D model forms an efficient foundation for 3D visualization, query and spatial analysis. As a development of the existing 3D models, this study proposes particular improvements in handling complex 3D objects. We present an object-oriented data model for handling complex 3D objects in GIS. First, the conceptual data model is developed based on the principle of object-oriented (OO) data modelling. This model is designed based on the following three basic geometric elements: node, segment and triangle. Accordingly, the abstract geometric objects are defined: including points, lines, surfaces and volumes. Second, the corresponding 3D logical model is designed based on the defined abstract objects and the relationships between them. Third, a formal representation of the 3D spatial objects is described in detail. Fourth, a prototype 3D GIS is developed based on the proposed 3D data model. Finally, we describe the results of an experimental study to reconstruct 3D objects using this 3D GIS and a comparison with the performance of other 3D data models. The proposed model is able to handle complex objects, such as complex buildings and TV towers, which is an essential functionality for building large-scale cyber cities, such as for Hong Kong. The proposed data model proves to be very efficient, particularly in visualization and rendering. The experimental results show which the data volume of the proposed model is compacted and the visualization speed for 3D objects is improved, compared with the existing models.

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.

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

We examine new I/O-efficient techniques for indexing problems in constraint and temporal data models. We present algorithms for these problems that are considerably simpler than previous solutions. Our solutions are unique in the sense that they only use B+-trees rather than special-purpose data structures. Indexing for many general constraint data models can be reduced to interval intersection. We present a new algorithm for this problem using a query-time/space tradeoff, which achieves the optimal query time O(logB n+t/B) I/O's in linear space O(n/B) using B+-trees. (Here, n is the number of intervals, t the number of intervals in the output of a query, and B the disk block size.) It is easy to update this data structure, but small worst-case bounds do not seem possible. Previous approaches have achieved these bounds but are fairly complex and rely mostly on reducing the interval intersection problem to special cases of two-dimensional search. Some of them can also handle updates in O(logB n) I/O's amortized. Indexing in many temporal models becomes a generalization of interval management, where each temporal object is characterized by an interval and a key. There are many different ways of querying these objects, and we achieve optimal bounds for many of these queries. These bounds are achieved using a modification of the technique used for the constraint indexing problem. Our technique is much simpler than other techniques that have been used for achieving similar bounds.KeywordsQuery PointTemporal IndexingDisk AccessTemporal ObjectSecondary StorageThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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.

Conceptual modeling is a learning task essential to students of computer science, software engineering and related programs. Construed as a complex task, surprisingly little is known about the actual act of conceptual modeling, and about modeling difficulties learners experience. Combining complementary modes of observation of learners’ modeling processes, we study modeling difficulties encountered while performing a data modeling task. Using the concept of cognitive breakdown, we analyze audiovisual protocols of the individual modelers’ modeling processes, recordings of their interactions with the employed modeling software tool and survey data of modelers about their perception of encountered modeling difficulties. In an exploratory study and a follow-up study, we identify eight types of modeling difficulties related to modeling entity types, generalization hierarchies, relationship types, attributes and cardinalities. The identified types of modeling difficulties contribute to a better and more complete understanding of data modeling processes intended to inform design science research on modeling assistance for data modelers at different stages of their learning and mastering of conceptual data modeling.

Effort and cost estimation is crucial in software management. Estimation of software size plays a key role in the estimation. Line of Code (LOC) is still a commonly used software size measure. Despite the fact that software sizing is well recognized as an important problem for more than two decades, there is still much problem in existing methods. Conceptual data model is widely used in the requirements analysis for information systems. It is also not difficult to construct conceptual data models in the early stage of developing information systems. Much characteristic of an information system is actually reflected from its conceptual data model. We explore into the use of conceptual data model for estimating LOC. This paper proposes a novel method for estimating LOC for an information system from its conceptual data model through the use of multiple linear regression model. We have validated the method through collecting samples from both the industry and open-source systems.

Data model for warehousing historical Web information

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.

Efficient data warehouse (DW) design depends on the quality of a conceptual model. In this paper, a set of quality metrics have been proposed along with the theoretical validation for quality measurement of conceptual level object-oriented multidimensional data model. The paper also describes the designer level and user level viewpoints of quality evaluation through the criteria like complexity, completeness, expressiveness and analysability. Finally, the work focuses on the empirical validation of the set of metrics and measurements. The empirical validation process shows the usefulness of the proposed metrics for the assessment of operability factor of conceptual level multidimensional data model. Significant correlations exist among the proposed quality measurements and operability factors. The proposed quality evaluation framework is verified in context of a graph semantic-based object-oriented multidimensional data model GOOMD.