Data Model, Moving Objects

Conventional data models are not satisfactory for modelling data base application systems. The features that they provide are too low level and representational to allow the semantics of a data base to be directly expressed in the schema. The semantic data model (SDM) has been designed as a natural application modelling mechanism that can capture and express the structure of an application environment. The features of the SDM correspond to the principal intensional structures naturally occurring in contemporary data base applications. Furthermore, facilities for expressing derived (redundant) information are an essential part of the SDM; derived information is as prominent in an SDM schema as is primitive data. The SDM is designed to enhance the effectiveness and usability of computerized data bases. It can serve as a formal specification and documentation mechanism for a data base, can support a variety of powerful user interface facilities, and can be used as a tool in the data base design process.

In this paper, a Graph-based semantic Data Model (GDM) is proposed with the primary objective of bridging the gap between the human perception of an enterprise and the needs of computing infrastructure to organize information in some particular manner for efficient storage and retrieval. The Graph Data Model (GDM) has been proposed as an alternative data model to combine the advantages of the relational model with the positive features of semantic data models. The proposed GDM offers a structural representation for interacting to the designer, making it always easy to comprehend the complex relations amongst basic data items. GDM allows an entire database to be viewed as a Graph (V, E) in a layered organization. Here, a graph is created in a bottom up fashion where V represents the basic instances of data or a functionally abstracted module, called primary semantic group (PSG) and secondary semantic group (SSG). An edge in the model implies the relationship among the secondary semantic groups. The contents of the lowest layer are the semantically grouped data values in the form of primary semantic groups. The SSGs are nothing but the higher-level abstraction and are created by the method of encapsulation of various PSGs, SSGs and basic data elements. This encapsulation methodology to provide a higher-level abstraction continues generating various secondary semantic groups until the designer thinks that it is sufficient to declare the actual problem domain. GDM, thus, uses standard abstractions available in a semantic data model with a structural representation in terms of a graph. The operations on the data model are formalized in the proposed graph algebra. A Graph Query Language (GQL) is also developed, maintaining similarity with the widely accepted user-friendly SQL. Finally, the paper also presents the methodology to make this GDM compatible with the distributed environment, and a corresponding query processing technique for distributed environment is also suggested for the sake of completeness.

This research presents a method for constructing a semantic data model for the field of material science based on ontology. The method involves designing the semantic data model and establishing mapping rules between the data model and the ontology. Data semantic analysis and annotation are carried out based on the semantic data model, supporting the integration and efficient retrieval of heterogeneous data in the material science field. Furthermore, the method utilizes the OWL-S technique to describe, issue, and acquire relevant data services, enabling higher-level service semantic synergy in the material science domain. The proposed method effectively enables semantic integration, intelligent inquiry, and personalized services for data in the material science field.

To develop semantically-oriented corporate applications, this chapter recommends a data model using RDF, RDFS, and OWL. Instead of static online, an interconnected semantic web is developed using RDF and OWL. Existing corporate applications’ data has been transformed into RDF/OWL equivalents in a number of different attempts to make it available on the semantic web. Even though relational database modeling was used in the majority of applications, in this chapter, we’ll go over why RDF/OWL semantic data modeling is so important and how to go about putting these best practices into action. Furthermore, we demonstrate the value of our method by contrasting it with conventional relational data modeling. RDFS, RDF, OWL, and SPARQL are just a handful of the terms we’ll employ.

A novel approach is described for building intelligent information systems (or knowledge-base management systems). The approach utilizes the knowledge data language, which is a schema specification language developed for the knowledge/data model. The model, referred to as a hypersemantic data model, captures both knowledge semantics, as specified in knowledge-based systems, and data semantics, as represented by semantic data models. Hypersemantic data models facilitate the incorporation of knowledge in the form of heuristics, uncertainty, constraints and other artificial intelligence concepts, together with object-oriented concepts found in semantic data models. The unified knowledge/data modeling features and constructs of the language are used to develop a prototype knowledge base management system, the KDL-advisor. >

A database management system (DBMS) should provide capabilities for defining and enforcing certain integrity constraints that must hold for the data recorded in the database. These constraints are derived from the semantics of the data and from the rules of the system under study (SuS) that database represents. Every DBMS is based on a data model that provides the means to achieve data abstraction and to express database schema. Each data model has a specific set of integrity constraints types. Some of constraint types are common for several data models and some of them are typical for a data model. Beyond well-known constraint types there are more complex ones that are difficult to express and enforce within the data model. In this paper we deal with the extended tuple constraint (ETC) type. It is the extension of tuple constraint that is one of constraint types of relational data model. In this paper we explain the usage of extended tuple constraint in relational data model and its implementation in relational DBMSs. XML data model does not recognize ETC type. We specify the extended tuple constraint in XML data model and propose two techniques for implementation of ETC in XML DBMSs. Each of them depends on the characteristics of a selected XML DBMS. We use XQuery functions in eXist DBMS, and triggers in Sedna DBMS.

The use of STA Data Model in Learning and Teaching of Data Modeling

Semantic data modeling of multimedia data is a fundamental problem in multimedia databases. The variety, context-dependency, and ambiguity of the semantics of multimedia data make it difficult to apply conventional data modeling techniques, which usually deal with explicit, self-contained alphanumeric data. Many semantic data modeling techniques are either designed for multimedia or can be used for modeling multimedia data. Specifically, we describe MediaView as a novel object-oriented view mechanism that can successfully capture the elusive nature of multimedia data.

There have been several efforts to use relational model and database to store and manipulate Resource Description Framework (RDF). They have one general disadvantage, i.e. one is forced to map the model of semantics of RDF into relational model, which will end up in constraints and additional properties, such as, validating each assertion against the RDF schema which also stored as a triplets table. In this paper, we introduce Semantic Data Model as a proposed data model language to store and manipulate Resource Description Framework. This study also tries to prescribe the procedure on transforming a semantic data model into a RDF data model. Keyworsd: Semantic Data Model, Resource Description Framework.

Product data models can be viewed as a class of semantic data models which take into account the needs of engineering data. In this paper the characteristics for semantic data models defined by Peckham and Maryanski [8] are taken as a starting point. The relevance of these characteristics to product data is shown. The capabilities of a prototype product description system are described in relation to the same characteristics. Additional data modeling characteristics such as parametrization and data sharing, which have been introduced to support engineering design, are then described. These can be viewed as extending beyond the accepted capabilities of semantic data models.

An increasing number of database management systems are expanding their functionality to work with various types of spatial data. This is true for both relational and NoSQL data models. The article describes the main features of those data models for which the functions of storing and processing spatial data are implemented. A comparative analysis of the performance of typical spatial queries for database management systems based on various data models, including multi-model ones, is carried out. The dataset on which the comparison is performed is presented in the form of three blocks of OpenStreetMap vector data for the territory of the Novosibirsk region. Based on the results of the study, recommendations are made on the use of certain data models, depending on the available data and the tasks to be solved.

Trajectories left behind cars, humans, birds or any other moving object are a new kind of data which can be very useful in decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery.

Emerging developments in geographic information systems and distributed computing offer a roadmap towards an unprecedented spatial data infrastructure in the climate sciences. Key to this are the standards developments for digital geographic information being led by the International Organisation for Standardisation (ISO) technical committee on geographic information/geomatics (TC211) and the Open Geospatial Consortium (OGC). These, coupled with the evolution of standardised web services for applications on the internet by the World Wide Web Consortium (W3C), mean that opportunities for both new applications and increased interoperability exist. These are exemplified by the ability to construct ISO‐compliant data models that expose legacy data sources through OGC web services. This paper concentrates on the applicability of these standards to climate data by introducing some examples and outlining the challenges ahead. An abstract data model is developed, based on ISO standards, and applied to a range of climate data –both observational and modelled. An OGC Web Map Server interface is constructed for numerical weather prediction (NWP) data stored in legacy data files. A W3C web service for remotely accessing gridded climate data is illustrated. Challenges identified include the following: first, both the ISO and OGC specifications require extensions to support climate data. Secondly, OGC services need to fully comply with W3C web services, and support complex access control. Finally, to achieve real interoperability, broadly accepted community‐based semantic data models are required across the range of climate data types. These challenges are being actively pursued, and broad data interoperability for the climate sciences appears within reach. Copyright © 2005 Royal Meteorological Society.

BackgroundRobust, extensible and distributed databases integrating clinical, imaging and molecular data represent a substantial challenge for modern neuroscience. It is even more difficult to provide extensible software environments able to effectively target the rapidly changing data requirements and structures of research experiments. There is an increasing request from the neuroscience community for software tools addressing technical challenges about: (i) supporting researchers in the medical field to carry out data analysis using integrated bioinformatics services and tools; (ii) handling multimodal/multiscale data and metadata, enabling the injection of several different data types according to structured schemas; (iii) providing high extensibility, in order to address different requirements deriving from a large variety of applications simply through a user runtime configuration.MethodsA dynamically extensible data structure supporting collaborative multidisciplinary research projects in neuroscience has been defined and implemented. We have considered extensibility issues from two different points of view. First, the improvement of data flexibility has been taken into account. This has been done through the development of a methodology for the dynamic creation and use of data types and related metadata, based on the definition of “meta” data model. This way, users are not constrainted to a set of predefined data and the model can be easily extensible and applicable to different contexts. Second, users have been enabled to easily customize and extend the experimental procedures in order to track each step of acquisition or analysis. This has been achieved through a process-event data structure, a multipurpose taxonomic schema composed by two generic main objects: events and processes. Then, a repository has been built based on such data model and structure, and deployed on distributed resources thanks to a Grid-based approach. Finally, data integration aspects have been addressed by providing the repository application with an efficient dynamic interface designed to enable the user to both easily query the data depending on defined datatypes and view all the data of every patient in an integrated and simple way.ResultsThe results of our work have been twofold. First, a dynamically extensible data model has been implemented and tested based on a “meta” data-model enabling users to define their own data types independently from the application context. This data model has allowed users to dynamically include additional data types without the need of rebuilding the underlying database. Then a complex process-event data structure has been built, based on this data model, describing patient-centered diagnostic processes and merging information from data and metadata. Second, a repository implementing such a data structure has been deployed on a distributed Data Grid in order to provide scalability both in terms of data input and data storage and to exploit distributed data and computational approaches in order to share resources more efficiently. Moreover, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.ConclusionsBased on such repository, data managing has been made possible through a friendly web interface. The driving principle of not being forced to preconfigured data types has been satisfied. It is up to users to dynamically configure the data model for the given experiment or data acquisition program, thus making it potentially suitable for customized applications.

Semantic and object-oriented data models, such as ER, OMT, IFO, and others, have been extensively used for modeling geographic applications. Despite their semantic expressiveness, such models present limitations to adequately model those applications, since they do not provide appropriate primitives for representing spatial data. This paper presents OMT-G, an object oriented data model for geographic applications. OMT-G provides primitives for modeling the geometry and the topology of spatial data, supporting different topological structures, multiple views of objects, and spatial relationships. OMT-G also includes tools to specify transformation processes and presentation alternatives, that allow, among many other possibilities, modeling for multiple representations and multiple presentations. In this way, it overcomes the main limitations of the existing models, thus providing more adequate tools for modeling geographic applications. A comparison with other data models is also presented in order to stress the main advantages of OMT-G.