Data and Systems Heterogeneity: Analysis on Data, Processing, Workload, and Infrastructure

Stan, Roxana-Gabriela; Negru, Cătălin; Băjenaru, Lidia; Pop, Florin

doi:10.1007/978-3-030-38836-2_4

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…It is designed to be run on Hadoop cluster using multiple commodity machines to analyse, summerize and query massive data that are stored in the Hadoop distributed file system. Hive is a data warehouse that performs different analytical features, such as windowing and partitioning [28], [29]. It is scalable system that can run queries using MapReduce programming model across several nodes of Hadoop cluster.…”

Section: Hive Apachementioning

confidence: 99%

Predictive analytics on COVID-19 data using Hive based on Hadoop cluster

Khaleel,

Kareem,

Mahdi

2023

IJEECS

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COVID-19 pandemic has received a serious attention from academia, industry and governments to stop the huge number of deaths and economic disruptions around the world. Many techniques have been used to control the spread of the pandemic by understanding its characteristics and behavior. However, because of the large amounts and complex characteristics of COVID-19 data, the querying and analysis of such data using conventional tools have become a challenging task. As a result, powerful and distributed tools are highly required for querying and analyzing this data effectively. In this paper, distributed system using Hive based on Hadoop cluster is used to query and analyze COVID-19 data to obtain meaningful information. Hadoop is employed as a scalable and reliable framework to accommodate such large amounts of data. Hive is used as a data warehouse that run on Hadoop cluster to perform querying and predictive analytics on huge COVID-19 datasets. Several experiments are performed to evaluate the performance of proposed system. Experiments show that the proposed system outperforms relational database management system (RDBMS) in terms of query processing time. Experiments also show that the proposed system has a better efficiency in terms of data load, I/O operation, reading and writing data.

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Section: Hive Apachementioning

confidence: 99%

Predictive analytics on COVID-19 data using Hive based on Hadoop cluster

Khaleel,

Kareem,

Mahdi

2023

IJEECS

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Implementing Replication of Objects in DOORS—The Object-Oriented Runtime System for Edge Computing

Palanciuc

Pop

2021

Sensors

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Aiming for simplicity and efficiency in the domain of edge computing, DOORS is a distributed system expected to scale up to hundreds of nodes, which encapsulates application state and behavior into objects and gives them the ability to exchange asynchronous messages. DOORS offers semi-synchronous replication and the ability to explicitly move objects from one node to another, as methods to achieve scalability and resilience. The present paper gives an outline of the system structure, describes how DOORS implements object replication, and describes a basic set of measurements, yielding an initial set of conclusions for the improvements of the design.

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Data and Systems Heterogeneity: Analysis on Data, Processing, Workload, and Infrastructure

Cited by 2 publications

References 21 publications

Predictive analytics on COVID-19 data using Hive based on Hadoop cluster

Predictive analytics on COVID-19 data using Hive based on Hadoop cluster

Implementing Replication of Objects in DOORS—The Object-Oriented Runtime System for Edge Computing

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