This document discusses challenges and outlooks related to big data. It begins with an introduction describing how big data is being collected and analyzed in various fields such as science, education, healthcare, urban planning, and more. It then outlines the key phases in big data analysis: data acquisition and recording, information extraction and cleaning, data integration and representation, query processing and analysis, and result interpretation. For each phase, it discusses challenges and how existing techniques can be applied or extended to address big data issues. Some of the major challenges discussed are data scale, heterogeneity, lack of structure, privacy, timeliness, provenance, and visualization across the entire big data analysis pipeline.
A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
A gigantic archive of terabytes of information is created every day from current data frameworks and computerized advances, for example, Internet of Things and distributed computing. Examination of these gigantic information requires a ton of endeavors at various levels to extricate information for dynamic. Hence, huge information examination is an ebb and flow region of innovative work. The essential goal of this paper is to investigate the likely effect of huge information challenges, and different instruments related with it. Accordingly, this article gives a stage to investigate enormous information at various stages. Moreover, it opens another skyline for analysts to build up the arrangement, in light of the difficulties and open exploration issues.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today
communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
Big data is a term that describes a large or complex
data volume. That data volume can be processes using traditional
data processing software or techniques that are insufficient to deal
with them. But big data is often noisy, heterogeneous, irrelevant
and untrustworthy. As the speed of information growth exceeds
Moore’s Law at the beginning of this new century, excessive data
is making great troubles to human beings. However this data with
special attributes can’t be managed and processed by the current
traditional software system, which become a real problem. In this
paper was discussed some big data challenges and problems that
are faced by organizations. These challenges may relate
heterogeneity, scale, timelines, privacy and human collaboration.
Survey method was used as a theoretical solution framework.
Survey method consists of a questionnaires report. Questionnaires
report consists of all challenges and problems faced by
organizations. After knowing the problem and challenges of
organizations, a solution was given to organization to solve big
data challenges.
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.
A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analyt...IJECEIAES
With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.
The Internet of Things, or the IoT is a vision for a ubiquitous society wherein people and “Things” are connected in an immersively networked computing environment, with the connected “Things” providing utility to people/enterprises and their digital shadows, through intelligent social and commercial services. However, translating this idea to a conceivable reality is a work in progress for close to two decades; mostly, due to assumptions favoured more towards a “Things”-centric rather than a “Human”-centric approach coupled with the evolution/deployment ecosystem of IoT technologies.
Estimates on the spread and economic impact of IoT over the next few years are in the neighborhood of 50 billion or more connected “Things” with a market exceeding $350 billion through smarter cities and infrastructure, intelligent appliances, and healthier lifestyles. While many of these potential benefits from IoT are real and achievable, the road to accomplish these may need an rethink.
In the last few years, there has been a realization that an effective architecture for IoT (particularly, for emerging nations with limited technology penetration at the national scale) that is both affordable and sustainable should be based on tangible technology advances in the present, ubiquitous capabilities of the present/future, and practical application scenarios of social and entrepreneurial value. Hence, there is a revitalized interest to rethink the above assumptions, and this exercise has led to a more plausible set of scenarios wherein humans along with data, communication and devices play key roles.
In this presentation, an attempt is made to disaggregate these core problems; and offer a trajectory with a set of design paradigms for a renewed IoT ecosystem.
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.
Hadoop and Big Data Readiness in Africa: A Case of Tanzaniaijsrd.com
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
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.
High Availability Architecture for Legacy Stuff - a 10.000 feet overviewMarco Amado
An overview of the tools and tricks you could use to turn a monolithic big pile of... Apache, PHP, and MariaDB into an awesome high-availability, load balanced, shiny new pile of... Apache, PHP, and MariaDB. Zero, or almost zero changes to the codebase.
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.
A Novel Integrated Framework to Ensure Better Data Quality in Big Data Analyt...IJECEIAES
With advent of Big Data Analytics, the healthcare system is increasingly adopting the analytical services that is ultimately found to generate massive load of highly unstructured data. We reviewed the existing system to find that there are lesser number of solutions towards addressing the problems of data variety, data uncertainty, and data speed. It is important that an errorfree data should arrive in analytics. Existing system offers single-hand solution towards single platform. Therefore, we introduced an integrated framework that has the capability to address all these three problems in one execution time. Considering the synthetic big data of healthcare, we carried out the investigation to find that our proposed system using deep learning architecture offers better optimization of computational resources. The study outcome is found to offer comparatively better response time and higher accuracy rate as compared to existing optimization technqiues that is found and practiced widely in literature.
The Internet of Things, or the IoT is a vision for a ubiquitous society wherein people and “Things” are connected in an immersively networked computing environment, with the connected “Things” providing utility to people/enterprises and their digital shadows, through intelligent social and commercial services. However, translating this idea to a conceivable reality is a work in progress for close to two decades; mostly, due to assumptions favoured more towards a “Things”-centric rather than a “Human”-centric approach coupled with the evolution/deployment ecosystem of IoT technologies.
Estimates on the spread and economic impact of IoT over the next few years are in the neighborhood of 50 billion or more connected “Things” with a market exceeding $350 billion through smarter cities and infrastructure, intelligent appliances, and healthier lifestyles. While many of these potential benefits from IoT are real and achievable, the road to accomplish these may need an rethink.
In the last few years, there has been a realization that an effective architecture for IoT (particularly, for emerging nations with limited technology penetration at the national scale) that is both affordable and sustainable should be based on tangible technology advances in the present, ubiquitous capabilities of the present/future, and practical application scenarios of social and entrepreneurial value. Hence, there is a revitalized interest to rethink the above assumptions, and this exercise has led to a more plausible set of scenarios wherein humans along with data, communication and devices play key roles.
In this presentation, an attempt is made to disaggregate these core problems; and offer a trajectory with a set of design paradigms for a renewed IoT ecosystem.
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.
Hadoop and Big Data Readiness in Africa: A Case of Tanzaniaijsrd.com
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
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.
High Availability Architecture for Legacy Stuff - a 10.000 feet overviewMarco Amado
An overview of the tools and tricks you could use to turn a monolithic big pile of... Apache, PHP, and MariaDB into an awesome high-availability, load balanced, shiny new pile of... Apache, PHP, and MariaDB. Zero, or almost zero changes to the codebase.
How OpenTable uses Big Data to impact growth by Raman MaryaData Con LA
Abstract:- We have created variety of Analytics Solutions combining data from our Data Lake with Traditional DW. Data API's which are fed into product for improving conversions, Churn prediction alogrithm to help account managers focus on high risk customers, using analytics as an edge to empower sales team to win prospective customers.
Everything generates logs. Applications, infrastructure, security ... everything. Keeping track of the flood of log data is a big challenge, yet critical to your ability to understand your systems and troubleshoot (or prevent) issues. In this session, we will use both Amazon CloudWatch and application logs to show you how to build an end-to-end log analytics solution. First, we cover how to configure an Amazon Elaticsearch Service domain and ingest data into it using Amazon Kinesis Firehose, demonstrating how easy it is to transform data with Firehose. We look at best practices for choosing instance types, storage options, shard counts, and index rotations based on the throughput of incoming data and configure a secure analytics environment. We demonstrate how to set up a Kibana dashboard and build custom dashboard widgets. Finally, we dive deep into the Elasticsearch query DSL and review approaches for generating custom, ad-hoc reports.
Big Data Commercialization and associated IoT Platform Implications by Ramnik...Data Con LA
Abstract:- IoT Market overview and Verizon’s focus on specific IoT verticals (AgTech, Energy, Share, etc.), Criteria for evaluation of IoT data analytics opportunities, Platform considerations for big data solutions (security, network and platform connectivity, data analytics processing/storage, applications etc.), Examples of a few big data solutions at Verizon
Lifehacking met Evernote is een Nederlandstalig handboek voor Evernote - het digitale notitieboekje voor je computer, tablet én smartphone.
Geschreven door Frank Meeuwsen, Oskar van Rijswijk en Patrick Mackaaij.
Why Cloud Native?
What is Cloud Native
Capgemini Cloud Choice
Cloud Native Apps –Our Approach
So how does this differ?
Keith KELLY, Cloud / DevOps Transformation Leader
SPSNL17 - Securing Office 365 and Microsoft Azure like a rock star (or groupi...DIWUG
Securing and maintaining a trustworthy Office 365 and Microsoft Azure deployment is not an easy task. In this session we'll take a look into how you can secure and control your cloud-based servers and services, data and users using Azure Active Directory, Azure Security Center, Privileged Identity Management and Advanced Security Management. In addition we’ll also take a look at how Operations Management Suite and Microsoft Advanced Threat Analytics can be used to provide better overall security for on-premises and hybrid deployments.
Cloud integration: what's in it for you? (Toon Vanhoutte & Massimo Crippa at ...Codit
This session focuses on the great opportunities that cloud integration brings to your business. Common challenges and pitfalls for hybrid messaging-based and API-based integration are discussed. Next to that, Codit's added-value approach to hybrid integration is presented, which combines a solid foundation of 15 years integration experience with an innovative and rapidly moving Azure cloud platform.
Red hat Open Source Day 2017, Milan - "From Mainframe to Container, a Cloud s...Kiratech
La presentazione, tenuta da Giulio Covassi, CEO di Kiratech, intende fare un excursus storico dall'invenzione dei primi computer al mainframe, dall'inizio dell'informatica moderna con Unix e Linux al Cloud Computing e alla virtualizzazione, fino ain container, all'intelligenza artificiale e il machine Learning.
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data, accumulating trillions of bytes of information about their customers, suppliers and operations. The advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters, automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In fact, as they go about their business and interact with individuals, they are producing an incredible amount of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions of individuals around the world to contribute to the amount of data available. In addition, the extremely increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security & Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources along with their characteristics in each domain. Later, it presents the highly productive and competitive big data applications with innovative big data technologies. Subsequently, the study showcases the impact of big data on each domain to capture value addition in its services. Finally, the study put forwards many more research opportunities as all these domains differ in their complexity and development in the usage of big data analytics
A COMPREHENSIVE STUDY ON POTENTIAL RESEARCH OPPORTUNITIES OF BIG DATA ANALYTI...ijcseit
Companies, organizations and policy makers shake out with flood flowing volume of transactional data,
accumulating trillions of bytes of information about their customers, suppliers and operations. The
advanced networked sensors are being implanted in devices such as mobile phones, smart energy meters,
automobiles and industrial machines that sense, generate and transfer data to multiple storage devices. In
fact, as they go about their business and interact with individuals, they are producing an incredible amount
of fatigue digital data. Social media sites, smart phones, and other customer devices have allowed billions
of individuals around the world to contribute to the amount of data available. In addition, the extremely
increasing size of multimedia data has also take part a key role in the rapid growth of data. The technology
of high-definition video creates more than 2,000 times as many bytes as necessary to store as normal text
data. Moreover, in a digitized world, consumers are leaving enormous amount of data about their day-today communicating, browsing, buying, sharing, searching and so on. As a result, it evolved as a big data
and in turn has motivated the advances in big data analytics paradigms, endorsed as a basic motivation
factor for the present researchers.
The authors in the present paper conduct a comprehensive study to explore the impact of big data analytics
in key domains namely, Health Care (HC), Retail Industry (RI), Public Governance (PG), Pubic Security &
Safety (PSS) and Personal Location Tracking (PLT). Initially, the study looks at the insights of data sources
along with their characteristics in each domain. Later, it presents the highly productive and competitive big
data applications with innovative big data technologies. Subsequently, the study showcases the impact of
big data on each domain to capture value addition in its services. Finally, the study put forwards many
more research opportunities as all these domains differ in their complexity and development in the usage of
big data analytics.
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
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.
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.
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.
Understand the Idea of Big Data and in Present ScenarioAI Publications
Big data analytics and deep learning are two of data science's most promising areas of convergence. The importance of Big Data has grown recently as several organizations, both public and commercial, have been amassing large amounts of region-specific data that may provide useful information on topics like as national information, advanced security, blackmail area, development, and prosperity informatics. For Big Data Analytics, where data is often unstructured and unlabeled, Deep Learning's ability to analyze and learn from large amounts of data on its own is a crucial feature. In this review, we look at how Deep Learning can be used to solve some of the most pressing problems in Big Data Analytics, including model isolation from large data sets, semantic querying, data marking, smart data recovery, and the automation of discriminative tasks.
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.
A forecasting of stock trading price using time series information based on b...IJECEIAES
Big data is a large set of structured or unstructured data that can collect, store, manage, and analyze data with existing database management tools. And it means the technique of extracting value from these data and interpreting the results. Big data has three characteristics: The size of existing data and other data (volume), the speed of data generation (velocity), and the variety of information forms (variety). The time series data are obtained by collecting and recording the data generated in accordance with the flow of time. If the analysis of these time series data, found the characteristics of the data implies that feature helps to understand and analyze time series data. The concept of distance is the simplest and the most obvious in dealing with the similarities between objects. The commonly used and widely known method for measuring distance is the Euclidean distance. This study is the result of analyzing the similarity of stock price flow using 793,800 closing prices of 1,323 companies in Korea. Visual studio and Excel presented calculate the Euclidean distance using an analysis tool. We selected “000100” as a target domestic company and prepared for big data analysis. As a result of the analysis, the shortest Euclidean distance is the code “143860” company, and the calculated value is “11.147”. Therefore, based on the results of the analysis, the limitations of the study and theoretical implications are suggested.
Isolating values from big data with the help of four v’seSAT Journals
Abstract
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. It includes business transactions, e-mail messages, photos, surveillance videos and activity logs. It also includes unstructured text posted on the Web, such as blogs and social media. Big Data has shown lot of potential in real world industry and research community. We support the power and Potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 4 Vs. 4th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 4 Vs. of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
Keywords: Big Data, Surveillance videos, blogs, social media, four Vs.
Light Dimmer with Implementation of Data Abstraction While CrashingIJCERT JOURNAL
The paper proposes a system to automatically dim the headlight of opposite vehicles by changing from high beam to low beam. The system envisioned is automatic collision avoidance and detection. Many methods are available to dim the headlight of vehicles. In the earlier days dip mechanism was used. Similarly there are several mechanisms for automatic accident detection. An automatic alarm gadget for traffic accidents is presented. It can consequently discover a car crash; hunt down the spot and after that send the essential data to hospitals. This system will highly aid the search and rescue of vehicles that have met with an accident.
Mining Opinion Features in Customer ReviewsIJCERT JOURNAL
Now days, E-commerce systems have become extremely important. Large numbers of customers are choosing online shopping because of its convenience, reliability, and cost. Client generated information and especially item reviews are significant sources of data for consumers to make informed buy choices and for makers to keep track of customer’s opinions. It is difficult for customers to make purchasing decisions based on only pictures and short product descriptions. On the other hand, mining product reviews has become a hot research topic and prior researches are mostly based on pre-specified product features to analyse the opinions. Natural Language Processing (NLP) techniques such as NLTK for Python can be applied to raw customer reviews and keywords can be extracted. This paper presents a survey on the techniques used for designing software to mine opinion features in reviews. Elven IEEE papers are selected and a comparison is made between them. These papers are representative of the significant improvements in opinion mining in the past decade.
Augmenting Publish/Subscribe System by Identity Based Encryption (IBE) Techni...IJCERT JOURNAL
Security is one of the extensive and complicated requirements that need to be provided in order to achieve few issues like confidentiality, integrity and authentication. In a content-based publish/subscribe system, authentication is difficult to achieve since there exists no strong bonding between the end parties. Similarly, Integrity and confidentiality needs arise in published events and subscription conflicts with content-based routing. The basic tool to support confidentiality, integrity is encryption. In this paper for providing security mechanism in broker-less content-based publish/subscribe system we adapt pairing-based cryptography mechanism. In this mechanism, we use Identity Based Encryption (IBE) technique to achieve the needs of publish/subscribe system. This approach helps in providing fine-grained key management, effective encryption, decryption operations and routing is carried out in the order of subscribed attributes
Design of an adaptive JPEG Steganalysis with UED IJCERT JOURNAL
Steganography is the art and science of writing hidden messages in such a way that no one apart from the sender and intended recipient suspects the existence of the message a form of security through obscurity. The internet as a whole does not use secure links, thus information in transit may be vulnerable to interception as well. The important of reducing a chance of the information being detected during the transmission is being an issue now days. In this paper, we proposed a class of new distortion functions known as uniform embedding distortion function (UED) is presented. By incorporating the syndrome trellis coding, the best code word with undetectable data hiding is achieved. Due to hiding more amounts of data into the intersected area, embedding capacity is increased. Our aim is to hide the secret information behind the image file. Steganography hides the secret message so that intruder’s can’t detect the communication. When hiding data into the intersected area, thus provides a higher level of security with more efficient data mean square error is reduced and embedding capacity is increased.
Investigation on Revocable Fine-grained Access Control Scheme for Multi-Autho...IJCERT JOURNAL
Cloud computing is one of the emerge technologies in order to outsource huge volume of data inters of storage and sharing. To protect the data and privacy of users the access control methods ensure that authorized users access the data and the system. Fine grained-approach is the appropriate method for data access control in cloud storage. However, CP-ABE schemes to data access control for cloud storage systems are difficult because of the attribute revocation problem. Specifically, in this paper we investigate on revocable multi-authority Fine-grained-Scheme performance.
A Secure Multi-Owner Data Sharing Scheme for Dynamic Group in Public Cloud. IJCERT JOURNAL
In cloud computing outsourcing group resource among cloud users is a major challenge, so cloud computing provides a low-cost and well-organized solution. Due to frequent change of membership, sharing data in a multi-owner manner to an untrusted cloud is still its challenging issue. In this paper we proposed a secure multi-owner data sharing scheme for dynamic group in public cloud. By providing AES encryption with convergent key while uploading the data, any cloud user can securely share data with others. Meanwhile, the storage overhead and encryption computation cost of the scheme are independent with the number of revoked users. In addition, I analyze the security of this scheme with rigorous proofs. One-Time Password is one of the easiest and most popular forms of authentication that can be used for securing access to accounts. One-Time Passwords are often referred to as secure and stronger forms of authentication in multi-owner manner. Extensive security and performance analysis shows that our proposed scheme is highly efficient and satisfies the security requirements for public cloud based secure group sharing.
Secure Redundant Data Avoidance over Multi-Cloud Architecture. IJCERT JOURNAL
Redundant data avoidance systems, the Private Cloud are involved as a proxy to allow data owner/users to securely perform duplicate check with differential privileges. Such architecture is practical and has attracted much attention from researchers. The data owners only outsource their data storage by utilizing public cloud while the data operation is managed in private cloud, in this connection our presented system has follows traditional encryption while providing data confidentiality, is incompatible with redundant data avoidance. Identical data copies of different users will lead to different ciphertexts, making data avoidance impossible. To address above issues convergent encryption technique has been proposed to encrypt the data before outsourcing. To better protect data security, this paper makes the first attempt to formally address the problem of authorized redundant data avoidance. Different from traditional redundant data avoidance systems, the differential privileges of users are further considered in duplicate check besides the data itself. We also present several new redundant data avoidance constructions supporting authorized duplicate check in a multi-cloud architecture. Security analysis demonstrates that our scheme is secure in terms of the definitions specified in the proposed security model. In order to perform secure access controlling scheme user may satisfy fine-grained approach at cloud level towards access restricting from unauthorized users or adversaries.
SURVEY ON IMPLEMANTATION OF COLUMN ORIENTED NOSQL DATA STORES ( BIGTABLE & CA...IJCERT JOURNAL
NOSQL is a database provides a mechanism for storage and retrieval of data that is modeled for huge amount of data which is used in big data and Cloud Computing . NOSQL systems are also called "Not only SQL" to emphasize that they may support SQL-like query languages. A basic classification of NOSQL is based on data model; they are like column, Document, Key-Value etc. The objective of this paper is to study and compare the implantation of various column oriented data stores like Bigtable, Cassandra.
Security Issues’ in Cloud Computing and its Solutions. IJCERT JOURNAL
Cloud computing is a set of IT services that are provided to a customer over a network on a leased basis and with the ability to scale up or down their service requirements. Usually cloud computing services are delivered by a third party provider who owns the infrastructure. It advantages to mention but a few include scalability, resilience, flexibility, efficiency and outsourcing non-core activities. Cloud computing offers an innovative business model for organizations to adopt IT services without upfront investment. Despite the potential gains achieved from the cloud computing, the organizations are slow in accepting it due to security issues and challenges associated with it. Security is one of the major issues which hamper the growth of cloud. The idea of handing over important data to another company is worrisome; such that the consumers need to be vigilant in understanding the risks of data breaches in this new environment. This paper introduces a detailed analysis of the cloud computing security issues and challenges focusing on the cloud computing types and the service delivery types.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
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.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks