Fuzzy XML data modeling with the UML and relational data models

2007 ◽  
Vol 63 (3) ◽  
pp. 972-996 ◽  
Author(s):  
Z.M. Ma ◽  
Li Yan
Keyword(s):  
Data Modeling ◽  
Data Models ◽  
Relational Data ◽  
Xml Data ◽  
Fuzzy Xml

Fuzzy XML Data Models

2013 ◽  
pp. 83-100
Author(s):  
Li Yan ◽  
Zongmin Ma ◽  
Fu Zhang
Keyword(s):  
Data Models ◽  
Xml Data ◽  
Fuzzy Xml

METHOD FOR SYNTHESIS OF CONCEPTUAL DATA MODELS BASED ON PROCESS DOMAIN MODELS

2020 ◽  
pp. 80-87
Author(s):  
С.И. Рябухин
Keyword(s):  
Data Modeling ◽  
Database Design ◽  
Data Models ◽  
Process Models ◽  
Domain Models ◽  
Conceptual Data

Процессные модели предметной области широко применяются при проектировании баз данных, а именно в ходе концептуального моделирования данных. Предлагается решение проблемы неоднозначности преобразования процессных доменных моделей типа SADT в концептуальные модели данных. Domain process models are widely used in database design, namely in conceptual data modeling. The solution of the problem of ambiguity of transformation of process domain models of the SADT type into conceptual data models is proposed.

XCase - A Tool for Conceptual XML Data Modeling

2010 ◽  
pp. 96-103 ◽  
Author(s):  
Jakub Klímek ◽  
Lukáš Kopenec ◽  
Pavel Loupal ◽  
Jakub Malý
Keyword(s):  
Data Modeling ◽  
Xml Data

A Meta-Analysis Comparing Relational and Semantic Models

2013 ◽  
pp. 394-409
Author(s):  
Keng Siau ◽  
Fiona F.H. Nah ◽  
Qing Cao
Keyword(s):  
Statistical Power ◽  
Meta Analysis ◽  
Data Modeling ◽  
Systems Development ◽  
Data Models ◽  
The Past ◽  
Semantic Data ◽  
Semantic Models ◽  
Semantic Data Modeling

Data modeling is the sine quo non of systems development and one of the most widely researched topics in the database literature. In the past three decades, semantic data modeling has emerged as an alternative to traditional relational modeling. The majority of the research in data modeling suggests that the use of semantic data models leads to better performance; however, the findings are not conclusive and are sometimes inconsistent. The discrepancies that exist in the data modeling literature and the relatively low statistical power in the studies make meta-analysis a viable choice in analyzing and integrating the findings of these studies.

XTEngine

2011 ◽  
pp. 174-213
Author(s):  
Kamal Taha
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Marked Improvement ◽  
Data Models ◽  
Data Element ◽  
Xml Data ◽  
Building Relationships ◽  
Search Techniques ◽  
Data Elements

There has been extensive research in XML Keyword-based and Loosely Structured querying. Some frameworks work well for certain types of XML data models while fail in others. The reason is that the proposed techniques overlook the context of elements when building relationships between the elements. The context of a data element is determined by its parent, because a data element is generally a characteristic of its parent. Overlooking the contexts of elements may result in relationships between the elements that are semantically disconnected, which lead to erroneous results. We present in this chapter a context-driven search engine called XTEngine for answering XML Keyword-based and Loosely Structured queries. XTEngine treats each set of elements consisting of a parent and its children data elements as one unified entity, and then uses context-driven search techniques for determining the relationships between the different unified entities. We evaluated XTEngine experimentally and compared it with three other search engines. The results showed marked improvement.

Data Mining on XML Data

2011 ◽  
pp. 506-510
Author(s):  
Qin Ding
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Efficient Algorithms ◽  
Relational Data ◽  
Xml Data ◽  
Structure Information ◽  
Data Mining Algorithms ◽  
Mining Algorithms ◽  
Naive Approach

With the growing usage of XML data for data storage and exchange, there is an imminent need to develop efficient algorithms to perform data mining on semistructured XML data. Mining on XML data is much more difficult than mining on relational data because of the complexity of structure in XML data. A naïve approach to mining on XML data is to first convert XML data into relational format. However the structure information may be lost during the conversion. It is desired to develop efficient and effective data mining algorithms that can be directly applied on XML data.

Converting Probabilistic Relational Data to Probabilistic XML Data Tree

2010 ◽  
Vol 9 (8) ◽  
pp. 1706-1712
Author(s):  
Jianwei Wang ◽  
Zhongxiao Hao
Keyword(s):  
Relational Data ◽  
Xml Data ◽  
Probabilistic Xml

Essential, Mandatory, and Shared Parts in Conceptual Data Models

2010 ◽  
pp. 17-52 ◽  
Author(s):  
Alessandro Artale ◽  
C. Maria Keet
Keyword(s):  
Life Cycle ◽  
Formal Semantics ◽  
Data Modeling ◽  
Data Models ◽  
Modeling Language ◽  
The Novel ◽  
Conceptual Data ◽  
Temporal Modality

This chapter focuses on formally representing life cycle semantics of part-whole relations in conceptual data models by utilizing the temporal modality. The authors approach this by resorting to the temporal conceptual data modeling language ERVT and extend it with the novel notion of status relations. This enables a precise axiomatization of the constraints for essential parts and wholes compared to mandatory parts and wholes, as well as introduction of temporally suspended part-whole relations. To facilitate usage in the conceptual stage, a set of closed questions and decision diagram are proposed. The longterm objectives are to ascertain which type of shareability and which lifetime aspects are possible for part-whole relations, investigate the formal semantics for sharability, and how to model these kind of differences in conceptual data models.

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