The concept of Big Data become extensively popular for their vast usage in emerging technologies. Despite being complex and dynamic, big data environment has been generating the colossal amount of data which is impossible to handle from traditional data processing applications. Nowadays, the Internet of things (IoT) and social media platforms like, Facebook, Instagram, Twitter, WhatsApp, LinkedIn, and YouTube generating data in various formats. Therefore, this promotes a drastic need for technology to store and process this tremendous volume of data. This research outlines the fundamental literature required to understand the concept of big data including its nature, definitions, types, and characteristics. Additionally, the primary focus of the current study is to deal with two fundamental issues; storing an enormous amount of data and fast data processing. Leading to objectives, the paper presents Hadoop as a solution to address the problem and discussed the Hadoop Distributed File System (HDFS) and MapReduce programming framework for storage and processing in Big Data efficiently. Future research directions in this field determined based on opportunities and several emerging issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal solutions to address Big Data storage and processing problems. Moreover, this study contributes to the existing body of knowledge by comprehensively addressing the opportunities and emerging issues of Big Data.
This document discusses big data and Hadoop frameworks for managing large volumes of data. It begins with an overview of how data generation has increased exponentially from employees to users to machines. Next, it discusses the history of big data technologies like Google File System and MapReduce, which were combined to create Hadoop. The document then covers sources of big data, challenges of big data, and how Hadoop provides a solution through distributed processing and its core components like HDFS and MapReduce. Finally, data processing techniques with traditional databases versus Hadoop are compared.
Big Data Systems: Past, Present & (Possibly) Future with @techmilindEMC
The document discusses the history and evolution of big data systems. It describes how traditional RDBMS databases could not handle the increasing volume, velocity, and variety of data being generated. This led to the development of new big data systems like Hadoop. Hadoop was developed based on Google's paper on its distributed file system GFS and MapReduce framework. Hadoop is now widely adopted and its ecosystem has grown to include components like Hive, Pig, and HBase. The document also discusses how big data systems are now used across many industries beyond internet companies to gain insights from large, diverse datasets.
IRJET- Youtube Data Sensitivity and Analysis using Hadoop FrameworkIRJET Journal
This document discusses analyzing YouTube data using the Hadoop framework. It proposes a system to filter and analyze YouTube comment content to remove sensitive data using natural language processing and store the data in Hadoop Distributed File System (HDFS). MapReduce is used to extract key-value pairs from the data and Hadoop provides a scalable platform for analyzing the large-scale YouTube data.
Lecture given at the University of Catania on December 2nd, 2014.
Start from Big Data definitions, continue with real life examples of successful Big Data Projects, go a little bit deeper with Sentiment Analysis, and conclude with a brief overview of Big Data tools and Big Data with Microsoft.
Summary:
1. What is Big Data? (includes the 5Vs of Big Data)
2. Big Data Examples (includes 6 Real Life Examples and comments on Privacy concerns)
3. How to Tackle a Big Data Problem (my 4 Universal Steps to follow)
4. Sentiment Analysis (what is sentiment analysis? Why do we care? A Technique and a plan)
5. Big Data tools (Hadoop, Hadoop Ecosystem, Hive, Pig, Sqoop, Oozie; Azure HDInsight, Excel Power Query, Power Pivot, Power View, Power Map)
Due to technological advances, vast data sets (e.g. big data) are increasing now days. Big Data a new term; is used
to identify the collected datasets. But due to their large size and complexity, we cannot manage with our current
methodologies or data mining software tools to extract those datasets. Such datasets provide us with unparalleled
opportunities for modelling and predicting of future with new challenges. So as an awareness of this and
weaknesses as well as the possibilities of these large data sets, are necessary to forecast the future. Today’s we
have an overwhelming growth of data in terms of volume, velocity and variety on web. Moreover this, from a
security and privacy views, both area have an unpredictable growth. So Big Data challenge is becoming one of the
most exciting opportunities for researchers in upcoming years.
Hence this paper discuss about this topic in a broad overview like; its current status; controversy; and challenges to
forecast the future. This paper defines at some of these problems, using illustrations with applications from various
areas. Finally this paper discuss secure management and privacy of big data as one of essential issues.
A Review Paper on Big Data: Technologies, Tools and TrendsIRJET Journal
This document provides a review of big data technologies, tools, and trends. It begins with an introduction to big data, discussing the rapid growth in data volumes and defining key characteristics like variety, velocity, and veracity. Common sources of big data are described, such as IoT devices, social media, and scientific projects. Hadoop is discussed as a major tool for big data management, with components like HDFS for scalable data storage. Overall, the document aims to discuss the state of big data technologies and challenges, as well as future domains and trends.
This document provides an overview of big data by discussing its background and definitions. It describes how data has grown exponentially in recent years due to factors like the internet, cloud computing, and internet of things. Big data is defined as data that cannot be processed by traditional technologies due to its huge size, speed of growth, and variety of data types. The document outlines several common definitions of big data, including the 3Vs (volume, velocity, variety) and 4Vs (volume, variety, velocity, value) models. It aims to provide readers with a comprehensive understanding of the emerging field of big data.
This document discusses big data and Hadoop frameworks for managing large volumes of data. It begins with an overview of how data generation has increased exponentially from employees to users to machines. Next, it discusses the history of big data technologies like Google File System and MapReduce, which were combined to create Hadoop. The document then covers sources of big data, challenges of big data, and how Hadoop provides a solution through distributed processing and its core components like HDFS and MapReduce. Finally, data processing techniques with traditional databases versus Hadoop are compared.
Big Data Systems: Past, Present & (Possibly) Future with @techmilindEMC
The document discusses the history and evolution of big data systems. It describes how traditional RDBMS databases could not handle the increasing volume, velocity, and variety of data being generated. This led to the development of new big data systems like Hadoop. Hadoop was developed based on Google's paper on its distributed file system GFS and MapReduce framework. Hadoop is now widely adopted and its ecosystem has grown to include components like Hive, Pig, and HBase. The document also discusses how big data systems are now used across many industries beyond internet companies to gain insights from large, diverse datasets.
IRJET- Youtube Data Sensitivity and Analysis using Hadoop FrameworkIRJET Journal
This document discusses analyzing YouTube data using the Hadoop framework. It proposes a system to filter and analyze YouTube comment content to remove sensitive data using natural language processing and store the data in Hadoop Distributed File System (HDFS). MapReduce is used to extract key-value pairs from the data and Hadoop provides a scalable platform for analyzing the large-scale YouTube data.
Lecture given at the University of Catania on December 2nd, 2014.
Start from Big Data definitions, continue with real life examples of successful Big Data Projects, go a little bit deeper with Sentiment Analysis, and conclude with a brief overview of Big Data tools and Big Data with Microsoft.
Summary:
1. What is Big Data? (includes the 5Vs of Big Data)
2. Big Data Examples (includes 6 Real Life Examples and comments on Privacy concerns)
3. How to Tackle a Big Data Problem (my 4 Universal Steps to follow)
4. Sentiment Analysis (what is sentiment analysis? Why do we care? A Technique and a plan)
5. Big Data tools (Hadoop, Hadoop Ecosystem, Hive, Pig, Sqoop, Oozie; Azure HDInsight, Excel Power Query, Power Pivot, Power View, Power Map)
Due to technological advances, vast data sets (e.g. big data) are increasing now days. Big Data a new term; is used
to identify the collected datasets. But due to their large size and complexity, we cannot manage with our current
methodologies or data mining software tools to extract those datasets. Such datasets provide us with unparalleled
opportunities for modelling and predicting of future with new challenges. So as an awareness of this and
weaknesses as well as the possibilities of these large data sets, are necessary to forecast the future. Today’s we
have an overwhelming growth of data in terms of volume, velocity and variety on web. Moreover this, from a
security and privacy views, both area have an unpredictable growth. So Big Data challenge is becoming one of the
most exciting opportunities for researchers in upcoming years.
Hence this paper discuss about this topic in a broad overview like; its current status; controversy; and challenges to
forecast the future. This paper defines at some of these problems, using illustrations with applications from various
areas. Finally this paper discuss secure management and privacy of big data as one of essential issues.
A Review Paper on Big Data: Technologies, Tools and TrendsIRJET Journal
This document provides a review of big data technologies, tools, and trends. It begins with an introduction to big data, discussing the rapid growth in data volumes and defining key characteristics like variety, velocity, and veracity. Common sources of big data are described, such as IoT devices, social media, and scientific projects. Hadoop is discussed as a major tool for big data management, with components like HDFS for scalable data storage. Overall, the document aims to discuss the state of big data technologies and challenges, as well as future domains and trends.
This document provides an overview of big data by discussing its background and definitions. It describes how data has grown exponentially in recent years due to factors like the internet, cloud computing, and internet of things. Big data is defined as data that cannot be processed by traditional technologies due to its huge size, speed of growth, and variety of data types. The document outlines several common definitions of big data, including the 3Vs (volume, velocity, variety) and 4Vs (volume, variety, velocity, value) models. It aims to provide readers with a comprehensive understanding of the emerging field of big data.
This document discusses data mining techniques for big data. It defines big data as large, complex collections of data from various sources that contain both structured and unstructured data. Big data is growing rapidly due to data from sources like social media, sensors, and digital content. Data mining can extract useful insights from big data by discovering patterns and relationships. The document outlines common data mining techniques like classification, prediction, clustering and association rule mining that can be applied to big data. It also discusses challenges of big data like its huge volume, variety of data types, and rapid growth that require new data management approaches.
Convergence Partners has released its latest research report on big data and its meaning for Africa. The report argues that big data poses a threat to those it overlooks, namely a large percentage of Africa’s populace, who remain on big data’s periphery.
The document discusses big data and how it is often misunderstood and overhyped. It argues that big data needs to be objectively defined in order to make meaningful claims and measurements regarding its success. Jumping into big data without proper preparation often leads to the same dismal results as failed IT projects. The document advocates separating the signal from the noise to truly understand big data.
Implementation of application for huge data file transferijwmn
Nowadays big data transfers make people’s life difficult. During the big data transfer, people waste so
much time. Big data pool grows everyday by sharing data. People prefer to keep their backups at the cloud
systems rather than their computers. Furthermore considering the safety of cloud systems, people prefer to
keep their data at the cloud systems instead of their computers. When backups getting too much size, their
data transfer becomes nearly impossible. It is obligated to transfer data with various algorithms for moving
data from one place to another. These algorithms constituted for transferring data faster and safer. In this
Project, an application has been developed to transfer of the huge files. Test results show its efficiency and
success.
This document discusses big data and Hadoop. It begins by describing the rapid growth of data from sources around the world. Hadoop provides a solution to challenges in storing and processing large volumes of unstructured data across distributed systems. The document then discusses key aspects of big data including the five V's (volume, velocity, variety, value and veracity). It provides examples of large companies using Hadoop and big data like Google, Facebook, Amazon and Twitter. The document concludes that Hadoop is well-suited for batch processing large datasets and provides advantages over relational database management systems.
The document discusses how data has become a central business asset and strategic advantage. It notes that the growth of data from sources like the Internet of Things means that variety, not just volume or velocity, will be important. New business processes will revolve around data, which will become more valuable over the next decade. It also provides examples of how companies like eBay and Groupon have used data for competitive advantages like identifying top sellers.
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
This document discusses big data and machine learning. It defines big data as large amounts of data that are analyzed by machines. It describes how data is increasingly coming from sources like smartphones, sensors, and the Internet. It also discusses how machine learning allows computers to learn from large amounts of data without being explicitly programmed, and how this is enabling automation and new applications of artificial intelligence.
Semantic Web Investigation within Big Data ContextMurad Daryousse
This document discusses how the semantic web can help address challenges associated with big data. It describes the 5 V's of big data: volume, variety, velocity, veracity, and value. For each V, it outlines related challenges in data acquisition, integration, and analysis. The document argues that semantic web concepts like ontologies, linked data, and reasoning can help solve problems of data heterogeneity, scale, and timeliness across different phases of the big data analysis pipeline, in order to ultimately extract value from data.
Big data document (basic concepts,3vs,Bigdata vs Smalldata,importance,storage...Taniya Fansupkar
This document provides an overview of big data, including its definition, origins, characteristics, importance, and opportunities and challenges. It describes big data as large volumes of diverse data that require new technologies and techniques to capture, curate, manage and process within a tolerable time. Big data is characterized by its volume, velocity and variety. Analyzing big data can provide benefits such as cost reductions, time reductions, new product development and smart decision making. It also discusses storing, processing and analyzing data at the edge of networks.
This document provides an overview of big data, including its definition, characteristics, examples, analysis methods, and challenges. It discusses how big data is characterized by its volume, variety, and velocity. Examples of big data are given from various industries like healthcare, retail, manufacturing, and web/social media. Analysis methods for big data like MapReduce, Hadoop, and HPCC are described and compared. The document also covers privacy and security issues that arise from big data analytics.
Interesting ways Big Data is used todayDaniel Sârbe
An overview on the Big Data field, interesting patterns on how data is used to make data mining, predictive analytics, machine learning and an overview on the jobs generated by the Big Data demand.
This document discusses the challenges of building a network infrastructure to support big data applications. Large amounts of data are being generated every day from a variety of sources and need to be aggregated and processed in powerful data centers. However, networks must be optimized to efficiently gather data from distributed sources, transport it to data centers over the Internet backbone, and distribute results. The unique demands of big data in terms of volume, variety and velocity are testing whether current networks can keep up. The document examines each segment of the required network from access networks to inter-data center networks and the challenges in supporting big data applications.
This document discusses huge data and data mining. It defines huge data and notes that huge amounts of data are being created daily from sources like social media, sensors, and digital content. It discusses some key aspects of huge data including that it can be structured or unstructured, comes from decentralized sources, and has complexity in relationships within the data. The 3Vs of huge data are also defined as volume, variety, and velocity. The document states that data mining techniques can be used to extract useful insights from huge data by discovering patterns and relationships within large datasets.
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
The story of how data became big starts many years before the current buzz around big data.The history of Big Data as a term may be brief – but many of the foundations it is built on were laid many years ago. Now, let’s look at a detailed account of the major milestones in the history of sizing data volumes in the evolution of the idea of “big data” and observations pertaining to data or information explosion:
The document discusses the concept of "Broad Data" which refers to the large amount of freely available but widely varied open data on the World Wide Web, including structured and semi-structured data. It provides examples such as the growing linked open data cloud and over 710,000 datasets available from governments around the world. Broad data poses new challenges for data search, modeling, integration and visualization of partially modeled datasets. International open government data search and linking government data to additional contexts are also discussed.
Al-Khouri, A.M. (2014) "Privacy in the Age of Big Data: Exploring the Role of Modern Identity Management Systems". World Journal of Social Science, Vol. 1, No. 1, pp. 37-47.
US EPA OSWER Linked Data Workshop 1-Feb-20133 Round Stones
Overview of US EPA's Linked Data Service to launch in early 2013. Open data published using the Linked Data model increases search engines' ability to find and display high value data sets. Linked Data enables policy makers, analysts and developers to more readily access and re-use data.
This document provides an overview of big data and commonly used methodologies. It defines big data as large volumes of complex data from various sources that is difficult to process using traditional data management tools. The key aspects of big data are volume, variety, and velocity. Hadoop is discussed as a popular framework for processing big data using the MapReduce programming model. HDFS is summarized as a distributed file system used with Hadoop to store and manage large datasets across clusters of computers. Challenges of big data such as storage capacity, processing large and complex datasets, and real-time analytics are also mentioned.
Big Data Handling Technologies ICCCS 2014_Love Arora _GNDU Love Arora
Big data came into existence when the traditional relational database systems were not able to handle the unstructured data (weblogs, videos, photos, social updates, human behaviour) generated today by organisation, social media, or from any other data generating source. Data that is so large in volume, so diverse in variety or moving with such velocity is called Big data. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant, flexible and scalable. The technologies used by big data application to handle the massive data are Hadoop, Map Reduce, Apache Hive, No SQL and HPCC. These technologies handle massive amount of data in MB, PB, YB, ZB, KB, and TB.
In this research paper various technologies for handling big data along with the advantages and disadvantages of each technology for catering the problems in hand to deal the massive data has discussed.
This document discusses data mining techniques for big data. It defines big data as large, complex collections of data from various sources that contain both structured and unstructured data. Big data is growing rapidly due to data from sources like social media, sensors, and digital content. Data mining can extract useful insights from big data by discovering patterns and relationships. The document outlines common data mining techniques like classification, prediction, clustering and association rule mining that can be applied to big data. It also discusses challenges of big data like its huge volume, variety of data types, and rapid growth that require new data management approaches.
Convergence Partners has released its latest research report on big data and its meaning for Africa. The report argues that big data poses a threat to those it overlooks, namely a large percentage of Africa’s populace, who remain on big data’s periphery.
The document discusses big data and how it is often misunderstood and overhyped. It argues that big data needs to be objectively defined in order to make meaningful claims and measurements regarding its success. Jumping into big data without proper preparation often leads to the same dismal results as failed IT projects. The document advocates separating the signal from the noise to truly understand big data.
Implementation of application for huge data file transferijwmn
Nowadays big data transfers make people’s life difficult. During the big data transfer, people waste so
much time. Big data pool grows everyday by sharing data. People prefer to keep their backups at the cloud
systems rather than their computers. Furthermore considering the safety of cloud systems, people prefer to
keep their data at the cloud systems instead of their computers. When backups getting too much size, their
data transfer becomes nearly impossible. It is obligated to transfer data with various algorithms for moving
data from one place to another. These algorithms constituted for transferring data faster and safer. In this
Project, an application has been developed to transfer of the huge files. Test results show its efficiency and
success.
This document discusses big data and Hadoop. It begins by describing the rapid growth of data from sources around the world. Hadoop provides a solution to challenges in storing and processing large volumes of unstructured data across distributed systems. The document then discusses key aspects of big data including the five V's (volume, velocity, variety, value and veracity). It provides examples of large companies using Hadoop and big data like Google, Facebook, Amazon and Twitter. The document concludes that Hadoop is well-suited for batch processing large datasets and provides advantages over relational database management systems.
The document discusses how data has become a central business asset and strategic advantage. It notes that the growth of data from sources like the Internet of Things means that variety, not just volume or velocity, will be important. New business processes will revolve around data, which will become more valuable over the next decade. It also provides examples of how companies like eBay and Groupon have used data for competitive advantages like identifying top sellers.
The document discusses big data challenges faced by organizations. It identifies several key challenges: heterogeneity and incompleteness of data, issues of scale as data volumes increase, timeliness in processing large datasets, privacy concerns, and the need for human collaboration in analyzing data. The document describes surveying various organizations in Pakistan, including educational institutions, telecommunications companies, hospitals, and electrical utilities, to understand the big data problems they face. Common challenges included data errors, missing or incomplete data, lack of data management tools, and issues integrating different data sources. The survey found that while some organizations used big data tools, many educational institutions in particular did not, limiting their ability to effectively manage and analyze their large and growing datasets.
This document discusses big data and machine learning. It defines big data as large amounts of data that are analyzed by machines. It describes how data is increasingly coming from sources like smartphones, sensors, and the Internet. It also discusses how machine learning allows computers to learn from large amounts of data without being explicitly programmed, and how this is enabling automation and new applications of artificial intelligence.
Semantic Web Investigation within Big Data ContextMurad Daryousse
This document discusses how the semantic web can help address challenges associated with big data. It describes the 5 V's of big data: volume, variety, velocity, veracity, and value. For each V, it outlines related challenges in data acquisition, integration, and analysis. The document argues that semantic web concepts like ontologies, linked data, and reasoning can help solve problems of data heterogeneity, scale, and timeliness across different phases of the big data analysis pipeline, in order to ultimately extract value from data.
Big data document (basic concepts,3vs,Bigdata vs Smalldata,importance,storage...Taniya Fansupkar
This document provides an overview of big data, including its definition, origins, characteristics, importance, and opportunities and challenges. It describes big data as large volumes of diverse data that require new technologies and techniques to capture, curate, manage and process within a tolerable time. Big data is characterized by its volume, velocity and variety. Analyzing big data can provide benefits such as cost reductions, time reductions, new product development and smart decision making. It also discusses storing, processing and analyzing data at the edge of networks.
This document provides an overview of big data, including its definition, characteristics, examples, analysis methods, and challenges. It discusses how big data is characterized by its volume, variety, and velocity. Examples of big data are given from various industries like healthcare, retail, manufacturing, and web/social media. Analysis methods for big data like MapReduce, Hadoop, and HPCC are described and compared. The document also covers privacy and security issues that arise from big data analytics.
Interesting ways Big Data is used todayDaniel Sârbe
An overview on the Big Data field, interesting patterns on how data is used to make data mining, predictive analytics, machine learning and an overview on the jobs generated by the Big Data demand.
This document discusses the challenges of building a network infrastructure to support big data applications. Large amounts of data are being generated every day from a variety of sources and need to be aggregated and processed in powerful data centers. However, networks must be optimized to efficiently gather data from distributed sources, transport it to data centers over the Internet backbone, and distribute results. The unique demands of big data in terms of volume, variety and velocity are testing whether current networks can keep up. The document examines each segment of the required network from access networks to inter-data center networks and the challenges in supporting big data applications.
This document discusses huge data and data mining. It defines huge data and notes that huge amounts of data are being created daily from sources like social media, sensors, and digital content. It discusses some key aspects of huge data including that it can be structured or unstructured, comes from decentralized sources, and has complexity in relationships within the data. The 3Vs of huge data are also defined as volume, variety, and velocity. The document states that data mining techniques can be used to extract useful insights from huge data by discovering patterns and relationships within large datasets.
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
The story of how data became big starts many years before the current buzz around big data.The history of Big Data as a term may be brief – but many of the foundations it is built on were laid many years ago. Now, let’s look at a detailed account of the major milestones in the history of sizing data volumes in the evolution of the idea of “big data” and observations pertaining to data or information explosion:
The document discusses the concept of "Broad Data" which refers to the large amount of freely available but widely varied open data on the World Wide Web, including structured and semi-structured data. It provides examples such as the growing linked open data cloud and over 710,000 datasets available from governments around the world. Broad data poses new challenges for data search, modeling, integration and visualization of partially modeled datasets. International open government data search and linking government data to additional contexts are also discussed.
Al-Khouri, A.M. (2014) "Privacy in the Age of Big Data: Exploring the Role of Modern Identity Management Systems". World Journal of Social Science, Vol. 1, No. 1, pp. 37-47.
US EPA OSWER Linked Data Workshop 1-Feb-20133 Round Stones
Overview of US EPA's Linked Data Service to launch in early 2013. Open data published using the Linked Data model increases search engines' ability to find and display high value data sets. Linked Data enables policy makers, analysts and developers to more readily access and re-use data.
This document provides an overview of big data and commonly used methodologies. It defines big data as large volumes of complex data from various sources that is difficult to process using traditional data management tools. The key aspects of big data are volume, variety, and velocity. Hadoop is discussed as a popular framework for processing big data using the MapReduce programming model. HDFS is summarized as a distributed file system used with Hadoop to store and manage large datasets across clusters of computers. Challenges of big data such as storage capacity, processing large and complex datasets, and real-time analytics are also mentioned.
Big Data Handling Technologies ICCCS 2014_Love Arora _GNDU Love Arora
Big data came into existence when the traditional relational database systems were not able to handle the unstructured data (weblogs, videos, photos, social updates, human behaviour) generated today by organisation, social media, or from any other data generating source. Data that is so large in volume, so diverse in variety or moving with such velocity is called Big data. Analyzing Big Data is a challenging task as it involves large distributed file systems which should be fault tolerant, flexible and scalable. The technologies used by big data application to handle the massive data are Hadoop, Map Reduce, Apache Hive, No SQL and HPCC. These technologies handle massive amount of data in MB, PB, YB, ZB, KB, and TB.
In this research paper various technologies for handling big data along with the advantages and disadvantages of each technology for catering the problems in hand to deal the massive data has discussed.
Analysis on big data concepts and applicationsIJARIIT
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
Moving Toward Big Data: Challenges, Trends and PerspectivesIJRESJOURNAL
Abstract: Big data refers to the organizational data asset that exceeds the volume, velocity, and variety of data typically stored using traditional structured database technologies. This type of data has become the important resource from which organizations can get valuable insightand make business decision by applying predictive analysis. This paper provides a comprehensive view of current status of big data development,starting from the definition and the description of Hadoop and MapReduce – the framework that standardizes the use of cluster of commodity machines to analyze big data. For the organizations that are ready to embrace big data technology, significant adjustments on infrastructure andthe roles played byIT professionals and BI practitioners must be anticipated which is discussed in the challenges of big data section. The landscape of big data development change rapidly which is directly related to the trend of big data. Clearly, a major part of the trend is the result ofthe attempt to deal with the challenges discussed earlier. Lastly the paper includes the most recent job prospective related to big data. The description of several job titles that comprise the workforce in the area of big data are also included.
This document discusses big data and its applications. It begins with an abstract that introduces big data and how extracting valuable information from large amounts of structured and unstructured data can help governments and organizations develop policies. It then discusses key aspects of big data including volume, velocity, and variety. Current big data technologies are outlined such as Hadoop, HBase, and Hive. Some big data problems and applications are also mentioned like using big data in commerce, business, and scientific research to improve forecasting, policies, productivity, and research.
Big Data and Big Data Management (BDM) with current Technologies –ReviewIJERA Editor
The emerging phenomenon called ―Big Data‖ is pushing numerous changes in businesses and several other organizations, Domains, Fields, areas etc. Many of them are struggling just to manage the massive data sets. Big data management is about two things - ―Big data‖ and ―Data Management‖ and these terms work together to achieve business and technology goals as well. In previous few years data generation have tremendously enhanced due to digitization of data. Day by day new computer tools and technologies for transmission of data among several computers through Internet is been increasing. It‗s relevance and importance in the context of applicability, usefulness for decision making, performance improvement etc in all areas have emerged very fast to be relevant in today‗s era. Big data management also has numerous challenges and common complexities include low organizational maturity relative to big data, weak business support, and the need to learn new technology approaches. This paper will discuss the impacts of Big Data and issues related to data management using current technologies
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value. We then present background technology for big data summarization brings to us. The objective of this paper is to discuss the big data summarization framework, challenges and possible solutions as well as methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.
We then present background technology for big data summarization brings to us. The objective of this
paper is to discuss the big data summarization framework, challenges and possible solutions as well as
methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of open problems and future directions..
BIG DATA SUMMARIZATION: FRAMEWORK, CHALLENGES AND POSSIBLE SOLUTIONSaciijournal
This document discusses big data summarization, including the challenges and potential solutions. It presents a framework for big data summarization with four main stages: 1) data clustering to group similar documents, 2) data generalization to abstract data to a higher conceptual level, 3) semantic term identification to identify metadata for more efficient data representation, and 4) evaluation of the summaries. Key challenges addressed include initializing clustering methods, selecting attributes to control generalization, and ensuring semantic associations in representations. Solutions proposed are detailed assessments of clustering initialization methods and statistical approaches for clustering, generalization and term identification.
Big Data Summarization : Framework, Challenges and Possible Solutionsaciijournal
In this paper, we first briefly review the concept of big data, including its definition, features, and value.
We then present background technology for big data summarization brings to us. The objective of this
paper is to discuss the big data summarization framework, challenges and possible solutions as well as
methods of evaluation for big data summarization. Finally, we conclude the paper with a discussion of
open problems and future directions..
This document provides an overview of big data and Hadoop. It defines big data as large volumes of diverse data that cannot be processed by traditional systems. Key characteristics are volume, velocity, variety, and veracity. Popular sources of big data include social media, emails, videos, and sensor data. Hadoop is presented as an open-source framework for distributed storage and processing of large datasets across clusters of computers. It uses HDFS for storage and MapReduce as a programming model. Major tech companies like Google, Facebook, and Amazon are discussed as big players in big data.
This document provides an overview of big data. It defines big data as large volumes of data that are high in velocity and variety, requiring new techniques and tools to analyze. Examples are given of the huge amounts of data generated daily by companies like Facebook, Twitter, and YouTube. The benefits of big data analytics are described as enabling better business decisions through hidden patterns, customer insights, and competitive advantages. The future of big data is promising, with the market expected to grow substantially in both revenue and jobs required to manage large amounts of data.
Mayank kaintura presents slides on big data to Miss Isha Pant. He thanks her for the opportunity to explore the concept beyond the syllabus, which helped him gain a good score and clearer understanding. Big data is growing exponentially as more devices generate data. It requires different techniques than traditional data due to its large size and diverse formats like text, images, videos. Organizations are using big data to gain insights, improve products/services, and make better decisions. It is an important and challenging area for IT with many job opportunities.
Data Mining in the World of BIG Data-A SurveyEditor IJCATR
Rapid development and popularization of internet and technological advancement introduced massive amount
of data and still increasing continuously and daily. A very large amount of data generated, collected, stored, transferred by
applications such as sensors, smart mobile devices, cloud systems and social networks put us on the era of BIG data, a data
with huge size, complex and unstructured data types from many origins. So converting these BIG data into useful information
is essential, the technique for discovering hidden interesting patterns and knowledge insights into BIG data introduced
as BIG data mining. BIG data have rises so many problems and challenges related with handling, storing, managing,
transferring, analyzing and mining but it has provides new directions and wide range of opportunities for research
and information extraction and future of some technologies such as data mining in the terms of BIG data mining. In this
paper, we present the concept of BIG data and BIG data mining and mentioned problems with BIG data mining and listed
new research directions for BIG data mining and problems with traditional data mining techniques while dealing with
BIG data as well as we have also discuss some comparison between traditional data mining algorithms and some big data
mining algorithms that will be useful for new BIG data mining technology future.
Big data PPT prepared by Hritika Raj (Shivalik college of engg.)Hritika Raj
This document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, risks and benefits. Big data is characterized by volume, velocity and variety of structured and unstructured data that is growing exponentially. It is generated from sources like mobile devices, sensors, social media and more. Tools like Hadoop, MapReduce and data analytics are used to extract value from big data. Potential applications include healthcare, security, manufacturing and more. Risks include privacy and scale, while benefits include improved decision making and new business opportunities. The big data industry is rapidly growing and transforming IT and business.
This document summarizes a research paper on big data and Hadoop. It begins by defining big data and explaining how the volume, variety and velocity of data makes it difficult to process using traditional methods. It then discusses Hadoop, an open source software used to analyze large datasets across clusters of computers. Hadoop uses HDFS for storage and MapReduce as a programming model to distribute processing. The document outlines some of the key challenges of big data including privacy, security, data access and analytical challenges. It also summarizes advantages of big data in areas like understanding customers, optimizing business processes, improving science and healthcare.
This document provides an overview of big data concepts and Hadoop. It discusses the characteristics of big data including volume, variety and velocity. It compares traditional data warehouses to Hadoop and explains when each is best suited. Use cases of big data from various companies are presented. The document also summarizes a survey on big data adoption trends and priorities across industries. Finally, it provides details on the Hadoop framework and its key components.
Advanced live Final Year CSE Academic IEEE Major Big Data Projects in Hyderabad for Final Year Students of Engineering. Computer Science and Engineering latest major Big Data Projects."
Similar a A Roadmap Towards Big Data Opportunities, Emerging Issues and Hadoop as a Solution (20)
A Critical Survey on Privacy Prevailing in Mobile Cloud Computing: Challenges...Rida Qayyum
With the explosive growth of mobile applications and extensive praxis of cloud computing, mobile cloud computing has been introduced to be a potential technology for mobile services. But privacy is the main concern for a mobile user in the modern era. In the current study, we address the privacy challenges faced by mobile users while outsourcing their data to the service provider for storage and processing. However, a secure mobile user is required to protect these fundamental privacy factors such as their personal data, real identity, current location and the actual query sent to the cloud vendor server while availing different cloud services. Under these privacy metrics, we evaluated the existing approaches that are counting privacy challenge in mobile cloud computing. The primary focus of this study is to presents a critical survey of recent privacy protection techniques. Leading to objective, the current study conduct a comparative analysis of these state of the art methods with their strong points, privacy level and scalability. After analysis, this paper suggests the pseudo-random permutation method could be a promising solution that can be taken into consideration for preserving user personal information and data query privacy in MCC more efficiently. Primarily, the purpose of the survey was to focus on further advancements of the suggested method. Furthermore, we present the future research directions in the mobile cloud computing paradigms.
Never ask anyone to assign you a research topic because you don't have enough knowledge without reading in this domain. Therefore, this presentation provides all necessary steps to find out a research topic.
External Defense (TTP based LBS System) Rida Qayyum
Muhammad Usman Ashraf, Rida Qayyum, and Hina Ejaz, ”STATE- OF-THE-ART, CHALLENGES: PRIVACY PROVISIONING IN TTP LOCATION BASED SERVICES SYSTEMS”, International Journal of Advanced Research in Computer Science (IJARCS), Vol. 10, No. 2, pp. 68-75, April 2019. DOI: https://doi.org/10.26483/ijarcs.v10i2
Rida Qayyum, Hina Ejaz “Provisioning Privacy for TIP Attribute in Trusted Third Party (TTP) Location Based Services (LBS) System”, May 2019. DOI: 10.13140/RG.2.2.25631.59041
Rida Qayyum, Hina Ejaz, " Data Security in Mobile Cloud Computing: A State of the Art Review", International Journal of Modern Education and Computer Science (IJMECS), Vol. 12, No. 2, pp. 30-35, April 2020. DOI: 10.5815/ijmecs.2020.02.04
Muhammad Usman Ashraf, Kamal M. Jambi, Rida Qayyum, Hina Ejaz, and Iqra Ilyas “IDP: A Privacy Provisioning Framework for TIP Attributes in Trusted Third Party-based Location-based Services Systems”, International Journal of Advanced Computer Science and Applications(IJACSA), 11(7), pp. 604-617, July 2020. DOI: 10.14569/IJACSA.2020.0110773
Rida Qayyum. " A Roadmap Towards Big Data Opportunities, Emerging Issues and Hadoop as a Solution ", International Journal of Education and Management Engineering (IJEME), Vol.10, No.4, pp.8-17, 2020. DOI: 10.5815/ijeme.2020.04.02
Rida Qayyum,Hina Ejaz."A Comparative Study of Location Based Services Simulators". International Journal of Computer Engineering In Research Trends (IJCERT) ,ISSN:2349-7084 ,Vol.7, Issue 11,pp.1-12, November 2020, DOI :10.22362/ijcert/2020/v7/i11/v7i1101
Provisioning Privacy for TIP Attribute in Trusted Third Party (TTP) Location ...Rida Qayyum
Currently, Location Based Services (LBS) System is rapidly growing due to radio communication services with wireless mobile devices having a positioning component in it. LBS System offers location-based services by knowing the actual user position. A mobile user uses LBS to access services relevant to their locations. In order to provide Point of Interest (POI), LBS confronts numerous privacy-related challenges. Conventionally, LBS systems are treat in three ways including Non-Trusted Third Party (NTTP), Trusted Third Party (TTP) and Mobile Peer-to-Peer (P2P). The current study emphasized the TTP based LBS system where the Location server does not provide full privacy to mobile users. In TTP based LBS system, a user’s privacy is concerned with personal identity, location information, and time information. In order to accomplish privacy under these concerns, state-of-the-art existing mechanisms have been reviewed. Hence, the aim to provide a promising roadmap to research and development communities for right selection of privacy approach has achieved by conducting a comparative survey of the TTP based approaches. Leading to these privacy attributes, current study addressed the privacy challenge by proposing a new privacy protection model named “Improved Dummy Position” (IDP) that protects TIP (Time, Identity, and Position) attributes under TTP LBS System. In order to validate the privacy level, a comparative analysis has been conduct by implementing the proposed IDP model in the simulation tool, Riverbed Modeler academic edition. The different scenarios of changing query transferring rate evaluate the performance of proposed model. Simulation results demonstrate that our IDP could be considered as a promising model to protect user’s TIP attributes in a TTP based LBS system due to better performance and improved privacy level. Further, the proposed model extensively compared with the existing work.
Data Security in Mobile Cloud Computing A State of the Art ReviewRida Qayyum
Due to tremendous use of smartphones the concern of cloud computing in mobile devices emerges, which is known as Mobile Cloud Computing (MCC). It involves the usage of mobile devices and cloud computing to perform resource intensive tasks using the internet with minimum impact on cellular resources. Nowadays, people are relying on mobile devices due to their small size and user friendly interface but due to its limited storage capacity, people can no more rely on internal RAM. Therefore, this promotes a drastic need for technology to make it possible for anyone to access their data anywhere anytime. As a result, Mobile Cloud Computing facilitates mobile users with its enticing technology by providing its on-demand and scalable services. But privacy and security are the main concern for a mobile user in the modern era. Thus, issues regarding security can be divided into cloud security and mobile network user’s security, respectively. However, the primary focus of this study is to analyze how to secure the user's data in a mobile cloud. Leading to objectives, the current study presents a comprehensive analysis of existing techniques that can be considered for securing data in MCC efficiently. Moreover, this work will contribute a state-of-the-art roadmap to research and development communities for the right selection of proposed approach.
A Comparative Study of Location Based Services SimulatorsRida Qayyum
Location-based services are now extremely prevalent due to their massive usage in current and emerging technologies. The use of simulation tools has been gaining popularity in the domain of LBS systems, where researchers take advantage of simulators for evaluating the behavior and performance of new architecture design. This popularity results from the availability of various powerful and sophisticated LBS simulators that are continuously verifying the flexibility of proposed models of LBS research projects. Despite its popularity worldwide, there is still a problem for researchers to choose the best simulator according to their needs and requirements, which provide them accurate results. Furthermore, conducting research on the physical LBS environment for individuals or small educational institutes is very challenging due to the cost involved in setting up location-based services live. Therefore, for selecting an appropriate LBS simulator, it is important to have knowledge of simulators that are currently available along with their features and selection criteria considered for conducting research in a particular type of problems in the LBS system. In the current study, we have presented various simulators that provide a cost-effective way of conducting LBS research projects. This paper compares 10 simulators to help researchers and developers for selecting the most appropriate simulation tool depending on selection criteria. Moreover, a detailed discussion with the recommendation for best practice in LBS simulation tools is also included in this paper, which would surely help new researchers to quickly identify the most suitable simulator according to their research problem.
IDP: A Privacy Provisioning Framework for TIP Attributes in Trusted Third Par...Rida Qayyum
Location-Based Services (LBS) System is rapidly growing due to radio communication services with wireless mobile devices having a positioning component in it. LBS System offers location-based services by knowing the actual user position. A mobile user uses LBS to access services relevant to their locations. In order to provide Point of Interest (POI), LBS confronts numerous privacy related challenges in three different formats including Non-Trusted Third Party (NTTP), Trusted Third Party (TTP), and Mobile Peer-to-Peer (P2P). The current study emphasized the TTP based LBS system where the Location server does not provide full privacy to mobile users. In TTP based LBS system, a user’s privacy is concerned with personal identity, location information, and time information. In order to accomplish privacy under these concerns, state-of-the-art existing mechanisms have been reviewed. Hence, the aim to provide a promising roadmap to research and development communities for the right selection of privacy approach has achieved by conducting a comparative survey of the TTP based approaches. Leading to these privacy attributes, the current study addressed the privacy challenge by proposing a new privacy protection model named “Improved Dummy Position” (IDP) that protects TIP (Time, Identity, and Position) attributes under TTP LBS System. In order to validate the privacy level, a comparative analysis has been conducted by implementing the proposed IDP model in the simulation tool, Riverbed Modeler academic edition. The different scenarios of changing query transferring rate evaluate the performance of the proposed model. Simulation results demonstrate that our IDP could be considered as a promising model to protect user’s TIP attributes in a TTP based LBS system due to better performance and improved privacy level. Further, the proposed model extensively compared with the existing work.
STATE-OF-THE-ART, CHALLENGES: PRIVACY PROVISIONING IN TTP LOCATION BASED SERV...Rida Qayyum
Nowadays, Location-based services (LBS) System is commonly used by Mobile users worldwide due to the immense growth of the Internet and Mobile devices. A mobile user uses LBS to access services relevant to their locations. LBS usage raises severe privacy concerns. A secure LBS system is required to protect three fundamentals metrics such as temporal information, user identity, and spatial information. Different models are being used to deal with such privacy metrics such as TTP and NTTP. In current study, we have conducted a comprehensive survey on TTP privacy protecting techniques which are being used in LBS systems. Primarily, it would be facilitating the mobile users with full privacy when they interact with the LBS system. Moreover, it is aimed to provide a promising roadmap to research and development communities for right selection of privacy approach.
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
Introduction of Cybersecurity with OSS at Code Europe 2024Hiroshi SHIBATA
I develop the Ruby programming language, RubyGems, and Bundler, which are package managers for Ruby. Today, I will introduce how to enhance the security of your application using open-source software (OSS) examples from Ruby and RubyGems.
The first topic is CVE (Common Vulnerabilities and Exposures). I have published CVEs many times. But what exactly is a CVE? I'll provide a basic understanding of CVEs and explain how to detect and handle vulnerabilities in OSS.
Next, let's discuss package managers. Package managers play a critical role in the OSS ecosystem. I'll explain how to manage library dependencies in your application.
I'll share insights into how the Ruby and RubyGems core team works to keep our ecosystem safe. By the end of this talk, you'll have a better understanding of how to safeguard your code.
Skybuffer SAM4U tool for SAP license adoptionTatiana Kojar
Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
Ocean lotus Threat actors project by John Sitima 2024 (1).pptxSitimaJohn
Ocean Lotus cyber threat actors represent a sophisticated, persistent, and politically motivated group that poses a significant risk to organizations and individuals in the Southeast Asian region. Their continuous evolution and adaptability underscore the need for robust cybersecurity measures and international cooperation to identify and mitigate the threats posed by such advanced persistent threat groups.
For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.
Monitoring and Managing Anomaly Detection on OpenShift.pdfTosin Akinosho
Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
Taking AI to the Next Level in Manufacturing.pdfssuserfac0301
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
2. Which barriers stand in the way of AI adoption.
3. How data quality and governance form the backbone of AI.
4. Organizational processes and structures that may inhibit effective AI adoption.
6. Ideas and approaches to help build your organization's AI strategy.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Generating privacy-protected synthetic data using Secludy and MilvusZilliz
During this demo, the founders of Secludy will demonstrate how their system utilizes Milvus to store and manipulate embeddings for generating privacy-protected synthetic data. Their approach not only maintains the confidentiality of the original data but also enhances the utility and scalability of LLMs under privacy constraints. Attendees, including machine learning engineers, data scientists, and data managers, will witness first-hand how Secludy's integration with Milvus empowers organizations to harness the power of LLMs securely and efficiently.