This presentation discusses fog computing and big data. It introduces the 5 V's of big data (volume, velocity, variety, veracity, value) and outlines a framework for managing big data that includes data preprocessing, clustering, feature extraction, classification, data mining, and visualization. It contrasts datasets, which are fixed, with data streams, which have continuous high velocity. Bio-inspired algorithms are presented as a way to process big data. Fog/edge computing is discussed as a solution to issues with processing big data solely in the cloud. A key challenge of fog computing is ensuring data quality given the 5V's, and a proposed solution is a quality-of-use framework that considers speed, size, and type of
This document discusses implementing hybrid recommender systems using web-based methods. It begins by introducing three basic recommendation approaches: demographic, content-based, and collaborative. It notes the disadvantages of each approach. The document then proposes that a hybrid approach can overcome the disadvantages by combining recommendation methods. It presents two consensus-based hybrid recommendation methods and provides examples of their implementation in different web-based systems.
The Big Data Importance – Tools and their UsageIRJET Journal
This document discusses big data, tools for analyzing big data, and opportunities that big data analytics provides. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses tools for storing, managing and processing big data like Hadoop, MapReduce and HDFS. Finally, it outlines how big data analytics can be applied across different domains to enable new insights and informed decision making through analyzing large datasets.
This document summarizes a survey on data mining. It discusses how data mining helps extract useful business information from large databases and build predictive models. Commonly used data mining techniques are discussed, including artificial neural networks, decision trees, genetic algorithms, and nearest neighbor methods. An ideal data mining architecture is proposed that fully integrates data mining tools with a data warehouse and OLAP server. Examples of profitable data mining applications are provided in industries such as pharmaceuticals, credit cards, transportation, and consumer goods. The document concludes that while data mining is still developing, it has wide applications across domains to leverage knowledge in data warehouses and improve customer relationships.
Mining Social Media Data for Understanding Drugs UsageIRJET Journal
This document discusses mining social media data to understand drug usage. It proposes using big data techniques like Hadoop and MapReduce to extract and analyze data from social networks about drug abuse. The methodology involves collecting data from platforms using crawlers, storing it in Hadoop, filtering it, then applying complex analysis using cloud computing. Prior work on extracting health information from social media and multi-scale community detection in networks is reviewed. The challenges of privacy preservation and scalability when anonymizing big healthcare datasets are also discussed.
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy.
This document provides an introduction to big data analytics. It discusses what big data is, key concepts and terminology, the characteristics of big data including the five Vs, different types of data, and case study background. It also covers big data drivers like marketplace dynamics, business architecture, and information and communications technology. The slides include information on data analytics categories, business intelligence, key performance indicators, and how big data relates to business layers and the feedback loop.
IRJET- Advances in Data Mining: Healthcare ApplicationsIRJET Journal
This document provides an overview of data mining and its applications in healthcare. It discusses how data mining can be used to extract useful information and patterns from large healthcare datasets. Some key applications mentioned include predicting hospital admissions and length of stay, improving diagnosis and treatment effectiveness, detecting healthcare abuse and fraud, and enhancing customer relationship management. The document also reviews several recent studies that have applied techniques like logistic regression, decision trees, naive Bayes classification, and neural networks to solve problems in areas such as predicting emergency department admissions, analyzing traumatic brain injury data, detecting heart failure, and diagnosing heart disease and cancer.
This document discusses implementing hybrid recommender systems using web-based methods. It begins by introducing three basic recommendation approaches: demographic, content-based, and collaborative. It notes the disadvantages of each approach. The document then proposes that a hybrid approach can overcome the disadvantages by combining recommendation methods. It presents two consensus-based hybrid recommendation methods and provides examples of their implementation in different web-based systems.
The Big Data Importance – Tools and their UsageIRJET Journal
This document discusses big data, tools for analyzing big data, and opportunities that big data analytics provides. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses tools for storing, managing and processing big data like Hadoop, MapReduce and HDFS. Finally, it outlines how big data analytics can be applied across different domains to enable new insights and informed decision making through analyzing large datasets.
This document summarizes a survey on data mining. It discusses how data mining helps extract useful business information from large databases and build predictive models. Commonly used data mining techniques are discussed, including artificial neural networks, decision trees, genetic algorithms, and nearest neighbor methods. An ideal data mining architecture is proposed that fully integrates data mining tools with a data warehouse and OLAP server. Examples of profitable data mining applications are provided in industries such as pharmaceuticals, credit cards, transportation, and consumer goods. The document concludes that while data mining is still developing, it has wide applications across domains to leverage knowledge in data warehouses and improve customer relationships.
Mining Social Media Data for Understanding Drugs UsageIRJET Journal
This document discusses mining social media data to understand drug usage. It proposes using big data techniques like Hadoop and MapReduce to extract and analyze data from social networks about drug abuse. The methodology involves collecting data from platforms using crawlers, storing it in Hadoop, filtering it, then applying complex analysis using cloud computing. Prior work on extracting health information from social media and multi-scale community detection in networks is reviewed. The challenges of privacy preservation and scalability when anonymizing big healthcare datasets are also discussed.
Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy.
This document provides an introduction to big data analytics. It discusses what big data is, key concepts and terminology, the characteristics of big data including the five Vs, different types of data, and case study background. It also covers big data drivers like marketplace dynamics, business architecture, and information and communications technology. The slides include information on data analytics categories, business intelligence, key performance indicators, and how big data relates to business layers and the feedback loop.
IRJET- Advances in Data Mining: Healthcare ApplicationsIRJET Journal
This document provides an overview of data mining and its applications in healthcare. It discusses how data mining can be used to extract useful information and patterns from large healthcare datasets. Some key applications mentioned include predicting hospital admissions and length of stay, improving diagnosis and treatment effectiveness, detecting healthcare abuse and fraud, and enhancing customer relationship management. The document also reviews several recent studies that have applied techniques like logistic regression, decision trees, naive Bayes classification, and neural networks to solve problems in areas such as predicting emergency department admissions, analyzing traumatic brain injury data, detecting heart failure, and diagnosing heart disease and cancer.
Data mining involves extracting patterns from large data sets. It is used to uncover hidden information and relationships within data repositories like databases, text files, social networks, and computer simulations. The patterns discovered can be used by organizations to make better business decisions. Some common applications of data mining include credit card fraud detection, customer segmentation for marketing, and scientific research. The process involves data preparation, algorithm selection, model building, and interpretation. While useful, data mining also raises privacy, security, and ethical concerns if misused.
This document discusses two case studies of organizations that partnered with Synoptek to improve their IT services and operations. The first case study was of a women's healthcare organization that wanted better infrastructure availability, performance, and security. With Synoptek's help, they reduced costs by 20%, improved IT performance, security, and availability. The second case study was of a community college that wanted to transform programs, optimize operations, and engage students. Synoptek helped them deliver on these goals through managed IT services and support.
The document discusses the steps involved in the data science life cycle (DSLC). It describes the main steps as business understanding, data acquisition and understanding, modeling, deployment, and customer acceptance. It provides details on several of these steps, including business understanding, data acquisition and understanding, data modeling, and initial data exploration. The goal is to clearly outline the typical process and considerations for a data science project from defining the problem to exploring the available data.
This document provides an introduction to data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-invariant, and non-volatile collection of data from multiple sources designed to support analysis and decision making. Data warehouses centralize data for analysis, allow analysis of broad business data over time, and are a core component of business intelligence. They improve decision making, increase productivity and efficiency, and provide competitive advantages for organizations. While data warehouses provide benefits, they also face challenges related to scalability, speed, and security.
Operations Research and ICT A Keynote AddressElvis Muyanja
By Prof. Venansius Baryamureeba, PhD
Uganda Technology And Management University (UTAMU)
www.utamu.ac.ug/barya ; barya@utamu.ac.ug
12th Operations Research Society for Eastern Africa (ORSEA) Conference, October 20-21, 2016, Hosted at the Faculty of Computing and Management Science Building, Makerere University Business School (MUBS), Kampala Uganda
Most of the time, when you hear about Artificial Intelligence (AI), people talk about new algorithms or even the computation power needed to train them. But Data is one of the most important factors in AI.
The document discusses the Internet of Things (IoT) and the data lifecycle in an IoT system. It describes how in an IoT system, things (devices) collect data and transfer it over a network. The data then goes through various steps including collection, aggregation, preprocessing, storage/updating, and archiving. It is stored and organized so it can be efficiently accessed, analyzed, and built upon to gain insights.
Presentation given to the BCS Data Management Specialist Group on 10th April 2018.
Data quality “tags” are a means of informing decision makers about the quality of the data they use within information systems. Unfortunately, these tags have not been successfully adopted because of the expense of maintaining them. This presentation will demonstrate an alternative approach that achieves improved decision making without the costly overheads.
Automating Data Science over a Human Genomics Knowledge BaseVaticle
# Automating Data Science over a Human Genomics Knowledge Base
Radouane Oudrhiri, the CTO of Eagle Genomics, will talk about how Eagle Genomics is building a platform for automating data science over a human genomics knowledge base. Rad will dive into the architecture Eagle Genomics and also discuss how Grakn serves as the knowledge base foundation of the system. Rad also give a brief history of databases, semantic expressiveness and how Grakn fits in the big picture.
# Radouane Oudrhiri, CTO, Eagle Genomics
Radouane has an extensive experience in leading world-class software and data-intensive system developments in different industries from Telecom to Healthcare, Nuclear, Automotive, Financials. Radouane is Lean/Six Sigma Master Black Belt with speciality in high-tech, IT and Software engineering and he is recognised as the leader and early adaptor of Lean/Six Sigma and DFSS to IT and Software. He is a fellow of the Royal Statistical Society (RSS) and member of the ISO Technical Committee (TC69: Applications of Statistical methods) where he is co-author of the Lean & Six Sigma Standard (ISO 18404) as well as the new standard under development (Design for Six Sigma). He is also part of the newly formed international Group on Big Data (nominated by BSI as the UK representative/expert). Radouane has also been Chair of the working group on Measurement Systems for Automated Processes/Systems within the ISPE (International Society for Pharmaceutical Engineering).
Making the Move to an Enterprise Clinical Trial Management SystemPerficient
The document discusses making the move to an enterprise clinical trial management system (CTMS) for organizations of any size. It outlines key indicators that a CTMS is needed, such as rapid growth, increased trial complexity, and a desire for real-time data integration. An internal analysis of current processes and identification of stakeholders and requirements is recommended. Selection considerations include system performance, customization options, and integration capabilities. The conclusion emphasizes analyzing needs, obtaining funding approval, and choosing a system and implementation partner carefully.
This document discusses different types of digital data including structured, unstructured, and semi-structured data. It provides examples and characteristics of each type of data. Structured data is organized in rows and columns, like in a database. Unstructured data lacks a predefined structure or organization, like text documents, images, and videos. Semi-structured data has some structure but not a rigid schema, like XML files. The majority of organizational data is unstructured. Big data is also discussed, which is high-volume, high-velocity, and high-variety data that requires new technologies to capture, store, manage and analyze.
Survey of the Euro Currency Fluctuation by Using Data Miningijcsit
Data mining or Knowledge Discovery in Databases (KDD) is a new field in information technology that emerged because of progress in creation and maintenance of large databases by combining statistical and artificial intelligence methods with database management. Data mining is used to recognize hidden patterns and provide relevant information for decision making on complex problems where conventional methods are inecient or too slow. Data mining can be used as a powerful tool to predict future trends and behaviors, and this prediction allows making proactive, knowledge-driven decisions in businesses. Since the automated prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools, it can answer the business questions which are traditionally time consuming to resolve. Based on this great advantage, it provides more interest for the government, industry and commerce. In this paper we have used this tool to investigate the Euro currency fluctuation.For this investigation, we have three different algorithms: K*, IBK and MLP and we have extracted.Euro currency volatility by using the same criteria for all used algorithms. The used dataset has
21,084 records and is collected from daily price fluctuations in the Euro currency in the period
of10/2006 to 04/2010.
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
This document discusses big data, including its characteristics of volume, velocity, and variety. It outlines issues related to big data such as storage and processing challenges due to the massive size of datasets. Privacy, security, and access are also concerns. Advantages include better understanding of customers, business optimization, improved science and healthcare. Effectively addressing the technical and analytical challenges will help realize big data's value.
Electronics health records and business analytics a cloud based approachIAEME Publication
This document discusses using business analytics and cloud computing to analyze electronic health records (EHRs). It proposes using pattern recognition algorithms within an intelligent agent on the cloud to better utilize resources and optimize the time needed to analyze EHR requests. The rest of the document outlines related work involving EHR and cloud environments, business scopes and trends related to EHR investments, and a proposed architectural model.
Full Paper: Analytics: Key to go from generating big data to deriving busines...Piyush Malik
This document discusses how analytics can help organizations derive business value from big data. It describes how statistical analysis, machine learning, optimization and text mining can extract meaningful insights from social media, online commerce, telecommunications, smart utility meters, and improve security. While tools exist to analyze big data, challenges remain around data security, privacy, and developing skilled talent. The paper aims to illustrate how existing algorithms can generate value from different industry use cases.
Big Data Presentation - Data Center Dynamics Sydney 2014 - Dez BlanchfieldDez Blanchfield
The document discusses the rise of big data and its impact on data centers. It defines what big data is and what it is not, providing examples of big data sources and uses. It also explores how the concept of a data center is evolving, as they must adapt to support new big data workloads. Traditional data center designs are no longer sufficient and distributed, modular, and software-defined approaches are needed to efficiently manage large and growing volumes of data.
Big data emerged in the early 2000s and was first adopted by online companies like Google, eBay, and Facebook. It refers to data that exceeds the processing capacity of traditional databases due to its large size, speed of creation, and unstructured nature. The key attributes of big data are volume, variety, velocity and complexity. It comes from a variety of sources like sensors, social media, web logs, and photos. Analyzing big data can provide competitive advantages through insights from hidden patterns. While big data offers opportunities, organizations must ensure they have the right skills, manage costs, and address privacy issues.
This document discusses challenges and solutions related to big data implementation. Some key challenges mentioned include reluctance to invest in big data strategies, integrating traditional and big data, and finding professionals with both big data and domain skills. The document recommends starting small with proofs of concept and taking an iterative approach to derive early benefits from big data before making larger investments. It also stresses the importance of having an enterprise-wide data strategy and acquiring various skills needed for big data projects.
High Performance Data Analytics and a Java Grande Run TimeGeoffrey Fox
There is perhaps a broad consensus as to important issues in practical parallel computing as applied to large scale simulations; this is reflected in supercomputer architectures, algorithms, libraries, languages, compilers and best practice for application development.
However the same is not so true for data intensive even though commercially clouds devote many more resources to data analytics than supercomputers devote to simulations.
Here we use a sample of over 50 big data applications to identify characteristics of data intensive applications and to deduce needed runtime and architectures.
We propose a big data version of the famous Berkeley dwarfs and NAS parallel benchmarks.
Our analysis builds on the Apache software stack that is well used in modern cloud computing.
We give some examples including clustering, deep-learning and multi-dimensional scaling.
One suggestion from this work is value of a high performance Java (Grande) runtime that supports simulations and big data
This document defines big data and discusses its key characteristics and applications. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data that is difficult to process using traditional methods. It then outlines the 5 Vs of big data: volume, velocity, variety, veracity, and variability. The document also discusses Hadoop as an open-source framework for distributed storage and processing of big data, and lists several applications of big data across various industries. Finally, it discusses both the risks and benefits of working with big data.
Data mining involves extracting patterns from large data sets. It is used to uncover hidden information and relationships within data repositories like databases, text files, social networks, and computer simulations. The patterns discovered can be used by organizations to make better business decisions. Some common applications of data mining include credit card fraud detection, customer segmentation for marketing, and scientific research. The process involves data preparation, algorithm selection, model building, and interpretation. While useful, data mining also raises privacy, security, and ethical concerns if misused.
This document discusses two case studies of organizations that partnered with Synoptek to improve their IT services and operations. The first case study was of a women's healthcare organization that wanted better infrastructure availability, performance, and security. With Synoptek's help, they reduced costs by 20%, improved IT performance, security, and availability. The second case study was of a community college that wanted to transform programs, optimize operations, and engage students. Synoptek helped them deliver on these goals through managed IT services and support.
The document discusses the steps involved in the data science life cycle (DSLC). It describes the main steps as business understanding, data acquisition and understanding, modeling, deployment, and customer acceptance. It provides details on several of these steps, including business understanding, data acquisition and understanding, data modeling, and initial data exploration. The goal is to clearly outline the typical process and considerations for a data science project from defining the problem to exploring the available data.
This document provides an introduction to data warehousing. It defines a data warehouse as a subject-oriented, integrated, time-invariant, and non-volatile collection of data from multiple sources designed to support analysis and decision making. Data warehouses centralize data for analysis, allow analysis of broad business data over time, and are a core component of business intelligence. They improve decision making, increase productivity and efficiency, and provide competitive advantages for organizations. While data warehouses provide benefits, they also face challenges related to scalability, speed, and security.
Operations Research and ICT A Keynote AddressElvis Muyanja
By Prof. Venansius Baryamureeba, PhD
Uganda Technology And Management University (UTAMU)
www.utamu.ac.ug/barya ; barya@utamu.ac.ug
12th Operations Research Society for Eastern Africa (ORSEA) Conference, October 20-21, 2016, Hosted at the Faculty of Computing and Management Science Building, Makerere University Business School (MUBS), Kampala Uganda
Most of the time, when you hear about Artificial Intelligence (AI), people talk about new algorithms or even the computation power needed to train them. But Data is one of the most important factors in AI.
The document discusses the Internet of Things (IoT) and the data lifecycle in an IoT system. It describes how in an IoT system, things (devices) collect data and transfer it over a network. The data then goes through various steps including collection, aggregation, preprocessing, storage/updating, and archiving. It is stored and organized so it can be efficiently accessed, analyzed, and built upon to gain insights.
Presentation given to the BCS Data Management Specialist Group on 10th April 2018.
Data quality “tags” are a means of informing decision makers about the quality of the data they use within information systems. Unfortunately, these tags have not been successfully adopted because of the expense of maintaining them. This presentation will demonstrate an alternative approach that achieves improved decision making without the costly overheads.
Automating Data Science over a Human Genomics Knowledge BaseVaticle
# Automating Data Science over a Human Genomics Knowledge Base
Radouane Oudrhiri, the CTO of Eagle Genomics, will talk about how Eagle Genomics is building a platform for automating data science over a human genomics knowledge base. Rad will dive into the architecture Eagle Genomics and also discuss how Grakn serves as the knowledge base foundation of the system. Rad also give a brief history of databases, semantic expressiveness and how Grakn fits in the big picture.
# Radouane Oudrhiri, CTO, Eagle Genomics
Radouane has an extensive experience in leading world-class software and data-intensive system developments in different industries from Telecom to Healthcare, Nuclear, Automotive, Financials. Radouane is Lean/Six Sigma Master Black Belt with speciality in high-tech, IT and Software engineering and he is recognised as the leader and early adaptor of Lean/Six Sigma and DFSS to IT and Software. He is a fellow of the Royal Statistical Society (RSS) and member of the ISO Technical Committee (TC69: Applications of Statistical methods) where he is co-author of the Lean & Six Sigma Standard (ISO 18404) as well as the new standard under development (Design for Six Sigma). He is also part of the newly formed international Group on Big Data (nominated by BSI as the UK representative/expert). Radouane has also been Chair of the working group on Measurement Systems for Automated Processes/Systems within the ISPE (International Society for Pharmaceutical Engineering).
Making the Move to an Enterprise Clinical Trial Management SystemPerficient
The document discusses making the move to an enterprise clinical trial management system (CTMS) for organizations of any size. It outlines key indicators that a CTMS is needed, such as rapid growth, increased trial complexity, and a desire for real-time data integration. An internal analysis of current processes and identification of stakeholders and requirements is recommended. Selection considerations include system performance, customization options, and integration capabilities. The conclusion emphasizes analyzing needs, obtaining funding approval, and choosing a system and implementation partner carefully.
This document discusses different types of digital data including structured, unstructured, and semi-structured data. It provides examples and characteristics of each type of data. Structured data is organized in rows and columns, like in a database. Unstructured data lacks a predefined structure or organization, like text documents, images, and videos. Semi-structured data has some structure but not a rigid schema, like XML files. The majority of organizational data is unstructured. Big data is also discussed, which is high-volume, high-velocity, and high-variety data that requires new technologies to capture, store, manage and analyze.
Survey of the Euro Currency Fluctuation by Using Data Miningijcsit
Data mining or Knowledge Discovery in Databases (KDD) is a new field in information technology that emerged because of progress in creation and maintenance of large databases by combining statistical and artificial intelligence methods with database management. Data mining is used to recognize hidden patterns and provide relevant information for decision making on complex problems where conventional methods are inecient or too slow. Data mining can be used as a powerful tool to predict future trends and behaviors, and this prediction allows making proactive, knowledge-driven decisions in businesses. Since the automated prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools, it can answer the business questions which are traditionally time consuming to resolve. Based on this great advantage, it provides more interest for the government, industry and commerce. In this paper we have used this tool to investigate the Euro currency fluctuation.For this investigation, we have three different algorithms: K*, IBK and MLP and we have extracted.Euro currency volatility by using the same criteria for all used algorithms. The used dataset has
21,084 records and is collected from daily price fluctuations in the Euro currency in the period
of10/2006 to 04/2010.
This document summarizes a literature review paper on big data analytics. It begins by defining big data as large datasets that are difficult to handle with traditional tools due to their size, variety, and velocity. It then discusses how big data analytics applies advanced analytics techniques to big data to extract valuable insights. The paper reviews literature on big data analytics tools and methods for storage, management, and analysis of big data. It also discusses opportunities that big data analytics provides for decision making in various domains.
This document discusses big data, including its characteristics of volume, velocity, and variety. It outlines issues related to big data such as storage and processing challenges due to the massive size of datasets. Privacy, security, and access are also concerns. Advantages include better understanding of customers, business optimization, improved science and healthcare. Effectively addressing the technical and analytical challenges will help realize big data's value.
Electronics health records and business analytics a cloud based approachIAEME Publication
This document discusses using business analytics and cloud computing to analyze electronic health records (EHRs). It proposes using pattern recognition algorithms within an intelligent agent on the cloud to better utilize resources and optimize the time needed to analyze EHR requests. The rest of the document outlines related work involving EHR and cloud environments, business scopes and trends related to EHR investments, and a proposed architectural model.
Full Paper: Analytics: Key to go from generating big data to deriving busines...Piyush Malik
This document discusses how analytics can help organizations derive business value from big data. It describes how statistical analysis, machine learning, optimization and text mining can extract meaningful insights from social media, online commerce, telecommunications, smart utility meters, and improve security. While tools exist to analyze big data, challenges remain around data security, privacy, and developing skilled talent. The paper aims to illustrate how existing algorithms can generate value from different industry use cases.
Big Data Presentation - Data Center Dynamics Sydney 2014 - Dez BlanchfieldDez Blanchfield
The document discusses the rise of big data and its impact on data centers. It defines what big data is and what it is not, providing examples of big data sources and uses. It also explores how the concept of a data center is evolving, as they must adapt to support new big data workloads. Traditional data center designs are no longer sufficient and distributed, modular, and software-defined approaches are needed to efficiently manage large and growing volumes of data.
Big data emerged in the early 2000s and was first adopted by online companies like Google, eBay, and Facebook. It refers to data that exceeds the processing capacity of traditional databases due to its large size, speed of creation, and unstructured nature. The key attributes of big data are volume, variety, velocity and complexity. It comes from a variety of sources like sensors, social media, web logs, and photos. Analyzing big data can provide competitive advantages through insights from hidden patterns. While big data offers opportunities, organizations must ensure they have the right skills, manage costs, and address privacy issues.
This document discusses challenges and solutions related to big data implementation. Some key challenges mentioned include reluctance to invest in big data strategies, integrating traditional and big data, and finding professionals with both big data and domain skills. The document recommends starting small with proofs of concept and taking an iterative approach to derive early benefits from big data before making larger investments. It also stresses the importance of having an enterprise-wide data strategy and acquiring various skills needed for big data projects.
High Performance Data Analytics and a Java Grande Run TimeGeoffrey Fox
There is perhaps a broad consensus as to important issues in practical parallel computing as applied to large scale simulations; this is reflected in supercomputer architectures, algorithms, libraries, languages, compilers and best practice for application development.
However the same is not so true for data intensive even though commercially clouds devote many more resources to data analytics than supercomputers devote to simulations.
Here we use a sample of over 50 big data applications to identify characteristics of data intensive applications and to deduce needed runtime and architectures.
We propose a big data version of the famous Berkeley dwarfs and NAS parallel benchmarks.
Our analysis builds on the Apache software stack that is well used in modern cloud computing.
We give some examples including clustering, deep-learning and multi-dimensional scaling.
One suggestion from this work is value of a high performance Java (Grande) runtime that supports simulations and big data
This document defines big data and discusses its key characteristics and applications. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data that is difficult to process using traditional methods. It then outlines the 5 Vs of big data: volume, velocity, variety, veracity, and variability. The document also discusses Hadoop as an open-source framework for distributed storage and processing of big data, and lists several applications of big data across various industries. Finally, it discusses both the risks and benefits of working with big data.
Every day we roughly create 2.5 Quintillion bytes of data; 90% of the worlds collected data has been generated only in the last 2 years. In this slide, learn the all about big data
in a simple and easiest way.
Multi-faceted Classification of Big Data Use Cases and Proposed Architecture ...Geoffrey Fox
Keynote at Sixth International Workshop on Cloud Data Management CloudDB 2014 Chicago March 31 2014.
Abstract: We introduce the NIST collection of 51 use cases and describe their scope over industry, government and research areas. We look at their structure from several points of view or facets covering problem architecture, analytics kernels, micro-system usage such as flops/bytes, application class (GIS, expectation maximization) and very importantly data source.
We then propose that in many cases it is wise to combine the well known commodity best practice (often Apache) Big Data Stack (with ~120 software subsystems) with high performance computing technologies.
We describe this and give early results based on clustering running with different paradigms.
We identify key layers where HPC Apache integration is particularly important: File systems, Cluster resource management, File and object data management, Inter process and thread communication, Analytics libraries, Workflow and Monitoring.
See
[1] A Tale of Two Data-Intensive Paradigms: Applications, Abstractions, and Architectures, Shantenu Jha, Judy Qiu, Andre Luckow, Pradeep Mantha and Geoffrey Fox, accepted in IEEE BigData 2014, available at: http://arxiv.org/abs/1403.1528
[2] High Performance High Functionality Big Data Software Stack, G Fox, J Qiu and S Jha, in Big Data and Extreme-scale Computing (BDEC), 2014. Fukuoka, Japan. http://grids.ucs.indiana.edu/ptliupages/publications/HPCandApacheBigDataFinal.pdf
Data science involves extracting knowledge and insights from structured, semi-structured, and unstructured data using scientific processes. It encompasses more than just data analysis. The data value chain describes the process of acquiring data and transforming it into useful information and insights. It involves data acquisition, analysis, curation, storage, and usage. There are three main types of data: structured data that follows a predefined model like databases, semi-structured data with some organization like JSON, and unstructured data like text without a clear model. Metadata provides additional context about data to help with analysis. Big data is characterized by its large volume, velocity, and variety that makes it difficult to process with traditional tools.
Big Data Analytics and Hadoop is presented. Key points include:
- Big data is large and complex data that is difficult to process using traditional methods. Domains that produce large datasets include meteorology, physics simulations, and internet search.
- The four V's of big data are volume, velocity, variety, and veracity. Hadoop is an open-source framework for distributed storage and processing of large datasets across clusters of computers. Its core components are HDFS for storage and MapReduce for processing.
- Apache Hadoop has gained popularity for big data analytics due to its ability to process large amounts of data in parallel using commodity hardware, its scalability, and automatic failover. A Hadoop ecosystem of
This document provides an introduction and overview of big data technologies. It begins with defining big data and its key characteristics of volume, variety and velocity. It discusses how data has exploded in recent years and examples of large scale data sources. It then covers popular big data tools and technologies like Hadoop and MapReduce. The document discusses how to get started with big data and learning related skills. Finally, it provides examples of big data projects and discusses the objectives and benefits of working with big data.
If Big Data is data that exceeds the processing capacity of conventional systems, thereby necessitating alternative processing measures, we are looking at an essentially technological challenge that IT managers are best equipped to address.
The DCC is currently working with 18 HEIs to support and develop their capabilities in the management of research data and, whilst the aforementioned challenge is not usually core to their expressed concerns, are there particular issues of curation inherent to Big Data that might force a different perspective?
We have some understanding of Big Data from our contacts in the Astronomy and High Energy Physics domains, and the scale and speed of development in Genomics data generation is well known, but the inability to provide sufficient processing capacity is not one of their more frequent complaints.
That’s not to say that Big Science and its Big Data are free of challenges in data curation; only that they are shared with their lesser cousins, where one might say that the real challenge is less one of size than diversity and complexity.
This brief presentation explores those aspects of data curation that go beyond the challenges of processing power but which may lend a broader perspective to the technology selection process.
This document provides an overview of handling and processing big data. It begins with defining big data and its key characteristics of volume, velocity, and variety. It then discusses several ways to effectively handle big data, such as outlining goals, securing data, keeping data protected, ensuring data is interlinked, and adapting to new changes. Metadata is also important for big data handling and processing. The document outlines the different types of metadata and closes by discussing technologies commonly used for big data processing like Hadoop, MapReduce, and Hive.
Big data analytics (BDA) involves examining large, diverse datasets to uncover hidden patterns, correlations, trends, and insights. BDA helps organizations gain a competitive advantage by extracting insights from data to make faster, more informed decisions. It supports a 360-degree view of customers by analyzing both structured and unstructured data sources like clickstream data. Businesses can leverage techniques like machine learning, predictive analytics, and natural language processing on existing and new data sources. BDA requires close collaboration between IT, business users, and data scientists to process and analyze large datasets beyond typical storage and processing capabilities.
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This document discusses the shifting landscape of data integration. It begins with an introduction by William McKnight, who is described as the "#1 Global Influencer in Data Warehousing". The document then discusses how challenges in data integration are shifting from dealing with volume, velocity and variety to dealing with dynamic, distributed and diverse data in the cloud. It also discusses IDC's view that this shift is occurring from the traditional 3Vs to the 3Ds. The rest of the document discusses Matillion, a vendor that provides a modern solution for cloud data integration challenges.
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আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
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(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
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ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
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Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
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The Paradigm of Fog Computing with Bio-inspired Search Methods and the “5Vs” of Big Data
1. The Paradigm of Fog Computing
with Bio-inspired Search Methods
and the “5Vs” of Big Data
Presenters:
Richard Millham, Israel Edem
Agbehadji, and Samuel Ofori Frimpong
Durban Univeristy of Technology, South Afrca
2. Outline
• Introduction
• Growth of Big Data
• The 5Vs of Big Data
• Framework to Manage Big Data
• Data Streaming vs Datasets
• Edge/Fog Computing paradigm
• Challenges of Fog computing and Potential
Solutions
• Conclusion
Durban Univeristy of Technology, South Afrca
3. Introduction
• This presentation seeks to briefly present some of the issues of
big data:
• What characteristics constitute big data?
• What methods and phases are needed to process big data?
• Datasets vs data streaming? What is the difference?
• What is the role and domain of bio-inspired algorithms?
• The drivers for fog/edge computing architecture?
Durban Univeristy of Technology, South Afrca
4. Big data
• Like many concepts, there is no consensus of what constitutes big data
• Many will say Big data is a voluminous amount of varied data available at
high rate, but it possesses other characteristics as well (5 Vs)
• Big data yields neither meaning nor value, it is important to understand
the unique features of data which may inform the analysis
• Any framework of analysing big data must address big data characteristics
namely velocity, variety, veracity, volume and value
• Sources of big data are numerous but have evolved with our changing
society
• IOT and smart entities
• Enterprise systems
• Social media
Durban Univeristy of Technology, South Afrca
5. The growth of IOT, along with the subsequent growth of IOT data, is one of
the main contributors to the growth of Big Data and the need for methods to
manage it Durban Univeristy of Technology, South Afrca
6. Smart Cities and IOT Sensors/Data Analytics
Smart City IOT/Data Analytics
• Smart cities enables its citizens to enjoy a
wide range of new services:
• health sector to monitor quality of
service delivery
• Government gains better insights for
better social intervention programs to
citizens
• Companies to customers to understand
customers perception of products
• These services are enabled through the use of
IOT sensors to monitor the environment and
data analytics to make sense of the
monitored data collected
Durban Univeristy of Technology, South Afrca
7. The 5-Vs of Big Data
Durban Univeristy of Technology, South Afrca
8. Big Data Framework
• To manage big data, a framework consisting of a set of steps
and phases. Although some of these phases may overlap and
the steps may vary, this framework is as follows:
• Data Pre-Processing
• Data Cleansing
• Acquire data from a multitude of heterogeneous
devices: social media, IOT sensors, mobile phones,
enterprise system transactions, GPS devices, etc
• Estimate missing values, if needed
• Remove redundant values
• Reformat heterogeneous data into a more uniform
format(s)
Durban Univeristy of Technology, South Afrca
9. Big Data Framework (cont)
Data Scattered in 3-D space Data Cleansing (Data Reduction)
• One of the most important steps
in data cleansing is data
reduction (reducing the amount
of data to be processed by later
stages). This can be
accomplished by:
• Removing outliers (noise)
• Removing redundant data
• Removing non-interesting data
(with little value)
Durban Univeristy of Technology, South Afrca
10. Big Data Framework (cont)
• After data cleaning is complete,
the next step is data clustering
or the combining of similar items
together into groups for easier
processing of data in later stages
• Clustering methods include:
• K-Nearest Neighbour
• Density-Based scan discovers
different cluster shapes
Durban Univeristy of Technology, South Afrca
11. Big Data Framework (cont)
Feature Extraction and Classification
• The next step after data clustering
is feature extraction and
classification where important
features are extracted from the
data and classified (labeled). This
reduces the amount of resources
used to describe a group of data
• Many tools may be used including:
• Autoencoder (to learn unlabeled
data)
Durban Univeristy of Technology, South Afrca
12. Big Data
Framework (cont)
• Data Mining Phase
• This phase involves finding relationships
among groups of data identified during the
previous phase
• These relationships include correlations
(dependencies among variables) and
association rules (if-then rules) among others
• Methods include Apriori, PageRank etc.
• Many data mining tools exist, using a variety
of methods, including:
• Orange
• Weka
• Apache Mahout
• RapidMiner
• KNIME integrates various components
for machine learning and data mining.
Durban Univeristy of Technology, South Afrca
13. Big Data
Framework
(cont)
• Visualisation/Business Intelligence Phase
• In this phase, the data relationships and classes identified in previous stages may be visualized
in the form of pie graphs, charts, linear diagrams, etc and/or incorporated into business rules
within the organization.
• Some examples:
• Linear graph may show the increase/decrease in sales of particular products based on
particular features offered. Hence, businesses may be able to determine the most
popular features for each price range
• Business rules may find associations between different itemsets. An example, a store
might find a strong association between the sale of hamburgers and rolls.
Durban Univeristy of Technology, South Afrca
14. Datasets vs Data
Streams
• Datasets may consist of high volume, veracity,
value and variety but are often fixed in terms of
velocity. In other words, these datasets may
contain the 4 Vs of big data and are modelled on
high velocity data coming in during the formation
of the dataset. However, once this dataset is
formed, they are stable. Consequently, many
different methods and tools may be used to
analyse them
• Data streaming, on the other hand, contains the
same characteristics of datasets but also contain
continuous high velocity with often changing
varieties, values, and veracities of data. Analysis
of this data, due to these characteristics, is
problematic and requires huge resources in
computation (i.e. a supercomputer)
Durban Univeristy of Technology, South Afrca
15. Datasets vs Data
Streams (cont)
• As this solution is not usually practical,
different methods must be used to
manage data streams including:
• Fixed or random sampling of the
stream (ex: 1 in 50 frames) to get a
snapshot of current data
• Sliding windows to contain these
samples and to ensure that these
samples are current as the streams
may change
• Potentially different methods that are
used for data streams in order to
handle the high velocity and produce
satisfactory results
Durban Univeristy of Technology, South Afrca
16. Big Data Analytics
• Following diagram shows some of
the methods mentioned or to be
mentioned in presentation under
the term Big Data Analytics
• Batch (dataset) vs stream processing
• Machine learning and advanced
learning (feature extraction,
classification, and business rules)
• Data mining
• Stochastic (probability) models for
preprocessing of noise, feature
extraction, classification, etc
• Edge computing and cloud computing
Durban Univeristy of Technology, South Afrca
18. Bio-inspired Computation
• Bio-inspired computation models the natural behavior of animals
(optimized over a very long time period) to achieve some set goal
• Numerous bio-inspired algorithms exist (200+) each with their
advantages and disadvantages
• One basic premise of these algorithms is exploration vs exploitation
• exploration:- search different regions of the solution space to find a global
solution
• exploitation:- search in a small region of the present solution in order to
improve its quality with a small perturbation
• Bio-inspired algorithms have been used in many application domains
such as route optimization, recommender systems, renewable energy
Durban Univeristy of Technology, South Afrca
21. Why is Edge/Fog Computing Needed?
Cloud Computing
Problems with Cloud – Need for New
Paradigm
• As illustrated in diagram, big data
(huge amounts from many types of
devices flow at high speed to the
cloud) to be processed using data
framework in cloud
• Network soon becomes overloaded
as many early phases
(preprocessing and data reduction)
are only done in the cloud
[Bottleneck]
Durban Univeristy of Technology, South Afrca
22. Fog Computing Paradigm
• The focus is on devices connected to the
edge of networks.
• The term fog computing or edge
computing operates on the concept that
instead of hosting devices to work from a
centralized location that is cloud server,
fog systems operate on network ends
(Naha et al. 2018).
• Advantage of fog computing is that it
avoids delay in processing of raw data
collected from edge networks rather than
sending it directly to the cloud for
processing
Durban Univeristy of Technology, South Afrca
25. Fog computing applications
SMART CITY
MONITORING
ENERGY EFFICIENT
MODEL
FOG COMPUTING IN
HEALTH MONITORING
Durban Univeristy of Technology, South Afrca
26. Quality Challenge of Fog computing and 5V’s and
Solution
• There are many issues in fog computing with big data but a key challenge is the issue of data quality.
• Solution: Fog Computing and “5Vs” for Quality-of-Use (QoU) Framework.
• This framework has analytical model that consider speed, size and type of data from
IoT devices and then determine the quality and importance of data to store on cloud
platform.
• The framework has two components, namely IoT (data) and fog computing
• The IoT (data) components is the location of sensors, Internet-enabled devices which
capture large data, at a speed and different types of data
• The data generated are processed and analyzed by fog computing component to
produce quality data that is useful
Durban Univeristy of Technology, South Afrca
27. More
Challenges in
Fog
Computing
and IoT
• The challenges include:
• energy consumption
• data distribution
• heterogeneity of edge devices
• dynamicity of fog network etc.
• This leads to finding new methods to
address the challenges
• One promising method is the use of bio-inspired
algorithms (a subset of Evolutionary algorithms)
to manage different aspects of these problems
Durban Univeristy of Technology, South Afrca
28. Fog Computing and Evolutionary
Algorithms Models
• Evolutionary Algorithm for Energy Efficient Model.
• Bio-Inspired Algorithm for Scheduling of Service Requests
to Virtual Machine (VMs).
• Bio-Inspired Algorithms and Fog Computing for Intelligent
Computing in Logistic Data Center.
• Ensemble of Swarm Algorithm for Fire-and-Rescue
Operations.
• Evolutionary Computation and Epidemic Models for Data
Availability in Fog Computing.
• Bio-Inspired Optimization for Job Scheduling in Fog
Computing.
Durban Univeristy of Technology, South Afrca
29. Conclusion
• This presentation is a brief overview of big data along with many of its aspects
• Increasing technological and societal changes make big data much more predominant
• With increasing prevalence of big data comes a demand to manage this data (particularly
data streams) through new methods and new architectures (edge/fog computing)
• Promising methods have emerged in the field of bio-inspired algorithms which have been
applied to a variety of domains, including challenges with new architectures
Durban Univeristy of Technology, South Afrca
Editor's Notes
K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions).
Auto-encoder: is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning)
PageRank is a link analysis algorithm and it assigns a numerical weighting to each element of a hyperlinked set of documents, such as the World Wide Web.