This document describes using big data techniques like Hadoop, Hive, Pig, and MapReduce to analyze global warming and CO2 emission data. The analysis finds that Southeast countries experience a major impact of global warming in terms of average temperature. Queries run on the data using the various big data tools show that Iraq, Saudi Arabia, Pakistan, India, and China are most affected by global warming and that CO2 emissions are correlated with higher temperatures. The analysis provides insights into countries with the most temperature uncertainty and those that have reached the highest maximum temperatures.
Hadoop for High-Performance Climate Analytics - Use Cases and Lessons LearnedDataWorks Summit
Scientific data services are a critical aspect of the NASA Center for Climate Simulation’s mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyberinfrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model intercomparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.
IJACT–International Journal of engineering science, computer science is indexed by major Indexing sites which are DOAJ, Index Copernicus,Google Scholar. For more: http://ijact.in
Leveraging Map Reduce With Hadoop for Weather Data Analytics iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Building a geospatial processing pipeline using Hadoop and HBase and how Mons...DataWorks Summit
Monsanto built a geospatial platform on Hadoop and HBase capable of managing over 120 billion polygons. As a result of the extreme data volumes and compute complexities we were forced to migrate our data processing from a more traditional RDBMS to a scale out Hadoop implementation. Data processing that took over 30 days on 8% of the data now runs in under 12 hours on the entire data set. Very little concrete material exist for how you process spatial data via MapReduce or model it in HBase. We will provide concrete and novel examples for processing and storing spatial data on Hadoop and HBase. As part of the data processing pipeline we integrated the popular open source geospatial processing library GDAL with MapReduce to convert all geospatial datasets to a common format and projection. We developed a method for splitting and processing images via MapReduce in which the boundaries of splits needed to be shared by multiple tasks due to the nature of the computation being performed on the data. Bulk writes to HBase were performed by writing HFiles directly. Finally we developed a novel method for storing geospatial data in HBase that met the needs of our access pattern.
OPTIMIZATION OF MULTIPLE CORRELATED QUERIES BY DETECTING SIMILAR DATA SOURCE ...Puneet Kansal
This research article experimentally shows, how Multiple Queries can be provided to Hive and their execution can be reduced by searching common expression and common data source. The TPC-H queries are used for demonstration and test data is generated in variation of 2 GB, 5 GB and 10 GB using DBGEN software. Technology used in this is HADOOP and Hive. HADOOP is configured in Single user over ubunto Operating system.
Model Building with RevoScaleR: Using R and Hadoop for Statistical ComputationRevolution Analytics
Slides from Joseph Rickert's presentation at Strata NYC 2013
"Using R and Hadoop for Statistical Computation at Scale"
http://strataconf.com/stratany2013/public/schedule/detail/30632
Hadoop for High-Performance Climate Analytics - Use Cases and Lessons LearnedDataWorks Summit
Scientific data services are a critical aspect of the NASA Center for Climate Simulation’s mission (NCCS). Hadoop, via MapReduce, provides an approach to high-performance analytics that is proving to be useful to data intensive problems in climate research. It offers an analysis paradigm that uses clusters of computers and combines distributed storage of large data sets with parallel computation. The NCCS is particularly interested in the potential of Hadoop to speed up basic operations common to a wide range of analyses. In order to evaluate this potential, we prototyped a series of canonical MapReduce operations over a test suite of observational and climate simulation datasets. The initial focus was on averaging operations over arbitrary spatial and temporal extents within Modern Era Retrospective- Analysis for Research and Applications (MERRA) data. After preliminary results suggested that this approach improves efficiencies within data intensive analytic workflows, we invested in building a cyberinfrastructure resource for developing a new generation of climate data analysis capabilities using Hadoop. This resource is focused on reducing the time spent in the preparation of reanalysis data used in data-model intercomparison, a long sought goal of the climate community. This paper summarizes the related use cases and lessons learned.
IJACT–International Journal of engineering science, computer science is indexed by major Indexing sites which are DOAJ, Index Copernicus,Google Scholar. For more: http://ijact.in
Leveraging Map Reduce With Hadoop for Weather Data Analytics iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Building a geospatial processing pipeline using Hadoop and HBase and how Mons...DataWorks Summit
Monsanto built a geospatial platform on Hadoop and HBase capable of managing over 120 billion polygons. As a result of the extreme data volumes and compute complexities we were forced to migrate our data processing from a more traditional RDBMS to a scale out Hadoop implementation. Data processing that took over 30 days on 8% of the data now runs in under 12 hours on the entire data set. Very little concrete material exist for how you process spatial data via MapReduce or model it in HBase. We will provide concrete and novel examples for processing and storing spatial data on Hadoop and HBase. As part of the data processing pipeline we integrated the popular open source geospatial processing library GDAL with MapReduce to convert all geospatial datasets to a common format and projection. We developed a method for splitting and processing images via MapReduce in which the boundaries of splits needed to be shared by multiple tasks due to the nature of the computation being performed on the data. Bulk writes to HBase were performed by writing HFiles directly. Finally we developed a novel method for storing geospatial data in HBase that met the needs of our access pattern.
OPTIMIZATION OF MULTIPLE CORRELATED QUERIES BY DETECTING SIMILAR DATA SOURCE ...Puneet Kansal
This research article experimentally shows, how Multiple Queries can be provided to Hive and their execution can be reduced by searching common expression and common data source. The TPC-H queries are used for demonstration and test data is generated in variation of 2 GB, 5 GB and 10 GB using DBGEN software. Technology used in this is HADOOP and Hive. HADOOP is configured in Single user over ubunto Operating system.
Model Building with RevoScaleR: Using R and Hadoop for Statistical ComputationRevolution Analytics
Slides from Joseph Rickert's presentation at Strata NYC 2013
"Using R and Hadoop for Statistical Computation at Scale"
http://strataconf.com/stratany2013/public/schedule/detail/30632
While much of the recent literature in spatial statistics has evolved around addressing the big data issue, practical implementations of these methods on high performance computing systems for truly large data are still rare. We discuss our explorations in this area at the National Center for Atmospheric Research for a range of applications, which can benefit from large scale computing infrastructure. These applications include extreme value analysis, approximate spatial methods, spatial localization methods and statistically-based data compression and are implemented in different programming languages. We will focus on timing results and practical considerations, such as speed vs. memory trade-offs, limits of scaling and ease of use.
27 Aug 2013 Webinar High Performance Predictive Analytics in Hadoop and R presented by Mario E. Inchiosa, PhD., US Data Scientist and Kathleen Rohrecker, Director of Product Marketing
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
HDFS-HC2: Analysis of Data Placement Strategy based on Computing Power of Nod...Xiao Qin
Hadoop and the term 'Big Data' go hand in hand. The information explosion caused
due to cloud and distributed computing lead to the curiosity to process and analyze massive
amount of data. The process and analysis helps to add value to an organization or derive
valuable information.
The current Hadoop implementation assumes that computing nodes in a cluster are
homogeneous in nature. Hadoop relies on its capability to take computation to the nodes
rather than migrating the data around the nodes which might cause a signicant network
overhead. This strategy has its potential benets on homogeneous environment but it might
not be suitable on an heterogeneous environment. The time taken to process the data on a
slower node on a heterogeneous environment might be signicantly higher than the sum of
network overhead and processing time on a faster node. Hence, it is necessary to study the
data placement policy where we can distribute the data based on the processing power of
a node. The project explores this data placement policy and notes the ramications of this
strategy based on running few benchmark applications.
Presentation given by US Chief Scientist, Mario Inchiosa, at the June 2013 Hadoop Summit in San Jose, CA.
ABSTRACT: Hadoop is rapidly being adopted as a major platform for storing and managing massive amounts of data, and for computing descriptive and query types of analytics on that data. However, it has a reputation for not being a suitable environment for high performance complex iterative algorithms such as logistic regression, generalized linear models, and decision trees. At Revolution Analytics we think that reputation is unjustified, and in this talk I discuss the approach we have taken to porting our suite of High Performance Analytics algorithms to run natively and efficiently in Hadoop. Our algorithms are written in C++ and R, and are based on a platform that automatically and efficiently parallelizes a broad class of algorithms called Parallel External Memory Algorithms (PEMA’s). This platform abstracts both the inter-process communication layer and the data source layer, so that the algorithms can work in almost any environment in which messages can be passed among processes and with almost any data source. MPI and RPC are two traditional ways to send messages, but messages can also be passed using files, as in Hadoop. I describe how we use the file-based communication choreographed by MapReduce and how we efficiently access data stored in HDFS.
WBDB 2015 Performance Evaluation of Spark SQL using BigBencht_ivanov
In this paper we present the initial results of our work to run BigBench on Spark. First, we evaluated the data scalability behavior of the existing MapReduce implementation of BigBench. Next, we executed the group of 14 pure HiveQL queries on Spark SQL and compared the results with the respective Hive results. Our experiments show that: (1) for both MapReduce and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive.
http://clds.sdsc.edu/wbdb2015.ca/program
Improving performance of apriori algorithm using hadoopeSAT Journals
Abstract Spatial data is a data having a geological information. This paper explores the use of Hadoop framework to improve the performance of Apriori algorithm for spatial data mining. FP growth algorithm is better than Apriori but it fails in certain situations. By applying the Apriori algorithm parallely using Hadoop framework to spatial data, we can perform well as compare to FP growth. This paper includes clustering based on geological location, classification based on mineral resource type and spatial coherence between mineral resources. Spatial data mining find out the different association rules by observing the spatial data by using Apriori algorithm. The result of the paper will indicate the accurate prediction of occurrence of commodity with respect to other commodity of mineral resources. Keywords: Hadoop, data mining, association rules, clustering, spatial coherence
Today’s era is generally treated as the era of data on each and every field of computing application huge amount of data is generated. The society is gradually more dependent on computers so large amount of data is generated in each and every second which is either in structured format, unstructured format or semi structured format. These huge amount of data are generally treated as big data. To analyze big data is a biggest challenge in current world. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage and it generally follows horizontal processing. Map Reduce programming is generally run over Hadoop Framework and process the large amount of structured and unstructured data. This Paper describes about different joining strategies used in Map reduce programming to combine the data of two files in Hadoop Framework and also discusses the skewness problem associate to it.
BDSE 2015 Evaluation of Big Data Platforms with HiBencht_ivanov
We evaluate the performance of DataStax Enterprise (DSE) using the HiBench benchmark suite and compare it with the corresponding Cloudera’s Distribution of Hadoop (CDH) results. Both systems, DSE and CDH were stress tested using CPU-bound (WordCount), I/O-bound (Enhanced DFSIO) and mixed (HiveBench) workloads.
While much of the recent literature in spatial statistics has evolved around addressing the big data issue, practical implementations of these methods on high performance computing systems for truly large data are still rare. We discuss our explorations in this area at the National Center for Atmospheric Research for a range of applications, which can benefit from large scale computing infrastructure. These applications include extreme value analysis, approximate spatial methods, spatial localization methods and statistically-based data compression and are implemented in different programming languages. We will focus on timing results and practical considerations, such as speed vs. memory trade-offs, limits of scaling and ease of use.
27 Aug 2013 Webinar High Performance Predictive Analytics in Hadoop and R presented by Mario E. Inchiosa, PhD., US Data Scientist and Kathleen Rohrecker, Director of Product Marketing
Survey on Performance of Hadoop Map reduce Optimization Methodspaperpublications3
Abstract: Hadoop is a open source software framework for storage and processing large scale of datasets on clusters of commodity hardware. Hadoop provides a reliable shared storage and analysis system, here storage provided by HDFS and analysis provided by MapReduce. MapReduce frameworks are foraying into the domain of high performance of computing with stringent non-functional requirements namely execution times and throughputs. MapReduce provides simple programming interfaces with two functions: map and reduce. The functions can be automatically executed in parallel on a cluster without requiring any intervention from the programmer. Moreover, MapReduce offers other benefits, including load balancing, high scalability, and fault tolerance. The challenge is that when we consider the data is dynamically and continuously produced, from different geographical locations. For dynamically generated data, an efficient algorithm is desired, for timely guiding the transfer of data into the cloud over time for geo-dispersed data sets, there is need to select the best data center to aggregate all data onto given that a MapReduce like framework is most efficient when data to be processed are all in one place, and not across data centers due to the enormous overhead of inter-data center data moving in the stage of shuffle and reduce. Recently, many researchers tend to implement and deploy data-intensive and/or computation-intensive algorithms on MapReduce parallel computing framework for high processing efficiency.
HDFS-HC2: Analysis of Data Placement Strategy based on Computing Power of Nod...Xiao Qin
Hadoop and the term 'Big Data' go hand in hand. The information explosion caused
due to cloud and distributed computing lead to the curiosity to process and analyze massive
amount of data. The process and analysis helps to add value to an organization or derive
valuable information.
The current Hadoop implementation assumes that computing nodes in a cluster are
homogeneous in nature. Hadoop relies on its capability to take computation to the nodes
rather than migrating the data around the nodes which might cause a signicant network
overhead. This strategy has its potential benets on homogeneous environment but it might
not be suitable on an heterogeneous environment. The time taken to process the data on a
slower node on a heterogeneous environment might be signicantly higher than the sum of
network overhead and processing time on a faster node. Hence, it is necessary to study the
data placement policy where we can distribute the data based on the processing power of
a node. The project explores this data placement policy and notes the ramications of this
strategy based on running few benchmark applications.
Presentation given by US Chief Scientist, Mario Inchiosa, at the June 2013 Hadoop Summit in San Jose, CA.
ABSTRACT: Hadoop is rapidly being adopted as a major platform for storing and managing massive amounts of data, and for computing descriptive and query types of analytics on that data. However, it has a reputation for not being a suitable environment for high performance complex iterative algorithms such as logistic regression, generalized linear models, and decision trees. At Revolution Analytics we think that reputation is unjustified, and in this talk I discuss the approach we have taken to porting our suite of High Performance Analytics algorithms to run natively and efficiently in Hadoop. Our algorithms are written in C++ and R, and are based on a platform that automatically and efficiently parallelizes a broad class of algorithms called Parallel External Memory Algorithms (PEMA’s). This platform abstracts both the inter-process communication layer and the data source layer, so that the algorithms can work in almost any environment in which messages can be passed among processes and with almost any data source. MPI and RPC are two traditional ways to send messages, but messages can also be passed using files, as in Hadoop. I describe how we use the file-based communication choreographed by MapReduce and how we efficiently access data stored in HDFS.
WBDB 2015 Performance Evaluation of Spark SQL using BigBencht_ivanov
In this paper we present the initial results of our work to run BigBench on Spark. First, we evaluated the data scalability behavior of the existing MapReduce implementation of BigBench. Next, we executed the group of 14 pure HiveQL queries on Spark SQL and compared the results with the respective Hive results. Our experiments show that: (1) for both MapReduce and Spark SQL, BigBench queries perform with the increase of the data size on average better than the linear scaling behavior and (2) pure HiveQL queries perform faster on Spark SQL than on Hive.
http://clds.sdsc.edu/wbdb2015.ca/program
Improving performance of apriori algorithm using hadoopeSAT Journals
Abstract Spatial data is a data having a geological information. This paper explores the use of Hadoop framework to improve the performance of Apriori algorithm for spatial data mining. FP growth algorithm is better than Apriori but it fails in certain situations. By applying the Apriori algorithm parallely using Hadoop framework to spatial data, we can perform well as compare to FP growth. This paper includes clustering based on geological location, classification based on mineral resource type and spatial coherence between mineral resources. Spatial data mining find out the different association rules by observing the spatial data by using Apriori algorithm. The result of the paper will indicate the accurate prediction of occurrence of commodity with respect to other commodity of mineral resources. Keywords: Hadoop, data mining, association rules, clustering, spatial coherence
Today’s era is generally treated as the era of data on each and every field of computing application huge amount of data is generated. The society is gradually more dependent on computers so large amount of data is generated in each and every second which is either in structured format, unstructured format or semi structured format. These huge amount of data are generally treated as big data. To analyze big data is a biggest challenge in current world. Hadoop is an open-source framework that allows to store and process big data in a distributed environment across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage and it generally follows horizontal processing. Map Reduce programming is generally run over Hadoop Framework and process the large amount of structured and unstructured data. This Paper describes about different joining strategies used in Map reduce programming to combine the data of two files in Hadoop Framework and also discusses the skewness problem associate to it.
BDSE 2015 Evaluation of Big Data Platforms with HiBencht_ivanov
We evaluate the performance of DataStax Enterprise (DSE) using the HiBench benchmark suite and compare it with the corresponding Cloudera’s Distribution of Hadoop (CDH) results. Both systems, DSE and CDH were stress tested using CPU-bound (WordCount), I/O-bound (Enhanced DFSIO) and mixed (HiveBench) workloads.
Large amount of data are produced daily from various fields such as science, economics,
engineering and health. The main challenge of pervasive computing is to store and analyze large amount of
data.This has led to the need for usable and scalable data applications and storage clusters. In this article, we
examine the hadoop architecture developed to deal with these problems. The Hadoop architecture consists of
the Hadoop Distributed File System (HDFS) and Mapreduce programming model, which enables storage and
computation on a set of commodity computers. In this study, a Hadoop cluster consisting of four nodes was
created.Regarding the data size and cluster size, Pi and Grep MapReduce applications, which show the effect of
different data sizes and number of nodes in the cluster, have been made and their results examined.
Web Oriented FIM for large scale dataset using Hadoopdbpublications
In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduce a new approach called data partitioning using Map Reduce programming model.In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.
Enhancing Big Data Analysis by using Map-reduce TechniquejournalBEEI
Database is defined as a set of data that is organized and distributed in a manner that permits the user to access the data being stored in an easy and more convenient manner. However, in the era of big-data the traditional methods of data analytics may not be able to manage and process the large amount of data. In order to develop an efficient way of handling big-data, this work enhances the use of Map-Reduce technique to handle big-data distributed on the cloud. This approach was evaluated using Hadoop server and applied on Electroencephalogram (EEG) Big-data as a case study. The proposed approach showed clear enhancement on managing and processing the EEG Big-data with average of 50% reduction on response time. The obtained results provide EEG researchers and specialist with an easy and fast method of handling the EEG big data.
We are in the age of big data which involves collection of large datasets.Managing and processing large data sets is difficult with existing traditional database systems.Hadoop and Map Reduce has become one of the most powerful and popular tools for big data processing . Hadoop Map Reduce a powerful programming model is used for analyzing large set of data with parallelization, fault tolerance and load balancing and other features are it is elastic,scalable,efficient.MapReduce with cloud is combined to form a framework for storage, processing and analysis of massive machine maintenance data in a cloud computing environment.
LOAD BALANCING LARGE DATA SETS IN A HADOOP CLUSTERijdpsjournal
With the interconnection of one to many computers online, the data shared by users is multiplying daily. As
a result, the amount of data to be processed by dedicated servers rises very quickly. However, the
instantaneous increase in the volume of data to be processed by the server comes up against latency during
processing. This requires a model to manage the distribution of tasks across several machines. This article
presents a study of load balancing for large data sets on a cluster of Hadoop nodes. In this paper, we use
Mapreduce to implement parallel programming and Yarn to monitor task execution and submission in a
node cluster.
Worldranking universities final documentationBhadra Gowdra
With the upcoming data deluge of semantic data, the fast growth of ontology bases has brought significant challenges in performing efficient and scalable reasoning. Traditional centralized reasoning methods are not sufficient to process large ontologies. Distributed searching methods are thus required to improve the scalability and performance of inferences. This paper proposes an incremental and distributed inference method for large-scale ontologies by using Map reduce, which realizes high-performance reasoning and runtime searching, especially for incremental knowledge base. By constructing transfer inference forest and effective assertion triples, the storage is largely reduced and the search process is simplified and accelerated. We propose an incremental and distributed inference method (IDIM) for large-scale RDF datasets via Map reduce. The choice of Map reduce is motivated by the fact that it can limit data exchange and alleviate load balancing problems by dynamically scheduling jobs on computing nodes. In order to store the incremental RDF triples more efficiently, we present two novel concepts, i.e., transfer inference forest (TIF) and effective assertion triples (EAT). Their use can largely reduce the storage and simplify the reasoning process. Based on TIF/EAT, we need not compute and store RDF closure, and the reasoning time so significantly decreases that a user’s online query can be answered timely, which is more efficient than existing methods to our best knowledge. More importantly, the update of TIF/EAT needs only minimum computation since the relationship between new triples and existing ones is fully used, which is not found in the existing literature. In order to store the incremental RDF triples more efficiently, we present two novel concepts, transfer inference forest and effective assertion triples. Their use can largely reduce the storage and simplify the searching process.
Big data is a popular term used to describe the large volume of data which includes structured, semi-structured and unstructured
data. Now-a-days, unstructured data is growing in an explosive speed with the development of Internet and social networks like Twitter,Facebook
& Yahoo etc., In order to process such colossal of data a software is required that does this efficiently and this is where Hadoop steps in. Hadoop
has become one of the most used frameworks when dealing with big data. It is used to analyze and process big data. In this paper, Apache Flume
is configured and integrated with spark streaming for streaming the data from twitter application. The streamed data is stored into Apache
Cassandra. After retrieving the data, the data is going to be analyzed by using the concept of Apache Zeppelin. The result will be displayed on
Dashboard and the dashboard result is also going to be analyzed and validating using JSON
Breast cancer detection from histopathological images is done using deep learning and transfer learning techniques. Image processing is done for better accuracy. CNN and DenseNet-121 algorithms are used. 90.9 % accuracy is achieved using CNN and 88% accuracy is achieved using Transfer learning.
Multiple Regression and Logistic Regression performed on data to evaluate the relation between birth rate and abortion rate for male and female using SPSS
Global warming big data is analyzed and processed using Pig, Hive, Mapreduce, HDFC, HBase technologies. Results are visualized using Tableau and stored in HBase in Programming in Data Analytics Project.
Twelve different machine learning models are built for disease prediction using safe machine learning. Homomorphic plaintext encryption is used for privacy-preserving.
Breast Cancer Detection from Mammography Images Using Machine Learning Algorithms (U-Net Segmentation and Dense Net Classifier implementation are in progress)
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
1. Global Warming Analysis using Big Data Techniques
Mansi Chowkkar
x18134599
MSc Data Analytics
PDA
National College of Ireland
Abstract— As data is growing rapidly in every field, new
technologies to handle such enormous big data and its
processing is also evolving. Hadoop is one of the popular
technologies due to its properties like distributive, scalable,
and open SRC framework. It is based on Map-reduce which
divide the task into smaller chunks and work. Pig and Hive
are based on Hadoop and they process data faster. All these
tools and technologies are freely available and open source.
In this project Hadoop, HDFS, MapReduce, HBase, Hive,
and Pig are used for analyzing global warming and CO2
emission data. Big data is being used for analysis and it is
accessed and stored using Hadoop distributed system. The
highest CO2 emission countries and the maximum global
warming temperature countries are related is confirmed from
this research. Also, southeast countries are having a major
impact of global warming on average temperature value is
observed from an analysis performed by above mentioned
technologies. keywords: Hadoop, Pig, Hive, HDFS, HBase
I. INTRODUCTION
As computation and technology are advancing, there are
many other problems which are increasing. For example,
increased global warming and pollution are a major concern
which needs attention. All the developing countries have
increased pollution levels and hence increased average
temperature. Earlier there was no uncertainty in temperature
prediction. But due to the global warming effect nowadays
uncertainty for weather prediction has increased [1].
To predict the pattern of each country from last 20/30 years
from the entire world, this study will use global warming
data from all previous years and for each country, city
with average temperature value and change in temperature
uncertainty value. Also to find whether CO2 emission has
any adverse effect on global warming, CO2 emission data
for all country from last 10 years will also be analyzed here.
Since data is very huge, for efficient and accurate analysis
this project will use Big Data Analytics programming
languages. Hadoop is a widely used Big data framework that
mainly uses the MapReduce framework which is popular
in data processing structures. Mapreduce with distributed
Hadoop will be used for analyzing pollution and global
warming issues without hampering the speed [2].
As Hadoop is used everywhere and it is open source,
technologies like Hive, Pig, Spark, etc. are built on top of
Hadoop. Pig and Hive are built on top of Hadoop which
process data queries faster than Map-reduce. Hive provides
SQL query language and transforms it into the task of
Map-reduce [3]. Pig run complex joins operations in real
time data queries. PigLatin comes with a novel debugging
environment which is useful when dealing with huge data
sets. The Pig and Hive are used for complex query execution.
For predicting temperature increase in each of the country
every year, Pig and Hive will be used to get results.
A. Business Queries:
TABLE I: Business query
Query Language Framework
Max value of CO2
emission in each country
Java Mapreduce,
Hadoop, HDFS,
HBase
In every year max value of
CO2
Java Mapreduce,
Hadoop, HDFS,
HBase
Max country count and
its corresponding avg
temperature
Pig Hadoop, HDFS,
HBase, Sqoop
Average of temperature of
all year data for each
country
Pig Hadoop, HDFS,
HBase, Sqoop
List of the countries who
arehaving temperature
greater than 29.5
Hive, HQL Hive, Hadoop,
HDFS, HBase,
Sqoop
List of the countries
with average value of the
temperature
Hive, HQL SQL, Hadoop,
HDFS, HBase,
Sqoop
List of the countries
with average value of the
temperatureHive
Hive, HQL SQL, Hadoop,
HDFS, HBase,
Sqoop
Top 5 countries with
maximum temperature
HQL,Hive SQL, Hadoop,
HDFS, HBase,
Sqoop
II. RESEARCH QUESTION
Analyzing global warming and pollution across the world
using Hadoop technology
III. MOTIVATION
The increasing population and pollution lead to the global
warming issue. There is a need for solving this global
warming issue. For finding a solution to this problem, the
deep study should be performed on available big data on
different parameters. Therefore Big data technology based
effective analysis should be performed with considering
various situations and variables from the data [4]. In Big data,
Hadoop is widely used and it is an open source technology,
therefore many technologies are built on Hadoop that can
be used for data analysis. MapReduce is an example of a
2. developed parallel technology which is based on Hadoop and
provides better performance in the big data processing. It can
be processed through Hadoop, Hive, Pig, and HDFS systems
for querying on data [?].
IV. LITERATURE REVIEW
There are lots of researches done in this field which
provides an overview for further study. In the study [5], the
author has carried out an experiment to investigate the effect
of top 5 countries global warming into tourist business using
automatic RSS feed big data technology. The temperature
change, global warming, and atmosphere change are studied
on top 5 tourist countries data to show climate change attracts
tourists in Thailand.
In another study [6] climate analysis has been done
using big data. In this study, the author explained big data
challenges in the field of MERRA analytic service which
enables MapReduce analysis over NASA analysis research
studies. Big data cannot move from one place to another
hence big data technology and cloud computing is used in
this study for climate data analysis. MapReduce provides an
approach for analysis with high performance and it has been
used in many research studies on climate data. MapReduce
has proven to be an effective technique for large text data,
complex data, and binary data.
In this technological era, Big data is increasing and
handling of these big data services are facing many problems.
To provide an efficient solution there is a mapping and
reducing technique which can be improved by shuffling
strategy. For counting of words, [7] study has explained
the shuffling technique for MapReduce. The MapReduce
is implemented on Hadoop for word counts. This shuffling
architecture is tested for repetition of words, duplicate entries
of words, and sentences from the paragraph for performance
testing.
[8] studied Apache Pig and Apache Hive configuration
over HDFS and problem faced during the experiment is
explained. Problems during the configuration of different jar
files and versions are discussed. For example, yarn uses a
new method for tracking jobs as compare to a MapReduce
job. This study inspires the usage and installation of newer
versions of big data sources and tools. This study compared
Pivotal HAWQ and Apache Hive for word count analysis
and it is found that Pivotal HAWQ is 7 times faster on
10 million rows of data. The study also confirmed that
there is no difference between Apache Pig and Apache Hive
performance.
V. METHODOLOGY
In this section process flow for the project will be
discussed.
A. Dataset
1. Data Collection: The global warming Data is selected
from the kaggle open source. The average temperature for
all countries from the year 1849 to 2013 including city,
longitude, and latitude is explained in this data set. Another
data for CO2 emission according to year and country with
country code is collected from the Air pollution world data
site.
2. Data Extraction For this project 2 data-sets are
selected, one with 2,37,000 rows and one with 2400 rows.
CO2 emission data is selected for studying co-relation
between increasing global warming temperature and CO2
emission in the world. The first dataset is downloaded
from Kaggle1
and another data is downloaded from the
OurWorldinData2
Website in .csv format. Data consists of
unwanted values, missing values, and null values. The second
dataset for CO2 emission is consists of years as column
names hence in the transpose format.
2. Data Reprocessing:
Data is cleaned using R programming language. In the
global warming data set, operations like extracting from .csv,
removing special characters, replacing or removing NA are
performed. In the CO2 data set data is first cleaned and
then transposed so the year is generated an new column
using API. After transposing, year string is added with extra
characters which are removed using conditional loop. One
of the data processing before and after stage is shown in the
fig(1).
3. Data Exploration and Transformation:
Fig. 1: Before and After cleaning
B. Project Flow
After cleaning of data, it is loaded to MySQL database.
From the MySQL it is loaded to HDFS for further processing
of queries on the dataset.
1. MApReduce Process and HDFS:
Using Apache sqoop data is loaded from MySQL to
HDFS. Pig, Hive, and Java accessedcthe stored data for the
query purpose.
2. MapReduce Process and Apache Hive:
Implementing business query hive is used, data is loaded
to hive using load command. After loading data, queries are
formed and saved in .hql file. Through the hive command
output file is generated and stored in HBase. Hive queries
will answers our three research objectives.
3. MapReduce process and Apache Pig:
To answer some of our business objectives Pig queries
will be used on the global warming data set. Pig is a faster
big data technique hence more than 2,37,000 rows data is
1https://www.kaggle.com/newyork167/
exploring-global-warming/data
2https://ourworldindata.org/co2-and-other-greenhouse-gas-emis
3. Fig. 2: Flow Diagram
Fig. 3: Data loading to HDFS
used for Pig query using HDFS storage. The load command
is used to load data from HDFS to Pig and then queries are
run using .pig file execution. The output is stored in HBase.
4. Java query using MapReduce Design:
MapReduce design pattern is used for writing three
classes: mapper for data type mapping with key, the reducer
is used for implementing query logic, and the driver is used
to executing main class and driving process for example
file reading writing tasks. Eclipse is used for MapReduce
execution using java code.
Fig. 4: Flow Diagram
Fig. 5: Pig query1
C. Technologies and Programming languages used:
Technologies are selected for business queries as per the
requirement and best suitability is discussed here.
MYSQL: It is a database that handles huge data. It works
on client-server modes where data is stored in the server and
it is able to send to the client location. Hence MySQL is used
in this project for initially storing data and then transferring
it to HDFS.
Hadoop: Hadoop and HDFS is are open source data
storing and managing distributed frameworks which are
widely used for big data. The processing of data is done
by MapReduce framework [9]. Hadoop cluster comprises of
many components, for example, MapReHDFS, Yarn, HDFS,
and some libraries which handle failure of the system.
Therefore, to achieve high performance for big data queries
Hadoop distributed system is used in this project.
HBase: HBase runs on top of the Hadoop cluster. It
provides storage for large tables which can be stored as
4. records. HBase provides red/write access during processing
of data hence in this project it is used for storage purposes.
MapReduce: Hadoop uses MapReduce for processing
large amounts of data. Every job is distributed as a mapping
task and reducing task. The map and reduce function, input,
output file location is required to complete mapreduce job
[10]. MapReduce distributes the task into small tasks hence
it processes data within less time. In this project, MapReduce
is used with mapper, reducer, and driver classes for 2 query
processing.
Sqoop: Sqoop is a interface for transferring big data from
one database to another similar to Cassandra. It internally
uses mapper and reducer functionality to process number
of tasks hence providing high data transfer and processing.
In this project, Sqooop is used for transferring data from
MySQL to HDFS.
Hive: Hive turns Hadoop into data warehouse which
makes query and data analyzing efficient. Hive uses some
of the concepts like tables, entity relationship, columns and
primitive data types from relational database management
system (RDBMS). Hive has its own declarative language
known as HiveQL(HQL) which is very similar to SQL
query language. Functioning of Hive is unique since it stores
schema inside the database but stores its data on HDFS
system [11]. Hive is used to process some of the business
queries using HQL language.
Pig: Pig uses Map-reduce in an indirect way which
was developed by Yahoo. Pig uses PigLatin high level
programming language similar to SQL. Pig has a local and
distributed type of execution environment. For executing
Pig queries, content needs to be saved in input file in
semi-structured format [12]. In this project, Pig is used to
find a solution for some of the complex queries. The input
file is copied to HDFS using sqoop and Pig command is
executed for finding query result from a file. Data is loaded
using PigStorage and output is stored in HDFS.
R Language: R programming language is used for
cleaning data sets in R studio. Different functions of R are
used for example gather(), gsub(), etc for cleaning and then
data is leaded.
Java Language: Hadoop based Mapreduce uses java
programming language for a map and reduce functionality.
In this project Mapper, reducer, and driver functionalities are
implemented in the Java programming language.
Tableau: For visualizing all outputs from queries, Tableau
visualizing tool is used. Different types of graphs are used
for explaining results from the queries.
VI. RESULTS
Query implemented in MapReduce using java
programming
Analysis : fig 6Query shows that united emirates and
Portugal are the country with maximum CO2 emission. From
the graph it has been seen that, Brazil, Italy, India, Greenland
are the countries with comparatively low CO2 emission
value.
Query implemented in MapReduce java programming
Fig. 6: Max CO2 emission for each country
Fig. 7: CO2 emission for every year
Analysis : fig 7This query represents the CO2 emission
value of all the years. From the graph, we can see that from
2006 the rate of CO2 emission is decreasing. Hence it is
observed that in recent years CO2 emission has decreased. To
resolve the global warming issue, countries are now actively
taking action to decrease pollution.
Fig. 8: Country count with max cities in global warming data
Query implemented in Pig
Analysis : fig 8 This graph represents country count for
the global warming data and its corresponding temperature.
It is seen that Chile, India, and Bangladesh countries have
maximum count hence they have more global warming effect
as compare to other countries in the world.
Query implemented in Pig
Analysis : fig 9 The graph represents the average value of
the global warming temperature in all countries. The result
shows that Sudan, Vietnam, and Somalia show the highest
value of avg temperature whereas, South Korea, Germany,
and Ethiopia have a minimum value. Hence, Germany, South
5. Fig. 9: Average value of global warming temperature
Korea doesn’t show major global warming effects.
Fig. 10: Countries with avg temperature greater than 29.5
Query implemented in Hive
Analysis : fig 10 This query list all the countries with a
global warming temperature greater than 29.5. This shows
that Pakistan, Iraq, Iran, Sudan, and Saudi Arabia are the
warmest countries and have a major global warming effect.
There is a total of 17 countries in the world with a
temperature greater than 29.5 which is a major concern that
needs to be resolved. We can see that all the countries listed
are South Eastern countries.
Fig. 11: Country count which are listed majorly for global warming
Query implemented in Hive
Analysis : fig 11 This query shows that Iraq, Saudi Arabia,
Pakistan, India, China, and Turkey are majorly suffering
countries in the global warming problem. These countries
are also listed in the CO2 emission country list. Hence it is
proved that CO2 emission affects the global warming issue.
Query implemented in Hive
Fig. 12: Countries with temperature uncertainty in change in
temperature
Analysis: fig 12 This query represents countries that are
most stable in temperature change. India, China, Brazil,
and Turkey show unstable temperatures throughout the year.
Hence these countries exhibit uncertainty in temperature
change. Whereas Egypt, Syria, South Africa, Kenya, and
Japan are more stable countries that have the lowest change
in temperature values.
Fig. 13: Top 5 Countries with maximum temperature reached
Query implemented in Hive
Analysis : fig 13 This result shows that London, Istanbul,
and Kiev has reached the maximum temperature in past years
due to the global warming effect. As we know London and
Berlin don’t have a maximum temperature in all months but
they have reached peak value as compare to other countries.
Istanbul is having maximum temperature in all of the months
as we can see from other query results and also reached the
maximum temperature.
From all the above results it has been seen that the
countries with maximum CO2 emission have the major
global warming effect. Hence CO2 is the major contributing
parameter for global warming temperature. It is also seen that
South-East countries suffering majorly from global warming
issues. Change in temperature is the variable which is also
directly proportional to the global warming problem.
VII. CHALLENGES AND LIMITATION
1. Challenges faced during the automation process while
storing output into HBase and automating shell script.
6. 2. Faced delay issue for running query on big data using
MapReduce.
3. Formation of complex query in Hive.
4. Virtual Machine speed for running all technologies.
4. All UI interfaces on open stack
5. Interfacing Machine learning techniques with Virtual
Box.
VIII. CONCLUSION AND FUTURE WORK
The global warming and CO2 data structured data are used
for the analysis of global warming countries and the impact
of CO2 emission. It is found that CO2 emission have an
effect on global warming data. In the future more related data
for example ozone layer value, other pollutants attributes can
be collected for further analysis. The Big Data techniques
used here are Hadoop, Hbase, Hive, Pig, and MapReduce.
It has been observed that Pig performed better in terms of
big query execution time and query complexity. MapReduce
performed well in query processing tasks. In the future, more
techniques can be explored for example Spark and Impala.
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