Enhanced Graph Transforming V2 Algorithm for Non-Simple Graph in Big Data Pre-Processing

Incapability of relational database in handling large-scale data triggers the development of NoSQL database that becomes part of a big data ecosystem. NoSQL database has different characteristics compared to the relational database. However, NoSQL database requires data from the relational database...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering Jg. 32; H. 1; S. 67 - 77
Hauptverfasser: Sutedi, Sutedi, Setiawan, Noor Akhmad, Adji, Teguh Bharata
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.01.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Big Data
Data management
Ecosystems
graph
loop
multigraph
multiple edges
NoSQL databases
Object oriented programming
Redundancy
Relational data bases
Relational databases
Software upgrading
SQL to NoSQL
Transformations
Transforms
ISSN:1041-4347, 1558-2191
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Abstract Incapability of relational database in handling large-scale data triggers the development of NoSQL database that becomes part of a big data ecosystem. NoSQL database has different characteristics compared to the relational database. However, NoSQL database requires data from the relational database as one of the structured data sources. Therefore, data pre-processing is required to ensure proper data migration from a relational database to NoSQL database. This data pre-processing is normally called data transformation. One of the simple and understandable transformation algorithms is graph transforming algorithm. However, the algorithm has a problem in solving a non-simple graph (multigraph). This research proposes an algorithm to overcome several multigraph problems. The experimental work confirms that the algorithm proposed in this research is able to transform data from a relational database to NoSQL schema that has a minimum number of redundant attributes while the data completeness is still maintained.
AbstractList Incapability of relational database in handling large-scale data triggers the development of NoSQL database that becomes part of a big data ecosystem. NoSQL database has different characteristics compared to the relational database. However, NoSQL database requires data from the relational database as one of the structured data sources. Therefore, data pre-processing is required to ensure proper data migration from a relational database to NoSQL database. This data pre-processing is normally called data transformation. One of the simple and understandable transformation algorithms is graph transforming algorithm. However, the algorithm has a problem in solving a non-simple graph (multigraph). This research proposes an algorithm to overcome several multigraph problems. The experimental work confirms that the algorithm proposed in this research is able to transform data from a relational database to NoSQL schema that has a minimum number of redundant attributes while the data completeness is still maintained.
Author Adji, Teguh Bharata
Sutedi, Sutedi
Setiawan, Noor Akhmad
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SubjectTerms Algorithms
Big Data
Data management
Ecosystems
graph
loop
multigraph
multiple edges
NoSQL databases
Object oriented programming
Redundancy
Relational data bases
Relational databases
Software upgrading
SQL to NoSQL
Transformations
Transforms
Title Enhanced Graph Transforming V2 Algorithm for Non-Simple Graph in Big Data Pre-Processing
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https://www.proquest.com/docview/2325184360
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