Building a Knowledge Vault with Effective Data Processing and Storage

Sathiya, R. R.; Swathi, S.; Nevedha, S.; Sruthi, U. Shanmuga

doi:10.1007/978-81-322-2674-1_15

Cited by 6 publications

(2 citation statements)

References 5 publications

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“…This work presents the work that advances cloud computing. Clustering approaches are also used to categorize the users [16]. Clustering approaches can be used in any domain or data which can be either structured unstructured or semi-structured [17] [18].…”

Section: Literaturesurveymentioning

confidence: 99%

CAI method in Cloud services for user workload using Cloudsim framework

2019

IJRTE

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In sighting the distinct patterns of processing capability in a cloud service is pedantic to enhance the resource management and operable conditions of the servers without compromising the Quality of Service is important. Simulations and models based on practicable parameters are required to understand the impact of the load on new system designs and policies. The proposed scheme and analysis provides a requirement for designing new systems which will be lessaffected by process loads. Classifying, analysis and improving (CAI) is done using real-time data center logs and simulations are done based on user requests and data center configurations. Simulations are created using cloudsim framework. Various simulations are done to provide a comprehensive result to improve the resource allocation for the system.

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Section: Literaturesurveymentioning

confidence: 99%

CAI method in Cloud services for user workload using Cloudsim framework

2019

IJRTE

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“…And also, bigram frequency is considered for receiving bigram weight. Bigram is the pair of sequential words [31]. While many have tried their hand at finding the best possible merging algorithm, one of the few earlier people is Craswell who introduced Feature Distance Ranking [22].…”

Section: Related Workmentioning

confidence: 99%

A Study of Various Result Merging Strategies for a Meta Search Engine

Sathiya¹,

Jayasree²,

Tangirala³

et al. 2018

IJET

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As the amount of data is growing day by day, the sources for these data are also growing simultaneously and to search through this very data, we need the use of search engines. Since each search engine is limited to its confined set of data, it would be even better to make use of a Meta search engine which will give us more relevant results than the ones obtained from any single search engine. It acts as an interface that provides the user with a single view from the various underlying search engines. The data is collected from these underlying search engines after they are accessed with the processed query from the Meta search engine. The collected data is merged using an algorithm and the algorithm will be a major factor in giving the best possible results. In this paper, we are going to discuss about the various existing metasearch engines and the different merging techniques and their approaches.

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Cloud Management: An Autoencoder-Based Clustering of Virtual Machines for Data Centre Workload

Sathiya

Prakash

2021

Advances in Automation, Signal Processing, Instrumentation, and Control

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Building a Knowledge Vault with Effective Data Processing and Storage

Cited by 6 publications

References 5 publications

CAI method in Cloud services for user workload using Cloudsim framework

CAI method in Cloud services for user workload using Cloudsim framework

A Study of Various Result Merging Strategies for a Meta Search Engine

Cloud Management: An Autoencoder-Based Clustering of Virtual Machines for Data Centre Workload

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