Cloud computing is designed to provide on-demand
resources or services over the Internet, usually... more Cloud computing is designed to provide on-demand resources or services over the Internet, usually at the scale and with the reliability level of a data center. MapReduce is a software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors. Google uses CMR to index its web pages. Traditional MapReduce has some major problems such as 1. It’s sequential in Map and Reduce parts and we need a way to process the data by parallelization. 2. It’s based on the cluster and due to this, it’s not scalable. 3. It does not support stream data processing. However, In cloud computing, all the hardware need to process an enormous amount of data, that cannot be handled by a single machine. It fixes the problems which we mentioned. This approach can improve the throughput and enhance the scalability for large data sets by using real-time data. In this papers, we will discuss the different enhancements of MapReduce methodology such as CMR(Cloud MapReduce), C-CMR(Continues cloud MapReduce), S-CMR(Spot Cloud MapReduce), SCMR(Smart Cloud MapReduce) and Amazon EMR(Elastic MapReduce) based on the cloud and also we will represent different experiments and compare them with traditional Hadoop MapReduce platform.
Web search engines are optimized to reduce the
high-percentile response time to consistently prov... more Web search engines are optimized to reduce the high-percentile response time to consistently provide fast responses to almost all user queries. A commercial web search engine shards its index among many servers(ISNs), and therefore the response time of a search query is dominated by the slowest server that processes the query. They predict query execution time, and if a query is predicted to be long-running, it runs in parallel, otherwise it runs sequentially. This paper focuses on different techniques for query execution estimation, prediction, pruning and also parallelization strategies to reduce tail latency of search engines.
Oil and Gas industry is a scope that is full of dangers. Most of the companies in this area deal ... more Oil and Gas industry is a scope that is full of dangers. Most of the companies in this area deal with huge amount of data from different phases such as exploration, drilling production that are increasing dramatically over the time. Hence, by increasing the data from these kinds of things, they have to use the state-of-the-art Big Data methodologies and technologies to analyse data to achieve better performance and reduce their costs effectively, improve business efficiency and performance and also make technical decisions. It gains if they can use real-time data that collect from the wells in drilling operations. At this paper I will do investigate on different methods of Big Data Analysis that are used in Oil and Gas companies, such as Hadoop, Microsoft MURA platform, IBM InfoSphere, Oracle and their applications.
Oil and Gas industry is a scope that is full of dangers. Most of the companies in this area deal ... more Oil and Gas industry is a scope that is full of dangers. Most of the companies in this area deal with huge amount of data from different phases such as exploration, drilling production that are increasing dramatically over the time. Hence, by increasing the data from these kinds of things, they have to use the state-of-the-art Big Data methodologies and technologies to analyse data to achieve better performance and reduce their costs effectively, improve business efficiency and performance and also make technical decisions. It gains if they can use real-time data that collect from the wells in drilling operations. At this paper I will do investigate on different methods of Big Data Analysis that are used in Oil and Gas companies, such as Hadoop, Microsoft MURA platform, IBM InfoSphere, Oracle and their applications.
Cloud computing is designed to provide on-demand
resources or services over the Internet, usually... more Cloud computing is designed to provide on-demand resources or services over the Internet, usually at the scale and with the reliability level of a data center. MapReduce is a software framework that allows developers to write programs that process massive amounts of unstructured data in parallel across a distributed cluster of processors. Google uses CMR to index its web pages. Traditional MapReduce has some major problems such as 1. It’s sequential in Map and Reduce parts and we need a way to process the data by parallelization. 2. It’s based on the cluster and due to this, it’s not scalable. 3. It does not support stream data processing. However, In cloud computing, all the hardware need to process an enormous amount of data, that cannot be handled by a single machine. It fixes the problems which we mentioned. This approach can improve the throughput and enhance the scalability for large data sets by using real-time data. In this papers, we will discuss the different enhancements of MapReduce methodology such as CMR(Cloud MapReduce), C-CMR(Continues cloud MapReduce), S-CMR(Spot Cloud MapReduce), SCMR(Smart Cloud MapReduce) and Amazon EMR(Elastic MapReduce) based on the cloud and also we will represent different experiments and compare them with traditional Hadoop MapReduce platform.
Web search engines are optimized to reduce the
high-percentile response time to consistently prov... more Web search engines are optimized to reduce the high-percentile response time to consistently provide fast responses to almost all user queries. A commercial web search engine shards its index among many servers(ISNs), and therefore the response time of a search query is dominated by the slowest server that processes the query. They predict query execution time, and if a query is predicted to be long-running, it runs in parallel, otherwise it runs sequentially. This paper focuses on different techniques for query execution estimation, prediction, pruning and also parallelization strategies to reduce tail latency of search engines.
Oil and Gas industry is a scope that is full of dangers. Most of the companies in this area deal ... more Oil and Gas industry is a scope that is full of dangers. Most of the companies in this area deal with huge amount of data from different phases such as exploration, drilling production that are increasing dramatically over the time. Hence, by increasing the data from these kinds of things, they have to use the state-of-the-art Big Data methodologies and technologies to analyse data to achieve better performance and reduce their costs effectively, improve business efficiency and performance and also make technical decisions. It gains if they can use real-time data that collect from the wells in drilling operations. At this paper I will do investigate on different methods of Big Data Analysis that are used in Oil and Gas companies, such as Hadoop, Microsoft MURA platform, IBM InfoSphere, Oracle and their applications.
Oil and Gas industry is a scope that is full of dangers. Most of the companies in this area deal ... more Oil and Gas industry is a scope that is full of dangers. Most of the companies in this area deal with huge amount of data from different phases such as exploration, drilling production that are increasing dramatically over the time. Hence, by increasing the data from these kinds of things, they have to use the state-of-the-art Big Data methodologies and technologies to analyse data to achieve better performance and reduce their costs effectively, improve business efficiency and performance and also make technical decisions. It gains if they can use real-time data that collect from the wells in drilling operations. At this paper I will do investigate on different methods of Big Data Analysis that are used in Oil and Gas companies, such as Hadoop, Microsoft MURA platform, IBM InfoSphere, Oracle and their applications.
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Papers by Hamed Hamzeiy
resources or services over the Internet, usually at the scale
and with the reliability level of a data center. MapReduce is
a software framework that allows developers to write programs
that process massive amounts of unstructured data in parallel
across a distributed cluster of processors. Google uses CMR to
index its web pages. Traditional MapReduce has some major
problems such as 1. It’s sequential in Map and Reduce parts
and we need a way to process the data by parallelization. 2.
It’s based on the cluster and due to this, it’s not scalable. 3.
It does not support stream data processing. However, In cloud
computing, all the hardware need to process an enormous amount
of data, that cannot be handled by a single machine. It fixes the
problems which we mentioned. This approach can improve
the throughput and enhance the scalability for large data sets
by using real-time data. In this papers, we will discuss
the different enhancements of MapReduce methodology such as
CMR(Cloud MapReduce), C-CMR(Continues cloud MapReduce),
S-CMR(Spot Cloud MapReduce), SCMR(Smart Cloud MapReduce)
and Amazon EMR(Elastic MapReduce) based on the cloud
and also we will represent different experiments and compare
them with traditional Hadoop MapReduce platform.
high-percentile response time to consistently provide fast responses
to almost all user queries. A commercial web search
engine shards its index among many servers(ISNs), and therefore
the response time of a search query is dominated by the slowest
server that processes the query. They predict query execution
time, and if a query is predicted to be long-running, it runs in
parallel, otherwise it runs sequentially. This paper focuses on
different techniques for query execution estimation, prediction,
pruning and also parallelization strategies to reduce tail latency
of search engines.
Drafts by Hamed Hamzeiy
resources or services over the Internet, usually at the scale
and with the reliability level of a data center. MapReduce is
a software framework that allows developers to write programs
that process massive amounts of unstructured data in parallel
across a distributed cluster of processors. Google uses CMR to
index its web pages. Traditional MapReduce has some major
problems such as 1. It’s sequential in Map and Reduce parts
and we need a way to process the data by parallelization. 2.
It’s based on the cluster and due to this, it’s not scalable. 3.
It does not support stream data processing. However, In cloud
computing, all the hardware need to process an enormous amount
of data, that cannot be handled by a single machine. It fixes the
problems which we mentioned. This approach can improve
the throughput and enhance the scalability for large data sets
by using real-time data. In this papers, we will discuss
the different enhancements of MapReduce methodology such as
CMR(Cloud MapReduce), C-CMR(Continues cloud MapReduce),
S-CMR(Spot Cloud MapReduce), SCMR(Smart Cloud MapReduce)
and Amazon EMR(Elastic MapReduce) based on the cloud
and also we will represent different experiments and compare
them with traditional Hadoop MapReduce platform.
high-percentile response time to consistently provide fast responses
to almost all user queries. A commercial web search
engine shards its index among many servers(ISNs), and therefore
the response time of a search query is dominated by the slowest
server that processes the query. They predict query execution
time, and if a query is predicted to be long-running, it runs in
parallel, otherwise it runs sequentially. This paper focuses on
different techniques for query execution estimation, prediction,
pruning and also parallelization strategies to reduce tail latency
of search engines.