W3C XML Schema vs Database Data Types

AI-ESTATE standard uses EXPRESS language to describe its diagnostic knowledge, but EXPRESS is not a programming language, which makes it difficult to implement diagnostics using EXPRESS language description. This makes it difficult to share and reuse diagnostic knowledge; XML language with its good flexibility, readability and extensibility, it has brought great convenience to information exchange. Mapping the EXPRESS language to XML Schema is of great significance for the sharing of diagnostic knowledge. First, the data types of the EXPRESS language and XML Schema are analysed; then, mapping mechanism of the simple data types, aggregate data type, and structural data types to XML data types in EXPRESS are studied separately. Finally, based on the analysis of the AI-ESTATE standard common element model data type, the process of describing the diagnostic knowledge of the common element model using XML Schema standardization is studied, and the diagnostic knowledge can be portable and reused.

Download Full-text

Programming Language Inherent Support for Constrained XML Schema Definition Data Types and OWL DL

21st IEEE/ACM International Conference on Automated Software Engineering (ASE'06) ◽

10.1109/ase.2006.56 ◽

2006 ◽

Cited By ~ 1

Author(s):

Alexander Paar ◽

Walter Tichy

Keyword(s):

Programming Language ◽

Xml Schema ◽

Data Types

Download Full-text

XML Schema Integration to Facilitate E-Commerce

Web-Enabled Systems Integration ◽

10.4018/978-1-59140-041-7.ch004 ◽

2011 ◽

pp. 66-90 ◽

Cited By ~ 1

Author(s):

Louise Lane ◽

Kalpdrum Passi ◽

Sanjay Madria ◽

Mukesh Mohania

Keyword(s):

Enterprise Resource Planning ◽

Data Model ◽

Information Exchange ◽

Graphical Representation ◽

Resource Planning ◽

Xml Schema ◽

Schema Integration ◽

Data Types ◽

Layered Architecture ◽

Global Schema

XML has become the de facto standard for Information Exchange protocol for e-commerce and many workgroup applications such as Enterprise Resource Planning (ERP). The availability of large amounts of heterogeneous distributed web data necessitates the integration of XML data from multiple XML sources for many reasons. Currently, there are many e-commerce companies, which sell similar products but represent them using different XML schemas with possibly different ontologies. When any two such companies merge, there is a need for a uniform schema integration methodology. In some applications like comparison-shopping, there is a need for an illusionary centralized homogeneous information system. In this chapter, we propose an XML Schema integration methodology. We define an object-oriented data model called XSDM (XML Schema Data Model) and present a graphical representation of XML Schema for the purpose of schema integration. We use a three-layered architecture for XML Schema integration, with each layer presenting an integrated view of the concepts that characterize the layer below. The three layers included are namely pre-integration, comparison and integration. During pre-integration, an analysis of the schemas to be integrated occurs. During the comparison phase of integration, correspondences as well as conflicts between elements are identified. During the integration phase, restructuring and merging of the initial schemas takes place to obtain the global schema. We define integration policies for integrating element definitions as well as their data types and attributes. The policies are also applicable in integrating DTD schemas with other DTD/XML Schemas.

Download Full-text

Integrating Visual and Bayesian Statistical Analyses in Single Case Experimental Research to Evaluate the Effectiveness and Magnitude of a Comprehensive Behavioral Intervention

10.31234/osf.io/54cj3 ◽

2018 ◽

Author(s):

Prathiba Natesan ◽

Smita Mehta

Keyword(s):

Behavioral Intervention ◽

Effect Size ◽

Rate Ratio ◽

Visual Analysis ◽

Single Case ◽

Small Sample ◽

Statistical Analyses ◽

Data Types ◽

Ratio Effect ◽

Small Sample Sizes

Single case experimental designs (SCEDs) have become an indispensable methodology where randomized control trials may be impossible or even inappropriate. However, the nature of SCED data presents challenges for both visual and statistical analyses. Small sample sizes, autocorrelations, data types, and design types render many parametric statistical analyses and maximum likelihood approaches ineffective. The presence of autocorrelation decreases interrater reliability in visual analysis. The purpose of the present study is to demonstrate a newly developed model called the Bayesian unknown change-point (BUCP) model which overcomes all the above-mentioned data analytic challenges. This is the first study to formulate and demonstrate rate ratio effect size for autocorrelated data, which has remained an open question in SCED research until now. This expository study also compares and contrasts the results from BUCP model with visual analysis, and rate ratio effect size with nonoverlap of all pairs (NAP) effect size. Data from a comprehensive behavioral intervention are used for the demonstration.

Download Full-text