Modeling a Geo-Spatial Database for Managing Travelers Demand

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.

<p>The heterogeneous nature of environmental systems poses a challenge to researchers constructing environmental models. Many simulation models of integrated systems need to incorporate phenomena that are represented as spatially and temporally continuous fields as well as phenomena that are modelled as spatially and temporally bounded agents. Examples include moving animals (agents) interacting with vegetation (fields) or static water reservoirs (agents) as components of hydrological catchments (fields). However, phenomena bounded in space and time have particular properties mainly because they require representation of multiple (sometimes mobile) objects that each exist in a small subdomain of the space-time domain of interest. Moreover, these subdomains of objects may overlap in space and time such as interleaving branches due to tree crown growth. Efficient storage and access of different types of phenomena requires an approach that integrates representation of fields and objects in a single data model.</p><p>We develop the open-source LUE data model that explicitly stores and separates domain information, i.e. where phenomena exist in the space-time domain, and property information, i.e. what attribute value the phenomenon has at a particular space-time location, for a particular object. Notable functionalities are support for multiple spatio-temporal objects, time domains, objects linked to multiple space and time domains, and relations between objects. The design of LUE is based on the conceptual data model of de Bakker (2017) and implemented as a physical data model using HDF5 and C++ (de Jong, 2019). Our LUE data model is part of a new modelling language implemented in Python, allowing for operations accepting both fields and agents as arguments, and therefore resembling and extending the map algebra approach to field-agent modelling.</p><p>We present the conceptual and physical data models and illustrate the usage by implementing a spatial agent-based model simulating changes in human nutrition. We thereby consider the interaction between personal demand and supply of healthy food of nearby stores as well as the influence of agent's social network.</p><p><br>

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.

Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were also developed or enhanced to handle geospatial data. For example, Rasdaman was developed to handle raster data and GeoSpark/SpatialHadoop were enhanced from Spark/Hadoop to handle vector data. However, there are few studies to systematically compare and evaluate the features and performances of these popular data containers. This paper provides a comprehensive evaluation of six popular data containers (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling multi-dimensional, array-based geospatial raster datasets. Their architectures, technologies, capabilities, and performance are compared and evaluated from two perspectives: (a) system design and architecture (distributed architecture, logical data model, physical data model, and data operations); and (b) practical use experience and performance (data preprocessing, data uploading, query speed, and resource consumption). Four major conclusions are offered: (1) no data containers, except ClimateSpark, have good support for the HDF data format used in this paper, requiring time- and resource-consuming data preprocessing to load data; (2) SciDB, Rasdaman, and MongoDB handle small/mediate volumes of data query well, whereas Spark and ClimateSpark can handle large volumes of data with stable resource consumption; (3) SciDB and Rasdaman provide mature array-based data operation and analytical functions, while the others lack these functions for users; and (4) SciDB, Spark, and Hive have better support of user defined functions (UDFs) to extend the system capability.

Extraction, Transformation and Loading (ETL) is a key process of data warehouse building. It integrates data sources with diverse features and structures. Numerous approaches and implementations of ETL have been introduced. However, they still have the following disadvantages: human-dependence, information integration only in syntactic levels, incomplete the homogeneity solution, difficulty to install and configure, etc. In this paper, we propose an alternative approach to the ETL process by attacking the homogeneity in data sources with an ontology-based methodology. Our approach can overcome the drawbacks of most existing approaches; as it automates the key activities of the process, such as: extraction of metainformation, generation of logical and physical data models, and transformation of information.

Data preparation is an iterative-agile process for exploring, combining, cleaning and transforming raw data into curated datasets for self-service data integration, data science, data discovery, and BI/analytics. To perform data preparation, data preparation tools are used by analysts, citizen data scientists and data scientists for self- service. The tools are also used by citizen integrators and data engineers for data enablement to reduce the time and complexity of interactively accessing, cataloging, harmonizing, transforming and modeling data for analytics in an agile manner with metadata and lineage support. These tools can provide data access for use in mostly analytical tasks that include storage, logical and physical data modeling, and data manipulation for data visualization, data integration and analytics. Some tools support machine-learning algorithms that can recommend or even automate actions to augment and accelerate data preparation. Keywords: Data Mining, Data preparation, Data Analysis, Big Data Analytics

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.

Intelligent vehicle monitoring system is collecting and processing the large amounts of information all the time. The paper applies data warehouse technique to intelligent vehicle monitoring system to store and manage the information. Based on the analysis of the subject field in the system and the design of dimension table, the system logical model and physical data model, the data warehouse of system is built. And, the data in the data warehouse are analyzed and summarized by the data analysis and display tools. Decision-makers can clearly and intuitively see the result, and this can offer the decision foundation for development of China's transportation.

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.

The amount of data has increased exponentially as a consequence of the availability of new data sources and the advances in data collection and storage. This data explosion was accompanied by the popularization of the Big Data term, addressing large volumes of data, with several degrees of complexity, often without structure and organization, which cannot be processed or analyzed using traditional processes or tools. Moving towards Big Data Warehouses (BDWs) brings new problems and implies the adoption of new logical data models and tools to query them. Hive is a DW system for Big Data contexts that organizes the data into tables, partitions and buckets. Several studies have been conducted to understand ways of optimizing its performance in data storage and processing, but few of them explore whether the way data is structured has any influence on how quickly Hive responds to queries. This paper investigates the role of data organization and modelling in the processing times of BDWs implemented in Hive, benchmarking multidimensional star schemas and fully denormalized tables with different Scale Factors (SFs), and analyzing the impact of adequate data partitioning in these two data modelling strategies.

Rapidly increasing storage device capacities at ever decreasing costs have resulted in mushrooming of publicly available large data sets on the Web. In this paper, we describe a novel approach to teaching relational database course by using such data repositories. We demonstrate our approach using the Amazon.com product database, though the approach is generic and is applicable to other data repositories. The Amazon database is supposedly the largest product database ever in existence. We have used the Amazon Web Services API and .NET/C# application to extract a subset of the product database to enhance student learning in a relational database course. This realistic data served various activities of the course and provided a rich backdrop to demonstrate more interesting features of SQL and Oracle cost-based query optimization. Central to the course is a semester-long team project. We discuss the details of data extraction from Amazon.com, conceptual and logical data modeling, logical and physical database design, database creation and data loading, database querying, and database application development.

Database system reengineering technique has been used to resolve the problem of converting from existing out-of-data database systems to new technique database systems in order to reduce the new system implementation cost. The technique consists of three parts: schema translation, data conversion, and program translation. The schema translation requires the transferring of existing database schema into the new database schema with better semantics. Recapturing the semantics is a complicated and difficult work. During the database design phase, the semantics has been lost from the conceptual data model to the logical data model. It is difficult to recapture it. After recapturing the semantics from the original conceptual database schema, we also need to have a sophisticated knowledge base to store the knowledge. The paper describes a new schema translation system, which can recapture the missing/hidden semantics of a database. The kernel of the system is an extended entity relationship (EER) data dictionary system (DDS), which can store all the semantics of the new database schema.

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.

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.

Data warehousing accounts for a significant fraction of database use today. As XML becomes ever more popular, more and more XML data finds its way into data warehouse repositories. This paper examines the modeling mismatch between the tree structure of XML data model and the multidimensional model of a typical data warehouse, and proposes an XML warehouse model based on the multi-colored trees (MCT) logical data model that resolves the modeling issue naturally. Furthermore, this data model ameliorates some well-known modeling limitations of the XML data warehouse. To cope with ad-hoc OLAP queries, we extend bit-map join indices to the XML context. We then tackle the difference between the bit-map and the stack-based structural join processing paradigm popular in XML query processing, permitting both styles of query processing to be used seamlessly in consort to evaluate queries. We demonstrate experimentally the benefit of bit-map join indices for typical queries, and particularly those with low cardinality or high selectivity.