Redirigiendo al acceso original de articulo en 23 segundos...
ARTÍCULO
TITULO

A Semantic Data Model: Meaning Making from Data Structures in the SQL Server

Sanjay Ramesh    
Anthony Henderson    

Resumen

Information systems designs are increasingly concerned with entity relationships and technical programmatic approaches to solutions architecture as opposed to semantic based, business focused information architecture that places business definitions at the centre of the information system design and implementation. The disconnect between information technology and business is perpetuated by an overly prescriptive information technology technical design method that fails to incorporate qualitative and normative aspects of business, where information is structured and delivered according to business. The paper will discuss various decision support and semantic approaches to information design and delivery and argue that the traditional modes of solution delivery do not include meaning making of the data elements which are essential to business information reporting and analytics. The meaning making aspect identified is linked to data dictionary or business data glossary that allows for the discovery of semantic meaning from the SQL Server. Using Christian Fürber?s methodology on semantic programming, the analytics team developed a semantic model that enabled detailed definition of fields and the discovery of information using semantic search functionality embedded in the SQL Server. The project provided semantic data framework that provided business with the capability for semantic reconciliation and data sets that were further integrated with Tableau visualization and SQL auto processes.

Palabras claves

 Artículos similares

       
 
Hao Liu, Bo Yang and Zhiwen Yu    
Multimodal sarcasm detection is a developing research field in social Internet of Things, which is the foundation of artificial intelligence and human psychology research. Sarcastic comments issued on social media often imply people?s real attitudes towa... ver más
Revista: Applied Sciences

 
Jie Wang, Jie Yang, Jiafan He and Dongliang Peng    
Semi-supervised learning has been proven to be effective in utilizing unlabeled samples to mitigate the problem of limited labeled data. Traditional semi-supervised learning methods generate pseudo-labels for unlabeled samples and train the classifier us... ver más
Revista: Algorithms

 
Chunling Wang, Tianyi Hang, Changke Zhu and Qi Zhang    
The Czech Republic is one of the countries along the Belt and Road Initiative, and classifying land cover in the Czech Republic helps to understand the distribution of its forest resources, laying the foundation for forestry cooperation between China and... ver más
Revista: Applied Sciences

 
Zongshun Wang, Ce Li, Jialin Ma, Zhiqiang Feng and Limei Xiao    
In this study, we introduce a novel framework for the semantic segmentation of point clouds in autonomous driving scenarios, termed PVI-Net. This framework uniquely integrates three different data perspectives?point clouds, voxels, and distance maps?exec... ver más
Revista: Information

 
Qishun Mei and Xuhui Li    
To address the limitations of existing methods of short-text entity disambiguation, specifically in terms of their insufficient feature extraction and reliance on massive training samples, we propose an entity disambiguation model called COLBERT, which f... ver más
Revista: Information