Providing Scalable Database Services on the Cloud

Architecture for Big Data Storage in Different Cloud Deployment Models

Research Anthology on Architectures, Frameworks, and Integration Strategies for Distributed and Cloud Computing ◽

10.4018/978-1-7998-5339-8.ch009 ◽

2021 ◽

pp. 178-208

Author(s):

Chandu Thota ◽

Gunasekaran Manogaran ◽

Daphne Lopez ◽

Revathi Sundarasekar

Keyword(s):

Cloud Computing ◽

Big Data ◽

Data Storage ◽

High Performance ◽

Data Services ◽

Big Data Applications ◽

Nosql Database ◽

Amazon Web Services ◽

Product Domains ◽

Scalable Database

Cloud Computing is a new computing model that distributes the computation on a resource pool. The need for a scalable database capable of expanding to accommodate growth has increased with the growing data in web world. More familiar Cloud Computing vendors such as Amazon Web Services, Microsoft, Google, IBM and Rackspace offer cloud based Hadoop and NoSQL database platforms to process Big Data applications. Variety of services are available that run on top of cloud platforms freeing users from the need to deploy their own systems. Nowadays, integrating Big Data and various cloud deployment models is major concern for Internet companies especially software and data services vendors that are just getting started themselves. This chapter proposes an efficient architecture for integration with comprehensive capabilities including real time and bulk data movement, bi-directional replication, metadata management, high performance transformation, data services and data quality for customer and product domains.

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Scalable Database Replication

10.1007/springerreference_65694 ◽

2011 ◽

Keyword(s):

Database Replication ◽

Scalable Database

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A scalable database of organ doses for common diagnostic fluoroscopy examinations of children: procedures of current practice at the University of Florida

Physics in Medicine and Biology ◽

10.1088/1361-6560/ab1bad ◽

2019 ◽

Vol 64 (13) ◽

pp. 135023 ◽

Cited By ~ 1

Author(s):

Emily L Marshall ◽

Dhanashree Rajderkar ◽

Justin L Brown ◽

Elliott J Stepusin ◽

David Borrego ◽

...

Keyword(s):

Current Practice ◽

Organ Doses ◽

University Of Florida ◽

Scalable Database ◽

The University

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Identification of Leishmania by Matrix-Assisted Laser Desorption Ionization–Time of Flight (MALDI-TOF) Mass Spectrometry Using a Free Web-Based Application and a Dedicated Mass-Spectral Library

Journal of Clinical Microbiology ◽

10.1128/jcm.00845-17 ◽

2017 ◽

Vol 55 (10) ◽

pp. 2924-2933 ◽

Cited By ~ 18

Author(s):

Laurence Lachaud ◽

Anna Fernández-Arévalo ◽

Anne-Cécile Normand ◽

Patrick Lami ◽

Cécile Nabet ◽

...

Keyword(s):

Mass Spectrometry ◽

Leishmania Donovani ◽

Identification System ◽

Enzyme Electrophoresis ◽

Mass Spectral Library ◽

Spectral Library ◽

Web Based ◽

Content Type ◽

Mass Spectral ◽

Scalable Database

ABSTRACT Human leishmaniases are widespread diseases with different clinical forms caused by about 20 species within the Leishmania genus. Leishmania species identification is relevant for therapeutic management and prognosis, especially for cutaneous and mucocutaneous forms. Several methods are available to identify Leishmania species from culture, but they have not been standardized for the majority of the currently described species, with the exception of multilocus enzyme electrophoresis. Moreover, these techniques are expensive, time-consuming, and not available in all laboratories. Within the last decade, mass spectrometry (MS) has been adapted for the identification of microorganisms, including Leishmania . However, no commercial reference mass-spectral database is available. In this study, a reference mass-spectral library (MSL) for Leishmania isolates, accessible through a free Web-based application (mass-spectral identification [MSI]), was constructed and tested. It includes mass-spectral data for 33 different Leishmania species, including species that infect humans, animals, and phlebotomine vectors. Four laboratories on two continents evaluated the performance of MSI using 268 samples, 231 of which were Leishmania strains. All Leishmania strains, but one, were correctly identified at least to the complex level. A risk of species misidentification within the Leishmania donovani , L. guyanensis , and L. braziliensis complexes was observed, as previously reported for other techniques. The tested application was reliable, with identification results being comparable to those obtained with reference methods but with a more favorable cost-efficiency ratio. This free online identification system relies on a scalable database and can be implemented directly in users' computers.

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AN ASSESSMENT OF A METRIC SPACE DATABASE INDEX TO SUPPORT SEQUENCE HOMOLOGY

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213005002430 ◽

2005 ◽

Vol 14 (05) ◽

pp. 867-885 ◽

Cited By ~ 9

Author(s):

RUI MAO ◽

WEIJIA XU ◽

NEHA SINGH ◽

DANIEL P. MIRANKER

Keyword(s):

Metric Space ◽

Sequence Data ◽

Large Data ◽

Peptide Sequence ◽

Data Sets ◽

Clustering Methods ◽

Storage And Retrieval ◽

Database Index ◽

Bulk Load ◽

Scalable Database

Hierarchical metric-space clustering methods have been commonly used to organize proteomes into taxonomies. Consequently, it is often anticipated that hierarchical clustering can be leveraged as a basis for scalable database index structures capable of managing the hyper-exponential growth of sequence data. M-tree is one such data structure specialized for the management of large data sets on disk. We explore the application of M-trees to the storage and retrieval of peptide sequence data. Exploiting a technique first suggested by Myers, we organize the database as records of fixed length substrings. Empirical results are promising. However, metric-space indexes are subject to "the curse of dimensionality" and the ultimate performance of an index is sensitive to the quality of the initial construction of the index. We introduce new hierarchical bulk-load algorithm that alternates between top-down and bottom-up clustering to initialize the index. Using the Yeast Proteomes, the bi-directional bulk load produces a more effective index than the existing M-tree initialization algorithms.

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