This document discusses the paradigm shift in data integration due to growing amounts of data from various sources. It outlines 5 principles and 5 capabilities of modern data integration, which takes processing to where the data lives, leverages multiple platforms, moves data point-to-point, manages rules centrally, and allows changes using existing logic. A case study shows how a bank migrated data to Hadoop in 3 weeks using these principles, lowering costs by 50% compared to traditional ETL. Looking ahead, real-time data access will become more important for businesses.
Data is not consistent, sometimes searches or general interest in certain topics, say social media or other types of data experienced peaks and valleys. Data analysis techniques allow the data scientist to mine this type of unstable data and still draw meaningful conclusions from it.
Overview of mit sloan case study on ge data and analytics initiative titled g...Gregg Barrett
GE collects sensor data from industrial equipment to analyze equipment performance and predict failures. It created a "data lake" to integrate raw flight data from 3.4 million flights with other data sources. This allows data scientists to identify issues reducing equipment uptime for customers. However, GE faces challenges in finding qualified analytics talent and establishing effective data governance as it scales its data and analytics efforts.
Capitalize On Social Media With Big Data AnalyticsHassan Keshavarz
This document discusses how companies can capitalize on social media through big data analytics. It notes that while social media promises benefits, most companies struggle to measure the true value and impact. To leverage social media effectively, the entire business must be aligned in their interactions. The document also discusses how analyzing large datasets through big data analytics can provide strategic insights for success, maximize product performance, and deliver real business value. It emphasizes the need for companies to measure social media's impact on key metrics and business goals.
Guidance for Incorporating Big Data into Humanitarian Operations - 2015 - web...Katie Whipkey
This document provides guidance on incorporating big data into humanitarian operations. It defines big data as large, complex datasets that exceed traditional data analysis capabilities. Big data is characterized by its volume, variety, velocity and value. The document outlines the history of big data and provides an overview of different big data types. It also discusses benefits and challenges, as well as important considerations around policy, acquisition, use, and timeline for humanitarian organizations looking to utilize big data.
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
ATAAS2016 - Big data analytics – data visualization himanshu and santoshAgile Testing Alliance
Data visualization can transform big data challenges by telling stories with data. It allows large amounts of complex data to be understood quickly through visual representations like charts and graphs. Effective data visualization improves communication, helps identify patterns and trends, and enables faster decision making. The right visualizations should be chosen based on the type of data to ensure the most insightful analysis.
This document discusses the paradigm shift in data integration due to growing amounts of data from various sources. It outlines 5 principles and 5 capabilities of modern data integration, which takes processing to where the data lives, leverages multiple platforms, moves data point-to-point, manages rules centrally, and allows changes using existing logic. A case study shows how a bank migrated data to Hadoop in 3 weeks using these principles, lowering costs by 50% compared to traditional ETL. Looking ahead, real-time data access will become more important for businesses.
Data is not consistent, sometimes searches or general interest in certain topics, say social media or other types of data experienced peaks and valleys. Data analysis techniques allow the data scientist to mine this type of unstable data and still draw meaningful conclusions from it.
Overview of mit sloan case study on ge data and analytics initiative titled g...Gregg Barrett
GE collects sensor data from industrial equipment to analyze equipment performance and predict failures. It created a "data lake" to integrate raw flight data from 3.4 million flights with other data sources. This allows data scientists to identify issues reducing equipment uptime for customers. However, GE faces challenges in finding qualified analytics talent and establishing effective data governance as it scales its data and analytics efforts.
Capitalize On Social Media With Big Data AnalyticsHassan Keshavarz
This document discusses how companies can capitalize on social media through big data analytics. It notes that while social media promises benefits, most companies struggle to measure the true value and impact. To leverage social media effectively, the entire business must be aligned in their interactions. The document also discusses how analyzing large datasets through big data analytics can provide strategic insights for success, maximize product performance, and deliver real business value. It emphasizes the need for companies to measure social media's impact on key metrics and business goals.
Guidance for Incorporating Big Data into Humanitarian Operations - 2015 - web...Katie Whipkey
This document provides guidance on incorporating big data into humanitarian operations. It defines big data as large, complex datasets that exceed traditional data analysis capabilities. Big data is characterized by its volume, variety, velocity and value. The document outlines the history of big data and provides an overview of different big data types. It also discusses benefits and challenges, as well as important considerations around policy, acquisition, use, and timeline for humanitarian organizations looking to utilize big data.
Open Innovation - Winter 2014 - Socrata, Inc.Socrata
As innovators around the world push the open data movement forward, Socrata features their stories, successes, advice, and ideas in our quarterly magazine, “Open Innovation.”
The Winter 2014 issue of Open Innovation is out. This special year-in-review edition contains stories about some of the biggest open data achievements in 2013, as well as expert insights into how open data can grow and where it may go in 2014.
ATAAS2016 - Big data analytics – data visualization himanshu and santoshAgile Testing Alliance
Data visualization can transform big data challenges by telling stories with data. It allows large amounts of complex data to be understood quickly through visual representations like charts and graphs. Effective data visualization improves communication, helps identify patterns and trends, and enables faster decision making. The right visualizations should be chosen based on the type of data to ensure the most insightful analysis.
Analytics, machine e deep learning, data/event streaming
Big data streaming: abilitare la macchina del tempo
Real time event streaming e nuovi paradigmi concettuali:
- Transazioni distribuite
- Consistenza eventuale
- Proiezioni materializzate
Real time event streaming e nuovi paradigmi architetturali:
- Enterprise service bus
- Event store
- Database delle proiezioni
Cenni di Domain Driven Design: una visione strategica della modellazione del proprio dominio di business nell'era dei bi Data.
This document provides an overview and introduction to big data implementation strategies using Hadoop and beyond. It discusses how big data has evolved from technologies pioneered by companies like Google to analyze vast amounts of diverse data cheaper and more effectively than traditional methods. It also outlines some of the key challenges organizations face as data volumes, varieties, and velocities outgrow existing systems, and how new big data technologies like Hadoop provide more cost-effective solutions to process and analyze data at scale. The document notes that big data represents a shift in computing paradigms rather than just data size alone.
Building an Infrastructure that Secures and Protects
In June and July 2011, the Economist Intelligence Unit conducted a global survey, sponsored by Booz Allen Hamilton, of 387 executives to assess attitudes toward cybersecurity, and their progress towards implementing resilience strategies. Learn more: http://www.boozallen.com/insights/expertvoices/cyber-power
- Governments could leverage graph databases to make their countries more secure, provide better services, and increase efficiency. Graph databases allow analysis of connections in data that may not be apparent using traditional databases.
- Examples of how governments use graph databases include combating money laundering by analyzing how funds travel between parties, improving law enforcement investigations by connecting related data like suspects, evidence and locations, and enhancing e-government services by eliminating duplicate records across systems.
- Graph databases provide benefits like easy maintenance of connected data models, intuitive querying of relationships, high performance, and minimal resource usage which can improve areas like border security, fraud detection, records management and more.
Smart Data Module 1 introduction to big and smart datacaniceconsulting
This document provides an overview of big and smart data. It defines big data as large volumes of structured, unstructured, and semi-structured data that is difficult to manage and process using traditional databases. It discusses how big data becomes smart data through analysis and insights. Examples of smart data applications are also provided across various industries like retail, healthcare, transportation and more. The document emphasizes that in order to start smart with data, companies need to review their existing data, ask the right questions, and form actionable insights rather than just conclusions.
Data science and its potential to change business as we know it. The Roadmap ...InnoTech
The document summarizes a presentation on data science and its potential to change business. It discusses how organizations can increase their data science maturity and capabilities to gain more value from data. As data volumes continue growing exponentially, data science can help organizations move from simple reporting to predictive analytics in order to make real-time decisions. The presentation examines how data science is an emerging field that incorporates techniques from many areas and how organizations can assess their analytics maturity.
IRJET - Big Data Analysis its ChallengesIRJET Journal
This document discusses big data analysis and its challenges. It begins by defining big data and business analytics, noting that large amounts of data are now being generated daily that require new techniques to analyze. It describes some of the key challenges in handling big data, including issues around storage, analysis, and reporting on large, complex datasets. The document then discusses the four Vs of big data - volume, variety, velocity, and veracity. It concludes by noting limitations in current research and opportunities for future work to better understand the impacts of big data and business analytics on competitive advantages.
This document discusses big data and provides an overview of key concepts and technologies. It defines big data as large volumes of data in various formats that are growing rapidly. It describes the four V's of big data - volume, velocity, variety, and value. The document then provides an overview of big data technologies like columnar databases, NoSQL, and Hadoop that are designed to handle large and complex data sets.
This document provides an overview of social media and big data analytics. It discusses key concepts like Web 2.0, social media platforms, big data characteristics involving volume, velocity, variety, veracity and value. The document also discusses how social media data can be extracted and analyzed using big data tools like Hadoop and techniques like social network analysis and sentiment analysis. It provides examples of analyzing social media data at scale to gain insights and make informed decisions.
This document provides an overview of big data, including its definition, size and growth, characteristics, analytics uses and challenges. It discusses operational vs analytical big data systems and technologies like NoSQL databases, Hadoop and MapReduce. Considerations for selecting big data technologies include whether they support online vs offline use cases, licensing models, community support, developer appeal, and enabling agility.
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.
Big Data Lecture given at the University of Balamand by Fady Sayah Digi Web Founder.
Why Big Data Now?
Types of Databases
The 4 Vs of Big Data
Big Data Challenges
Big Data & Marketing
Big Data Impact on Social Media
Big Data & Hospitality
Big Data Scalable systems
BIg Data and Higher Education
Big Data Success Stories
You can view the presentation on this link.
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...IRJET Journal
This document discusses big data analytical methods, cloud computing, and how they can be combined. It explains that big data involves large amounts of structured, semi-structured, and unstructured data from various sources that requires significant computing resources to analyze. Cloud computing provides a way for big data analytics to be offered as a service and processed efficiently using cloud resources. The integration of big data and cloud computing allows organizations to gain business intelligence from large datasets in a flexible, scalable and cost-effective manner.
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 big data characteristics, issues, challenges, and technologies. It describes the key characteristics of big data as volume, velocity, variety, value, and complexity. It outlines issues related to these characteristics like data volume and velocity. Challenges of big data include privacy and security, data access and sharing, analytical challenges, human resources, and technical challenges around fault tolerance, scalability, data quality, and heterogeneous data. The document also discusses technologies used for big data like Hadoop, HDFS, and cloud computing and provides examples of big data projects.
TechWise with Eric Kavanagh, Dr. Robin Bloor and Dr. Kirk Borne
Live Webcast on July 23, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=59d50a520542ee7ed00a0c38e8319b54
Analytical applications are everywhere these days, and for good reason. Organizations large and small are using analytics to better understand any aspect of their business: customers, processes, behaviors, even competitors. There are several critical success factors for using analytics effectively: 1) know which kind of apps make sense for your company; 2) figure out which data sets you can use, both internal and external; 3) determine optimal roles and responsibilities for your team; 4) identify where you need help, either by hiring new employees or using consultants 5) manage your program effectively over time.
Register for this episode of TechWise to learn from two of the most experienced analysts in the business: Dr. Robin Bloor, Chief Analyst of The Bloor Group, and Dr. Kirk Borne, Data Scientist, George Mason University. Each will provide their perspective on how companies can address each of the key success factors in building, refining and using analytics to improve their business. There will then be an extensive Q&A session in which attendees can ask detailed questions of our experts and get answers in real time. Registrants will also receive a consolidated deck of slides, not just from the main presenters, but also from a variety of software vendors who provide targeted solutions.
Visit InsideAnlaysis.com for more information.
A next generation introduction to data science and its potential to change bu...InnoTech
The document discusses the rise of data science and its potential to change business. It notes that the amount of data being generated is growing exponentially and will soon exceed 40 zettabytes. However, most companies feel overwhelmed by the data they have. Data science uses techniques from many fields to extract meaningful insights from vast amounts of data. It has become a critical business asset for companies in almost every industry. The emergence of data science is enabling real-time, predictive analytics beyond what was previously possible.
Data Lake-based Approaches to Regulatory-Driven Technology ChallengesBooz Allen Hamilton
The document discusses how a data lake approach can help financial institutions address regulatory challenges more effectively than traditional ETL approaches. A data lake allows raw data to be ingested rapidly and indexed as needed for analysis, reducing preparation time. It also enables unified queries across all data sources and quick fusion of multiple sources. This significantly reduces operational complexity and costs while improving security, flexibility, and the ability to address evolving requirements. The data lake approach is well-suited for challenges involving streaming analytics, point-to-point data marts, or data-heavy ETL requirements. Booz Allen has successfully implemented this approach for government clients to prototype solutions around critical applications.
Young people are increasingly living digital lives as computers and the internet have moved from large rooms into pockets through mobile devices. Social media plays a large role in this as it is mobile, personal, communal, and popular among youth who spend more time with social networking sites than watching television. This document discusses examining the social aspects of social media for young people through a survey and interviews to understand their new media behaviors in this changing digital world.
Analytics, machine e deep learning, data/event streaming
Big data streaming: abilitare la macchina del tempo
Real time event streaming e nuovi paradigmi concettuali:
- Transazioni distribuite
- Consistenza eventuale
- Proiezioni materializzate
Real time event streaming e nuovi paradigmi architetturali:
- Enterprise service bus
- Event store
- Database delle proiezioni
Cenni di Domain Driven Design: una visione strategica della modellazione del proprio dominio di business nell'era dei bi Data.
This document provides an overview and introduction to big data implementation strategies using Hadoop and beyond. It discusses how big data has evolved from technologies pioneered by companies like Google to analyze vast amounts of diverse data cheaper and more effectively than traditional methods. It also outlines some of the key challenges organizations face as data volumes, varieties, and velocities outgrow existing systems, and how new big data technologies like Hadoop provide more cost-effective solutions to process and analyze data at scale. The document notes that big data represents a shift in computing paradigms rather than just data size alone.
Building an Infrastructure that Secures and Protects
In June and July 2011, the Economist Intelligence Unit conducted a global survey, sponsored by Booz Allen Hamilton, of 387 executives to assess attitudes toward cybersecurity, and their progress towards implementing resilience strategies. Learn more: http://www.boozallen.com/insights/expertvoices/cyber-power
- Governments could leverage graph databases to make their countries more secure, provide better services, and increase efficiency. Graph databases allow analysis of connections in data that may not be apparent using traditional databases.
- Examples of how governments use graph databases include combating money laundering by analyzing how funds travel between parties, improving law enforcement investigations by connecting related data like suspects, evidence and locations, and enhancing e-government services by eliminating duplicate records across systems.
- Graph databases provide benefits like easy maintenance of connected data models, intuitive querying of relationships, high performance, and minimal resource usage which can improve areas like border security, fraud detection, records management and more.
Smart Data Module 1 introduction to big and smart datacaniceconsulting
This document provides an overview of big and smart data. It defines big data as large volumes of structured, unstructured, and semi-structured data that is difficult to manage and process using traditional databases. It discusses how big data becomes smart data through analysis and insights. Examples of smart data applications are also provided across various industries like retail, healthcare, transportation and more. The document emphasizes that in order to start smart with data, companies need to review their existing data, ask the right questions, and form actionable insights rather than just conclusions.
Data science and its potential to change business as we know it. The Roadmap ...InnoTech
The document summarizes a presentation on data science and its potential to change business. It discusses how organizations can increase their data science maturity and capabilities to gain more value from data. As data volumes continue growing exponentially, data science can help organizations move from simple reporting to predictive analytics in order to make real-time decisions. The presentation examines how data science is an emerging field that incorporates techniques from many areas and how organizations can assess their analytics maturity.
IRJET - Big Data Analysis its ChallengesIRJET Journal
This document discusses big data analysis and its challenges. It begins by defining big data and business analytics, noting that large amounts of data are now being generated daily that require new techniques to analyze. It describes some of the key challenges in handling big data, including issues around storage, analysis, and reporting on large, complex datasets. The document then discusses the four Vs of big data - volume, variety, velocity, and veracity. It concludes by noting limitations in current research and opportunities for future work to better understand the impacts of big data and business analytics on competitive advantages.
This document discusses big data and provides an overview of key concepts and technologies. It defines big data as large volumes of data in various formats that are growing rapidly. It describes the four V's of big data - volume, velocity, variety, and value. The document then provides an overview of big data technologies like columnar databases, NoSQL, and Hadoop that are designed to handle large and complex data sets.
This document provides an overview of social media and big data analytics. It discusses key concepts like Web 2.0, social media platforms, big data characteristics involving volume, velocity, variety, veracity and value. The document also discusses how social media data can be extracted and analyzed using big data tools like Hadoop and techniques like social network analysis and sentiment analysis. It provides examples of analyzing social media data at scale to gain insights and make informed decisions.
This document provides an overview of big data, including its definition, size and growth, characteristics, analytics uses and challenges. It discusses operational vs analytical big data systems and technologies like NoSQL databases, Hadoop and MapReduce. Considerations for selecting big data technologies include whether they support online vs offline use cases, licensing models, community support, developer appeal, and enabling agility.
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.
Big Data Lecture given at the University of Balamand by Fady Sayah Digi Web Founder.
Why Big Data Now?
Types of Databases
The 4 Vs of Big Data
Big Data Challenges
Big Data & Marketing
Big Data Impact on Social Media
Big Data & Hospitality
Big Data Scalable systems
BIg Data and Higher Education
Big Data Success Stories
You can view the presentation on this link.
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...IRJET Journal
This document discusses big data analytical methods, cloud computing, and how they can be combined. It explains that big data involves large amounts of structured, semi-structured, and unstructured data from various sources that requires significant computing resources to analyze. Cloud computing provides a way for big data analytics to be offered as a service and processed efficiently using cloud resources. The integration of big data and cloud computing allows organizations to gain business intelligence from large datasets in a flexible, scalable and cost-effective manner.
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 big data characteristics, issues, challenges, and technologies. It describes the key characteristics of big data as volume, velocity, variety, value, and complexity. It outlines issues related to these characteristics like data volume and velocity. Challenges of big data include privacy and security, data access and sharing, analytical challenges, human resources, and technical challenges around fault tolerance, scalability, data quality, and heterogeneous data. The document also discusses technologies used for big data like Hadoop, HDFS, and cloud computing and provides examples of big data projects.
TechWise with Eric Kavanagh, Dr. Robin Bloor and Dr. Kirk Borne
Live Webcast on July 23, 2014
Watch the archive: https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=59d50a520542ee7ed00a0c38e8319b54
Analytical applications are everywhere these days, and for good reason. Organizations large and small are using analytics to better understand any aspect of their business: customers, processes, behaviors, even competitors. There are several critical success factors for using analytics effectively: 1) know which kind of apps make sense for your company; 2) figure out which data sets you can use, both internal and external; 3) determine optimal roles and responsibilities for your team; 4) identify where you need help, either by hiring new employees or using consultants 5) manage your program effectively over time.
Register for this episode of TechWise to learn from two of the most experienced analysts in the business: Dr. Robin Bloor, Chief Analyst of The Bloor Group, and Dr. Kirk Borne, Data Scientist, George Mason University. Each will provide their perspective on how companies can address each of the key success factors in building, refining and using analytics to improve their business. There will then be an extensive Q&A session in which attendees can ask detailed questions of our experts and get answers in real time. Registrants will also receive a consolidated deck of slides, not just from the main presenters, but also from a variety of software vendors who provide targeted solutions.
Visit InsideAnlaysis.com for more information.
A next generation introduction to data science and its potential to change bu...InnoTech
The document discusses the rise of data science and its potential to change business. It notes that the amount of data being generated is growing exponentially and will soon exceed 40 zettabytes. However, most companies feel overwhelmed by the data they have. Data science uses techniques from many fields to extract meaningful insights from vast amounts of data. It has become a critical business asset for companies in almost every industry. The emergence of data science is enabling real-time, predictive analytics beyond what was previously possible.
Data Lake-based Approaches to Regulatory-Driven Technology ChallengesBooz Allen Hamilton
The document discusses how a data lake approach can help financial institutions address regulatory challenges more effectively than traditional ETL approaches. A data lake allows raw data to be ingested rapidly and indexed as needed for analysis, reducing preparation time. It also enables unified queries across all data sources and quick fusion of multiple sources. This significantly reduces operational complexity and costs while improving security, flexibility, and the ability to address evolving requirements. The data lake approach is well-suited for challenges involving streaming analytics, point-to-point data marts, or data-heavy ETL requirements. Booz Allen has successfully implemented this approach for government clients to prototype solutions around critical applications.
Young people are increasingly living digital lives as computers and the internet have moved from large rooms into pockets through mobile devices. Social media plays a large role in this as it is mobile, personal, communal, and popular among youth who spend more time with social networking sites than watching television. This document discusses examining the social aspects of social media for young people through a survey and interviews to understand their new media behaviors in this changing digital world.
The document discusses using PowerShell and Boo programming languages together to build a testing framework. It provides an overview of PowerShell as a command line shell with dynamic objects and Boo as a .NET language based on Python with static typing. It then covers examples of using each language, building custom objects in both languages, and how to mix the dynamic and static objects together to create testing frameworks and other applications.
Digital cultural heritage class at IMT Lucca Spring 2015 day 1Stefano A Gazziano
This document provides information about a seminar on using digital technologies to add value to cultural heritage sites. It discusses topics like augmented reality, virtual reality, analyzing visitor data, and using websites and social media. The seminar aims to expose students to state-of-the-art tools and applications for improving the online presence and visitor experience of cultural sites. Students will learn how to effectively manage the digital aspects of museums and cultural heritage sites.
The document appears to contain a single year - 1982. In 3 sentences or less, this document is about a year and does not contain much additional context or information to summarize further.
09NTC: Your Website as an Experience of Your Brand (Environmental Defense Fund)Farra Trompeter, Big Duck
The document discusses the benefits of exercise for mental health. Regular physical activity can help reduce anxiety and depression and improve mood and cognitive functioning. Exercise causes chemical changes in the brain that may help protect against mental illness and improve symptoms.
O documento descreve a criação de uma nova linguagem de programação pelo programador apaixonado pelo framework .NET que queria uma sintaxe mais leve, maior extensibilidade e produtividade. A nova linguagem teria importações simplificadas, inferência de tipos, construção de objetos simplificada, atributos sintáticos, macros, pipeline de compilação extensível, funções de primeira classe, geradores e console interativo.
JIMS IT Flash , a monthly newsletter-An Initiative by the students of IT Department, shares the knowledge to its readers about the latest IT Innovations, Technologies and News.Your suggestions, thoughts and comments about latest in IT are always welcome at itflash@jimsindia.org.
Visit Website : http://jimsindia.org/
An Encyclopedic Overview Of Big Data AnalyticsAudrey Britton
This document provides an overview of big data analytics. It discusses the characteristics of big data, known as the 5 V's: volume, velocity, variety, veracity, and value. It describes how Hadoop has become the standard for storing and processing large datasets across clusters of servers. The challenges of big data are also summarized, such as dealing with the speed, scale, and inconsistencies of data from a variety of structured and unstructured sources.
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.
Introduction to big data – convergences.saranya270513
Big data is high-volume, high-velocity, and high-variety data that is too large for traditional databases to handle. The volume of data is growing exponentially due to more data sources like social media, sensors, and customer transactions. Data now streams in continuously in real-time rather than in batches. Data also comes in more varieties of structured and unstructured formats. Companies use big data to gain deeper insights into customers and optimize business processes like supply chains through predictive analytics.
Einstein published his ideas and became a pivotal element in shifting the way we think about physics - from the Newtonian model to the Quantum - in turn this changed the way we think about the world and allowed us to develop new ways of engaging with the world.
We are at a similar juncture. The development of computational technologies allows us to think about astronomical volumes of data and to make meaning of that data.
The mindshift that occurs is that “the machine is our friend”. The computer, like all machines, extends our capabilities. As a consequence the types of thinking now required in industry are those that get away from thinking like a computer and shift towards creative engagement with possibilities. Logical thinking is still necessary but it starts to be driven by imagination.
Computational thinking and data science change the way we think about defining and solving problems.
The age of creativity - which increasingly extends its impact from arts applications to business, scientific, technological, entrepreneurship, political, and other contexts.
Big Data & Analytics Trends 2016 Vin MalhotraVin Malhotra
This document discusses several trends in analytics for 2016:
1. Data security is a major concern as data volumes grow exponentially and security risks increase. Analytics can help secure data but requires integration across innovation, analytics, connectivity and technology.
2. The Internet of Things generates massive sensor data that requires new analytics to extract value, though challenges remain in integrating sensor and structured data in real time.
3. Open source analytics solutions like Hadoop are increasingly used by enterprises but also require careful risk management and a clear strategy to ensure they align with technology needs.
Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
IRJET- Big Data Management and Growth EnhancementIRJET Journal
1. The document discusses big data management and growth, including definitions of big data, properties of big data like volume, variety, and velocity, and applications of big data in various domains.
2. It describes how big data is used in education to improve student outcomes, in healthcare to enable prevention and more personalized care, and in industries like banking and fraud detection to enhance customer segmentation and risk assessment.
3. Big data analytics refers to analyzing large and complex datasets to extract useful insights and make better decisions. The document provides examples of machine learning and predictive analytics techniques used for big data analysis.
Python's Role in the Future of Data AnalysisPeter Wang
Why is "big data" a challenge, and what roles do high-level languages like Python have to play in this space?
The video of this talk is at: https://vimeo.com/79826022
Data foundation for analytics excellenceMudit Mangal
The document discusses predictive analytics and business insights. It covers what data analytics is and its challenges, the importance of data foundation and governance, security issues with data, and a retail use case. The future of data analytics is also discussed, with more structured, human interaction, and machine data expected to be analyzed. Establishing a robust data foundation is key to enabling trusted reporting and analytics.
MAKING SENSE OF IOT DATA W/ BIG DATA + DATA SCIENCE - CHARLES CAIBig Data Week
Charles Cai has more than two decades of experience and track records of global transformational programme deliveries – from vision, evangelism to end-to-end execution in global investment banks, and energy trading companies, where he excels at designing and building innovative, large scale, Big Data systems in high volume low latency trading, global Energy Trading & Risk Management, and advanced temporal and geospatial predictive analytics, as Chief Front Office Technical Architect and Head of Data Science. He’s also a frequent speaker at Google Campus, Big Data Innovation Summit, Cloud World Forum, Data Science London, QCon London and MoD CIO Symposium etc, to promote knowledge and best practice sharing, with audience ranging from developers, data scientists, to CXO level senior executives from both IT and business background. He has in-depth knowledge and experience Scala, Python, C# / F#, C++, Node.js, Java, R, Haskell programming languages in Mobile, Desktop, Hadoop/Spark, Cloud IoT/MCU and BlockChain etc, and TOGAF9, EMC-DS, AWS CNE4 etc. certifications.
This document discusses the role of data scientists in analyzing large and complex datasets to help answer critical questions. It notes that over 95% of digital data is unstructured and organizations lose millions annually due to inefficient use of information. Data scientists can help transform this data into usable knowledge by developing expertise in both data management and specific domains. They work with infrastructure experts and domain experts to analyze "big data" and solve grand challenges across many fields.
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Digital cultural heritage spring 2015 day 2
1. Seminar at IMT Lucca - Spring 2015
Prof. Stefano Gazziano sgazziano@johncabot.eu
Data, Value, People
2. Internet is a powerful a channel to spread info, and culture,
which power towards management of cultural heritages is
just being unleashed.
Topics
Pros and cons of using internet in managing cultural
heritage assets.
The "death of distance" and motivation to cross real
distances. "Being digital" helps increase real visits.
Virtual Museums, Virtual reality, Augmented reality:
technologies and content to improve the user experience
of cultural heritage sites
Internet platforms, on-site installations, mobile devices,
cloud computing platforms.
Stefano A Gazziano
sgazziano@johncabot.edu 2
3. Internet is a gold mine, users are the nuggets. Let us learn
how we can enrich culture.
Topics
What is “Big data” and what use it is.
“Analytics” or who are our internet visitors, what are they
looking for, and do they found it on our internet presence ?
Data acquisition. Open data standards.
Digital contact with users. Before and after the visit.
Museum analytics, assessing user satisfaction. Case study.
Stefano A Gazziano
sgazziano@johncabot.edu 3
4. Internet has rules, netiquette, and we must conform and
be smart. A few “musts” to put cultural heritage on the
net.
Topics
Search Engine Optimization. Content updates, internet
staff.
Web reputation management.
Search engine marketing: crawling, indexing, ranking.
Analitycs and conversions of a web site.
Stefano A Gazziano
sgazziano@johncabot.edu 4
5. The web is really a wide world, and there is a lot more to
do than just publish a web site.
Topics
Social networks: engagement techniques and online
tools.
Going viral. Case study
Stefano A Gazziano
sgazziano@johncabot.edu 5
6. Internet is a gold mine, users are the nuggets. Let us learn
how we can enrich culture.
Topics
What is “Big data” and what use it is.
“Analytics” or who are our internet visitors, what are they
looking for, and do they found it on our internet presence ?
Data acquisition. Open data standards.
Digital contact with users.
Museum analytics, assessing user satisfaction. Case study.
Stefano A Gazziano
sgazziano@johncabot.edu 6
7. As a general reference: Head First Data Analysis - A learner's guide to big numbers,
statistics, and good decisions By Michael Milton Publisher: O'Reilly Media - July 2009
SAS Institute, International Institute for Analytics. Big Data in Big Companies - May 2013
Authored by:Thomas H. Davenport, Jill Dyché. http://www.sas.com/resources/asset/Big-
Data-in-Big-Companies.pdf
Web analytics on Wikipedia: http://en.wikipedia.org/wiki/Web_analytics
Google Analytics Home Page http://www.google.com/analytics/
Open Web analytics http://www.openwebanalytics.com/
Open data Wikipedia page http://en.wikipedia.org/wiki/Open_data
Opencultuurdata http://www.opencultuurdata.nl/english/ at the Rijksmuseum, the
Regionaal Archief Leiden and Visserijmuseum Zoutkamp, The Netherelands.
The Rijksmuseum API (Application Programming Interface)
https://www.rijksmuseum.nl/en/api
How the Rijksmuseum opened up its collection - a case study http://pro.europeana.eu/pro-
blog/-/blogs/how-the-rijksmuseum-opened-up-its-collection-a-case-study
http://www.museumsandtheweb.com/mw2012/papers/sharing_cultural_heritage_the_lin
ked_open_data
Museum Analytics http://www.museum-analytics.org/
Stefano A Gazziano
sgazziano@johncabot.edu 7
8. Now: a
Video !!
And a loong one on
visual overviews, just
in case (MIT video,
such stuff!)
Stefano A Gazziano
sgazziano@johncabot.edu 8
11. There are few technology phenomena that have taken both the
technical and the mainstream media by storm than “big data.”
From the analyst communities to the front pages of the most
respected sources of journalism, the world seems to be awash in big
data projects, activities, analyses, and so on.
However, as with many technology fads, there is some murkiness in its
definition, which lends to confusion, uncertainty, and doubt when
attempting to understand how the methodologies can benefit the
organization. Therefore, it is best to begin with a definition of big data.
The analyst firm Gartner can be credited with the most-frequently
used (and perhaps, somewhat abused) definition:
Big data is high-volume, high-velocity and high-variety information assets
that demand cost-effective, innovative forms of information processing
for enhanced insight and decision making.
Stefano A Gazziano
sgazziano@johncabot.edu 11
12. For the most part, in popularizing the big data concept, the
analyst community and the media have seemed to latch onto
the alliteration that appears at the beginning of the definition,
hyperfocusing on what is referred to as the “3Vs—volume,
velocity, and variety.” Others have built upon that meme to
inject additional Vs such as“value”or “variability,” intended to
capitalize on an apparent improvement to the definition.
The challenge with Gartner’s definition is twofold. First, the
impact of truncating the definition to concentrate on the Vs
effectively distils out two other critical components of the
message:
1. “cost-effective innovative forms of information processing” (the
means by which the benefit can be achieved);
2. “enhanced insight and decision-making”(the desired outcome)
Stefano A Gazziano
sgazziano@johncabot.edu 12
13. Big data is fundamentally about applying innovative
and cost-effective techniques for solving existing and
future business problems whose resource
requirements (for data management space,
computation resources, or immediate, inmemory
representation needs) exceed the capabilities of
traditional computing environments as currently
configured within the enterprise.
Stefano A Gazziano
sgazziano@johncabot.edu 13
16. Stefano A Gazziano
sgazziano@johncabot.edu 16
Main » TERM » U » unstructured data Related Terms structured data data structuredata dynamic data structure static data structure SQL
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Stefano Gazziano <sgazziano@johncabot.edu> database database software ODBC - Open DataBase Connectivity cloud database By
Vangie Beal The phrase "unstructured data" usually refers to information that doesn't reside in a traditional row-column database. As
you might expect, it's the opposite of structured data -- the data stored in fields in a database. Unstructured data files often include text
and multimedia content. Examples include e-mail messages, word processing documents, videos, photos, audio files, presentations,
webpages and many other kinds of business documents. Note that while these sorts of files may have an internal structure, they are still
considered "unstructured" because the data they contain doesn't fit neatly in a database. Experts estimate that 80 to 90 percent of the
data in any organization is unstructured. And the amount of : with unstructured data. Big data refers to extremely large datasets that are
difficult to analyze with traditional tools. Big data can include both structured and unstructured data, but IDC estimates that 90 percent
of big data is unstructured data. Many of the tools designed to analyze big data can handle unstructured data. Implementing
Unstructured Data Management Organizations use of variety of different software tools to help them organize and manage
unstructured data. These can include the following: Big data tools: Software like Hadoop can process stores of both unstructured and
structured data that are extremely large, very complex and changing rapidly. Business intelligence software: Also known as BI, this is a
broad category of analytics, data mining, dashboards and reporting tools that help companies make sense of their structured and
unstructured data for the purpose of making better business decisions. Data integration tools: These tools combine data from disparate
sources so that they can be viewed or analyzed from a single application. They sometimes include the capability to unify structured and
unstructured data. Document management systems: Also called "enterprise content management systems," a DMS can track, store and
share unstructured data that is saved in the form of document files. Information management solutions: This type of software tracks
structured and unstructured enterprise data throughout its lifecycle. Search and indexing tools: These tools retrieve information from
unstructured data files such as documents, Web pages and photos. Unstructured Data Technology A group called the Organization for
the Advancement of Structured Information Standards (OASIS) has published the Unstructured Information Management Architecture
(UIMA) standard. The UIMA "defines platform-independent data representations and interfaces for software components or services
called analytics, which analyze unstructured information and assign semantics to regions of that unstructured information." Many
industry watchers say that Hadoop has become the de facto industry standard for managing Big Data. This open source project is
managed by the Apache Software Foundation. PREVIOUS unpackNEXT unusual software bug
24. The current work on e-Infrastructures relevant to digital
cultural heritage, such as DARIAH and CLARIN, and large-
scale aggregators of digital content, like Europeana,
changes the current landscape of digital cultural heritage.
Better understanding of big data implications on content,
architectures, functionality of large digital collections and
the effects on the users, quality and policy aspects is
needed.
The digital cultural heritage community forum to discuss
current work and theoretical advancements, and
consolidate state-of-the-art research, provide a forum to
discuss current experiences, and brainstorm future
developments in the area.
Stefano A Gazziano
sgazziano@johncabot.edu 24
26. Being a new domain, it also requires an in-depth discussion on integrating aspects of
big data in curricula in librarianship, information science, archival science and a range of
Humanities disciplines. Novel research relates to big data in the following domains:
◦ Cultural heritage objects and big data:
◦ aspects of capture, storage, sharing, and analysis
◦ Visualisation of large digital cultural heritage collections
◦ Curation of big cultural heritage collections
◦ Searching big data: Information retrieval and data mining
◦ Natural language processing: statistical NLP in cultural heritage
◦ Semantic web technologies and large scales of cultural data
◦ Web intelligence Cultural cloud
◦ Issues of aggregation of vast resources
◦ Distributed service architectures: SaaS, PaaS, IaaS
◦ Big data economics and digital heritage
◦ Evaluation, usability and use
◦ Visualisation methods and tools
◦ e-Infrastructures and large digital resources
◦ Citizen science: the challenges of scale in engaging citizens
◦ Educational aspects: how to introduce big data aspects in digital humanities and in Library and
Information Science schools?
Stefano A Gazziano
sgazziano@johncabot.edu 26
28. Justify and quantify NH impact to the communities they
serve while knowing relatively little about their visitors.
Understanding of visitor behavior in museums significantly
lags common practice in the commercial sector to provide
adequate insight into how best to achieve the field’s
mission.
Simple attendance statistics are not enough.
Invest little in the detailed understanding of the actions,
experiences, and ongoing participation of visitors once they
enter the building.T
Tools to know how to achieve long-term relevance.
Stefano A Gazziano
sgazziano@johncabot.edu 28
31. Surveys v/s Digital interaction
The danger of garbage in / garbage out
Wrong email (misspelling), Incorrect
statistical sampling and “confounders“
The importance of digital
interaction
Stefano A Gazziano
sgazziano@johncabot.edu 31
32. Actually, we’ll present a brief overview, just what is
necessary to interact then with a data analyst and not
look too dumb
Stefano A Gazziano
sgazziano@johncabot.edu 32
37. My problem ? Get more votes than others.
A tough job that requires quantitative directions. The best
agency (progressive) is probably GQRR Research . I thank IPR
Marketing, who graciously allowed me to disclose this study
for IMT
Get voters to the polls
Create consensus on your proposal and candidate
Case study : Italian parliamentary 2013.
Stefano A Gazziano
sgazziano@johncabot.edu 37
38. Identify segments
of electorate
Survey voters
Target segments
with proper
message
Focus groups
Evaluate results
Stefano A Gazziano
sgazziano@johncabot.edu 38
39. Loyal voters
Stefano A Gazziano
sgazziano@johncabot.edu 39
Mobile voters
Swing voters
Non voters
40. Loyal voters
Stefano A Gazziano
sgazziano@johncabot.edu 40
Mobile voters
Swing voters
Non voters
Now: profile, profile and profile again (8 – 12)
41. We want to get as
much votes as
possible given the
campaign budget
Where to allocate
how much given
the data analysis
results ?
Constraints:
“profitability” of target by
segments
Total campaign budget
Time to election day
Decision variable:
How many ads to run
per target
Stefano A Gazziano
sgazziano@johncabot.edu 41
44. Beyond paper: actual observational digital data
Web site analytics, user experience
Social networks engagement
Direct contact by targeted mail
Digital membership programs
Online polls
Newsletters
Virtual / 3D museums
Augmented reality
Marketing & Upselling
E-commerce
Stefano A Gazziano
sgazziano@johncabot.edu 44
47. Sorry but the technicalia is exactly the same for a
Museum and a Supermarket
Surveys are not enough, and are expensive
Social networks and web site presence could offer a
deluge of data
Day 3 will exactly be on how to produce content
suitable for data collection.
Day 4 will focus on activity to engage prospects on
social networks
Today we have a look at how selected CH institutions
assess user satisfaction
Stefano A Gazziano
sgazziano@johncabot.edu 47
51. The ten largest museums in the world: off and online
Stefano A Gazziano
sgazziano@johncabot.edu 51
52. The annual conference of Museums and the Web
◦ April 2-5, 2014 Baltimore, MD, USA
MW2014: Museums and the Web 2014
Tourist Satisfaction with Cultural Heritage destinations in India:
with special reference to Kolkata, West Bengal
TOURIST SATISFACTION WITH CULTURAL / HERITAGE SITES: The
Virginia Historic Triangle
A Study of Service Quality and Satisfaction for Museums - Taking
the National Museum of Prehistory as an Example
The Contribution of Technology-Based Heritage Interpretation to
the Visitor Satisfaction in Museums
Stefano A Gazziano
sgazziano@johncabot.edu 52