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.
Processing Geospatial at Scale at LocationTechRob Emanuele
These slides were for a talk at EclipseCon Europe 2015, about LocationTech projects that provide capabilities of processing geospatial data at scale. Video of the talk should be released at some point through the Eclipse Foundation.
Enabling Access to Big Geospatial Data with LocationTech and Apache projectsRob Emanuele
LocationPowers OGC BigGeoData 2016
This presentation will discuss tools in the open source landscape that are used to handle big geospatial data. In particular, we will focus on how Apache frameworks such as Spark and Accumulo are "geospatially enabled" by four projects: GeoTrellis, GeoWave, GeoMesa, and GeoJinni. These four projects all participate in LocationTech, a working group under the Eclipse Foundation. In particular, we will discuss how each of these LocationTech technologies implement spatial indexing (e.g. by using space filling curves) in order to provide quick access to data, and other common themes among the four projects. Attendees should walk away from this presentation understanding important parts of the Apache big data ecosystem, a set of LocationTech projects that belong to the cutting edge of enabling those Apache project's handling of geospatial data, as well as some solutions to common problems when dealing with large geospatial data.
This is a slide deck that I have been using to present on GeoTrellis for various meetings and workshops. The information is speaks to GeoTrellis pre-1.0 release in Q4 of 2016.
Processing Geospatial at Scale at LocationTechRob Emanuele
These slides were for a talk at EclipseCon Europe 2015, about LocationTech projects that provide capabilities of processing geospatial data at scale. Video of the talk should be released at some point through the Eclipse Foundation.
Enabling Access to Big Geospatial Data with LocationTech and Apache projectsRob Emanuele
LocationPowers OGC BigGeoData 2016
This presentation will discuss tools in the open source landscape that are used to handle big geospatial data. In particular, we will focus on how Apache frameworks such as Spark and Accumulo are "geospatially enabled" by four projects: GeoTrellis, GeoWave, GeoMesa, and GeoJinni. These four projects all participate in LocationTech, a working group under the Eclipse Foundation. In particular, we will discuss how each of these LocationTech technologies implement spatial indexing (e.g. by using space filling curves) in order to provide quick access to data, and other common themes among the four projects. Attendees should walk away from this presentation understanding important parts of the Apache big data ecosystem, a set of LocationTech projects that belong to the cutting edge of enabling those Apache project's handling of geospatial data, as well as some solutions to common problems when dealing with large geospatial data.
This is a slide deck that I have been using to present on GeoTrellis for various meetings and workshops. The information is speaks to GeoTrellis pre-1.0 release in Q4 of 2016.
Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Sc...Dawn Wright
Invited talk for 2016 AGU Fall Meeting Session IN12A Big Data Analytics I
Introduced is a new approach for processing spatiotemporal big data by leveraging distributed analytics and storage. A suite of temporally-aware analysis tools summarizes data nearby or within variable windows, aggregates points (e.g., for various sensor observations or vessel positions), reconstructs time-enabled points into tracks (e.g., for mapping and visualizing storm tracks), joins features (e.g., to find associations between features based on attributes, spatial relationships, temporal relationships or all three simultaneously), calculates point densities, finds hot spots (e.g., in species distributions), and creates space-time slices and cubes (e.g., in microweather applications with temperature, humidity, and pressure, or within human mobility studies). These “feature geo analytics” tools run in both batch and streaming spatial analysis mode as distributed computations across a cluster of servers on typical “big” data sets, where static data exist in traditional geospatial formats (e.g., shapefile) locally on a disk or file share, attached as static spatiotemporal big data stores, or streamed in near-real-time. In other words, the approach registers large datasets or data stores with ArcGIS Server, then distributes analysis across a cluster of machines for parallel processing. Several brief use cases will be highlighted based on a 16-node server cluster at 14 Gb RAM per node, allowing, for example, the buffering of over 8 million points or thousands of polygons in ~1 minute. The approach is “hybrid” in that ArcGIS Server integrates open-source big data frameworks such as Apache Hadoop and Apache Spark on the cluster in order to run the analytics. In addition, the user may devise and connect custom open-source interfaces and tools developed in Python or Python Notebooks; the common denominator being the familiar REST API.
Adding Location and Geospatial Analytics to Big Data Analytics (BDT210) | AWS...Amazon Web Services
(Presented by Esri)
When people analyze a problem, they often include location at the core of the analysis. Location and spatial context, combined with geographical knowledge, can make the biggest difference in understanding a problem and analyzing it in a more meaningful way.
In this session, we show how Amazon EMR can be used with location and geospatial analytics, and how the Amazon EMR API and the Python SDK were used to build tools that integrate Big Data and geospatial analysis. We also show powerful visualization options for displaying your results, using maps which can be shared in reports or distributed online and to mobile apps.
"Machine Learning and Internet of Things, the future of medical prevention", ...Dataconomy Media
"Machine Learning and Internet of Things, the future of medical prevention", Pierre Gutierrez, Sr. Data Scientist at Dataiku
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
https://www.youtube.com/c/DataNatives
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Pierre Gutierrez is a senior data scientist at Dataiku. As a data science expert and consultant, Pierre has worked in diverse sectors such as e-business, retail, insurance or telcos. He has experience in various topics such as smart cities, fraud detection, recommender systems, or IoT.
ESIP 2018 - The Case for Archives of ConvenienceDan Pilone
Earth Science data is measured in petabytes and represents decades of data collection, evolution of technology and practices, and provides an unparalleled view of our planet. The pace of change is only accelerating: NASA and other agencies are on their way to making hundreds of Petabytes of data available in the cloud, highly scalable processing and analysis architectures and tools are in active use with more being developed every day, and each of these brings with it opportunities for optimization and innovation. This talk demonstrates leveraging the elastic nature of the cloud using GOES-16 data to create ephemeral Archives of Convenience, targeting individual researcher needs, optimized for their problems and tool suites, instead of trying to settle on a single "cloud optimized" solution.
This tool solves a real problem in the environmental inventory industry and makes a valuable open data set more accessible.
The NJDEP maintains a statewide wildlife habitat data set that details conservation requirements related to environmental regulations. This is an open data set, but accessibility is limited since working with the one million habitat areas often requires knowledge of GIS software. Using desktop GIS software, a site-specific search is a time-intensive process, taking minutes or hours to run geoprocessing operations for specific properties.
Now, a user can draw a custom area in a browser window and return results in seconds.
Learn about how the project was built in this presentation.
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
Analyzing Larger RasterData in a Jupyter Notebook with GeoPySpark on AWS - FO...Rob Emanuele
Slides from the 2017 FOSS4G Workshop "Analyzing Larger RasterData in a Jupyter Notebook with GeoPySpark on AWS"
See the repository at https://github.com/lossyrob/foss4g-2017-geopyspark-workshop
Delegating Data Management to the Cloud: A Case Study in a Telecommunications...Giuseppe Procaccianti
Qing Gu, Patricia Lago and Simone Potenza (VU University, The Netherlands)
Presented by: Giuseppe Procaccianti
In 2013 IEEE 7th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems (MESOCA '13)
Feature Geo Analytics and Big Data Processing: Hybrid Approaches for Earth Sc...Dawn Wright
Invited talk for 2016 AGU Fall Meeting Session IN12A Big Data Analytics I
Introduced is a new approach for processing spatiotemporal big data by leveraging distributed analytics and storage. A suite of temporally-aware analysis tools summarizes data nearby or within variable windows, aggregates points (e.g., for various sensor observations or vessel positions), reconstructs time-enabled points into tracks (e.g., for mapping and visualizing storm tracks), joins features (e.g., to find associations between features based on attributes, spatial relationships, temporal relationships or all three simultaneously), calculates point densities, finds hot spots (e.g., in species distributions), and creates space-time slices and cubes (e.g., in microweather applications with temperature, humidity, and pressure, or within human mobility studies). These “feature geo analytics” tools run in both batch and streaming spatial analysis mode as distributed computations across a cluster of servers on typical “big” data sets, where static data exist in traditional geospatial formats (e.g., shapefile) locally on a disk or file share, attached as static spatiotemporal big data stores, or streamed in near-real-time. In other words, the approach registers large datasets or data stores with ArcGIS Server, then distributes analysis across a cluster of machines for parallel processing. Several brief use cases will be highlighted based on a 16-node server cluster at 14 Gb RAM per node, allowing, for example, the buffering of over 8 million points or thousands of polygons in ~1 minute. The approach is “hybrid” in that ArcGIS Server integrates open-source big data frameworks such as Apache Hadoop and Apache Spark on the cluster in order to run the analytics. In addition, the user may devise and connect custom open-source interfaces and tools developed in Python or Python Notebooks; the common denominator being the familiar REST API.
Adding Location and Geospatial Analytics to Big Data Analytics (BDT210) | AWS...Amazon Web Services
(Presented by Esri)
When people analyze a problem, they often include location at the core of the analysis. Location and spatial context, combined with geographical knowledge, can make the biggest difference in understanding a problem and analyzing it in a more meaningful way.
In this session, we show how Amazon EMR can be used with location and geospatial analytics, and how the Amazon EMR API and the Python SDK were used to build tools that integrate Big Data and geospatial analysis. We also show powerful visualization options for displaying your results, using maps which can be shared in reports or distributed online and to mobile apps.
"Machine Learning and Internet of Things, the future of medical prevention", ...Dataconomy Media
"Machine Learning and Internet of Things, the future of medical prevention", Pierre Gutierrez, Sr. Data Scientist at Dataiku
Watch more from Data Natives Berlin 2016 here: http://bit.ly/2fE1sEo
Visit the conference website to learn more: www.datanatives.io
Follow Data Natives:
https://www.facebook.com/DataNatives
https://twitter.com/DataNativesConf
https://www.youtube.com/c/DataNatives
Stay Connected to Data Natives by Email: Subscribe to our newsletter to get the news first about Data Natives 2017: http://bit.ly/1WMJAqS
About the Author:
Pierre Gutierrez is a senior data scientist at Dataiku. As a data science expert and consultant, Pierre has worked in diverse sectors such as e-business, retail, insurance or telcos. He has experience in various topics such as smart cities, fraud detection, recommender systems, or IoT.
ESIP 2018 - The Case for Archives of ConvenienceDan Pilone
Earth Science data is measured in petabytes and represents decades of data collection, evolution of technology and practices, and provides an unparalleled view of our planet. The pace of change is only accelerating: NASA and other agencies are on their way to making hundreds of Petabytes of data available in the cloud, highly scalable processing and analysis architectures and tools are in active use with more being developed every day, and each of these brings with it opportunities for optimization and innovation. This talk demonstrates leveraging the elastic nature of the cloud using GOES-16 data to create ephemeral Archives of Convenience, targeting individual researcher needs, optimized for their problems and tool suites, instead of trying to settle on a single "cloud optimized" solution.
This tool solves a real problem in the environmental inventory industry and makes a valuable open data set more accessible.
The NJDEP maintains a statewide wildlife habitat data set that details conservation requirements related to environmental regulations. This is an open data set, but accessibility is limited since working with the one million habitat areas often requires knowledge of GIS software. Using desktop GIS software, a site-specific search is a time-intensive process, taking minutes or hours to run geoprocessing operations for specific properties.
Now, a user can draw a custom area in a browser window and return results in seconds.
Learn about how the project was built in this presentation.
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
Analyzing Larger RasterData in a Jupyter Notebook with GeoPySpark on AWS - FO...Rob Emanuele
Slides from the 2017 FOSS4G Workshop "Analyzing Larger RasterData in a Jupyter Notebook with GeoPySpark on AWS"
See the repository at https://github.com/lossyrob/foss4g-2017-geopyspark-workshop
Delegating Data Management to the Cloud: A Case Study in a Telecommunications...Giuseppe Procaccianti
Qing Gu, Patricia Lago and Simone Potenza (VU University, The Netherlands)
Presented by: Giuseppe Procaccianti
In 2013 IEEE 7th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Systems (MESOCA '13)
Internal meeting developed at Institut des Matériaux Jean Rouxel (Nantes, France) during January 2014, regarding nickel oxide deposited under different parameters.
Is the 2013 Ford Focus a truly competitive compact?managingeditor
The 2013 Ford Focus is arguably the best-looking, best-driving domestic compact car of all time (if you're more interested in quiet comfort, the Chevy Cruze isn't bad, either).
But is it good enough to recommend over a category stalwart like the Honda Civic? You're just 22 slides away from being a casual expert on the 2013 Ford Focus.
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.
LARGE-SCALE DATA PROCESSING USING MAPREDUCE IN CLOUD COMPUTING ENVIRONMENTijwscjournal
The computer industry is being challenged to develop methods and techniques for affordable data processing on large datasets at optimum response times. The technical challenges in dealing with the increasing demand to handle vast quantities of data is daunting and on the rise. One of the recent processing models with a more efficient and intuitive solution to rapidly process large amount of data in parallel is called MapReduce. It is a framework defining a template approach of programming to perform large-scale data computation on clusters of machines in a cloud computing environment. MapReduce provides automatic parallelization and distribution of computation based on several processors. It hides the complexity of writing parallel and distributed programming code. This paper provides a comprehensive systematic review and analysis of large-scale dataset processing and dataset handling challenges and
requirements in a cloud computing environment by using the MapReduce framework and its open-source implementation Hadoop. We defined requirements for MapReduce systems to perform large-scale data processing. We also proposed the MapReduce framework and one implementation of this framework on Amazon Web Services. At the end of the paper, we presented an experimentation of running MapReduce
system in a cloud environment. This paper outlines one of the best techniques to process large datasets is MapReduce; it also can help developers to do parallel and distributed computation in a cloud environment.
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.
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.
Introduction to Big Data and Hadoop using Local Standalone Modeinventionjournals
Big Data is a term defined for data sets that are extreme and complex where traditional data processing applications are inadequate to deal with them. The term Big Data often refers simply to the use of predictive investigation on analytic methods that extract value from data. Big data is generalized as a large data which is a collection of big datasets that cannot be processed using traditional computing techniques. Big data is not purely a data, rather than it is a complete subject involves various tools, techniques and frameworks. Big data can be any structured collection which results incapability of conventional data management methods. Hadoop is a distributed example used to change the large amount of data. This manipulation contains not only storage as well as processing on the data. Hadoop is an open- source software framework for dispersed storage and processing of big data sets on computer clusters built from commodity hardware. HDFS was built to support high throughput, streaming reads and writes of extremely large files. Hadoop Map Reduce is a software framework for easily writing applications which process vast amounts of data. Wordcount example reads text files and counts how often words occur. The input is text files and the result is wordcount file, each line of which contains a word and the count of how often it occurred separated by a tab.
Design Issues and Challenges of Peer-to-Peer Video on Demand System cscpconf
P2P media streaming and file downloading is most popular applications over the Internet.
These systems reduce the server load and provide a scalable content distribution. P2P
networking is a new paradigm to build distributed applications. It describes the design
requirements for P2P media streaming, live and Video on demand system comparison based on their system architecture. In this paper we described and studied the traditional approaches for P2P streaming systems, design issues, challenges, and current approaches for providing P2P VoD services.
Survey of Parallel Data Processing in Context with MapReduce cscpconf
MapReduce is a parallel programming model and an associated implementation introduced by
Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. The original MapReduce implementation by Google, as well as its open-source counterpart,Hadoop, is aimed for parallelizing computing in large clusters of commodity machines.This paper gives an overview of MapReduce programming model and its applications. The author has described here the workflow of MapReduce process. Some important issues, like fault tolerance, are studied in more detail. Even the illustration of working of Map Reduce is given. The data locality issue in heterogeneous environments can noticeably reduce the Map Reduce performance. In this paper, the author has addressed the illustration of data across nodes in a way that each node has a balanced data processing load stored in a parallel manner. Given a data intensive application running on a Hadoop Map Reduce cluster, the auhor has exemplified how data placement is done in Hadoop architecture and the role of Map Reduce in the Hadoop Architecture. The amount of data stored in each node to achieve improved data-processing performance is explained here.
DESIGN ARCHITECTURE-BASED ON WEB SERVER AND APPLICATION CLUSTER IN CLOUD ENVI...cscpconf
Cloud has been a computational and storage solution for many data centric organizations. The
problem today those organizations are facing from the cloud is in data searching in an efficient
manner. A framework is required to distribute the work of searching and fetching from
thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The
major idea is to design a web server in the map phase using the jetty web server which will give
a fast and efficient way of searching data in MapReduce paradigm. For real time processing on
Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in
web server with multi-level index keys. The web server uses to handle traffic throughput. By web
clustering technology we can improve the application performance. To keep the work down, the
load balancer should automatically be able to distribute load to the newly added nodes in the
server.
Cloud has been a computational and storage solution for many data centric organizations. The
problem today those organizations are facing from the cloud is in data searching in an efficient
manner. A framework is required to distribute the work of searching and fetching from
thousands of computers. The data in HDFS is scattered and needs lots of time to retrieve. The
major idea is to design a web server in the map phase using the jetty web server which will give
a fast and efficient way of searching data in MapReduce paradigm. For real time processing on
Hadoop, a searchable mechanism is implemented in HDFS by creating a multilevel index in
web server with multi-level index keys. The web server uses to handle traffic throughput. By web
clustering technology we can improve the application performance. To keep the work down, the
load balancer should automatically be able to distribute load to the newly added nodes in the
server.
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.
There is a growing trend of applications that ought to handle huge information. However, analysing huge information may be a terribly difficult drawback nowadays. For such data many techniques can be considered. The technologies like Grid Computing, Volunteering Computing, and RDBMS can be considered as potential techniques to handle such data. We have a still in growing phase Hadoop Tool to handle such data also. We will do a survey on all this techniques to find a potential technique to manage and work with Big Data.
An Examination of Effectuation Dimension as Financing Practice of Small and M...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Does Goods and Services Tax (GST) Leads to Indian Economic Development?iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Childhood Factors that influence success in later lifeiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Emotional Intelligence and Work Performance Relationship: A Study on Sales Pe...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Customer’s Acceptance of Internet Banking in Dubaiiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study of Employee Satisfaction relating to Job Security & Working Hours amo...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Consumer Perspectives on Brand Preference: A Choice Based Model Approachiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Student`S Approach towards Social Network Sitesiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Broadcast Management in Nigeria: The systems approach as an imperativeiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study on Retailer’s Perception on Soya Products with Special Reference to T...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A Study Factors Influence on Organisation Citizenship Behaviour in Corporate ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Consumers’ Behaviour on Sony Xperia: A Case Study on Bangladeshiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Design of a Balanced Scorecard on Nonprofit Organizations (Study on Yayasan P...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Public Sector Reforms and Outsourcing Services in Nigeria: An Empirical Evalu...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Media Innovations and its Impact on Brand awareness & Considerationiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Customer experience in supermarkets and hypermarkets – A comparative studyiosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Social Media and Small Businesses: A Combinational Strategic Approach under t...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Secretarial Performance and the Gender Question (A Study of Selected Tertiary...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Implementation of Quality Management principles at Zimbabwe Open University (...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Organizational Conflicts Management In Selected Organizaions In Lagos State, ...iosrjce
IOSR Journal of Business and Management (IOSR-JBM) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of business and managemant and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications inbusiness and management. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
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Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
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Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
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About
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Technical Specifications
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
Key Features
Indigenized remote control interface card suitable for MAFI system CCR equipment. Compatible for IDM8000 CCR. Backplane mounted serial and TCP/Ethernet communication module for CCR remote access. IDM 8000 CCR remote control on serial and TCP protocol.
• Remote control: Parallel or serial interface
• Compatible with MAFI CCR system
• Copatiable with IDM8000 CCR
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
Application
• Remote control: Parallel or serial interface.
• Compatible with MAFI CCR system.
• Compatible with IDM8000 CCR.
• Compatible with Backplane mount serial communication.
• Compatible with commercial and Defence aviation CCR system.
• Remote control system for accessing CCR and allied system over serial or TCP.
• Indigenized local Support/presence in India.
• Easy in configuration using DIP switches.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
Leveraging Map Reduce With Hadoop for Weather Data Analytics
1. IOSR Journal of Computer Engineering (IOSR-JCE)
e-ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. II (May – Jun. 2015), PP 06-12
www.iosrjournals.org
DOI: 10.9790/0661-17320612 www.iosrjournals.org 6 | Page
Leveraging Map Reduce With Hadoop for Weather Data
Analytics
Riyaz P.A.1
, Surekha Mariam Varghese2
1,2.
(Dept. of CSE, Mar Athanasius College of Engineering, Kothamangalam, Kerala, India)
Abstract : Collecting, storing and processing of huge amounts of climatic data is necessary for accurate
prediction of weather. Meteorological departments use different types of sensors such as temperature, humidity
etc. to get the values. Number of sensors and volume and velocity of data in each of the sensors makes the data
processing time consuming and complex. Leveraging MapReduce with Hadoop to process the massive amount
of data. Hadoop is an open framework suitable for large scale data processing. MapReduce programming model
helps to process large data sets in parallel, distributed manner. This project aims to build a data analytical engine
for high velocity, huge volume temperature data from sensors using MapReduce on Hadoop.
Keywords – Bigdata, MapReduce, Hadoop, Weather, Analytics, Temperature
I. Introduction
Big Data has become one of the buzzwords in IT during the last couple of years. Initially it was shaped
by organizations which had to handle fast growth rates of data like web data, data resulting from scientific or
business simulations or other data sources. Some of those companies’ business models are fundamentally based
on indexing and using this large amount of data. The pressure to handle the growing data amount on the web
e.g. lead Google to develop the Google File System [1] and MapReduce [2].
Bigdata and Internet of things are related areas. The sensor data is a kind of Bigdata. The sensor data
have high velocity. The large number of sensors generates high volume of data. While the term “Internet of
Things” encompasses all kinds of gadgets and devices, many of which are designed to perform a single task
well. The main focus with respect to IoT technology is on devices that share information via a connected
network.
India is an emerging country. Now most of the cities have become smart. Different sensors employed
in smart city can be used to measure weather parameters. Weather forecast department has begun collect and
analysis massive amount of data like temperature. They use different sensor values like temperature, humidity to
predict the rain fall etc. When the number of sensors increases, the data becomes high volume and the sensor
data have high velocity data. There is a need of a scalable analytics tool to process massive amount of data.
The traditional approach of process the data is very slow. Process the sensor data with MapReduce in
Hadoop framework which remove the scalability bottleneck. Hadoop is an open framework used for Bigdata
analytics. Hadoop’s main processing engine is MapReduce, which is currently one of the most popular big-data
processing frameworks available. MapReduce is a framework for executing highly parallelizable and
distributable algorithms across huge data sets using a large number of commodity computers. Using Mapreduce
with Hadoop, the temperature can be analyse without scalability issues. The speed of processing data can
increase rapidly when across multi cluster distributed network.
In this paper a new Temperature Data Anlaytical Engine is proposed, which leverages MapReduce with
Hadoop framework of producing results without scalability bottleneck. This paper is organized as follows: in
section II, the works related to MapReduce is discussed. Section III describes the Temperature Data Analytical
Engine implementation. Section IV describes the analytics of NCDC Data. Finally the section V gives the
conclusion for the work.
II. Related Works
2.1 Hadoop
Hadoop is widely used in big data applications in the industry, e.g., spam filtering, network searching,
click-stream analysis, and social recommendation. In addition, considerable academic research is now based on
Hadoop. Some representative cases are given below. As declared in June 2012, Yahoo runs Hadoop in 42,000
servers at four data centers to support its products and services, e.g.,searching and spam filtering, etc. At
present, the biggest Hadoop cluster has 4,000 nodes, but the number of nodes will be increased to 10,000 with
the release of Hadoop 2.0. In the same month, Facebook announced that their Hadoop cluster can process 100
PB data, which grew by 0.5 PB per day as in November 2012. Some well-known agencies that use Hadoop to
conduct distributed computation are listed in [3].
2. Leveraging Map Reduce with Hadoop for Weather Data Analytics
DOI: 10.9790/0661-17320612 www.iosrjournals.org 7 | Page
In addition, many companies provide Hadoop commercial execution and/or support, including
Cloudera, IBM, MapR, EMC, and Oracle. According to the Gartner Research, Bigdata Analytics is a trending
topic in 2014 [4]. Hadoop is an open framework mostly used for Bigdata Analytics. MapReduce is a
programming paradigm associated with the Hadoop.
Fig.1: Hadoop Environment
Some of its characteristics of Hadoop are following. It is an open-source system developed by Apache
in Java. It is designed to handle very large data sets. It is designed to scale to very large clusters. It is designed to
run on commodity hardware. It offers resilience via data replication. It offers automatic failover in the event of a
crash. It automatically fragments storage over the cluster. It brings processing to the data. Its supports large
volumes of files into the millions. The Hadoop environment as shown in Figure 1
2.2 Map Reduce
Hadoop using HDFS for data storing and using MapReduce to processing that data. HDFS is Hadoop’s
implementation of a distributed filesystem. It is designed to hold a large amount of data, and provide access to
this data to many clients distributed across a network [5]. MapReduce is an excellent model for distributed
computing, introduced by Google in 2004. Each MapReduce job is composed of a certain number of map and
reduce tasks. The MapReduce model for serving multiple jobs consists of a processor sharing queue for the Map
Tasks and a multi-server queue for the Reduce Tasks [6].
To run a MapReduce job, users should furnish a map function, a reduce function, input data, and an
output data location as shown in figure 2. When executed, Hadoop carries out the following steps:
Hadoop breaks the input data into multiple data items by new lines and runs the map function once for
each data item, giving the item as the input for the function. When executed, the map function outputs one or
more key-value pairs.Hadoop collects all the key-value pairs generated from the map function, sorts them by the
key, and groups together the values with the same key.
For each distinct key, Hadoop runs the reduce function once while passing the key and list of values for
that key as input.The reduce function may output one or more key-value pairs, and Hadoop writes them to a file
as the final result.Hadoop allows the user to configure the job, submit it, control its execution, and query the
state. Every job consists of independent tasks, and all the tasks need to have a system slot to run [6]. In Hadoop
all scheduling and allocation decisions are made on a task and node slot level for both the map and reduce
phases[7].
There are three important scheduling issues in MapReduce such as locality, synchronization and
fairness. Locality is defined as the distance between the input data node and task-assigned node.
Synchronization is the process of transferring the intermediate output of the map processes to the reduce process
3. Leveraging Map Reduce with Hadoop for Weather Data Analytics
DOI: 10.9790/0661-17320612 www.iosrjournals.org 8 | Page
Fig.2: MapReduce Framework
As input is also consider as a factor which affects the performance. Fairness finiteness have trade-offs
between the locality and dependency between the map and reduce phases. Due to the important issues and many
more problems in scheduling of MapReduce, the Scheduling is one of the most critical aspects of MapReduce.
There are many algorithms to solve this issue with different techniques and approaches. Some of them get focus
to improvement data locality and some of them implements to provide Synchronization processing and many of
them have been designed to minimizing the total completion time.
E.Dede et.al [8] proposed MARISSA allows for: Iterative application support: The ability for an
application to assess its output and schedule further executions. The ability to run a different executable on each
node of the cluster. The ability to run different input datasets on different nodes. The ability for all or a subset of
the nodes to run duplicates of the same task, allowing the reduce step to decide which result to select.
Thilina Gunarthane et.al [9] introduce AzureMapReduce, a novel MapReduce runtime built using the
Microsoft Azure cloud infrastructure services. AzureMapReduce architecture successfully leverages the high
latency, eventually consistent, yet highly scalable Azure infrastructure services to provide an efficient, on
demand alternative to traditional MapReduce clusters. Further evaluate the use and performance of MapReduce
frameworks, including AzureMapReduce, in cloud environments for scientific applications using sequence
assembly and sequence alignment as use cases.
Vinay Sudakaran et.al [10] introduce analyses of surface air temperature and ocean surface temperature
changes are carried out by several groups, including the Goddard institute of space studies (GISS) [11] and the
National Climatic Data Center based on the data available from a large number of land based weather stations
and ship data, which forms an instrumental source of measurement of global climate change.
Uncertainties in the collected data from both land and ocean, with respect to their quality and
uniformity, force analysis of both the land based station data and the combined data to estimate the global
temperature change. Estimating long term global temperature change has significant advantages over restricting
the temperature analysis to regions with dense station coverage, providing a much better ability to identify
phenomenon that influence the global climate change, such as increasing atmospheric CO2 [12]. This has been
the primary goal of GISS analysis, and an area with potential to make more efficient through use of MapReduce
to improve throughput.
Ekanayake et al. [13] evaluated the Hadoop implementation of MapReduce with High Energy Physics
data analysis. The analyses were conducted on a collection of data files produced by high energy physics
experiments, which is both data and compute intensive. As an outcome of this porting, it was observed that
scientific data analysis that has some form of SPMD (Single- Program Multiple Data) algorithm is more likely
to benefit from MapReduce when compared to others. However, the use of iterative algorithms required by
many scientific applications were seen as a limitation to the existing MapReduce implementations.
III. Implementation
The input weather dataset contain the values of temperature, time, place etc. The input weather dataset
file on the left of Figure 3 is split into chunks of data. The size of these splits is controlled by the InputSplit
method within the FileInputFormat class of the Map Reduce job. The number of splits is influenced by the
HDFS block size, and one mapper job is created for each split data chunk. Each split data chunk that is, “a
record,” is sent to a Mapper process on a Data Node server. In the proposed system, the Map process creates a
series of key-value pairs where the key is the word, for instance, “place” and the value is the temperature. These
key-value pairs are then shuffled into lists by key type. The shuffled lists are input to Reduce tasks, which
4. Leveraging Map Reduce with Hadoop for Weather Data Analytics
DOI: 10.9790/0661-17320612 www.iosrjournals.org 9 | Page
reduce the dataset volume by the values. The Reduce output is then a simple list of averaged key-value pairs.
The Map Reduce framework takes care of all other tasks, like scheduling and resources.
Fig.3: Proposed MapReduce Framework
The proposed MapReduce application is shown in figure 3. The mapper function will find the average
temperature and associated with the key as place. The all values are combined ie reduce to form the final result.
The average temperature, maximum temperature, minimum temperature etc can be calculated in the reduce
phase.
3.1 Driver Operation
The driver is what actually sets up the job, submits it, and waits for completion. The driver is driven
from a configuration file for ease of use for things like being able to specify the input/output directories. It can
also accept Groovy script based mappers and reducers without recompilation.
3.2 Mapper Operation
The actual mapping routine was a simple filter so that only variables that matched certain criteria
would be sent to the reducer. It was initially assumed that the mapper would act like a distributed search
capability and only pull the <key, value> pairs out of the file that matched the criteria. This turned out not to be
the case, and the mapping step took a surprisingly long amount of time.
The default Hadoop input file format reader opens the files and starts reading through the files for the
<key, value> pairs. Once it finds a <key, value> pair, it reads both the key and all the values to pass that along
to the mapper. In the case of the NCDC data, the values can be quite large and consume a large amount of
resources to read and go through all <key, value> pairs within a single file.
Currently, there is no concept within Hadoop to allow for a “lazy” loading of the values. As they are
accessed, the entire <key, value> must be read. Initially, the mapping operator was used to try to filter out the
<key, value> pairs that did not match the criteria. This did not have a significant impact on performance, since
the mapper is not actually the part of Hadoop that is reading the data. The mapper is just accepting data from the
input file format reader.
Subsequently, modified the default input file format reader that Hadoop uses to read the sequenced
files. This routine basically opens a file and performs a simple loop to read every <key, value> within the file.
This routine was modified to include an accept function in order to filter the keys prior to actually reading in the
values. If the filter matched the desired key, then the values are read into memory and passed to the mapper. If
the filter did not match, then the values were skipped. There is some values in the temperature data may be null.
In the mapper the null values were filtered.
Based on the position of sensor values, the mapper function assigns the values to the <Key, Value>
pairs. Place id can be used as Key to get the entire calculation of a place. Alternatively combination of place and
date can be used as Key. The values are temperature in an hour.
3.3 Reduce Operation
With the sequencing and mapping complete, the resulting <key, value> pairs that matched the criteria
to be analyzed were forwarded to the reducer. While the actual simple averaging operation was straightforward
and relatively simple to set up, the reducer turned out to be more complex than expected. Once a <key, value>
5. Leveraging Map Reduce with Hadoop for Weather Data Analytics
DOI: 10.9790/0661-17320612 www.iosrjournals.org 10 | Page
object has been created, a comparator is also needed to order the keys. If the data is to be grouped, a group
comparator is also needed. In addition, a partitioner must be created in order to handle the partitioning of the
data into groups of sorted keys.
With all these components in place, Hadoop takes the <key, value> pairs generated by the mappers and
groups and sorts them as specified as shown in figure 4. By default, Hadoop assumes that all values that share a
key will be sent to the same reducer. Hence, a single operation over a very large data set will only employ one
reducer, i.e., one node. By using partitions, sets of keys to group can be created to pass these grouped keys to
different reducers and parallelize the reduction operation. This may result in multiple output files so that an
additional combination step may be needed to handle the consolidation of all results.
Fig 4 : Inside Mapreduce
3.4 Map Reduce Process
MapReduce is a framework for processing highly distributable problems across huge data sets using a
large number of computers (nodes). In a “map” operation the head node takes the input, partitions it into smaller
sub-problems, and distributes them to data nodes. A data node may do this again in turn, leading to a multi-level
tree structure. The data node processes the smaller problem, and passes the answer back to a reducer node to
perform the reduction operation. In a “reduce” step, the reducer node then collects the answers to all the sub-
problems and combines them in some way to form the output, the answer to the problem it was originally trying
to solve. The map and reduce functions of Map-Reduce are both defined with respect to data structured in <key,
value> pairs.
The following describes the overall general MapReduce process that is executed for the averaging
operation, maximum operation and minimum operation on the NCDC data:
The NCDC files were processed into Hadoop sequence files on the HDFS Head Node. The files were
read from the local NCDC directory, sequenced, and written back out to a local disk.
The resulting sequence files were then ingested into the Hadoop file system with the default replica
factor of three and, initially, the default block size of 64 MB.
The job containing the actual MapReduce operation was submitted to the Head Node to be run.
Along with the JobTracker, the Head Node schedules and runs the job on the cluster. Hadoop
distributes all the mappers across all data nodes that contain the data to be analyzed.
On each data node, the input format reader opens up each sequence file for reading and passes all the
<key,value> pairs to the mapping function.
6. Leveraging Map Reduce with Hadoop for Weather Data Analytics
DOI: 10.9790/0661-17320612 www.iosrjournals.org 11 | Page
The mapper determines if the key matches the criteria of the given query. If so, the mapper saves the
<key, value> pair for delivery back to the reducer. If not, the <key, value> pair is discarded. All keys and values
within a file are read and analyzed by the mapper.
Once the mapper is done, all the <key, value> pairs that match the query are sent back to the reducer.
The reducer then performs the desired averaging operation on the sorted <key, value> pairs to create a final
<key, value> pair result.
This final result is then stored as a sequence file within the HDFS.
IV. Analysing NCDC Data
National Climatic Data Center (NCDC) have provide weather datasets [14]. Daily Global Weather
Measurements 1929-2009 (NCDC, GSOD) dataset is one of the biggest dataset available for weather forecast.
Its total size is around 20 GB. It is available on amazon web services [15].The United States National Climatic
Data Center (NCDC), previously known as the National Weather Records Center (NWRC), in Asheville, North
Carolina is the world's largest active archive of weather data. The Center has more than 150 years of data on
hand with 224 gigabytes of new information added each day. NCDC archives 99 percent of all NOAA data,
including over 320 million paper records; 2.5 million microfiche records; over 1.2 petabytes of digital data
residing in a mass storage environment. NCDC has satellite weather images back to 1960.
Proposed System used the temperature dataset of NCDC in 2014. The records are stored in HDFS.
They are splitted and goes to different mappers. Finally all results goes to reducer. Due to practical limit, the
analysis is executed in Hadoop standalone mode. MapReduce Framework execution as shown in figure 5. The
results shows that adding more number of systems to the network will speed up the entire data processing. That
is the major advantage of MapReduce with Hadoop framework.
Fig 5 : MapReduce Framework Execution