Big data refers to large volumes of complex, variable data that require advanced techniques for capture, storage, distribution and analysis. It is defined by 3 characteristics: volume relating to size, variety referring to different data types and sources, and velocity regarding frequency of data generation. Data is gathered from online transactions, social media, sensors and devices. Big data provides benefits like new business opportunities but also challenges around privacy as it reveals details about individuals' lives. Companies use big data for business intelligence and analytics to understand customers better.
Broad view of the new decade and the new paradigm of Innovation and Knowledge Management. Argues that KM happens at three levels, individual, organizational, societal and we need to focus on all the three levels
THE DIGITAL UNIVERSE IN 2020: Big Data,
Bigger Digital Shadows, and Biggest Growth in
the Far East, Decemeber 2012, IDC Whitepaper sponsored by EMC, http://bit.ly/Ux3kxq
How Can Policymakers and Regulators Better Engage the Internet of Things? Mercatus Center
The world today is seemingly always plugged into the Internet and technologies are constantly sharing data about our personal and professional lives. Device connectivity is on an upward trend with Cisco estimating that 50 billion devices will be connected to the Internet by 2020. Collection and data sharing by these devices introduces a host of new vulnerabilities, raising concerns about safety, security, and privacy for policymakers and regulators.
Broad view of the new decade and the new paradigm of Innovation and Knowledge Management. Argues that KM happens at three levels, individual, organizational, societal and we need to focus on all the three levels
THE DIGITAL UNIVERSE IN 2020: Big Data,
Bigger Digital Shadows, and Biggest Growth in
the Far East, Decemeber 2012, IDC Whitepaper sponsored by EMC, http://bit.ly/Ux3kxq
How Can Policymakers and Regulators Better Engage the Internet of Things? Mercatus Center
The world today is seemingly always plugged into the Internet and technologies are constantly sharing data about our personal and professional lives. Device connectivity is on an upward trend with Cisco estimating that 50 billion devices will be connected to the Internet by 2020. Collection and data sharing by these devices introduces a host of new vulnerabilities, raising concerns about safety, security, and privacy for policymakers and regulators.
Age Friendly Economy - Legislation and Ethics of Data UseAgeFriendlyEconomy
Upon completion of this module you will:
- Be able to recognize the necessity of regulating big data
- Understand the difference between privacy and data protection
- Know how to implement actions of data protection into your own (future) company
Duration of the module: approximately 1 – 2 hours
Mobile technology in libraries is a must for the future. See what university libraries, public libraries and school libraries are doing to market their services using mobile technologies.
This is a White Paper by Dave Evans, Cisco's Chief Futurist on the IoT, what it is and why it is important. I particularly the like the simple definition of IoT.
"The Internet of Things (IoT) - a definition - is simply the time when there are more objects connected to the Internet than people. this happened sometime in 2008/9."
DIsampaikan dalam seminar IoT, UPN Veteran Jakarta, 29 September 2018
IoT merupakan salah satu ciri dari revolusi industri 4.0. Definisi, studi kasus IoT di pertanian, energy, building management dan perikanan diberikan contoh. Device dan protocol juga disingung.
We have never lived in a world of faster and more wide-reaching technology innovations.
Our jobs, businesses, and how we operate as societies are being transformed by
technology, and the current global pandemic is only fast-tracking the digital
transformation. With this post, I want to delve into the top 10 tech trends that are driving the 4th Industrial Revolution, and that will define the next decade.
The world has developed faster than humans even imagined it to. It is coming to a stage where almost everything is beginning to depend on internet. So, what exactly is Internet of Things? Anything and everything which can be assigned an IP address and provided the ability to transfer data can be branched under internet of things.
While it is still a human who operates a computer, when it comes to storing and remembering data, a computer might perform better. This being said, humans have become completely dependent on a system in every walk of their life. Some companies have utilized this opportunity and also leveraged it to derive profits. This has also helped people keep track of latest happenings, of things that they like and more.
The COVID-19 coronavirus has impacted countries, communities and individuals in countless ways, from school closures to health-care insurance issues not to undermined loss of lives.
As governments scramble to address these problems, different solutions based on blockchain technologies have sprung up to help deal with the worldwide health crisis. Blockchain will surely not prevent the emergence of new viruses itself, but what it can do is create the first line of rapid protection through a network of connected devices whose primary goal is to remain alert about disease outbreaks.
Therefore, the use of blockchain-enabled platforms can help prevent these pandemics by enabling early detection of epidemics, fast-tracking drug trials, and impact management of outbreaks and treatment.
“Permissionless Innovation” & the Clash of Visions over Emerging TechnologiesAdam Thierer
"Permissionless Innovation & the Clash of Visions over Emerging Technologies." A presentation created by Adam Thierer (Mercatus Center at George Mason University). It focuses on coming public policy fights over various emerging technologies, such as: driverless cars, the Internet of Things, wearable technology, commercial drones, mobile medical innovations, virtual reality, and more.
This presentation has been updated to reflect most recent version.
Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbour algorithm is discreetly chosen among them and described along with an example.
An Investigation on Scalable and Efficient Privacy Preserving Challenges for ...IJERDJOURNAL
ABSTRACT:- Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big data refers to huge amount of digital information collected from multiple and different sources. With the development of application of Internet/Mobile Internet, social networks, Internet of Things, big data has become the hot topic of research across the world, at the same time; big data faces security risks and privacy protection during collecting, storing, analyzing and utilizing. Since a key point of big data is to access data from multiple and different domains security and privacy will play an important role in big data research and technology. Traditional security mechanisms, which are used to secure small scale static data, are inadequate. So the question is which security and privacy technology is adequate for efficient access to big data. This paper introduces the functions of big data, and the security threat faced by big data, then proposes the technology to solve the security threat, finally, discusses the applications of big data in information security. Main expectation from the focused challenges is that it will bring a novel focus on the big data infrastructure.
The simplest definition of Big Data is large and complex unstructured data (images posted on Facebook, email, text messages, GPS signals from mobile phones, tweets, and other social media updates…etc.) that cannot be processed by traditional database tools.
Age Friendly Economy - Legislation and Ethics of Data UseAgeFriendlyEconomy
Upon completion of this module you will:
- Be able to recognize the necessity of regulating big data
- Understand the difference between privacy and data protection
- Know how to implement actions of data protection into your own (future) company
Duration of the module: approximately 1 – 2 hours
Mobile technology in libraries is a must for the future. See what university libraries, public libraries and school libraries are doing to market their services using mobile technologies.
This is a White Paper by Dave Evans, Cisco's Chief Futurist on the IoT, what it is and why it is important. I particularly the like the simple definition of IoT.
"The Internet of Things (IoT) - a definition - is simply the time when there are more objects connected to the Internet than people. this happened sometime in 2008/9."
DIsampaikan dalam seminar IoT, UPN Veteran Jakarta, 29 September 2018
IoT merupakan salah satu ciri dari revolusi industri 4.0. Definisi, studi kasus IoT di pertanian, energy, building management dan perikanan diberikan contoh. Device dan protocol juga disingung.
We have never lived in a world of faster and more wide-reaching technology innovations.
Our jobs, businesses, and how we operate as societies are being transformed by
technology, and the current global pandemic is only fast-tracking the digital
transformation. With this post, I want to delve into the top 10 tech trends that are driving the 4th Industrial Revolution, and that will define the next decade.
The world has developed faster than humans even imagined it to. It is coming to a stage where almost everything is beginning to depend on internet. So, what exactly is Internet of Things? Anything and everything which can be assigned an IP address and provided the ability to transfer data can be branched under internet of things.
While it is still a human who operates a computer, when it comes to storing and remembering data, a computer might perform better. This being said, humans have become completely dependent on a system in every walk of their life. Some companies have utilized this opportunity and also leveraged it to derive profits. This has also helped people keep track of latest happenings, of things that they like and more.
The COVID-19 coronavirus has impacted countries, communities and individuals in countless ways, from school closures to health-care insurance issues not to undermined loss of lives.
As governments scramble to address these problems, different solutions based on blockchain technologies have sprung up to help deal with the worldwide health crisis. Blockchain will surely not prevent the emergence of new viruses itself, but what it can do is create the first line of rapid protection through a network of connected devices whose primary goal is to remain alert about disease outbreaks.
Therefore, the use of blockchain-enabled platforms can help prevent these pandemics by enabling early detection of epidemics, fast-tracking drug trials, and impact management of outbreaks and treatment.
“Permissionless Innovation” & the Clash of Visions over Emerging TechnologiesAdam Thierer
"Permissionless Innovation & the Clash of Visions over Emerging Technologies." A presentation created by Adam Thierer (Mercatus Center at George Mason University). It focuses on coming public policy fights over various emerging technologies, such as: driverless cars, the Internet of Things, wearable technology, commercial drones, mobile medical innovations, virtual reality, and more.
This presentation has been updated to reflect most recent version.
Big data refers to huge set of data which is very common these days due to the increase of internet utilities. Data generated from social media is a very common example for the same. This paper depicts the summary on big data and ways in which it has been utilized in all aspects. Data mining is radically a mode of deriving the indispensable knowledge from extensively vast fractions of data which is quite challenging to be interpreted by conventional methods. The paper mainly focuses on the issues related to the clustering techniques in big data. For the classification purpose of the big data, the existing classification algorithms are concisely acknowledged and after that, k-nearest neighbour algorithm is discreetly chosen among them and described along with an example.
An Investigation on Scalable and Efficient Privacy Preserving Challenges for ...IJERDJOURNAL
ABSTRACT:- Big data is a relative term describing a situation where the volume, velocity and variety of data exceed an organization’s storage or compute capacity for accurate and timely decision making. Big data refers to huge amount of digital information collected from multiple and different sources. With the development of application of Internet/Mobile Internet, social networks, Internet of Things, big data has become the hot topic of research across the world, at the same time; big data faces security risks and privacy protection during collecting, storing, analyzing and utilizing. Since a key point of big data is to access data from multiple and different domains security and privacy will play an important role in big data research and technology. Traditional security mechanisms, which are used to secure small scale static data, are inadequate. So the question is which security and privacy technology is adequate for efficient access to big data. This paper introduces the functions of big data, and the security threat faced by big data, then proposes the technology to solve the security threat, finally, discusses the applications of big data in information security. Main expectation from the focused challenges is that it will bring a novel focus on the big data infrastructure.
The simplest definition of Big Data is large and complex unstructured data (images posted on Facebook, email, text messages, GPS signals from mobile phones, tweets, and other social media updates…etc.) that cannot be processed by traditional database tools.
over the past ten years, data has grown on the Internet, and we are the fuel and haste of this increase. Business owners, they produce apps for us, and we feed these companies with our data, unfortunately, it is all our private data. In the end, we become, through our private data, a commodity that is sold to the highest bidder.
Without security, not even privacy. Ethical oversight and constraints are needed to ensure that an appropriate balance. This article will cover: the contents of big data, what it includes, how data is collected, and the process of involving it on the Internet. In addition, it discuss the analysis of data, methods of collecting it, and factors of ethical challenges. Furthermore, the user's rights, which must be observed, and the privacy the user has.
Isolating values from big data with the help of four v’seSAT Journals
Abstract
Big Data refers to the massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. It includes business transactions, e-mail messages, photos, surveillance videos and activity logs. It also includes unstructured text posted on the Web, such as blogs and social media. Big Data has shown lot of potential in real world industry and research community. We support the power and Potential of it in solving real world problems. However, it is imperative to understand Big Data through the lens of 4 Vs. 4th V as ‘Value’ is desired output for industry challenges and issues. We provide a brief survey study of 4 Vs. of Big Data in order to understand Big Data and extract Value concept in general. Finally we conclude by showing our vision of improved healthcare, a product of Big Data Utilization, as a future work for researchers and students, while moving forward.
Keywords: Big Data, Surveillance videos, blogs, social media, four Vs.
Convergence of AI, IoT, Big Data and Blockchain: A Review.
Kefa Rabah .
Mara Research, Nairobi, Kenya .
Abstract
Data is the lifeblood of any business. Today, big data has applications in just about every industry – retail, healthcare,
financial services, government, agriculture, customer service among others. Any organization that can assimilate data
to answer nagging questions about their operations can benefit from big data. In overall, the demand for big data
transcend across all sectors and business. Those who work to understand their customers’ business and their problems
will be able to proactively identify big data solutions appropriate to their needs, and thus gain competitive advantage
over their competitors. Job demand for people with big data skill-set is also in the rise especially professional,
scientific and technical services; information technology; manufacturing; and finance and insurance; and retail.
DevOps is baseless without the cloud. IoT needs cloud to operate efficiently, for computing is required by the cloud
operate efficiently. AI remained only as model up until the advent of big data. Blockchain and related distributed
ledger technologies are disrupting the technology sector as we know it. The confluence of technologies is just
inevitable and often they are beneficial especially today when usher in the 4th industrial revolution (Rabah, 2017a)
and the forth coming machine economy (Rabah, 2018). More-so, data is a key ingredient of approaches to developing
AI and machine learning, which are now being applied to a wide variety of uses, from stock trading to chatbots to
self-driving cars. There is barely a business or human activity today that is not considered as a target for AI in future
years and decades.
Due to technological advances, vast data sets (e.g. big data) are increasing now days. Big Data a new term; is used
to identify the collected datasets. But due to their large size and complexity, we cannot manage with our current
methodologies or data mining software tools to extract those datasets. Such datasets provide us with unparalleled
opportunities for modelling and predicting of future with new challenges. So as an awareness of this and
weaknesses as well as the possibilities of these large data sets, are necessary to forecast the future. Today’s we
have an overwhelming growth of data in terms of volume, velocity and variety on web. Moreover this, from a
security and privacy views, both area have an unpredictable growth. So Big Data challenge is becoming one of the
most exciting opportunities for researchers in upcoming years.
Hence this paper discuss about this topic in a broad overview like; its current status; controversy; and challenges to
forecast the future. This paper defines at some of these problems, using illustrations with applications from various
areas. Finally this paper discuss secure management and privacy of big data as one of essential issues.
Due to the arrival of new technologies, devices, and communication means, the amount of data produced by mankind is growing rapidly every year. This gives rise to the era of big data. The term big data comes with the new challenges to input, process and output the data. The paper focuses on limitation of traditional approach to manage the data and the components that are useful in handling big data. One of the approaches used in processing big data is Hadoop framework, the paper presents the major components of the framework and working process within the framework.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
2. This Photo by Unknown Author is licensed under CC BY-SA
This Photo by Unknown Author is licensed under CC
BY-SA
• It's not regular data.
• It's not business as usual.
• It's not something that an
experienced data analyst
may be ready to deal with.
Big data is NOT…
3. In its simplest possible
definition,
big data is data that's
just too big to work
on your computer.
This Photo by Unknown Author is licensed under CC BY-SA
(Lynda.com, 2019)
4. “it describes large volumes of high velocity,
complex and variable data that require
advanced techniques and technologies to
enable the capture, storage, distribution,
management, and analysis of the
information.”
(TechAmerica Foundation’s Federal Big Data Commission, 2012)
Big data is…
5. Volume
VelocityVariety
Adapted from: Laney, 2001.
Big Data is defined based on 3 primary characteristics, also known as the 3Vs.
• Volume relates to the data’s size (terabytes,
petabytes, or zettabytes)
• Variety refers to the type of data and its source
(sensors, devices, social networks, the Web,
mobile phones, and so on)
• Velocity means how frequently the data is
generated (for instance, every millisecond,
second, minute, hour, day, week, month, or
year)
(Gandomi and Haider, 2015; Perera et al., 2015)
6. Data gathering
• Data are generated from online transactions,
email, video, images, clickstream, logs, search
queries, health records, and social networking
interactions; collected from increasingly
pervasive sensors deployed in infrastructure
such as communications networks, electric
grids, global positioning satellites, roads and
bridges, as well as in homes, clothing, and
mobile phones.
(Tene and Polonetsky, 2013, p. 240)
8. Benefits
The extraordinary societal benefits of big data must be
reconciled with increased risks to individuals’ privacy
(Tene and Polonetsky, 2013, p. 241)
Big data boosts the economy, transforming traditional
business models and creating new opportunities
through the use of business intelligence, sentiment
analysis, and analytics.
9. Major stakeholders
responsible for protecting
user privacy
• Protecting user privacy is a
responsibility not only of device
manufactures, services, and
app developers but also of
users themselves. Government
also has a key role to play in
governing standardization
processes (Perera et al., 2015, p.
32).
Source: Perera et al., 2015, p. 36.
10. Reading
• Little privacy in the age of big data (Claire
Porter, in The Guardian 20 June 2014)
• Turning big data into money proves harder
than expected (Richard Waters, in Financial
Times 4 October 2018)
• What is Big Data? (Bernard Marr, YouTube
2:05m, September 2014)
• Big data and little privacy: there is no
alternative? | Bart Preneel | TEDxULB
(TEDx Talks, June 2015)
11. What is Big Data?
by Bernard Marr, YouTube video 2:05m
What is the
features of
Big Data?
1
How is data
gathered?
2
What are the
benefits of
BD?
3
What is the
challenge?
4
How are
companies
using BD?
5
How does it
impact on
our life?
6
12. What is the
features of Big
Data? Our life is
online, high
volume of data
1
How is data
gathered?
Devices,
computers
smartphones etc
2
What are the
benefits of BD?
Window to the life
of customers, also
us as individuals
3
What is the
challenge? How to
turn this info into
knowledge, pick
up the relevant
parts
4
How are
companies using
BD? Consider BD
or left behind
5
How does it
impact on our
contemporary life?
6
13. This Photo by Unknown Author is licensed under CC BY-SA-NC
14. References
• Tene, O. and Polonetsky, J. (2012) Big data for all: Privacy and user control in the age of analytics. Nw. J. Tech. & Intell.
Prop., 11, p.xxvii.
• Perera, C., Ranjan, R., Wang, L., Khan, S.U. and Zomaya, A.Y. (2015) Big data privacy in the internet of things era. IT
Professional, 17(3), pp.32-39.
• FT.com 1/11/2018 John Burn-Murdoch “How data analysis helps football clubs make better signings”
https://www.ft.com/content/84aa8b5e-c1a9-11e8-84cd-9e601db069b8
• The Guardian (2014) Little privacy in the age of big data. 20 June 2014.
https://www.theguardian.com/technology/2014/jun/20/little-privacy-in-the-age-of-big-data
• Lynda.com (2019) Big Data Foundations: Techniques and Concepts. https://www.lynda.com/Data-Science-tutorials/three-
Vs-big-data/158656/190769-4.html?org=northampton.ac.uk
• Gandomi & Haider (2015) Beyond the hype: Big data concepts, methods, and analytics. International Journal of
Information Management, 35(2), pp.137–144.
• Laney, D. (2001, February 6). 3-D data management: Controlling data volume, velocity and variety. Application Delivery
Strategies by META Group Inc. Retrievedfrom http://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-
Management-Controlling-Data-Volume-Velocity-and-Variety.pdf