Hadoop Projects

HADOOP IEEE PROJECTS

HADOOP IEEE PROJECTS

Sentiment Analysis Using Twitter Data

Sentiment Analysis Using Twitter Data – Hadoop Project In today’s world, opinions and reviews accessible to us are one of the most critical factors in formulating our views and influencing the success of a brand, product or service. With the advent and growth of social media in the world, stakeholders often take to expressing their…

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FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce

FiDoop: Parallel Mining of Frequent Itemsets Using MapReduce Existing parallel mining algorithms for frequent itemsets lack a mechanism that enables automatic parallelization, load balancing, data distribution, and fault tolerance on large clusters. As a solution to this problem, we design a parallel frequent itemsets mining algorithm called FiDoop using the MapReduce programming model. To achieve…

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HConfig: Resource Adaptive Fast Bulk Loading in HBase

HConfig: Resource Adaptive Fast Bulk Loading in HBase NoSQL (Not only SQL) data stores become a vital component in many big data computing platforms due to its inherent horizontal scalability. HBase is an open-source distributed NoSQL store that is widely used by many Internet enterprises to handle their big data computing applications (e.g. Facebook handles…

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Self-Adjusting Slot Configurations for Homogeneous and Heterogeneous Hadoop Clusters

Self-Adjusting Slot Configurations for Homogeneous and Heterogeneous Hadoop Clusters The MapReduce framework and its open source implementation Hadoop have become the defacto platform for scalable analysis on large data sets in recent years. One of the primary concerns in Hadoop is how to minimize the completion length (i.e., makespan) of a set of MapReduce jobs.…

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Frequent Itemset Mining for Big Data in social media using ClustBigFIM algorithm

Frequent Itemset Mining for Big Data in social media using ClustBigFIM algorithm Abstract – Tremendous amount of data is getting explored through IOT (Internet of Things) from variety of sources such as sensor network, social media feed, internet applications, called as Big Data. Big Data cannot be handled by conventional tools and techniques. Social networks…

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FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments

FastRAQ: A Fast Approach to Range-Aggregate Queries in Big Data Environments Range-aggregate queries are to apply a certain aggregate function on all tuples within given query ranges. Existing approaches to range-aggregate queries are insufficient to quickly provide accurate results in big data environments. FastRAQ—a fast approach to range-aggregate queries is proposed in big data environments.…

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