Big Data and MapReduce Challenges, Opportunities and Trends

Thanekar, Sachin Arun; Subrahmanyam, K.; Bagwan, A. B.

doi:10.11591/ijece.v6i6.pp2911-2919

Cited by 9 publications

(6 citation statements)

References 4 publications

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“…Performance concerns are not only influenced by location alone but by data security issues also. Security concerns arise because of the nature and characteristics of Big Data (the huge volume, velocity, variety and veracity of data) [29]. One of such concerns is how to query encrypted database systems without degrading performance of applications [30,31].…”

Section: Resultsmentioning

confidence: 99%

Performance Benchmarking of Key-Value Store NoSQL Databases

Omoruyi

Okokpujie

Nkordeh

et al. 2018

IJECE

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<p>Increasing requirements for scalability and elasticity of data storage for web applications has made Not Structured Query Language NoSQL databases more invaluable to web developers. One of such NoSQL Database solutions is Redis. A budding alternative to Redis database is the SSDB database, which is also a key-value store but is disk-based. The aim of this research work is to benchmark both databases (Redis and SSDB) using the Yahoo Cloud Serving Benchmark (YCSB). YCSB is a platform that has been used to compare and benchmark similar NoSQL database systems. Both databases were given variable workloads to identify the throughput of all given operations. The results obtained shows that SSDB gives a better throughput for majority of operations to Redis’s performance.</p>

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Section: Resultsmentioning

confidence: 99%

Performance Benchmarking of Key-Value Store NoSQL Databases

Omoruyi

Okokpujie

Nkordeh

et al. 2018

IJECE

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“…The sequence being considered is inspired by the thought process of the human beings while framing meaningful sentences. MapReduce model [23] is used to preprocess and classify the comment's as Uni-gram (one word in the comment), Bi-gram (two words in the comment), or N-gram (N words in the comment). The pictorical representation of classification along with an example is shown in Figure 2 and Figure 3.…”

Section: Methodsmentioning

confidence: 99%

Predicting the Brand Popularity from the Brand Metadata

Bhargavi¹,

Babu²,

Iyengar³

2018

IJECE

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Social networks have become one of the primary sources of big data, where a variety of posts related to brands are liked, shared, and commented, which are collectively called as brand metadata. Due to the increased boom in E/M-commerce, buyers often refer the brand metadata as a valuable source of information to make their purchasing decision. From the literature study, we found that there are not many works on predicting the popularity of the brand based on the combination of brand metadata and comment’s thoughtfulness analysis. This paper proposes a novel framework to classify the comment’s as thoughtful favored or disfavored comment’s, and later combines them with the brand metadata to forecast the popularity of the brand in near future. The performance of the proposed framework is compared with some of the recent works w.r.t. thoughtful comment’s identification accuracy, execution time, prediction accuracy and prediction time, the results obtained are found to be very encouraging.

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“…A lot of research has been carried out, in providing the solutions to the challenges [5] in handling the large volumes of data using the Map-Reduce framework. The parallel processing approach [6] is the core part of Big Data processing is implemented with the Map-Reduce technique.…”

Section: Literature Surveymentioning

confidence: 99%

An Analytical Performance Evaluation of MapReduce Model Using Transient Queuing Model

Darapaneni¹,

Rao²,

Prasad³

et al. 2021

AMA_B

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Today the MapReduce frameworks become the standard distributed computing mechanisms to store, process, analyze, query and transform the Bigdata. While processing the Bigdata, evaluating the performance of the MapReduce framework is essential, to understand the process dependencies and to tune the hyper-parameters. Unfortunately, the scope of the MapReduce framework in-built functions is limited to evaluate the performance till some extent. A reliable analytical performance model is required in this area to evaluate the performance of the MapReduce frameworks. The main objective of this paper is to investigate the performance effect of the MapReduce computing models under various configurations. To accomplish this job, we proposed an analytical transient queuing model, which evaluates the MapReduce model performance for different job arrival rates at mappers and various job completion times of mappers as well as the reducers too. In our transient queuing model, we appointed an efficient multi-server queuing model M/M/C for optimal waiting queue management. To conduct the experiments on proposed analytics model, we selected the Bigdata applications with three mappers and two reducers, under various configurations. As part of the experiments, the transient differential equations, average queue lengths, mappers blocking probability, shuffle waiting probabilities and transient states are evaluated. MATLAB based numerical simulations presented the analytical results for various combinations of the input parameters like λ, µ1 and µ2 and their effect on queue length.

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Big Data and MapReduce Challenges, Opportunities and Trends

Cited by 9 publications

References 4 publications

Performance Benchmarking of Key-Value Store NoSQL Databases

Performance Benchmarking of Key-Value Store NoSQL Databases

Predicting the Brand Popularity from the Brand Metadata

An Analytical Performance Evaluation of MapReduce Model Using Transient Queuing Model

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