- Big data has revolutionized many fields by enabling the extraction of useful insights from vast amounts of data.
- The document discusses the evolution of big data and its applications in areas like healthcare, search engines, transportation, finance, social media, and government identification systems.
- It also covers technologies used for big data like machine learning, artificial intelligence, the internet of things, and highlights challenges of collecting, analyzing, and managing large datasets.
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
Hadoop is an open-source software framework used for storing and processing large datasets in a distributed manner across commodity hardware. It was created in 2005 by Doug Cutting and Mike Cafarella to address the issue of processing big data at a reasonable cost and time. Hadoop uses HDFS for storage and MapReduce for processing data distributed over a network of nodes in parallel. It allows organizations to gain insights from vast amounts of structured and unstructured data faster and at lower costs than traditional approaches.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Governing Big Data : Principles and practicesPiyush Malik
This document summarizes key points from a special issue of the IBM Journal that focuses on applications, analytics, software, and hardware technologies for massive-scale analytics and processing big data. It begins with an introduction that defines big data and provides examples of how it is used. The remainder of the document provides summaries of 15 articles in the special issue that cover topics like governing big data, trends in massive-scale analytics stacks, designing systems for big data workloads, platforms for extreme analytics, and using big data for applications like social network analysis and underwater acoustic monitoring.
Big Data Applications | Big Data Application Examples | Big Data Use Cases | ...Simplilearn
In this Big Data presentation, we will be discussing the Big data growth over the last few years followed by the various big data applications. We will look into the various sectors where big data is used such as weather forecast, healthcare, media and entertainment, logistics, travel & tourism and finally in the government & law enforcement sector.
We will be discussing how below industries are using Big Data presentation:
1. Weather forecast
2. Media and entertainment
3. Healthcare
4. Logistics
5. Travel n tourism
6. Government and law enforcement
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
What is the impact of Big Data on Analytics from a Data Science perspective.
Presented at the Big Data and Analytics Summit 2014, Nasscom by Mamatha Upadhyaya.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
Hadoop is an open-source software framework used for storing and processing large datasets in a distributed manner across commodity hardware. It was created in 2005 by Doug Cutting and Mike Cafarella to address the issue of processing big data at a reasonable cost and time. Hadoop uses HDFS for storage and MapReduce for processing data distributed over a network of nodes in parallel. It allows organizations to gain insights from vast amounts of structured and unstructured data faster and at lower costs than traditional approaches.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Governing Big Data : Principles and practicesPiyush Malik
This document summarizes key points from a special issue of the IBM Journal that focuses on applications, analytics, software, and hardware technologies for massive-scale analytics and processing big data. It begins with an introduction that defines big data and provides examples of how it is used. The remainder of the document provides summaries of 15 articles in the special issue that cover topics like governing big data, trends in massive-scale analytics stacks, designing systems for big data workloads, platforms for extreme analytics, and using big data for applications like social network analysis and underwater acoustic monitoring.
Big Data Applications | Big Data Application Examples | Big Data Use Cases | ...Simplilearn
In this Big Data presentation, we will be discussing the Big data growth over the last few years followed by the various big data applications. We will look into the various sectors where big data is used such as weather forecast, healthcare, media and entertainment, logistics, travel & tourism and finally in the government & law enforcement sector.
We will be discussing how below industries are using Big Data presentation:
1. Weather forecast
2. Media and entertainment
3. Healthcare
4. Logistics
5. Travel n tourism
6. Government and law enforcement
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Module 6 The Future of Big and Smart Data- Online caniceconsulting
This document provides an overview of the future predictions and trends related to big data. Some of the key predictions discussed include machine learning becoming prominent in big data analysis, privacy emerging as a major challenge, and the creation of chief data officer positions. Emerging trends covered include the growth of open source solutions like Hadoop, the use of in-memory technologies to speed processing, and the incorporation of machine learning and predictive analytics. The document also discusses opportunities that big data presents for industries like increased productivity and sales.
Big data is impacting individuals in several ways based on their digital footprint and interactions online. As more data is collected through mobile devices, social media, and ecommerce sites about things like demographics, preferences, and behaviors, companies can use analytics to better target marketing and tailor product offerings. This results in personalized ads and discounts. Additionally, employers are able to gain deeper insights into current and potential employees by analyzing data from HR systems, social profiles, and other sources to inform talent management, succession planning, and career predictions. So in many areas of life, big data collected from individuals online is being used to shape the experiences, opportunities, and interactions they receive.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
1) The document discusses how Avelo, a UK financial software vendor, can leverage big data to provide additional value to its clients in the financial services sector.
2) It examines potential big data projects Avelo could implement related to performance management, data exploration, social analytics, and decision science.
3) It also discusses how Avelo has begun constructing a big data capability by developing a common enterprise data model to unify its applications and data.
Big Data Tools PowerPoint Presentation SlidesSlideTeam
The document discusses big data analysis requirements and tools. It covers where big data comes from both internally and externally. It then discusses tools for analyzing big data such as BI tools, in-database analytics, Hadoop, decision management, and discovery tools. Techniques for analyzing big data like classification tree analysis, genetic algorithms, regression analysis, machine learning, and sentiment analysis are also covered. The key benefits and a successful implementation roadmap for big data in an organization are summarized.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
A study on web analytics with reference to select sports websitesBhanu Prakash
This document is a project report submitted by Y. Bhanu Prakash to GITAM Institute of Management in partial fulfillment of the degree of Bachelor of Business Administration in Business Analytics. The report is on the topic of web analytics with reference to select sports websites. It includes declarations by the student and certification by the guide, as well as acknowledgements. The report will consist of 5 chapters - an introduction to analytics, a profile of Alexa.com, methodology, analysis and interpretation of data, and observations and conclusions.
Analytics 3.0 Measurable business impact from analytics & big dataMicrosoft
Presentación del evento de Harvard Business Review sobre Analítica y Big Data
(15 de Octubre 2013)
"Featuring analytics expert Tom Davenport, author of Competing on Analytics, Analytics at Work, and the just-released Keeping Up with the Quants" 
This document provides an overview and introduction to big data concepts. It defines big data as covering everything that is digitized, including both structured and unstructured data from various sources. The key aspects that define big data are time, location, amount, and data type. Big data represents an opportunity to analyze vast data sets and gain insights in real-time to improve business operations, predictive analytics, and transparency. While traditional tools are not suited for big data, new techniques and startups are addressing big data challenges and transforming how businesses use data.
This document provides an overview of predictive analytics and its growing importance. It discusses how advances in technologies like cloud computing and the internet of things are enabling businesses to gather and analyze vast amounts of data. While descriptive and diagnostic analytics describe what happened in the past, predictive analytics uses statistical techniques to create models that forecast future outcomes. The document outlines several key drivers that are pushing predictive analytics towards mainstream adoption over the next few years, including easier-to-use tools, open source software, innovation from startups, and the availability of cloud-based solutions. It concludes that the combination of big data and predictive analytics will continue to accelerate innovation across industries.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
Big data refers to huge amounts of data from various sources that traditional data management systems cannot handle. It is characterized by volume, velocity, variety, and veracity. Handling big data requires expertise in security, management, and analytics. Data scientists use descriptive, diagnostic, predictive, and prescriptive analytics techniques on big data to create business insights and decisions using business intelligence tools. While big data offers opportunities, it also poses risks like bad data, security issues, and costs if not properly analyzed and managed.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Module 6 The Future of Big and Smart Data- Online caniceconsulting
This document provides an overview of the future predictions and trends related to big data. Some of the key predictions discussed include machine learning becoming prominent in big data analysis, privacy emerging as a major challenge, and the creation of chief data officer positions. Emerging trends covered include the growth of open source solutions like Hadoop, the use of in-memory technologies to speed processing, and the incorporation of machine learning and predictive analytics. The document also discusses opportunities that big data presents for industries like increased productivity and sales.
Big data is impacting individuals in several ways based on their digital footprint and interactions online. As more data is collected through mobile devices, social media, and ecommerce sites about things like demographics, preferences, and behaviors, companies can use analytics to better target marketing and tailor product offerings. This results in personalized ads and discounts. Additionally, employers are able to gain deeper insights into current and potential employees by analyzing data from HR systems, social profiles, and other sources to inform talent management, succession planning, and career predictions. So in many areas of life, big data collected from individuals online is being used to shape the experiences, opportunities, and interactions they receive.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
1) The document discusses how Avelo, a UK financial software vendor, can leverage big data to provide additional value to its clients in the financial services sector.
2) It examines potential big data projects Avelo could implement related to performance management, data exploration, social analytics, and decision science.
3) It also discusses how Avelo has begun constructing a big data capability by developing a common enterprise data model to unify its applications and data.
Big Data Tools PowerPoint Presentation SlidesSlideTeam
The document discusses big data analysis requirements and tools. It covers where big data comes from both internally and externally. It then discusses tools for analyzing big data such as BI tools, in-database analytics, Hadoop, decision management, and discovery tools. Techniques for analyzing big data like classification tree analysis, genetic algorithms, regression analysis, machine learning, and sentiment analysis are also covered. The key benefits and a successful implementation roadmap for big data in an organization are summarized.
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
A study on web analytics with reference to select sports websitesBhanu Prakash
This document is a project report submitted by Y. Bhanu Prakash to GITAM Institute of Management in partial fulfillment of the degree of Bachelor of Business Administration in Business Analytics. The report is on the topic of web analytics with reference to select sports websites. It includes declarations by the student and certification by the guide, as well as acknowledgements. The report will consist of 5 chapters - an introduction to analytics, a profile of Alexa.com, methodology, analysis and interpretation of data, and observations and conclusions.
Analytics 3.0 Measurable business impact from analytics & big dataMicrosoft
Presentación del evento de Harvard Business Review sobre Analítica y Big Data
(15 de Octubre 2013)
"Featuring analytics expert Tom Davenport, author of Competing on Analytics, Analytics at Work, and the just-released Keeping Up with the Quants" 
This document provides an overview and introduction to big data concepts. It defines big data as covering everything that is digitized, including both structured and unstructured data from various sources. The key aspects that define big data are time, location, amount, and data type. Big data represents an opportunity to analyze vast data sets and gain insights in real-time to improve business operations, predictive analytics, and transparency. While traditional tools are not suited for big data, new techniques and startups are addressing big data challenges and transforming how businesses use data.
This document provides an overview of predictive analytics and its growing importance. It discusses how advances in technologies like cloud computing and the internet of things are enabling businesses to gather and analyze vast amounts of data. While descriptive and diagnostic analytics describe what happened in the past, predictive analytics uses statistical techniques to create models that forecast future outcomes. The document outlines several key drivers that are pushing predictive analytics towards mainstream adoption over the next few years, including easier-to-use tools, open source software, innovation from startups, and the availability of cloud-based solutions. It concludes that the combination of big data and predictive analytics will continue to accelerate innovation across industries.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
Big data refers to huge amounts of data from various sources that traditional data management systems cannot handle. It is characterized by volume, velocity, variety, and veracity. Handling big data requires expertise in security, management, and analytics. Data scientists use descriptive, diagnostic, predictive, and prescriptive analytics techniques on big data to create business insights and decisions using business intelligence tools. While big data offers opportunities, it also poses risks like bad data, security issues, and costs if not properly analyzed and managed.
The document discusses big data analytics and provides tips for organizations looking to implement big data initiatives. It notes that while organizations have large amounts of customer, sales, and other operational data, most are not effectively analyzing and extracting insights from this data. The value is in using analytics to uncover hidden patterns and correlations to help businesses make better decisions. However, most companies currently take a slow, manual approach to data compilation and analysis. The document recommends that organizations consider big data as a business solution rather than just an IT problem. It suggests taking a journey approach, focusing on insights over data, using proven analytics tools, and delivering early business value from big data projects in order to justify further investment.
Big Data is the lastest cashcow. Data Analytics has now a crucial role for industries. This article describes as to what is Big Data and Analytics and how a Chartered Accountant will be able to provide value in this field.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
The document discusses big data, including its definition, types, benefits, and challenges. It describes how big data is generated from a variety of sources and is characterized by its volume, velocity, and variety (the 3Vs). Big data provides benefits like improved customer insights and business optimization. However, it also poses challenges to deal with its huge volume, high velocity, varied types (structured and unstructured), and issues of data veracity (uncertainty). Techniques to address these challenges include using distributed file systems, parallel processing frameworks like Hadoop, and data fusion or advanced mathematics to manage uncertainty.
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
Big Data In Small Steps is a document that addresses common questions about big data including what it is, how it can provide value, and how to implement it. It defines big data using the four V's of volume, velocity, variety and veracity. It provides examples of how insurance and telecom companies can use big data for customer loyalty, risk management, claims processing, segmentation, capacity planning and promotional optimization. The document recommends establishing a center of excellence and identifies the key roles needed including an executive leader, project manager, data technologist, data scientist and data analyst. It advocates prototyping solutions and developing repeatable processes for extracting value from big data.
1. Data science involves applying scientific methods and processes to extract knowledge and insights from data. It includes techniques like machine learning, statistical analysis, and data visualization.
2. Data science has many applications in fields like marketing, healthcare, banking, and government. It helps with tasks like demand forecasting, fraud detection, personalized recommendations, and policymaking.
3. The key characteristics of data science include business understanding, intuition, curiosity, and skills in areas like machine learning algorithms, statistics, programming, and communication. Data scientists help organizations make better decisions using data-driven insights.
Big data refers to the vast amounts of digital data created every day from people's online activities. As more of our lives move online and everything from social media to smart devices generates data, the potential for analyzing this "big data" can provide valuable insights for businesses. The evolution of big data saw its first use of the term in 1999 and the development of Hadoop, a major big data framework, in 2005. Nowadays, big data is commonly described using the four Vs: volume refers to the vast amounts of data, velocity is the speed at which data is created and processed, variety indicates the different data types, and veracity relates to the reliability of the data. Businesses are using big data analytics to better understand
IRJET- Big Data Management and Growth EnhancementIRJET Journal
1. The document discusses big data management and growth, including definitions of big data, properties of big data like volume, variety, and velocity, and applications of big data in various domains.
2. It describes how big data is used in education to improve student outcomes, in healthcare to enable prevention and more personalized care, and in industries like banking and fraud detection to enhance customer segmentation and risk assessment.
3. Big data analytics refers to analyzing large and complex datasets to extract useful insights and make better decisions. The document provides examples of machine learning and predictive analytics techniques used for big data analysis.
This document provides an overview of big data, including definitions of key terms like data, big data, and examples of big data. It describes why big data is important, how big data analytics works, and the benefits it provides. It outlines different types of big data like structured, unstructured, and semi-structured data. It also discusses characteristics of big data like volume, velocity, variety, and veracity. Additionally, it identifies primary sources of big data and examples of big data tools and software. Finally, it briefly discusses how big data and machine learning are related and how AI can be used to enhance big data analytics.
Why Data Science is Getting Popular in 2023?kavyagaur3
Data science employs mathematics, statistics, advanced programming techniques, analytics and artificial intelligence (AI) to uncover insights that drive business value for their organisation. Then, this information can be used for strategic planning and decision-making.
Data has flooded in massive amounts as a result of digitization. Businesses are making their utmost efforts to take advantage of every opportunity to increase their businesses. This makes the best opportunity for individuals who want to pursue Data Science. The first step is to get the best data science training.
This document discusses how life insurance companies can leverage big data analytics across their value chain. It begins by explaining how data sources have expanded dramatically in recent years due to factors like the growth of digital devices and the internet of things. It then outlines how big data can be used in various parts of the insurance lifecycle from product development to claims processing. The document presents a four stage framework for life insurers to adopt big data analytics and provides examples of how some companies have realized benefits. It concludes by noting that while insurers recognize big data's potential, many challenges remain in analyzing diverse and voluminous unstructured data.
Similar to IRJET - Big Data: Evolution Cum Revolution (20)
TUNNELING IN HIMALAYAS WITH NATM METHOD: A SPECIAL REFERENCES TO SUNGAL TUNNE...IRJET Journal
1) The document discusses the Sungal Tunnel project in Jammu and Kashmir, India, which is being constructed using the New Austrian Tunneling Method (NATM).
2) NATM involves continuous monitoring during construction to adapt to changing ground conditions, and makes extensive use of shotcrete for temporary tunnel support.
3) The methodology section outlines the systematic geotechnical design process for tunnels according to Austrian guidelines, and describes the various steps of NATM tunnel construction including initial and secondary tunnel support.
STUDY THE EFFECT OF RESPONSE REDUCTION FACTOR ON RC FRAMED STRUCTUREIRJET Journal
This study examines the effect of response reduction factors (R factors) on reinforced concrete (RC) framed structures through nonlinear dynamic analysis. Three RC frame models with varying heights (4, 8, and 12 stories) were analyzed in ETABS software under different R factors ranging from 1 to 5. The results showed that displacement increased as the R factor decreased, indicating less linear behavior for lower R factors. Drift also decreased proportionally with increasing R factors from 1 to 5. Shear forces in the frames decreased with higher R factors. In general, R factors of 3 to 5 produced more satisfactory performance with less displacement and drift. The displacement variations between different building heights were consistent at different R factors. This study evaluated how R factors influence
A COMPARATIVE ANALYSIS OF RCC ELEMENT OF SLAB WITH STARK STEEL (HYSD STEEL) A...IRJET Journal
This study compares the use of Stark Steel and TMT Steel as reinforcement materials in a two-way reinforced concrete slab. Mechanical testing is conducted to determine the tensile strength, yield strength, and other properties of each material. A two-way slab design adhering to codes and standards is executed with both materials. The performance is analyzed in terms of deflection, stability under loads, and displacement. Cost analyses accounting for material, durability, maintenance, and life cycle costs are also conducted. The findings provide insights into the economic and structural implications of each material for reinforcement selection and recommendations on the most suitable material based on the analysis.
Effect of Camber and Angles of Attack on Airfoil CharacteristicsIRJET Journal
This document discusses a study analyzing the effect of camber, position of camber, and angle of attack on the aerodynamic characteristics of airfoils. Sixteen modified asymmetric NACA airfoils were analyzed using computational fluid dynamics (CFD) by varying the camber, camber position, and angle of attack. The results showed the relationship between these parameters and the lift coefficient, drag coefficient, and lift to drag ratio. This provides insight into how changes in airfoil geometry impact aerodynamic performance.
A Review on the Progress and Challenges of Aluminum-Based Metal Matrix Compos...IRJET Journal
This document reviews the progress and challenges of aluminum-based metal matrix composites (MMCs), focusing on their fabrication processes and applications. It discusses how various aluminum MMCs have been developed using reinforcements like borides, carbides, oxides, and nitrides to improve mechanical and wear properties. These composites have gained prominence for their lightweight, high-strength and corrosion resistance properties. The document also examines recent advancements in fabrication techniques for aluminum MMCs and their growing applications in industries such as aerospace and automotive. However, it notes that challenges remain around issues like improper mixing of reinforcements and reducing reinforcement agglomeration.
Dynamic Urban Transit Optimization: A Graph Neural Network Approach for Real-...IRJET Journal
This document discusses research on using graph neural networks (GNNs) for dynamic optimization of public transportation networks in real-time. GNNs represent transit networks as graphs with nodes as stops and edges as connections. The GNN model aims to optimize networks using real-time data on vehicle locations, arrival times, and passenger loads. This helps increase mobility, decrease traffic, and improve efficiency. The system continuously trains and infers to adapt to changing transit conditions, providing decision support tools. While research has focused on performance, more work is needed on security, socio-economic impacts, contextual generalization of models, continuous learning approaches, and effective real-time visualization.
Structural Analysis and Design of Multi-Storey Symmetric and Asymmetric Shape...IRJET Journal
This document summarizes a research project that aims to compare the structural performance of conventional slab and grid slab systems in multi-story buildings using ETABS software. The study will analyze both symmetric and asymmetric building models under various loading conditions. Parameters like deflections, moments, shears, and stresses will be examined to evaluate the structural effectiveness of each slab type. The results will provide insights into the comparative behavior of conventional and grid slabs to help engineers and architects select appropriate slab systems based on building layouts and design requirements.
A Review of “Seismic Response of RC Structures Having Plan and Vertical Irreg...IRJET Journal
This document summarizes and reviews a research paper on the seismic response of reinforced concrete (RC) structures with plan and vertical irregularities, with and without infill walls. It discusses how infill walls can improve or reduce the seismic performance of RC buildings, depending on factors like wall layout, height distribution, connection to the frame, and relative stiffness of walls and frames. The reviewed research paper analyzes the behavior of infill walls, effects of vertical irregularities, and seismic performance of high-rise structures under linear static and dynamic analysis. It studies response characteristics like story drift, deflection and shear. The document also provides literature on similar research investigating the effects of infill walls, soft stories, plan irregularities, and different
This document provides a review of machine learning techniques used in Advanced Driver Assistance Systems (ADAS). It begins with an abstract that summarizes key applications of machine learning in ADAS, including object detection, recognition, and decision-making. The introduction discusses the integration of machine learning in ADAS and how it is transforming vehicle safety. The literature review then examines several research papers on topics like lightweight deep learning models for object detection and lane detection models using image processing. It concludes by discussing challenges and opportunities in the field, such as improving algorithm robustness and adaptability.
Long Term Trend Analysis of Precipitation and Temperature for Asosa district,...IRJET Journal
The document analyzes temperature and precipitation trends in Asosa District, Benishangul Gumuz Region, Ethiopia from 1993 to 2022 based on data from the local meteorological station. The results show:
1) The average maximum and minimum annual temperatures have generally decreased over time, with maximum temperatures decreasing by a factor of -0.0341 and minimum by -0.0152.
2) Mann-Kendall tests found the decreasing temperature trends to be statistically significant for annual maximum temperatures but not for annual minimum temperatures.
3) Annual precipitation in Asosa District showed a statistically significant increasing trend.
The conclusions recommend development planners account for rising summer precipitation and declining temperatures in
P.E.B. Framed Structure Design and Analysis Using STAAD ProIRJET Journal
This document discusses the design and analysis of pre-engineered building (PEB) framed structures using STAAD Pro software. It provides an overview of PEBs, including that they are designed off-site with building trusses and beams produced in a factory. STAAD Pro is identified as a key tool for modeling, analyzing, and designing PEBs to ensure their performance and safety under various load scenarios. The document outlines modeling structural parts in STAAD Pro, evaluating structural reactions, assigning loads, and following international design codes and standards. In summary, STAAD Pro is used to design and analyze PEB framed structures to ensure safety and code compliance.
A Review on Innovative Fiber Integration for Enhanced Reinforcement of Concre...IRJET Journal
This document provides a review of research on innovative fiber integration methods for reinforcing concrete structures. It discusses studies that have explored using carbon fiber reinforced polymer (CFRP) composites with recycled plastic aggregates to develop more sustainable strengthening techniques. It also examines using ultra-high performance fiber reinforced concrete to improve shear strength in beams. Additional topics covered include the dynamic responses of FRP-strengthened beams under static and impact loads, and the performance of preloaded CFRP-strengthened fiber reinforced concrete beams. The review highlights the potential of fiber composites to enable more sustainable and resilient construction practices.
Survey Paper on Cloud-Based Secured Healthcare SystemIRJET Journal
This document summarizes a survey on securing patient healthcare data in cloud-based systems. It discusses using technologies like facial recognition, smart cards, and cloud computing combined with strong encryption to securely store patient data. The survey found that healthcare professionals believe digitizing patient records and storing them in a centralized cloud system would improve access during emergencies and enable more efficient care compared to paper-based systems. However, ensuring privacy and security of patient data is paramount as healthcare incorporates these digital technologies.
Review on studies and research on widening of existing concrete bridgesIRJET Journal
This document summarizes several studies that have been conducted on widening existing concrete bridges. It describes a study from China that examined load distribution factors for a bridge widened with composite steel-concrete girders. It also outlines challenges and solutions for widening a bridge in the UAE, including replacing bearings and stitching the new and existing structures. Additionally, it discusses two bridge widening projects in New Zealand that involved adding precast beams and stitching to connect structures. Finally, safety measures and challenges for strengthening a historic bridge in Switzerland under live traffic are presented.
React based fullstack edtech web applicationIRJET Journal
The document describes the architecture of an educational technology web application built using the MERN stack. It discusses the frontend developed with ReactJS, backend with NodeJS and ExpressJS, and MongoDB database. The frontend provides dynamic user interfaces, while the backend offers APIs for authentication, course management, and other functions. MongoDB enables flexible data storage. The architecture aims to provide a scalable, responsive platform for online learning.
A Comprehensive Review of Integrating IoT and Blockchain Technologies in the ...IRJET Journal
This paper proposes integrating Internet of Things (IoT) and blockchain technologies to help implement objectives of India's National Education Policy (NEP) in the education sector. The paper discusses how blockchain could be used for secure student data management, credential verification, and decentralized learning platforms. IoT devices could create smart classrooms, automate attendance tracking, and enable real-time monitoring. Blockchain would ensure integrity of exam processes and resource allocation, while smart contracts automate agreements. The paper argues this integration has potential to revolutionize education by making it more secure, transparent and efficient, in alignment with NEP goals. However, challenges like infrastructure needs, data privacy, and collaborative efforts are also discussed.
A REVIEW ON THE PERFORMANCE OF COCONUT FIBRE REINFORCED CONCRETE.IRJET Journal
This document provides a review of research on the performance of coconut fibre reinforced concrete. It summarizes several studies that tested different volume fractions and lengths of coconut fibres in concrete mixtures with varying compressive strengths. The studies found that coconut fibre improved properties like tensile strength, toughness, crack resistance, and spalling resistance compared to plain concrete. Volume fractions of 2-5% and fibre lengths of 20-50mm produced the best results. The document concludes that using a 4-5% volume fraction of coconut fibres 30-40mm in length with M30-M60 grade concrete would provide benefits based on previous research.
Optimizing Business Management Process Workflows: The Dynamic Influence of Mi...IRJET Journal
The document discusses optimizing business management processes through automation using Microsoft Power Automate and artificial intelligence. It provides an overview of Power Automate's key components and features for automating workflows across various apps and services. The document then presents several scenarios applying automation solutions to common business processes like data entry, monitoring, HR, finance, customer support, and more. It estimates the potential time and cost savings from implementing automation for each scenario. Finally, the conclusion emphasizes the transformative impact of AI and automation tools on business processes and the need for ongoing optimization.
Multistoried and Multi Bay Steel Building Frame by using Seismic DesignIRJET Journal
The document describes the seismic design of a G+5 steel building frame located in Roorkee, India according to Indian codes IS 1893-2002 and IS 800. The frame was analyzed using the equivalent static load method and response spectrum method, and its response in terms of displacements and shear forces were compared. Based on the analysis, the frame was designed as a seismic-resistant steel structure according to IS 800:2007. The software STAAD Pro was used for the analysis and design.
Cost Optimization of Construction Using Plastic Waste as a Sustainable Constr...IRJET Journal
This research paper explores using plastic waste as a sustainable and cost-effective construction material. The study focuses on manufacturing pavers and bricks using recycled plastic and partially replacing concrete with plastic alternatives. Initial results found that pavers and bricks made from recycled plastic demonstrate comparable strength and durability to traditional materials while providing environmental and cost benefits. Additionally, preliminary research indicates incorporating plastic waste as a partial concrete replacement significantly reduces construction costs without compromising structural integrity. The outcomes suggest adopting plastic waste in construction can address plastic pollution while optimizing costs, promoting more sustainable building practices.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
DEEP LEARNING FOR SMART GRID INTRUSION DETECTION: A HYBRID CNN-LSTM-BASED MODELgerogepatton
As digital technology becomes more deeply embedded in power systems, protecting the communication
networks of Smart Grids (SG) has emerged as a critical concern. Distributed Network Protocol 3 (DNP3)
represents a multi-tiered application layer protocol extensively utilized in Supervisory Control and Data
Acquisition (SCADA)-based smart grids to facilitate real-time data gathering and control functionalities.
Robust Intrusion Detection Systems (IDS) are necessary for early threat detection and mitigation because
of the interconnection of these networks, which makes them vulnerable to a variety of cyberattacks. To
solve this issue, this paper develops a hybrid Deep Learning (DL) model specifically designed for intrusion
detection in smart grids. The proposed approach is a combination of the Convolutional Neural Network
(CNN) and the Long-Short-Term Memory algorithms (LSTM). We employed a recent intrusion detection
dataset (DNP3), which focuses on unauthorized commands and Denial of Service (DoS) cyberattacks, to
train and test our model. The results of our experiments show that our CNN-LSTM method is much better
at finding smart grid intrusions than other deep learning algorithms used for classification. In addition,
our proposed approach improves accuracy, precision, recall, and F1 score, achieving a high detection
accuracy rate of 99.50%.
Harnessing WebAssembly for Real-time Stateless Streaming PipelinesChristina Lin
Traditionally, dealing with real-time data pipelines has involved significant overhead, even for straightforward tasks like data transformation or masking. However, in this talk, we’ll venture into the dynamic realm of WebAssembly (WASM) and discover how it can revolutionize the creation of stateless streaming pipelines within a Kafka (Redpanda) broker. These pipelines are adept at managing low-latency, high-data-volume scenarios.
Introduction- e - waste – definition - sources of e-waste– hazardous substances in e-waste - effects of e-waste on environment and human health- need for e-waste management– e-waste handling rules - waste minimization techniques for managing e-waste – recycling of e-waste - disposal treatment methods of e- waste – mechanism of extraction of precious metal from leaching solution-global Scenario of E-waste – E-waste in India- case studies.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Literature Review Basics and Understanding Reference Management.pptxDr Ramhari Poudyal
Three-day training on academic research focuses on analytical tools at United Technical College, supported by the University Grant Commission, Nepal. 24-26 May 2024
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.