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Jasmine extends the relational database technology.
Parallel relational database technology has proven ineffective in analyzing massive amounts of data [23 25].
Relational database technology can commonly become a bottleneck in Big Data challenges.
This paper shows how the SMWP approach can be put into practice using off-the-shelf relational database technology.
Data sparsity and schema evolution issues affecting clinical informatics and bioinformatics communities have led to the adoption of vertical or object-attribute value-based database schemas tobject-attribute value-based when using cobject-attribute value-basedase technology.
One common component of most of the occurrences of information technology, such as client-server computing, business intelligence and data warehousing, packaged software for enterprise applications, call center management, and sales force automation is relational database technology with Database Management System (DBMS) products based on SQL.
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However, recently some vendors such as Splunk [74] have adopted relational database technologies in order to better address Big Data challenges.
The phasor data from PMU's will be accumulated in peta-bytes in coming years, which exceeds the capability of conventional relational database technologies.
For example, commercial SIEMs that use relational database technologies for their storage repositories will find the databases becoming bottlenecks in deployments at larger enterprises: storage and retrieval of data begins to take longer than is acceptable.
Enormous strides will be required to emulate the advanced functionality of rigorously tested relational database technologies.
The present work show how object relational database management system technology can be used to support an finite element analysis process that includes component mode synthesis using the Craig Bampton method for calculating reduced mass and stiffness matrices.
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