Handling XML to Relational Database Transformation Using Model-Based Mapping Approaches

Song, Emyliana; Haw, Su-Cheng; Chua, Fang-Fang

doi:10.1109/icos.2018.8632805

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…Nowadays, many tools are available for processing raw data by cleaning and transforming it into a specific destination format [45,46]. Song et al [47] discussed and reviewed the techniques used to transform XML data into a relational database. Zhu et al [48] proposed an efficient transformation method of XML-based data into a relational database by analyzing complex XML schema.…”

Section: Related Workmentioning

confidence: 99%

Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping

Aftab

Iqbal

Mellouli

et al. 2020

Scientific Programming

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Recently, the use of NoSQL databases has grown to manage unstructured data for applications to ensure performance and scalability. However, many organizations prefer to transfer data from an operational NoSQL database to a SQL-based relational database for using existing tools for business intelligence, analytics, decision making, and reporting. The existing methods of NoSQL to relational database transformation require manual schema mapping, which requires domain expertise and consumes noticeable time. Therefore, an efficient and automatic method is needed to transform an unstructured NoSQL database into a structured database. In this paper, we proposed and evaluated an efficient method to transform a NoSQL database into a relational database automatically. In our experimental evaluation, we used MongoDB as a NoSQL database, and MySQL and PostgreSQL as relational databases to perform transformation tasks for different dataset sizes. We observed excellent performance, compared to the existing state-of-the-art methods, in transforming data from a NoSQL database into a relational database.

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Section: Related Workmentioning

confidence: 99%

Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping

Aftab

Iqbal

Mellouli

et al. 2020

Scientific Programming

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“…Advantages of RDBM: The Relational model structures data in a manner that avoids complexity, it is more flexible than other types to collect data from different sources. Relational database offers reliability, simple indexing to organize data and maintaining data integrity by setting up defined constraints [10]. Relational model has special tools and characteristics and can be used with all programming languages.…”

Section: Fig 2 -Relational Data Modelmentioning

confidence: 99%

A Comparative Study of Data Management Systems

Salim¹,

Saringat²,

Mostafa³

2020

JSCDM

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Databases play an important role in all areas that use computers such as industry, agriculture, trade, engineering, medicine, etc. Where relevant data is stored in a database. Information systems programmers have long been building their programs on data files and files have played an important role in storing information. In the data file processing system, each program is designed to perform a specific purpose and has its own files without a general framework linking all the programs and possibly repeated data in more than one file. The use of these files many problems and disadvantages such as data redundancy, heterogeneity or data compatibility, limited participation, and the difficulty of maintenance. This paper introduces the overview of Database Management System (DBMS) such as its components, purposes, and data models which are describing and manipulating data, relationships between data, and constraints on the data in an organization.

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“…Nevertheless, in structural-based mapping, the approaches can support the relationship among nodes (Parent-Child (P-C), Ancestor-Descendant (A-D), sibling and level) effectively [4]. On the other hand, model-based mapping approach involves in a fixed relational schema which is built to store XML document without any support of DTD or XSD [5]. In this approach, they support every XML applications either in static and dynamic.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation on structural mapping choices for data-centric XML documents

Haw

Soong

2020

IJEECS

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<span>eXtensible Mark-up Language (XML) has been widely used as the de facto standard for data exchange over the Web. It is crucial to ensure that the data can be mapped correctly into the underlying data storage format, that is, without any lost of information. The two mapping strategies are structural-based and model-based. The structural-based mapping involves the presence of Data Type Definition (DTD) for schema mapping while the model-based mapping does not require the present of DTD or any schema for the mapping purpose. The structural-based mapping is good especially for data-centric type of data, i.e., data which is structured and can be binded into certain schema. As such, this paper evaluates and compares the performances of two selected existing structural-based mapping via simulation. Two main evaluations are: (i) storing the XML data into relational database (RDB), <br /> and (ii) querying the XML data from the RDB. The time taken for each respective process will be recorded and compared. From the experimental results, it is observed that the s-XML approach outperformed the SAX approach in terms of storing and query evaluations for most of the test cases conducted.</span>

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Handling XML to Relational Database Transformation Using Model-Based Mapping Approaches

Cited by 4 publications

References 12 publications

Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping

Automatic NoSQL to Relational Database Transformation with Dynamic Schema Mapping

A Comparative Study of Data Management Systems

Performance evaluation on structural mapping choices for data-centric XML documents

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