This document provides an overview of big data. It begins with definitions of big data and its key characteristics, including volume, velocity, and variety. It then discusses how big data is stored, selected, and processed. Examples of big data sources and tools are provided. The document outlines several applications of big data across different industries like healthcare, manufacturing, and retail. It also discusses risks of big data like privacy issues and costs. The future of big data is presented, with projections that the big data market will grow significantly in coming years. In closing, references are provided for additional information on big data.
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data.
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
Big Data is a new term used to identify datasets that we can not manage with current methodologies or data mining software tools due to their large size and complexity. Big Data mining is the capability of extracting useful information from these large datasets or streams of data. New mining techniques are necessary due to the volume, variability, and velocity, of such data.
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
everyone need to some storage and data.this big data is increase the data capacity and processing power.
Big Data may well be the Next Big Thing in the IT world.
• Big data burst upon the scene in the first decade of the 21st century.
• The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
• Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
Introduction to Big Data (non-technical) and the importance of Data Science to create meaning.
First of all we define Big Data in the light of the 3 Vs: volume, velocity and variety; next we move on to redefine Big Data, and we touch the topic of a data lake. We envision that Big Data will become mainstream for small organisations as well, what we can do with Big Data, how to tackle Big Data projects, what challenges lie ahead, but what opportunities are there to reap. And of course how important data science is to find the meaning in all the data.
General introduction to Big Data terms and technologies: Velocity, Volume, Variety (3V) and Veracity (4V), NoSQL, Data Science, main data stores (key-value, column, document, graph), Elasticsearch, ...
Presentation of data.be products leveraging Big Data & Elasticsearch
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Big Stream Processing Systems, Big GraphsPetr Novotný
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.
This episode of our series will be focussed on processing systems capable of dealing with Big Streams. But analysing data lacking graphical representation will not be very convenient for us. And this is where we have to use a platform capable of visualising Big Graphs. All these topics will be covered in today’s presentation.
#CHEDTEB
www.chedteb.eu
Big data is a term that describes the large volume of data may be both structured and unstructured.
That inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters.
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
everyone need to some storage and data.this big data is increase the data capacity and processing power.
Big Data may well be the Next Big Thing in the IT world.
• Big data burst upon the scene in the first decade of the 21st century.
• The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
• Like many new information technologies, big data can bring about dramatic cost reductions, substantial improvements in the time required to perform a computing task, or new product and service offerings.
Introduction to Big Data (non-technical) and the importance of Data Science to create meaning.
First of all we define Big Data in the light of the 3 Vs: volume, velocity and variety; next we move on to redefine Big Data, and we touch the topic of a data lake. We envision that Big Data will become mainstream for small organisations as well, what we can do with Big Data, how to tackle Big Data projects, what challenges lie ahead, but what opportunities are there to reap. And of course how important data science is to find the meaning in all the data.
General introduction to Big Data terms and technologies: Velocity, Volume, Variety (3V) and Veracity (4V), NoSQL, Data Science, main data stores (key-value, column, document, graph), Elasticsearch, ...
Presentation of data.be products leveraging Big Data & Elasticsearch
In this presentation, I have talked about Big Data and its importance in brief. I have included the very basics of Data Science and its importance in the present day, through a case study. You can also get an idea about who a data scientist is and what all tasks he performs. A few applications of data science have been illustrated in the end.
Big Stream Processing Systems, Big GraphsPetr Novotný
Big Data, a recent phenomenon. Everyone talks about it, but do you really know what Big Data is? Join our four-part series about Big Data and you will get answers to your questions!
We will cover Introduction to Big Data and available platforms which we can use to deal with Big Data. And in the end, we are going to give you an insight into the possible future of dealing with Big Data.
After the two previous episodes you know the basics about Big Data. Yet, it might get a bit more complicated than that. Usually when you have to deal with data which is generated in real-time. In this case, you are dealing with Big Stream.
This episode of our series will be focussed on processing systems capable of dealing with Big Streams. But analysing data lacking graphical representation will not be very convenient for us. And this is where we have to use a platform capable of visualising Big Graphs. All these topics will be covered in today’s presentation.
#CHEDTEB
www.chedteb.eu
The Play's the Thing: Shakespeare in Animesketchydetails
Originally presented at ConnectiCon 2014, The Play's the Thing: Shakespeare in Anime takes a look at the the many interesting adaptations and uses of William Shakespeare and his plays in Japanese animation.
Topics include:
My Mental Choices Are Completely Interfering with My School Romantic Comedy
Future Diary
Psycho-Pass
Nisekoi
Blast of Tempest
Romeo X Juliet
View the slideshow at http://thesketchydetails.net to see all the included video.
Content1. Introduction2. What is Big Data3. Characte.docxdickonsondorris
Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing,selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
Introduction
• Big Data may well be the Next Big Thing in the IT
world.
• Big data burst upon the scene in the first decade of the
21st century.
• The first organizations to embrace it were online and
startup firms. Firms like Google, eBay, LinkedIn, and
Facebook were built around big data from the
beginning.
• Like many new information technologies, big data can
bring about dramatic cost reductions, substantial
improvements in the time required to perform a
computing task, or new product and service offerings.
• ‘Big Data’ is similar to ‘small data’, but bigger in
size
• but having data bigger it requires different
approaches:
– Techniques, tools and architecture
• an aim to solve new problems or old problems in a
better way
• Big Data generates value from the storage and
processing of very large quantities of digital
information that cannot be analyzed with
traditional computing techniques.
What is BIG DATA?
What is BIG DATA
• Walmart handles more than 1 million customer
transactions every hour.
• Facebook handles 40 billion photos from its user base.
• Decoding the human genome originally took 10years to
process; now it can be achieved in one week.
Three Characteristics of Big Data V3s
Volume
• Data
quantity
Velocity
• Data
Speed
Variety
• Data
Types
1st Character of Big Data
Volume
•A typical PC might have had 10 gigabytes of storage in 2000.
•Today, Facebook ingests 500 terabytes of new data every day.
•Boeing 737 will generate 240 terabytes of flight data during a single
flight across the US.
• The smart phones, the data they create and consume; sensors
embedded into everyday objects will soon result in billions of new,
constantly-updated data feeds containing environmental, location,
and other information, including video.
2nd Character of Big Data
Velocity
• Clickstreams and ad impressions capture user behavior at
millions of events per second
• high-frequency stock trading algorithms reflect market
changes within microseconds
• machine to machine processes exchange data between
billions of devices
• infrastructure and sensors generate massive log data in real-
time
• on-line gaming systems support millions of concurrent
users, each producing multiple inputs per second.
3rd Character of Big Data
Variety
• Big Data isn't just numbers, dates, and strings. Big
Data is also geospatial data, 3D data, audio and
video, and unstructured text, including log files and
social media.
• Traditional database systems were designed to
address smaller volumes of structured data, fewer
updates or a predictable, consistent data stru.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves.
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
4. Content
1. Introduction
2. What is Big Data
3. Characteristic of Big Data
4. Storing, selecting and processing of Big Data
5. Why Big Data
6. How it is Different
7. Big Data sources
8. Tools used in Big Data
9. Application of Big Data
10. Risks of Big Data
11. Benefits of Big Data
12. How Big Data Impact on IT
13. Future of Big Data
5. Application Of Big Data analytics
Homeland
Security
Smarter
Healthcare
Multi-channel
sales
Telecom
Manufacturing
Traffic Control
Trading
Analytics
Search
Quality
13. The Structure of Big Data
Structured
• Most traditional data sources
Semi-structured
• Many sources of big data
Unstructured
• Video data, audio data
19. Risks of Big Data
• Will be so overwhelmed
• Need the right people and solve the right
problems
• Costs escalate too fast
• Isn’t necessary to capture 100%
• Many sources of big data
is privacy
• self-regulation
• Legal regulation 19
20. Leading Technology Vendors
Example Vendors
• IBM – Netezza
• EMC – Greenplum
• Oracle – Exadata
Commonality
• MPP architectures
• Commodity Hardware
• RDBMS based
• Full SQL compliance
22. Future of Big Data
• $15 billion on software firms only specializing in
data management and analytics.
• This industry on its own is worth more than
$100 billion and growing at almost 10% a year
which is roughly twice as fast as the software
business as a whole.
• In February 2012, the open source analyst firm
Wikibon released the first market forecast for
Big Data , listing $5.1B revenue in 2012 with
growth to $53.4B in 2017
• The McKinsey Global Institute estimates that
data volume is growing 40% per year, and will
grow 44x between 2009 and 2020.