The Experts below are selected from a list of 72 Experts worldwide ranked by ideXlab platform

Anthony Deese - One of the best experts on this subject based on the ideXlab platform.

  • Implementation of Unsupervised k-Means Clustering Algorithm Within Amazon Web Services Lambda
    2018 18th IEEE ACM International Symposium on Cluster Cloud and Grid Computing (CCGRID), 2018
    Co-Authors: Anthony Deese
    Abstract:

    This work demonstrates how an unsupervised learning algorithm based on k-Means Clustering with Kaufman Initialization may be implemented effectively as an Amazon Web Services Lambda Function, within their serverless cloud computing service. It emphasizes the need to employ a lean and modular design philosophy, transfer data efficiently between Lambda and Dynamodb, as well as employ Lambda Functions within mobile applications seamlessly and with negligible latency. This work presents a novel application of serverless cloud computing and provides specific examples that will allow readers to develop similar algorithms. The author provides compares the computation speed and cost of machine learning implementations on traditional PC and mobile hardware (running locally) as well as implementations that employ Lambda.

Simone Brunozzi - One of the best experts on this subject based on the ideXlab platform.

  • Big Data and NoSQL with Amazon Dynamodb
    Proceedings of the 2012 workshop on Management of big data systems - MBDS '12, 2012
    Co-Authors: Simone Brunozzi
    Abstract:

    This technical talk will briefly introduce the NoSQL approach, describe why it's important for Big Data tasks, and then switch to Dynamodb, a NoSQL managed database that runs on the Amazon Web Services cloud. The talk will then cover Dynamodb's data model, compare it with other NoSQL solutions, and provide some examples and a demo of the service.

Malka N Halgamuge - One of the best experts on this subject based on the ideXlab platform.

  • big data nosql databases a comparison and analysis of big table Dynamodb and cassandra
    International Conference on Big Data, 2017
    Co-Authors: Sultana Kalid, Ali Syed, Azeem Mohammad, Malka N Halgamuge
    Abstract:

    The growth and enhancement of technology in the corporate society has led to data storage and confidentiality issues. The problem arises from the management of trillions of data, generated every second in corporations, precisely known as “Big Data”. Big Data needs to be stored and managed by larger companies that do not have the right storage systems, as there is not any developed yet. The aim of this paper is to find a solution to this growing problem by analyzing gaps in the literature, and to evaluate possible solutions. This study has analyzed content from top reviewed scientific publications, to gather compared and contrasted data from articles and highlight gaps. The highlighted literature will address this problems, and find solutions by contrasting BigData management approaches of NoSQL databases; BigTable, Dynamodb, and Cassandra. The findings summarized from publications are highlighted and the main features of all three databases and their applications are displayed. The system performances are analyzed based on their consistency, availability and partition intolerance. The study concluded that Google's BigTable and Amazon's Dynamodb are also critical and efficient on their own, and also found that the combination of both systems had caused the development of Cassandra. Cassandra is now the primary focus of numerous companies to develop different applications. Furthermore, all three systems are NoSQL storage systems, and BigTable, and based on one master node approach, unlike Dynamo, and Cassandra, it follows a Peer-to-Peer system. BigTable however, with some additional features from Dynamodb has helped the development of Cassandra, which is the basis of various modern applications available both open source and socially.

Sultana Kalid - One of the best experts on this subject based on the ideXlab platform.

  • big data nosql databases a comparison and analysis of big table Dynamodb and cassandra
    International Conference on Big Data, 2017
    Co-Authors: Sultana Kalid, Ali Syed, Azeem Mohammad, Malka N Halgamuge
    Abstract:

    The growth and enhancement of technology in the corporate society has led to data storage and confidentiality issues. The problem arises from the management of trillions of data, generated every second in corporations, precisely known as “Big Data”. Big Data needs to be stored and managed by larger companies that do not have the right storage systems, as there is not any developed yet. The aim of this paper is to find a solution to this growing problem by analyzing gaps in the literature, and to evaluate possible solutions. This study has analyzed content from top reviewed scientific publications, to gather compared and contrasted data from articles and highlight gaps. The highlighted literature will address this problems, and find solutions by contrasting BigData management approaches of NoSQL databases; BigTable, Dynamodb, and Cassandra. The findings summarized from publications are highlighted and the main features of all three databases and their applications are displayed. The system performances are analyzed based on their consistency, availability and partition intolerance. The study concluded that Google's BigTable and Amazon's Dynamodb are also critical and efficient on their own, and also found that the combination of both systems had caused the development of Cassandra. Cassandra is now the primary focus of numerous companies to develop different applications. Furthermore, all three systems are NoSQL storage systems, and BigTable, and based on one master node approach, unlike Dynamo, and Cassandra, it follows a Peer-to-Peer system. BigTable however, with some additional features from Dynamodb has helped the development of Cassandra, which is the basis of various modern applications available both open source and socially.

Noh, Jin Hyeok - One of the best experts on this subject based on the ideXlab platform.

  • Creating an Internet of Things Platform for Storing Smart Sensor Data Using Amazon Web Services
    eGrove, 2019
    Co-Authors: Noh, Jin Hyeok
    Abstract:

    Water is one of the essential resources that we should be aware of conserving, but lawn sprinkler systems in Mississippi have inefficient resource management systems. During my fundamental research, I read an article about IoT (Internet of Things) and how important it will be in the future. Both of these topics inspired prototype research. In order to create the prototype, a Raspberry Pi, breadboard, keyboard, monitor, and cables were used. Using the Raspberry Pi, monitors, and keyboard, the software was developed in order to create a RESTFUL API gateway to connect to AWS (Amazon Web Service). In the AWS console, an AWS Lambda function was created to store data to AWS Dynamodb. Then, a breadboard and sensor were connected to the Raspberry Pi. When the sensor detects water, the data is transferred to the Raspberry Pi, and the Raspberry Pi sends the data to the AWS API Gateway. The API Gateway then sends the data to AWS Lambda, and the process continues from AWS Lambda to AWS Dynamodb. Dynamodb can display the type of sensor, date, IP address, user id, and eventid. This platform will make it easy to monitor energy efficiency and water waste