Managing Big data using Hadoop Map Reduce in Telecom DomainAM Publications
Map reduce is a programming model for analysing and processing large massive data sets. Apache Hadoop is an efficient frame work and the most popular implementation of the map reduce model. Hadoop’s success has motivated research interest and has led to different modifications as well as extensions to framework. In this paper, the challenges faced in different domains like data storage, analytics, online processing and privacy/ security issues while handling big data are explored. Also, the various possible solutions with respect to Telecom domain with Hadoop Map reduce implementation is discussed in this paper.
A Generic Model for Student Data Analytic Web Service (SDAWS)Editor IJCATR
Any university management system accumulates a cartload of data and analytics can be applied on it to gather useful
information to aid the academic decision making process. This paper is a novel attempt to demonstrate the significance of a data
analytic web service in the education domain. This can be integrated with the University Management System or any other application
of the university easily. Analytics as a web service offers much benefits over the traditional analysis methods. The web service can be
hosted on a web server and accessed over the internet or on to the private cloud of the campus. The data from various courses from
different departments can be uploaded and analyzed easily. In this paper we design a web service framework to be used in educational
data mining that provide analysis as a service.
Growth of relational model: Interdependence and complementary to big data IJECEIAES
A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system.
Data is the most valuable entity in today’s world which has to be managed. The huge data
available is to be processed for knowledge and predictions.This huge data in other words big data is
available from various sources like Facebook, twitter and many more resources. The processing time taken
by the frameworks such as Spark ,MapReduce Hierachial Distributed Matrix(HHDM) is more. Hence
Hybrid Hierarchically Distributed Data Matrix(HHHDM) is proposed. This framework is used to develop
Bigdata applications. In existing system developed programs are by default or automatically roughly
defined, jobs are without any functionality being described to be reusable.It also reduces the ability to
optimize data flow of job sequences and pipelines. To overcome the problems of existing framework we
introduce a HHHDM method for developing the big data processing jobs. The proposed method is a Hybrid
method which has the advantages of Hierarchial Distributed Matrix (HHDM) which is functional,
stronglytyped for writing big data applications which are composable. To improve the performance of
executing HHHDM jobs multiple optimizationsis applied to the HHHDM method. The experimental results
show that the improvement of the processing time is 65-70 percent when compared to the existing
technology that is spark.
TREND-BASED NETWORKING DRIVEN BY BIG DATA TELEMETRY FOR SDN AND TRADITIONAL N...ijngnjournal
Organizations face a challenge of accurately analyzing network data and providing automated action
based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and
improve the performance of the network services, but organizations use different network management
tools to understand and visualize the network traffic with limited abilities to dynamically optimize the
network. This research focuses on the development of an intelligent system that leverages big data
telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web
application for effortless management of all subsystems, and the system and application developed in
this research demonstrate the true potential for a scalable system capable of effectively benchmarking
the network to set the expected behavior for comparison and trend analysis. Moreover, this research
provides a proof of concept of how trend analysis results are actioned in both a traditional network and
a software-defined network (SDN) to achieve dynamic, automated load balancing.
A tutorial on secure outsourcing of large scalecomputation for big dataredpel dot com
A tutorial on secure outsourcing of large scalecomputation for big data
for more ieee paper / full abstract / implementation , just visit www.redpel.com
Managing Big data using Hadoop Map Reduce in Telecom DomainAM Publications
Map reduce is a programming model for analysing and processing large massive data sets. Apache Hadoop is an efficient frame work and the most popular implementation of the map reduce model. Hadoop’s success has motivated research interest and has led to different modifications as well as extensions to framework. In this paper, the challenges faced in different domains like data storage, analytics, online processing and privacy/ security issues while handling big data are explored. Also, the various possible solutions with respect to Telecom domain with Hadoop Map reduce implementation is discussed in this paper.
A Generic Model for Student Data Analytic Web Service (SDAWS)Editor IJCATR
Any university management system accumulates a cartload of data and analytics can be applied on it to gather useful
information to aid the academic decision making process. This paper is a novel attempt to demonstrate the significance of a data
analytic web service in the education domain. This can be integrated with the University Management System or any other application
of the university easily. Analytics as a web service offers much benefits over the traditional analysis methods. The web service can be
hosted on a web server and accessed over the internet or on to the private cloud of the campus. The data from various courses from
different departments can be uploaded and analyzed easily. In this paper we design a web service framework to be used in educational
data mining that provide analysis as a service.
Growth of relational model: Interdependence and complementary to big data IJECEIAES
A database management system is a constant application of science that provides a platform for the creation, movement, and use of voluminous data. The area has witnessed a series of developments and technological advancements from its conventional structured database to the recent buzzword, bigdata. This paper aims to provide a complete model of a relational database that is still being widely used because of its well known ACID properties namely, atomicity, consistency, integrity and durability. Specifically, the objective of this paper is to highlight the adoption of relational model approaches by bigdata techniques. Towards addressing the reason for this in corporation, this paper qualitatively studied the advancements done over a while on the relational data model. First, the variations in the data storage layout are illustrated based on the needs of the application. Second, quick data retrieval techniques like indexing, query processing and concurrency control methods are revealed. The paper provides vital insights to appraise the efficiency of the structured database in the unstructured environment, particularly when both consistency and scalability become an issue in the working of the hybrid transactional and analytical database management system.
Data is the most valuable entity in today’s world which has to be managed. The huge data
available is to be processed for knowledge and predictions.This huge data in other words big data is
available from various sources like Facebook, twitter and many more resources. The processing time taken
by the frameworks such as Spark ,MapReduce Hierachial Distributed Matrix(HHDM) is more. Hence
Hybrid Hierarchically Distributed Data Matrix(HHHDM) is proposed. This framework is used to develop
Bigdata applications. In existing system developed programs are by default or automatically roughly
defined, jobs are without any functionality being described to be reusable.It also reduces the ability to
optimize data flow of job sequences and pipelines. To overcome the problems of existing framework we
introduce a HHHDM method for developing the big data processing jobs. The proposed method is a Hybrid
method which has the advantages of Hierarchial Distributed Matrix (HHDM) which is functional,
stronglytyped for writing big data applications which are composable. To improve the performance of
executing HHHDM jobs multiple optimizationsis applied to the HHHDM method. The experimental results
show that the improvement of the processing time is 65-70 percent when compared to the existing
technology that is spark.
TREND-BASED NETWORKING DRIVEN BY BIG DATA TELEMETRY FOR SDN AND TRADITIONAL N...ijngnjournal
Organizations face a challenge of accurately analyzing network data and providing automated action
based on the observed trend. This trend-based analytics is beneficial to minimize the downtime and
improve the performance of the network services, but organizations use different network management
tools to understand and visualize the network traffic with limited abilities to dynamically optimize the
network. This research focuses on the development of an intelligent system that leverages big data
telemetry analysis in Platform for Network Data Analytics (PNDA) to enable comprehensive trendbased networking decisions. The results include a graphical user interface (GUI) done via a web
application for effortless management of all subsystems, and the system and application developed in
this research demonstrate the true potential for a scalable system capable of effectively benchmarking
the network to set the expected behavior for comparison and trend analysis. Moreover, this research
provides a proof of concept of how trend analysis results are actioned in both a traditional network and
a software-defined network (SDN) to achieve dynamic, automated load balancing.
A tutorial on secure outsourcing of large scalecomputation for big dataredpel dot com
A tutorial on secure outsourcing of large scalecomputation for big data
for more ieee paper / full abstract / implementation , just visit www.redpel.com
[This is work presented at SIGMOD'13.]
The use of large-scale data mining and machine learning has proliferated through the adoption of technologies such as Hadoop, with its simple programming semantics and rich and active ecosystem. This paper presents LinkedIn's Hadoop-based analytics stack, which allows data scientists and machine learning researchers to extract insights and build product features from massive amounts of data. In particular, we present our solutions to the "last mile" issues in providing a rich developer ecosystem. This includes easy ingress from and egress to online systems, and managing workflows as production processes. A key characteristic of our solution is that these distributed system concerns are completely abstracted away from researchers. For example, deploying data back into the online system is simply a 1-line Pig command that a data scientist can add to the end of their script. We also present case studies on how this ecosystem is used to solve problems ranging from recommendations to news feed updates to email digesting to descriptive analytical dashboards for our members.
This poster represents 4 months of work on the MSc project while doing a double degree at Heriot-Watt University.
£50 have been given for rewarding this work.
Data Exchange Design with SDMX Format for Interoperability Statistical DataNooria Sukmaningtyas
Today’s concept of Open Government Data (OGD) for openness, transparency and ease of
access of data owned by government agencies becomes increasingly important. This initiative emerges
from the demand of data usersforthe data belongs to the government agencies. The data services
providing an easy access, cheap, fast, and interoperability are needed by the users and becomes
important indicator performance for respective government agencies. Statistical Data and Metadata
Exchange (SDMX) is a new standard format in the data dissemination activities particularly in the
exchange of statistical data and metadata via Internet. In this respect SDMX support the implementation of
OGD project. This paper is on the technical design, development and implementation of data and
metadata exchange service of statistical data using SDMX format to support interoperability data through
web services. Three results are proposed: (i) framework for standardization of structure of statistical
publications data model with SDMX; (ii) design architecture of data sharing model; and (iii) web service
implementation of data and metadata exchange service using Service Oriented Analysis and Design
(SOAD) method. Implementation at Statistics Indonesia (BPS) is chosen as a case study to prove the
design concept. It is shown through quantitative assessment and black box testing that the design
achieves its objective.
An Algorithm to synchronize the local database with cloud DatabaseAM Publications
Since the cloud computing [1] platform is widely accepted by the industry, variety of applications are designed targeting to a cloud platform. Database as a Service (DaaS) is one of the powerful platform of cloud computing. There are many research issues in DaaS platform and one among them is the data synchronization issue. There are many approaches suggested in the literature to synchronise a local database by being in cloud environment. Unfortunately, very few work only available in the literature to synchronise a cloud database by being in the local database. The aim of this paper is to provide an algorithm to solve the problem of data synchronization from local database to cloud database.
An efficient resource sharing technique for multi-tenant databases IJECEIAES
Multi-tenancy is a key component of Software as a Service (SaaS) paradigm. Multi-tenant software has gained a lot of attention in academics, research and business arena. They provide scalability and economic benefits for both cloud service providers and tenants by sharing same resources and infrastructure in isolation of shared databases, network and computing resources with Service level agreement (SLA) compliances. In a multitenant scenario, active tenants compete for resources in order to access the database. If one tenant blocks up the resources, the performance of all the other tenants may be restricted and a fair sharing of the resources may be compromised. The performance of tenants must not be affected by resource-intensive activities and volatile workloads of other tenants. Moreover, the prime goal of providers is to accomplish low cost of operation, satisfying specific schemas/SLAs of each tenant. Consequently, there is a need to design and develop effective and dynamic resource sharing algorithms which can handle above mentioned issues. This work presents a model referred as MultiTenant Dynamic Resource Scheduling Model (MTDRSM) embracing a query classification and worker sorting technique enabling efficient and dynamic resource sharing among tenants. The experiments show significant performance improvement over existing model.
A CASE STUDY OF INNOVATION OF AN INFORMATION COMMUNICATION SYSTEM AND UPGRADE...ijaia
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).
Building an enterprise Natural Language Search Engine with ElasticSearch and ...Debmalya Biswas
Presented at Berlin Buzzwords 2019
https://berlinbuzzwords.de/19/session/building-enterprise-natural-language-search-engine-elasticsearch-and-facebooks-drqa
EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Syngenta's Predictive Analytics Platform for Seeds R&DMichael Swanson
Syngenta’s Predictive Analytics Platform for Seeds R&D
A journey from on-premise Hadoop to AWS’s Big Data Serverless Analytics stack
Amazon Athena and AWS Glue Summit – Boston
October 9, 2018
Michael Swanson
Domain Architect - Insights and Decisions, Syngenta
A Case Study of Innovation of an Information Communication System and Upgrade...gerogepatton
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication
system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is
gained by means of different tools that are able to provide data and information having different formats
and structures into an unique bus system connected to a Big Data. The initial part of the research is
focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the
proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz
Information Miner) Gradient Boosted Trees workflow processing data of the communication system which
travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the
new KB collected into a Cassandra big data system could be processed through the ESB by predictive
algorithms solving possible conflicts between hardware and software. The conflicts are due to the
integration of different database technologies and data structures. In order to check the outputs of the
Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been
tested. The test has been performed on a prototype network system modeling a part of the whole
communication system. The paper shows how to validate industrial research by following a complete
design and development of a whole communication system network improving business intelligence (BI).
[This is work presented at SIGMOD'13.]
The use of large-scale data mining and machine learning has proliferated through the adoption of technologies such as Hadoop, with its simple programming semantics and rich and active ecosystem. This paper presents LinkedIn's Hadoop-based analytics stack, which allows data scientists and machine learning researchers to extract insights and build product features from massive amounts of data. In particular, we present our solutions to the "last mile" issues in providing a rich developer ecosystem. This includes easy ingress from and egress to online systems, and managing workflows as production processes. A key characteristic of our solution is that these distributed system concerns are completely abstracted away from researchers. For example, deploying data back into the online system is simply a 1-line Pig command that a data scientist can add to the end of their script. We also present case studies on how this ecosystem is used to solve problems ranging from recommendations to news feed updates to email digesting to descriptive analytical dashboards for our members.
This poster represents 4 months of work on the MSc project while doing a double degree at Heriot-Watt University.
£50 have been given for rewarding this work.
Data Exchange Design with SDMX Format for Interoperability Statistical DataNooria Sukmaningtyas
Today’s concept of Open Government Data (OGD) for openness, transparency and ease of
access of data owned by government agencies becomes increasingly important. This initiative emerges
from the demand of data usersforthe data belongs to the government agencies. The data services
providing an easy access, cheap, fast, and interoperability are needed by the users and becomes
important indicator performance for respective government agencies. Statistical Data and Metadata
Exchange (SDMX) is a new standard format in the data dissemination activities particularly in the
exchange of statistical data and metadata via Internet. In this respect SDMX support the implementation of
OGD project. This paper is on the technical design, development and implementation of data and
metadata exchange service of statistical data using SDMX format to support interoperability data through
web services. Three results are proposed: (i) framework for standardization of structure of statistical
publications data model with SDMX; (ii) design architecture of data sharing model; and (iii) web service
implementation of data and metadata exchange service using Service Oriented Analysis and Design
(SOAD) method. Implementation at Statistics Indonesia (BPS) is chosen as a case study to prove the
design concept. It is shown through quantitative assessment and black box testing that the design
achieves its objective.
An Algorithm to synchronize the local database with cloud DatabaseAM Publications
Since the cloud computing [1] platform is widely accepted by the industry, variety of applications are designed targeting to a cloud platform. Database as a Service (DaaS) is one of the powerful platform of cloud computing. There are many research issues in DaaS platform and one among them is the data synchronization issue. There are many approaches suggested in the literature to synchronise a local database by being in cloud environment. Unfortunately, very few work only available in the literature to synchronise a cloud database by being in the local database. The aim of this paper is to provide an algorithm to solve the problem of data synchronization from local database to cloud database.
An efficient resource sharing technique for multi-tenant databases IJECEIAES
Multi-tenancy is a key component of Software as a Service (SaaS) paradigm. Multi-tenant software has gained a lot of attention in academics, research and business arena. They provide scalability and economic benefits for both cloud service providers and tenants by sharing same resources and infrastructure in isolation of shared databases, network and computing resources with Service level agreement (SLA) compliances. In a multitenant scenario, active tenants compete for resources in order to access the database. If one tenant blocks up the resources, the performance of all the other tenants may be restricted and a fair sharing of the resources may be compromised. The performance of tenants must not be affected by resource-intensive activities and volatile workloads of other tenants. Moreover, the prime goal of providers is to accomplish low cost of operation, satisfying specific schemas/SLAs of each tenant. Consequently, there is a need to design and develop effective and dynamic resource sharing algorithms which can handle above mentioned issues. This work presents a model referred as MultiTenant Dynamic Resource Scheduling Model (MTDRSM) embracing a query classification and worker sorting technique enabling efficient and dynamic resource sharing among tenants. The experiments show significant performance improvement over existing model.
A CASE STUDY OF INNOVATION OF AN INFORMATION COMMUNICATION SYSTEM AND UPGRADE...ijaia
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is gained by means of different tools that are able to provide data and information having different formats and structures into an unique bus system connected to a Big Data. The initial part of the research is focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz Information Miner) Gradient Boosted Trees workflow processing data of the communication system which travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the new KB collected into a Cassandra big data system could be processed through the ESB by predictive algorithms solving possible conflicts between hardware and software. The conflicts are due to the integration of different database technologies and data structures. In order to check the outputs of the Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been tested. The test has been performed on a prototype network system modeling a part of the whole communication system. The paper shows how to validate industrial research by following a complete design and development of a whole communication system network improving business intelligence (BI).
Building an enterprise Natural Language Search Engine with ElasticSearch and ...Debmalya Biswas
Presented at Berlin Buzzwords 2019
https://berlinbuzzwords.de/19/session/building-enterprise-natural-language-search-engine-elasticsearch-and-facebooks-drqa
EMR: A Scalable Graph-based Ranking Model for Content-based Image Retrieval1crore projects
IEEE PROJECTS 2015
1 crore projects is a leading Guide for ieee Projects and real time projects Works Provider.
It has been provided Lot of Guidance for Thousands of Students & made them more beneficial in all Technology Training.
Dot Net
DOTNET Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
Java Project Domain list 2015
1. IEEE based on datamining and knowledge engineering
2. IEEE based on mobile computing
3. IEEE based on networking
4. IEEE based on Image processing
5. IEEE based on Multimedia
6. IEEE based on Network security
7. IEEE based on parallel and distributed systems
ECE IEEE Projects 2015
1. Matlab project
2. Ns2 project
3. Embedded project
4. Robotics project
Eligibility
Final Year students of
1. BSc (C.S)
2. BCA/B.E(C.S)
3. B.Tech IT
4. BE (C.S)
5. MSc (C.S)
6. MSc (IT)
7. MCA
8. MS (IT)
9. ME(ALL)
10. BE(ECE)(EEE)(E&I)
TECHNOLOGY USED AND FOR TRAINING IN
1. DOT NET
2. C sharp
3. ASP
4. VB
5. SQL SERVER
6. JAVA
7. J2EE
8. STRINGS
9. ORACLE
10. VB dotNET
11. EMBEDDED
12. MAT LAB
13. LAB VIEW
14. Multi Sim
CONTACT US
1 CRORE PROJECTS
Door No: 214/215,2nd Floor,
No. 172, Raahat Plaza, (Shopping Mall) ,Arcot Road, Vadapalani, Chennai,
Tamin Nadu, INDIA - 600 026
Email id: 1croreprojects@gmail.com
website:1croreprojects.com
Phone : +91 97518 00789 / +91 72999 51536
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Syngenta's Predictive Analytics Platform for Seeds R&DMichael Swanson
Syngenta’s Predictive Analytics Platform for Seeds R&D
A journey from on-premise Hadoop to AWS’s Big Data Serverless Analytics stack
Amazon Athena and AWS Glue Summit – Boston
October 9, 2018
Michael Swanson
Domain Architect - Insights and Decisions, Syngenta
A Case Study of Innovation of an Information Communication System and Upgrade...gerogepatton
In this paper, a case study is analyzed. This case study is about an upgrade of an industry communication
system developed by following Frascati research guidelines. The knowledge Base (KB) of the industry is
gained by means of different tools that are able to provide data and information having different formats
and structures into an unique bus system connected to a Big Data. The initial part of the research is
focused on the implementation of strategic tools, which can able to upgrade the KB. The second part of the
proposed study is related to the implementation of innovative algorithms based on a KNIME (Konstanz
Information Miner) Gradient Boosted Trees workflow processing data of the communication system which
travel into an Enterprise Service Bus (ESB) infrastructure. The goal of the paper is to prove that all the
new KB collected into a Cassandra big data system could be processed through the ESB by predictive
algorithms solving possible conflicts between hardware and software. The conflicts are due to the
integration of different database technologies and data structures. In order to check the outputs of the
Gradient Boosted Trees algorithm an experimental dataset suitable for machine learning testing has been
tested. The test has been performed on a prototype network system modeling a part of the whole
communication system. The paper shows how to validate industrial research by following a complete
design and development of a whole communication system network improving business intelligence (BI).
DESIGN PATTERNS IN THE WORKFLOW IMPLEMENTATION OF MARINE RESEARCH GENERAL INF...AM Publications
This paper proposes the use of design patterns in a marine research general information platform. The development of the platform refers to a design of complicated system architecture. Creation and execution of the research workflow nodes and designing of visualization library suited for marine users play an important role in the whole software architecture. This paper studies the requirements characteristic in marine research fields and has implemented a series of framework to solve these problems based on object-oriented and design patterns techniques. These frameworks make clear the relationship in all directions between modules and layers of software, which communicate through unified abstract interface and reduce the coupling between modules and layers. The building of these frameworks is importantly significant in advancing the reusability of software and strengthening extensibility and maintainability of the system.
Running head NETWORK DIAGRAM AND WORKFLOW1NETWORK DIAGRAM AN.docxjeanettehully
Running head: NETWORK DIAGRAM AND WORKFLOW 1
NETWORK DIAGRAM AND WORKFLOW 2
Network and Workflow for Data Analytics Company
Name: sunil patel
Running head: Network diagram and work Flow 1
MIT 681: capstone Assignment 4
Network and Workflow for Data Analytics Company
Every company requires a workflow that is consistent with the network in order to achieve productivity and value addition. There are different network setups for different companies based on the services and products offered. The main idea behind formulation of a network is to map the workflow of the company to different resources available. The network design must guarantee effective connection among the staff members in a company. The relationship between network diagram and company’s workflow is based on the idea to maintain communication in the company among the existing staff members. The achievement of the company’s objective is based on the nature and performance of the network as well as the workflow of the respective company (Kumar & Kirthika, 2017). This paper outlines the design and architecture of a good practice network and workflow for a big data company.
The implementation of the network for a data analytics company requires planning based on the requirements and user roles. Connecting the staff is the main idea in a network architecture. The requirements for a data analytics company shall need data analysts for decision making and identification of opportunities in the market sector (Kumar & Kirthika, 2017). A good network workflow shall guarantee improvement in service offering for the company in context. A highly skilled team shall be required for the achievement of the objectives with the use of the network workflow.
The composition of a data analytics company is made of the leader director of analytics, data science manager and analytics manager. The director is in charge of management of the analytics and data science manager. He/she shall oversee the activities of the lower position leaders. The exploratory and description of the analyses shall be conducted by the data engineers in the lower departments. All data scientists shall be controlled by the data science manager in their respective roles. The company shall also have professionals in different fields and qualifications in other areas who shall collaborate in the achievement of objectives and goals of the institution. The skill set shall include; data architects, statisticians, software engineers, business analysts and data visualizers.
The major component of the network in the data analytics company is the software applied for data analysis. The software is usually created by the software engineers and comprises of components that are able to collect and process data (Cao, Chen, Zhao & Li, 2009). The role of the software engineers in this case is to guide the company with their skills and expertise for the best technology and implementation procedures. The advice and edu ...
THE DEVELOPMENT AND STUDY OF THE METHODS AND ALGORITHMS FOR THE CLASSIFICATIO...IJCNCJournal
This paper represents the results of the research, which have allowed us to develop a hybrid
approach to the processing, classification, and control of traffic routes. The approach enables to
identify traffic flows in the virtual data center in real-time systems. Our solution is based on the
methods of data mining and machine learning, which enable to classify traffic more accurately
according to more criteria and parameters. As a practical result, the paper represents the
algorithmic solution of the classification of the traffic flows of cloud applications and services
embodied in a module for the controller of the software-defined network. This solution enables to
increase the efficiency of handling user requests to cloud applications and reduce the response
time, which has a positive effect on the quality of service in the network of the virtual data center
Service Level Comparison for Online Shopping using Data MiningIIRindia
The term knowledge discovery in databases (KDD) is the analysis step of data mining. The data mining goal is to extract the knowledge and patterns from large data sets, not the data extraction itself. Big-Data Computing is a critical challenge for the ICT industry. Engineers and researchers are dealing with the cloud computing paradigm of petabyte data sets. Thus the demand for building a service stack to distribute, manage and process massive data sets has risen drastically. We investigate the problem for a single source node to broadcast the big chunk of data sets to a set of nodes to minimize the maximum completion time. These nodes may locate in the same datacenter or across geo-distributed data centers. The Big-data broadcasting problem is modeled into a LockStep Broadcast Tree (LSBT) problem. And the main idea of the LSBT is defining a basic unit of upload bandwidth, r, a node with capacity c broadcasts data to a set of [c=r] children at the rate r. Note that r is a parameter to be optimized as part of the LSBT problem. The broadcast data are further divided into m chunks. In a pipeline manner, these m chunks can then be broadcast down the LSBT. In a homogeneous network environment in which each node has the same upload capacity c, the optimal uplink rate r, of LSBT is either c=2 or 3, whichever gives the smaller maximum completion time. For heterogeneous environments, an O(nlog2n) algorithm is presented to select an optimal uplink rate r, and to construct an optimal LSBT. With lower computational complexity and low maximum completion time, the numerical results shows better performance.The methodology includes Various Web applications Building and Broadcasting followed by the Gateway Application and Batch Processing over the TSV Data after which the Web Crawling for Resources and MapReduce process takes place and finally Picking Products from Recommendations and Purchasing it.
Effective performance engineering is a critical factor in delivering meaningful results. The implementation must be built into every aspect of the business, from IT and business management to internal and external customers and all other stakeholders. Convetit brought together ten experts in the field of performance engineering to delve into the trends and drivers that are defining the space. This Foresights discussion will directly influence Business and Technology Leaders that are looking to stay ahead of the challenges they face with delivering high performing systems to their end users, today and in the next 2-5 years.
A distributed system can be viewed as an environment in which, number of computers/nodes are connected and resources are shared among these computers/nodes. But unfortunately, distributed systems often face the problem of traffic, which can degrade the performance of the system. Traffic management is used to improve scalability and overall system throughput in distributed systems using Software Defined Network (SDN) based systems. Traffic management improves system performance by dividing the work traffic effectively among the participating computers/nodes. Many algorithms were proposed for traffic management and their performance is measured based on certain parameters such as response time, resource utilization, and fault tolerance. Traffic management algorithms are broadly classified into two categories- scheduling and machine learning traffic management. This work presents the study of performance analysis of traffic management algorithms. This analysis can further help in the design of new algorithms. However, when multiple servers are assigned to compile the mysterious code, different kinds of techniques are used. One common example is traffic management. The processes are managed based on power efficiency, networking bandwidth, Processor speed. The desired output will again send back to the developer. If multiple programs have to be compiled then appropriate technique such as scheduling algorithm is used. So the compilation process becomes faster and also the other process can get a chance to compile. SDN based clustering algorithm based on Simulated Annealing whose main goal is to increase network lifetime while maintaining adequate sensing coverage in scenarios where sensor nodes produce uniform or non-uniform data traffic.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
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.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.