This document discusses big data challenges for e-governance systems in distributed systems. It proposes a map reducing architecture model to provide the basics for building interoperable big data infrastructure for e-governance. The model includes stages for data management, access control, and security. It explains how this can be implemented using distributed structures and a provisioning model. Key challenges addressed include the exponential growth of data from various sources, need for data sharing across organizations, and ensuring security, access control, and data integrity in distributed systems.
Big data a possible game changer for e-governanceSomenath Nag
Big data is an IT trend on the fast track. It is one of the most disruptive IT trends that will change the way business is done today. It will make the organizations a proactive one from the current reactive state through the insights generated from the vast volume of data that is getting generated across different medium. There is a huge potential of using Big Data in e-governance projects for improving efficiency, transparency, and resource utilization of the system.
Connected Healthcare - New PerspectiveSomenath Nag
An IDC source says, the healthcare industry is one of the highest-ranked industries for year-over-year growth and five-year compound annual growth rates with a worldwide average of 7.0% growth for FY12 in software.
There has been a significant investment in the form of health modernization and stimulus funding to leverage technology to cut down rising healthcare costs.
This presentation discusses the concepts of connected healthcare and how it will change the Healthcare Industry.
Taking full advantage of data-driven efficiency makes your operations more precise, predictable and efficient.
Take control of your resources and inventory. Let big data work for you.
Big data a possible game changer for e-governanceSomenath Nag
Big data is an IT trend on the fast track. It is one of the most disruptive IT trends that will change the way business is done today. It will make the organizations a proactive one from the current reactive state through the insights generated from the vast volume of data that is getting generated across different medium. There is a huge potential of using Big Data in e-governance projects for improving efficiency, transparency, and resource utilization of the system.
Connected Healthcare - New PerspectiveSomenath Nag
An IDC source says, the healthcare industry is one of the highest-ranked industries for year-over-year growth and five-year compound annual growth rates with a worldwide average of 7.0% growth for FY12 in software.
There has been a significant investment in the form of health modernization and stimulus funding to leverage technology to cut down rising healthcare costs.
This presentation discusses the concepts of connected healthcare and how it will change the Healthcare Industry.
Taking full advantage of data-driven efficiency makes your operations more precise, predictable and efficient.
Take control of your resources and inventory. Let big data work for you.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
How smart, connected products are transforming companies presentation (edit...Fahmy Amrillah
Information technology is revolutionizing products. Once composed solely of mechanical and electrical parts, products have become complex systems that combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways. These “smart, connected products”—made possible by vast improvements in processing power and device miniaturization and by the network benefits of ubiquitous wireless connectivity—have unleashed a new era of competition.
by Michael E. Porter and James E. Heppelmann
Definition, architecture, general applications, and energy management specified application of expert systems - Class presentation - University of Tabriz 2019
Brace Yourselves Because The Internet of Things Is ComingCherwell Software
Amy DeMartine, Senior Analyst Forrester— Her research focus is on IT service management and IT asset management, including topics such as knowledge management, collaboration opportunities, and customer experience management. Amy helps IT organizations improve their customer experience with their lines of business by analyzing paradigm shifts in the services and support IT provides.
From grid infrastructure analytics to consumer analytics, the true power of data is starting to be realized. Greentech Media Co-Founder and President, Rick Thompson, sets the stage for the days presentations and panels.
Big Data BlackOut: Are Utilities Powering Up Their Data Analytics?Capgemini
Analytics is seeing greater recognition amongst utility executives. Our research showed that 80% of utilities consider big data analytics as a source of new business opportunities and 75% see it as crucial for future success. Big Data indeed offers an exciting opportunity to transform utility operational effectiveness, while at the same time dealing with the historical problem of low customer satisfaction. Take operational efficiency alone. The annual cost of weather-related power outages to the U.S. economy is estimated to be between $18 billion to $33 billion. Organizations can use Big Data analytics to detect operational challenges and prevent outages, substantially reducing costs. Big Data also affords opportunities to utilities for inventing new business models through the data generated by the smart infrastructure.
The analytics opportunity for utilities is clear, but there continues to be a lack of real impetus and value delivery. Only 20% have already implemented big data analytics initiatives. What is putting the brakes on utilities?
In this paper, we highlight the big data opportunities that utilities can leverage and identify the challenges that are currently holding them back. We conclude the paper with concrete recommendations on how to ensure analytics drive business value.
IMMERSIVE TECHNOLOGIES IN 5G-ENABLED APPLICATIONS: SOME TECHNICAL CHALLENGES ...ijcsit
5G next-generation networking paradigm with its envisioned capacity, coverage, and data transfer rates
provide a developmental field for novel applications scenarios. Virtual, Mixed, and Augmented Reality will
play a key role as visualization, interaction, and information delivery platforms. The recent hardware and
software developments in immersive technologies including AR, VR and MR in terms of the commercial
availability of advanced headsets equipped with XR-accelerated processing units and Software
Development Kits (SDKs) are significantly increasing the penetration of such devices for entertainment,
corporate and industrial use. This trend creates next-generation usage models which rise serious technical
challenges within all networking and software architecture levels to support the immersive digital
transformation. The focus of this paper is to detect, discuss and propose system development approaches
and architectures for successful integration of the immersive technologies in the future information and
communication concepts like Tactile Internet and Internet of Skills.
Intelligent Maintenance: Mapping the #IIoT ProcessDan Yarmoluk
A presentation about Industrial IoT, the value chain and real-world use cases; how to create value with IoT at your organization with an emphasis on predictive maintenance (bearing fault detection).
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
Artificial intelligence has been a buzz word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards sustainable development. In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studied and provided in order to handle and extract value and knowledge from these datasets for different industries and business operations. Numerous use cases have shown that AI can ensure an effective supply of information to citizens, users and customers in times of crisis. This paper aims to analyse some of the different methods and scenario which can be applied to AI and big data, as well as the opportunities provided by the application in various business operations and crisis management domains.
A survey of big data and machine learning IJECEIAES
This paper presents a detailed analysis of big data and machine learning (ML) in the electrical power and energy sector. Big data analytics for smart energy operations, applications, impact, measurement and control, and challenges are presented in this paper. Big data and machine learning approaches need to be applied after analyzing the power system problem carefully. Determining the match between the strengths of big data and machine learning for solving the power system problem is of utmost important. They can be of great help to plan and operate the traditional grid/smart grid (SG). The basics of big data and machine learning are described in detailed manner along with their applications in various fields such as electrical power and energy, health care and life sciences, government, telecommunications, web and digital media, retailers, finance, e-commerce and customer service, etc. Finally, the challenges and opportunities of big data and machine learning are presented in this paper.
How smart, connected products are transforming companies presentation (edit...Fahmy Amrillah
Information technology is revolutionizing products. Once composed solely of mechanical and electrical parts, products have become complex systems that combine hardware, sensors, data storage, microprocessors, software, and connectivity in myriad ways. These “smart, connected products”—made possible by vast improvements in processing power and device miniaturization and by the network benefits of ubiquitous wireless connectivity—have unleashed a new era of competition.
by Michael E. Porter and James E. Heppelmann
Definition, architecture, general applications, and energy management specified application of expert systems - Class presentation - University of Tabriz 2019
Brace Yourselves Because The Internet of Things Is ComingCherwell Software
Amy DeMartine, Senior Analyst Forrester— Her research focus is on IT service management and IT asset management, including topics such as knowledge management, collaboration opportunities, and customer experience management. Amy helps IT organizations improve their customer experience with their lines of business by analyzing paradigm shifts in the services and support IT provides.
From grid infrastructure analytics to consumer analytics, the true power of data is starting to be realized. Greentech Media Co-Founder and President, Rick Thompson, sets the stage for the days presentations and panels.
Big Data BlackOut: Are Utilities Powering Up Their Data Analytics?Capgemini
Analytics is seeing greater recognition amongst utility executives. Our research showed that 80% of utilities consider big data analytics as a source of new business opportunities and 75% see it as crucial for future success. Big Data indeed offers an exciting opportunity to transform utility operational effectiveness, while at the same time dealing with the historical problem of low customer satisfaction. Take operational efficiency alone. The annual cost of weather-related power outages to the U.S. economy is estimated to be between $18 billion to $33 billion. Organizations can use Big Data analytics to detect operational challenges and prevent outages, substantially reducing costs. Big Data also affords opportunities to utilities for inventing new business models through the data generated by the smart infrastructure.
The analytics opportunity for utilities is clear, but there continues to be a lack of real impetus and value delivery. Only 20% have already implemented big data analytics initiatives. What is putting the brakes on utilities?
In this paper, we highlight the big data opportunities that utilities can leverage and identify the challenges that are currently holding them back. We conclude the paper with concrete recommendations on how to ensure analytics drive business value.
IMMERSIVE TECHNOLOGIES IN 5G-ENABLED APPLICATIONS: SOME TECHNICAL CHALLENGES ...ijcsit
5G next-generation networking paradigm with its envisioned capacity, coverage, and data transfer rates
provide a developmental field for novel applications scenarios. Virtual, Mixed, and Augmented Reality will
play a key role as visualization, interaction, and information delivery platforms. The recent hardware and
software developments in immersive technologies including AR, VR and MR in terms of the commercial
availability of advanced headsets equipped with XR-accelerated processing units and Software
Development Kits (SDKs) are significantly increasing the penetration of such devices for entertainment,
corporate and industrial use. This trend creates next-generation usage models which rise serious technical
challenges within all networking and software architecture levels to support the immersive digital
transformation. The focus of this paper is to detect, discuss and propose system development approaches
and architectures for successful integration of the immersive technologies in the future information and
communication concepts like Tactile Internet and Internet of Skills.
Intelligent Maintenance: Mapping the #IIoT ProcessDan Yarmoluk
A presentation about Industrial IoT, the value chain and real-world use cases; how to create value with IoT at your organization with an emphasis on predictive maintenance (bearing fault detection).
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
Big Data is the new technology or science to make the well informed decision in
business or any other science discipline with huge volume of data from new sources of
heterogeneous data. . Such new sources include blogs, online media, social network, sensor network,
image data and other forms of data which vary in volume, structure, format and other factors. Big
Data applications are increasingly adopted in all science and engineering domains, including space
science, biomedical sciences and astronomic and deep space studies. The major challenges of big
data mining are in data accessing and processing, data privacy and mining algorithms. This paper
includes the information about what is big data, data mining with big data, the challenges in big data
mining and what are the currently available solutions to meet those challenges.
Content an Insight to Security Paradigm for BigData on Cloud: Current Trend a...IJECEIAES
The sucesssive growth of collabrative applications producing Bigdata on timeline leads new opprutinity to setup commodities on cloud infrastructure. Mnay organizations will have demand of an efficient data storage mechanism and also the efficient data analysis. The Big Data (BD) also faces some of the security issues for the important data or information which is shared or transferred over the cloud. These issues include the tampering, losing control over the data, etc. This survey work offers some of the interesting, important aspects of big data including the high security and privacy issue. In this, the survey of existing research works for the preservation of privacy and security mechanism and also the existing tools for it are stated. The discussions for upcoming tools which are needed to be focused on performance improvement are discussed. With the survey analysis, a research gap is illustrated, and a future research idea is presented
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
Efficient data filtering algorithm for Big Data technology Telecommunication is a concept aimed at effectively filtering desired information for preventive purposes, the challenges posed by unprecedented rise in volume, variety and velocity of information has necessitated the need for exploring various methods Big Data which is simply a data sets that are so large and complex that traditional data processing tools and technologies cannot cope with is been considered. The process of examining such data to uncover hidden patterns in them was evolved, this was achieved by coming up with an Algorithm comprising of various stages like Artificial neural Network, Backtracking Algorithm, Depth First Search, Branch and Bound and dynamic programming and error check. The algorithm developed gave rise to the flowchart, with each line of block representing a sub-algorithm.
BIG DATA IN CLOUD COMPUTING REVIEW AND OPPORTUNITIESijcsit
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review, opportunities and challenges of transforming big data using cloud computing resources.
Big Data is used in decision making process to gain useful insights hidden in the data for business and engineering. At the same time it presents challenges in processing, cloud computing has helped in advancement of big data by providing computational, networking and storage capacity. This paper presents the review, opportunities and challenges of transforming big data using cloud computing resources.
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.
Here's how big data and the Internet of Things work together: a vast network of sensors (IoT) collect a boatload of information (big data) that is then used to improve services and products in various industries, which in turn generate revenue.
Big data is to be implemented in as full way in real-time; it is still in a research. People
need to know what to do with enormous data. Insurance agencies are actively participating for the
analysis of patient's data which could be used to extract some useful information. Analysis is done in
term of discharge summary, drug & pharma, diagnostics details, doctor’s report, medical history,
allergies & insurance policies which are made by the application of map reduce and useful data is
extracted. We are analysing more number of factors like disease Types with its agreeing reasons,
insurance policy details along with sanctioned amount, family grade wise segregation.
Keywords: Big data, Stemming, Map reduce Policy and Hadoop.
Impact of big data congestion in IT: An adaptive knowledgebased Bayesian networkIJECEIAES
Recent progress on real-time systems are growing high in information technology which is showing importance in every single innovative field. Different applications in IT simultaneously produce the enormous measure of information that should be taken care of. In this paper, a novel algorithm of adaptive knowledge-based Bayesian network is proposed to deal with the impact of big data congestion in decision processing. A Bayesian system show is utilized to oversee learning arrangement toward all path for the basic leadership process. Information of Bayesian systems is routinely discharged as an ideal arrangement, where the examination work is to find a development that misuses a measurably inspired score. By and large, available information apparatuses manage this ideal arrangement by methods for normal hunt strategies. As it required enormous measure of information space, along these lines it is a tedious method that ought to be stayed away from. The circumstance ends up unequivocal once huge information include in hunting down ideal arrangement. A calculation is acquainted with achieve quicker preparing of ideal arrangement by constraining the pursuit information space. The proposed algorithm consists of recursive calculation intthe inquiry space. The outcome demonstrates that the ideal component of the proposed algorithm can deal with enormous information by processing time, and a higher level of expectation rates.
The software development process is complete for computer project analysis, and it is important to the evaluation of the random project. These practice guidelines are for those who manage big-data and big-data analytics projects or are responsible for the use of data analytics solutions. They are also intended for business leaders and program leaders that are responsible for developing agency capability in the area of big data and big data analytics .
For those agencies currently not using big data or big data analytics, this document may assist strategic planners, business teams and data analysts to consider the value of big data to the current and future programs.
This document is also of relevance to those in industry, research and academia who can work as partners with government on big data analytics projects.
Technical APS personnel who manage big data and/or do big data analytics are invited to join the Data Analytics Centre of Excellence Community of Practice to share information of technical aspects of big data and big data analytics, including achieving best practice with modeling and related requirements. To join the community, send an email to the Data Analytics Centre of Excellence
Characterizing and Processing of Big Data Using Data Mining TechniquesIJTET Journal
Abstract— Big data is a popular term used to describe the exponential growth and availability of data, both structured and unstructured. It concerns Large-Volume, Complex and growing data sets in both multiple and autonomous sources. Not only in science and engineering big data are now rapidly expanding in all domains like physical, bio logical etc...The main objective of this paper is to characterize the features of big data. Here the HACE theorem, that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective, is used. The aggregation of mining, analysis, information sources, user interest modeling, privacy and security are involved in this model. To explore and extract the large volumes of data and useful information or knowledge respectively is the most fundamental challenge in Big Data. So we should have a tendency to analyze these problems and knowledge revolution.
SECURITY AND PRIVACY AWARE PROGRAMMING MODEL FOR IOT APPLICATIONS IN CLOUD EN...ijccsa
The introduction of Internet of Things (IoT) applications into daily life has raised serious privacy concerns
among consumers, network service providers, device manufacturers, and other parties involved. This paper
gives a high-level overview of the three phases of data collecting, transmission, and storage in IoT systems
as well as current privacy-preserving technologies. The following elements were investigated during these
three phases:(1) Physical and data connection layer security mechanisms(2) Network remedies(3)
Techniques for distributing and storing data. Real-world systems frequently have multiple phases and
incorporate a variety of methods to guarantee privacy. Therefore, for IoT research, design, development,
and operation, having a thorough understanding of all phases and their technologies can be beneficial. In
this Study introduced two independent methodologies namely generic differential privacy (GenDP) and
Cluster-Based Differential privacy ( Cluster-based DP) algorithms for handling metadata as intents and
intent scope to maintain privacy and security of IoT data in cloud environments. With its help, we can
virtual and connect enormous numbers of devices, get a clearer understanding of the IoT architecture, and
store data eternally. However, due of the dynamic nature of the environment, the diversity of devices, the
ad hoc requirements of multiple stakeholders, and hardware or network failures, it is a very challenging
task to create security-, privacy-, safety-, and quality-aware Internet of Things apps. It is becoming more
and more important to improve data privacy and security through appropriate data acquisition. The
proposed approach resulted in reduced loss performance as compared to Support Vector Machine (SVM) ,
Random Forest (RF) .
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
Culturally, the Romans were eclectic, absorbing and adapting elements from the civilizations they encountered, particularly the Greeks. Roman art, literature, and philosophy reflected this synthesis, creating a rich cultural tapestry. Latin, the Roman language, became the lingua franca of the Western world, influencing numerous modern languages.
Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
Ethnobotany and Ethnopharmacology:
Ethnobotany in herbal drug evaluation,
Impact of Ethnobotany in traditional medicine,
New development in herbals,
Bio-prospecting tools for drug discovery,
Role of Ethnopharmacology in drug evaluation,
Reverse Pharmacology.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
The Indian economy is classified into different sectors to simplify the analysis and understanding of economic activities. For Class 10, it's essential to grasp the sectors of the Indian economy, understand their characteristics, and recognize their importance. This guide will provide detailed notes on the Sectors of the Indian Economy Class 10, using specific long-tail keywords to enhance comprehension.
For more information, visit-www.vavaclasses.com
Sectors of the Indian Economy - Class 10 Study Notes pdf
Big data challenges for e governance
1. International Journal of Soft Computing and Engineering (IJSCE)
ISSN: 2231-2307, Volume-3, Issue-5, November 2013
16
Big Data Challenges for E-governess System in
Distributing Systems
M.Suresh, R.Parthasarathy, M.Prabakaran, S.Raja
Abstract—This paper discusses the challenges that are imposed
by E-governess on the modern and future infrastructure. This
paper refers to map reducing algorithm to define the
requirements on data management, access control and
security. This model that includes all the major stages and
reflect specifying data management in modern E-government.
This paper proposes the map reducing architecture model that
provides the basic for building interoperable data. The paper
explain how the implemented using Distributed structures and
provisioning model.
Index Terms —Big Data, Map reducing, Distributed structures.
I. INTRODUCTION
Modern infrastructure allows targeting new large scale
problem-governesses which solution was not possible before
,e.g. Banking, Media, Airlines, Telecom, Entertainment
News, Sports, Astrology, Movie Tickets, Public Works
Monitoring, Electricity Board, Health etc. e-governess
typically produces a huge amount of data that need to be
supported by a new type of e-Infrastructure capable to store,
distribute, process, preserve, and curate these data:
We refer to these new infrastructures as E-governess Data
Infrastructure. In e-governess, the data are complex
multifaceted objects with
the complex internal relations, they are becoming an
infrastructure of their own and need to be supported by
corresponding physical or logical infrastructures to store,
access and manage these data.
The emerging E-GOVERNESS should allow different
groups of researchers to work on the same data sets,
constructing their own distributed approach and
collaborative environments, safely store and retrieved
intermediate results, and later share the discovered results.
New data provide, Third party security and access control
mechanisms and tools will allow researchers to link their e-
governess results with the initial data and intermediate data
to allow future re-use/re-purpose of big data, e.g. with the
improved research technique and tools.
The paper is organized as follows. Section 2 gives an
overview of the main research communities and summarizes
requirements to future E-GOVERNESS. Section 3 discusses
challenges to data engagement in Big Data E-governess,
including Map reduce discussion. Section 4 introduces the
proposed E-governess architecture model that is intended to
answer the future big data challenges and requirements.
Section 5 discusses e-governess implementation using
Distributed technologies.
Manuscript received on November, 2013.
M,Suresh,, Computer Science Engineering- Muthayammal Engineering
College-India.
R.Parthasarathy Computer Science Engineering- Muthayammal
Engineering College-India.
M,suresh,, Computer Science Engineering- Muthayammal Engineering
College-India.
S.Raja,, Computer Science Engineering- Muthayammal Engineering
College-India.
II. GENERAL REQUIREMENTS TO BIG DATA
E-GOVERNESS
Big Data e- governess is becoming a new technology driver
and requires re-thinking a number of infrastructure
components, solutions and processes to address the
following general challenges:
Exponential growth of data volume produced by
different research instruments and/or collected from
sensors
Need to consolidate e-Infrastructure as persistent
research platform to ensure research continuity and
cross-disciplinary collaboration, deliver/offer persistent
services, with adequate governance model. The recent
advancements in the general ICT and big data
technologies facilitate the paradigm change in modern
e-governess.
E-governess that is characterized by the following features:
Automation of all e- governess processes including data
collection, storing, classification, indexing and other
components of the general data duration and
provenance Transformation all processes, events and
products into digital form by means of multi-
dimensional multifaceted measurements, monitoring
and control; digitising existing artefacts and other
content.
Possibility to re-use the initial and published E-
governess with possible data re-purposing for secondary
research
Global data availability and access over network for
cooperative group of public user including wide public
access to e-governess
Advanced security and access control technologies that
ensure secure operation of the complex research
infrastructures and e-governess instruments and allow
creating trusted secure environment for cooperating
groups and individual researchers
Multidimensional data can involved and distributing the
data management efficiently
Monitoring the heterogeneous data might be evaluated
constructing in the structure manner
The data management efficiently could be stored and
retrieved by using the big data much effectively in
corresponding technology
Figure: Big data structures
2. Big Data Challenges for E-governess System in Distributing Systems
17
A) E-governess Requirements in Research communities
Informatics has emerged as the thrust area for the
Government as it can enable the administration to re-
engineer and improve its processes, connect citizens and
build interactions with and within the society by ringing
radical changes in its functioning leading to Simple, Moral,
Accountable, Responsive and Transparent (SMART)
governance. This new way of governance adopted by the
public administration for the delivery of services on the
Internet and Intranet, constitute the concept of Electronic
Governance (E-Governance).
E-Governance to change potentials for sufficient
development:
Automation replacing existing manual processes,
which involve accepting, storing, processing,
outputting or transmitting data/information was more
efficient
Corresponding information supporting current
processes of decision-making and implementation.
Transmission of information by extensive use of
Electronic Forms and Interfaces through use of web
and Internet technology (Paperless Government-On-
Line).
With the availability of information technologies including
Database Management, Data Warehousing and Data
Mining, e-Governance is aimed at converting the
transactional data into business relevant information and
managing this repository. This asset, in turn, is made
accessible to the decision makers by computer based
Decision Support System (DSS) using alphanumeric
information in various application areas.
Figure: E-governess data Management structures
The infrastructure requirements to E-GOVERNESS for
emerging Big Data E-governess:
High Volume of data supported very long time
Large Volumes of generated data at high speed
Multi dimensional data distribution and replication
Support of virtual e-governess communities
Security environment for data storage and retrieval
processing
Data integrity, confidentiality, accountability
Binding the privacy by policy
Distributed computing
Distributed computing is a technique to optimize use of
resources over the networking. It is a way to increase the
capacity or add capabilities dynamically without investing in
new infrastructure, training new personnel, or licensing new
software. It extends Information Technology’s (IT) existing
capabilities. Distributed computing entrusts remote services
with a user's data, software and computation. Moving data
into the Distributed offers great convenience to users since
they don’t have to care about the complexities of direct
hardware management. For a quality service data security is
a necessary thing, security must be imposed on data by
using encryption strategies to achieve secured data storage
and access. The transparent nature of Distributed it is
necessary to anxious about the security issues. But
distributed infrastructure even more reliable and powerful
then personal computing, but wide range of internal,
external threats for data stored on the distributed system.
Since the data are not stored in client area, implementing
security measures cannot be applied directly on e -
governance.
We can use the Distributed computing on every field of e –
Governance.
Government to Citizen (G2C)
Government to Government (G2G)
Government to Business (G2B)
Government to Enterprise (G2E)
Government to NGO (G2N)
III. DATA MANAGEMENT IN BIG DATA AN E-
GOVERNESS
Emergence of computer aided research methods is
transforming the way how research are done and e-
governess data are used. The following types of e-governess
data are defined [4]:
Raw data collected from observation and from
Experiment (according to an initial research model)
Structured data and datasets that went through data
Filtering and processing (supporting some particular
formal model)
Published data that supports one or another e-governess
hypothesis, research result or statement Data linked to
publications to support the wide research consolidation,
integration, and openness.
Volume refers to larger amounts of data being
Generated from a range of sources For example, big data
can include data gathered from the Internet of Things (Iota).
As originally conceived, 3 Iota referred to the data gathered
from a range of devices and sensors networked together,
over the Internet. RFID tags appear on inventory items
capturing transaction data as goods are shipped through the
supply chain. Big data can also refer to the exploding
information available on social Media such as Face book
and Twitter.
Variety refers to using multiple kinds of data to Analyze a
situation or event. On the Iota, millions of devices
generating a constant flow of data results in not only a large
volume of data but different types of data characteristic of
different situations. For example, in addition to WSN, heart
monitors in patients and Global position System all generate
different types of structured data However, devices and
sensors aren’t the only sources of data. Additionally, people
on the Internet generate a highly diverse set of structured
3. International Journal of Soft Computing and Engineering (IJSCE)
ISSN: 2231-2307, Volume-3, Issue-5, November 2013
18
and unstructured data. Web browsing data, captured as a
sequence of clicks, is structured data. However, there’s also
substantial unstructured data. For example, according to
kingdom, 4 in 2012 there were 600 million websites and
more than 125 million blogs, with many including non
structured multidimensional data base..
As a result, there’s an assemblage of data emerging through
the ―Internet of People and Things‖5 and the ―Internet of
Everything.‖
Velocity of data also is on demand rapidly over time for
semi structure data band there’s a need for more frequent
decision making about that data. As the world becomes
more global and developed, and as the Iota builds, there’s an
increasing frequency of data capture and decision making
about those ―things‖ as they move through the world.
Further, the velocity of social media use is increasing. For
example, there are more than 250 million face book per
day.4 Face book lead to decisions about other Face book,
escalating the velocity. Further, unlike classic data
warehouses that generally ―store‖ data, big data is more
dynamic. As decisions are made using big data, those
decisions ultimately can influence the next data that’s
gathered and analyzed, adding another dimension to
velocity.
IV. MAPREDUCE AND HADOOP
Map Reduce has been used by Google, facebook, amazon,
yahoo etc to generate scalable applications. Inspired by the
―map‖ and ―reduce‖ functions in Lisp, Map Reduce breaks
an application into several small portions of the problem,
each of which can be executed across any node in a
computer cluster. The ―map‖ stage gives sub problems to
nodes of computers, and the ―reduce‖ combines the results
from all of those different sub problems. Map Reduce
provides an interface that allows distributed Computing and
parallelization on clusters of computers Map Reduce is used
at Google, facebook,amazon,yahoo etc for a large number of
activities, including data mining and machine learning.
Hadoop (http://hadoop.apache.org), named after a boy’s toy
elephant, is an open source version of Map Reduce.
Apparently,
Facebookhttp://developer.facebook.com/hadoop) is the
largest user (developer and tester) of Hadoop,with more than
550 million users per month and billions of transactions per
day using multiple pet bytes of data.7 As an example of the
use of the Map Reduce approach, consider a Face book front
page that might be broken into multiple categories—such as
advertisements (optimized for the user), must-see videos
(subject to content optimization), news (subject to content
management), and so on—where each category could be
handled by different clusters of computers. Further, within
each of those areas, problems might be further decomposed;
facilitating even faster response.Map Reduce allows the
development of approaches that can handle larger volumes
of data using larger numbers of processors. As a result,
some of the issues caused by increasing volumes and
velocities of data can be addressed using parallel-based
approaches.
Reduced the complexity
More Effectiveness
Robustness
Scalable and Elastics
Wide range of application
Figure: map reducing algorithm
V. DISTRIBUTED VIRTUAL TECHNOLOGY
REQUIREMENTS
Technologies for a distributed solution for a comprehensive
e-governance solution, that meets the Objectives defined in
the earlier sections, will have to Address many diverse
requirements that may be present due to various reasons.
These reasons may be economic, political, technical and
cultural amongst others. The requirements are classified into
two categories, (a) distributed technology requirements that
discusses the core technology requirements and (b)
application requirements, which discusses abstraction of
common code required for multiple applications/
departments
VM technologies’ increasing ubiquity has enabled users to
create customized environments atop physical infrastructure
and has facilitated the emergence of business models such as
Distributed computing. VMs’ use has several benefits:
Server consolidation, which lets system administrators
place the workloads of several underutilized servers in
fewer machines; the ability to create VMs to run
legacy code without interfering with other applications’
APIs;
improved security through the creation of sandboxes for
running applications with questionable reliability; and
Performance isolation, letting providers offer some
guarantees and better quality of service to customers’
applications.
Existing VM-based resource management systems can
manage a cluster of computers within a site, allowing users
to create virtual workspaces5 or clusters.6 Such systems can
bind resources to virtual clusters or workspaces according to
a user’s demand. They commonly provide an interface
through which users can allocate VMs and configure them
with a chosen operating system and software. These
resource managers, or virtual infrastructure engines (VIEs),
let users create customized virtual clusters by using shares
of the physical machines available at the site.
4. Big Data Challenges for E-governess System in Distributing Systems
19
Figure: Distributed Virtual Infrastructures
Distributed Computing Services
Distributed Services Delivery Platform:
This is essentially a workflow engine that executes the
application which - as we described in the previous
section, is ideally composed as business workflow that
orchestrates a number of distributable workflow
elements. This defines the services dial tone in our
reference architecture model.
Distributed Services Creation Platform:
This layer provides the tools that developers will use to
create applications defined as collection of services
which can be composed, decomposed and distributed on
the fly to virtual servers that are automatically created
and managed by the distributed services assurance
platform.
Big Data Security System as Distributed Computing
Advantages
There are many points for database security system as
follows
A. Reduced Costs
Today we know that the Business and IT leaders understand
the need for accurate and timely information when making
decisions that impact their business. And calculate the
accurate figure to our business aspects. To ensure that the
right information is available when it is needed, IT projects
often spend a large portion of their time, resources and
money to create what amounts to individual information
silos, with distinct requirements, configurations, and support
models. Historically, production environments have been
configured for peak load, peak performance and
uninterrupted business continuity.
B. Distributed Computing Framework Increase the Service
Levels
In these fields the service orientation aspects of DBaaS
architectures benefit both IT providers and consumers.
Providers benefit from being able to develop and offer pre-
defined services for their consumers to use – minimizing
vendor, software version and configuration diversity. This
reduced diversity supports business goals of agility,
efficiency and improved quality of service through the
development of standardized processes, common support
mechanisms and focused skills development.
C. Access the Enhanced Information to server to client
Another common practice within organizations stems from
the misperception that information requirements are so
unique that each line of business or region must maintain
separate and dedicated database environments.
D.Distributed Big Data Storage Model
In the Distributed Computing System end user always
store there data in Distributed not in their local system. Then
it’s must that distributed computing Give a effectiveness and
correct Secure Data in distributed Distributed Storage. It’s
possible that an unauthorized person modify the data or
access data.
the distributed case when such inconsistencies
are successfully detected, to find which server the data error
lies in is also of great significance, since it can be the first
step to fast recover the storage errors.
The homomorphic token is introduced. The token
computation function we are considering belongs to a family
of universal hash function. It is also shown how to derive a
challenge response protocol for verifying the storage
correctness as well as identifying misbehaving servers.
Finally, the procedure for file retrieval and error recovery
based on erasure-correcting code is outlined.
VI. FUTURE RESEARCH AND DEVELOPMENT
The future research and development will include further
SDLM definition, e-governess and Map Reduce components
definition and development with focus on infrastructure
components of e-governess. Special attention will be given
to defining the whole cycle of the provisioning e-governess
services on-demand specifically tailored to support instant e-
governess workflows using Distributed platforms. This
research will be also supported by development of the
corresponding Big Data e-governess processes and Map
reduces operation.
REFERENCES
[1] Bansal, V. and J. Bhattacharya. E-governance solution for
government of Maharashtra. Technology whitepaper, India Research
Lab, IBM, 2000.
[2] Batra, V.; J. Bhattacharya; H. Chauhan; A. Gupta; M.Mohania; U.
Sharma. 2002. ―Policy Driven Data.
[3] Administration‖. In POLICY 2002, IEEE 3rd
International Workshop
on Policies for Distributed Systems and Networks.
[4] Gillick et al., 2006, Gillick D., Faria A., DeNero J., and MapReduce:
Distributed Computing for Machine Learning, Berkley, and
December 18, 2006.
[5] K. Shvachko, Hairong Kuang, S. Radia and R Chansler – The Hadoop
Distributed File System. Mass Storage Systems and Technologies
(MSST), 2010 IEEE 26th Symposium 3-7 May 2010.
[6] F. Chang, J. Dean, S. Ghemawat, W. Hsieh, D. Wallach,M. Burrows,
T. Chandra, A. Fikes, and R. Gruber, ―Bigtable: A distributed
structured data storage system,‖ in 7th OSDI,2006, pp. 305–314.
[7] A. Stupar, S. Michel, and R. Schenkel, ―Rankreduce–processing k-
nearest neighbor queries on top of mapreduce,‖ in Proceedings of the
8th Workshop on Large-Scale Distributed Systems for Information
Retrieval, 2010, pp. 13–18.
[8] J. Ekanayake, H. Li, B. Zhang, T. Gunarathne, S. Bae, J. Qiu, and G.
Fox, ―Twister: a runtime for iterative mapreduce,‖ in Proceedings of
the 19th ACM International Symposium on High Performance
Distributed Computing. ACM, 2010, pp. 810–818.
[9] J.S. Chase et al., ―Dynamic Virtual Clusters in a Grid Site Manager,‖
Proc. 12th IEEE Int’l Symp. High Performance Distributed
Computing (HPDC 03), IEEE CS Press, 2003, p. 90.