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.
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.
Presentation from David Simoes-Brown, Strategy Partner at 100%Open on: The Do's and Dont's of Opening Up Data.
Seminar summary slide.
Presented at Ordnance Survey hosted Science and Innovation 2010 Seminar: Underpinning innovation with geography, launching this year's GeoVation Challenge - "How can Britain feed itself?"
A talk at the Urban Science workshop at the Puget Sound Regional Council July 20 2014 organized by the Northwest Institute for Advanced Computing, a joint effort between Pacific Northwest National Labs and the University of Washington.
Knowledge Architecture: Graphing Your KnowledgeNeo4j
Ask any project manager and they will tell you the importance of reviewing lessons learned prior to starting a new project. The lesson learned databases are filled with nuggets of valuable information to help project teams increase the likelihood of project success. Why then do most lesson learned databases go unused by project teams? In my experience, they are difficult to search through and require hours of time to review the result set.
Recently I had a project engineer ask me if we could search our lessons learned using a list of 22 key terms the team was interested in. Our current keyword search engine would require him to enter each term individually, select the link, and save the document for review. Also, there was no way to search only the database, the query would search our entire corpus, close to 20 million URLs. This would not do. I asked our search team if they would run a special query against the lesson database only, using the terms provided. They returned a spreadsheet with a link to each document containing the terms. The engineer had his work cut out for him: over 1100 documents were on the list;.
I started thinking there had to be a better way. I had been experimenting with topic modeling, in particular to assist our users in connecting seemingly disparate documents through an easier visualization mechanism. Something better than a list of links on multiple pages. I gathered my toolbox: R/RStudio, for the topic modeling and exploring the data; Neo4j, for modeling and visualizing the topics; and Linkurious, a web front end for our users to search and visualize the graph database.
Data Science For Social Good: Tackling the Challenge of HomelessnessAnita Luthra
A talk presented at the Champions Leadership Conference Series - leveraging data provided by New York City’s Department of Homeless Services, software vendor Tibco partnered with SumAll.Org to help tackle the societal challenge of homelessness in New York City.
Algorithms are biased because we are. Are we willing to change?Gregory Menvielle
Algorithms are just as biased as we are. But how do we identify these biases and how do we change our ways to make our technology better and more impactful for everyone?
Presentation given at the Intel Dev Fest 2019
Presentation slide used during the meetup on Artificial Intelligence and Its Ecosystem organized by Developer Session. In the presentation, I highlighted why open data is one of the key parts of AI ecosystem and the situation of Open Data in Nepal.
Data: A Timeline - How Data Came To Rule The WorldRibbonfish
Data: A Timeline - How Data Came To Rule The World
At Ribbonfish, we work with data all the time. Organisations use data to understand their customers, test new products, manage processes, and much more. This presentation looks at the timeline of how data came to such importance in this noisy world.
David Meza's slides from his talk at Connected Data London. David is a Chief Knowledge Architect at NASA, Johnson Space Centre. His keynote talk proposed how combining strategy, data science and information architecture can help transform data to knowledge.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
Tim Estes - Information Systems in an Entity Centric WorldDigital Reasoning
Tim Estes, CEO of Digital Reasoning, talks about the use of Hadoop and other scalable technologies along with Digital Reasoning's analytics for automated understanding of cloud-scale text challenges.
This presentation was delivered at Hadoop World in New York in Oct 2010
An invited talk in the Big Data session of the Industrial Research Institute meeting in Seattle Washington.
Some notes on how to train data science talent and exploit the fact that the membrane between academia and industry has become more permeable.
Keynote presentation given at the 10th anniversary of the 4TU.researchdata repository https://data.4tu.nl/info/en/news-events/training-events/news-item/4turesearchdatas-role-in-fostering-open-science-10th-anniversary-celebration-29-sep-2020-1530-1730-c/
Analysis on big data concepts and applicationsIJARIIT
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
Presentation from David Simoes-Brown, Strategy Partner at 100%Open on: The Do's and Dont's of Opening Up Data.
Seminar summary slide.
Presented at Ordnance Survey hosted Science and Innovation 2010 Seminar: Underpinning innovation with geography, launching this year's GeoVation Challenge - "How can Britain feed itself?"
A talk at the Urban Science workshop at the Puget Sound Regional Council July 20 2014 organized by the Northwest Institute for Advanced Computing, a joint effort between Pacific Northwest National Labs and the University of Washington.
Knowledge Architecture: Graphing Your KnowledgeNeo4j
Ask any project manager and they will tell you the importance of reviewing lessons learned prior to starting a new project. The lesson learned databases are filled with nuggets of valuable information to help project teams increase the likelihood of project success. Why then do most lesson learned databases go unused by project teams? In my experience, they are difficult to search through and require hours of time to review the result set.
Recently I had a project engineer ask me if we could search our lessons learned using a list of 22 key terms the team was interested in. Our current keyword search engine would require him to enter each term individually, select the link, and save the document for review. Also, there was no way to search only the database, the query would search our entire corpus, close to 20 million URLs. This would not do. I asked our search team if they would run a special query against the lesson database only, using the terms provided. They returned a spreadsheet with a link to each document containing the terms. The engineer had his work cut out for him: over 1100 documents were on the list;.
I started thinking there had to be a better way. I had been experimenting with topic modeling, in particular to assist our users in connecting seemingly disparate documents through an easier visualization mechanism. Something better than a list of links on multiple pages. I gathered my toolbox: R/RStudio, for the topic modeling and exploring the data; Neo4j, for modeling and visualizing the topics; and Linkurious, a web front end for our users to search and visualize the graph database.
Data Science For Social Good: Tackling the Challenge of HomelessnessAnita Luthra
A talk presented at the Champions Leadership Conference Series - leveraging data provided by New York City’s Department of Homeless Services, software vendor Tibco partnered with SumAll.Org to help tackle the societal challenge of homelessness in New York City.
Algorithms are biased because we are. Are we willing to change?Gregory Menvielle
Algorithms are just as biased as we are. But how do we identify these biases and how do we change our ways to make our technology better and more impactful for everyone?
Presentation given at the Intel Dev Fest 2019
Presentation slide used during the meetup on Artificial Intelligence and Its Ecosystem organized by Developer Session. In the presentation, I highlighted why open data is one of the key parts of AI ecosystem and the situation of Open Data in Nepal.
Data: A Timeline - How Data Came To Rule The WorldRibbonfish
Data: A Timeline - How Data Came To Rule The World
At Ribbonfish, we work with data all the time. Organisations use data to understand their customers, test new products, manage processes, and much more. This presentation looks at the timeline of how data came to such importance in this noisy world.
David Meza's slides from his talk at Connected Data London. David is a Chief Knowledge Architect at NASA, Johnson Space Centre. His keynote talk proposed how combining strategy, data science and information architecture can help transform data to knowledge.
We are good IEEE java projects development center in Chennai and Pondicherry. We guided advanced java technologies projects of cloud computing, data mining, Secure Computing, Networking, Parallel & Distributed Systems, Mobile Computing and Service Computing (Web Service).
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/java-projects/
Tim Estes - Information Systems in an Entity Centric WorldDigital Reasoning
Tim Estes, CEO of Digital Reasoning, talks about the use of Hadoop and other scalable technologies along with Digital Reasoning's analytics for automated understanding of cloud-scale text challenges.
This presentation was delivered at Hadoop World in New York in Oct 2010
An invited talk in the Big Data session of the Industrial Research Institute meeting in Seattle Washington.
Some notes on how to train data science talent and exploit the fact that the membrane between academia and industry has become more permeable.
Keynote presentation given at the 10th anniversary of the 4TU.researchdata repository https://data.4tu.nl/info/en/news-events/training-events/news-item/4turesearchdatas-role-in-fostering-open-science-10th-anniversary-celebration-29-sep-2020-1530-1730-c/
Analysis on big data concepts and applicationsIJARIIT
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
A Roadmap Towards Big Data Opportunities, Emerging Issues and Hadoop as a Sol...Rida Qayyum
The concept of Big Data become extensively popular for their vast usage in emerging technologies. Despite being complex and dynamic, big data environment has been generating the colossal amount of data which is impossible to handle from traditional data processing applications. Nowadays, the Internet of things (IoT) and social media platforms like, Facebook, Instagram, Twitter, WhatsApp, LinkedIn, and YouTube generating data in various formats. Therefore, this promotes a drastic need for technology to store and process this tremendous volume of data. This research outlines the fundamental literature required to understand the concept of big data including its nature, definitions, types, and characteristics. Additionally, the primary focus of the current study is to deal with two fundamental issues; storing an enormous amount of data and fast data processing. Leading to objectives, the paper presents Hadoop as a solution to address the problem and discussed the Hadoop Distributed File System (HDFS) and MapReduce programming framework for storage and processing in Big Data efficiently. Future research directions in this field determined based on opportunities and several emerging issues in Big Data domination. These research directions facilitate the exploration of the domain and the development of optimal solutions to address Big Data storage and processing problems. Moreover, this study contributes to the existing body of knowledge by comprehensively addressing the opportunities and emerging issues of Big Data.
Hadoop and Big Data Readiness in Africa: A Case of Tanzaniaijsrd.com
Big data has been referred to as a forefront pillar of any modern analytics application. Together with Hadoop which is open source software, they have emerged to be a solution to the processing of massive generated both structured and unstructured data. With different strategies and initiatives taken by governments and private institutions in the world towards deployment and support of big data analytics and hadoop, Africa cannot be left isolated. In this paper, we assessed the readiness of Africa with a case study of Tanzania in harnessing the power of big data analytics and hadoop as a tool for drawing insights that might help them make crucial decisions. We used a survey in collecting the data using questionnaires. Results reveal that majority of the companies are either not aware of the technologies or still in their infancy stages in using big data analytics and hadoop. We identified that most companies are in either awakening or advancing stages of the big data continuum. This is attributed by challenges such as lack of IT skills to manage big data projects, cost of technology infrastructure, making decision on which data are relevant, lack of skills to analyze the data, lack of business support and deciding on what technology is best compared to others. It has also been found out that most of the companies' IT officers are not aware with the concepts and techniques of big data analytics and hadoop.
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.
Communications of the Association for Information SystemsV.docxmonicafrancis71118
Communications of the Association for Information Systems
Volume 34 Article 65
5-2014
Tutorial: Big Data Analytics: Concepts,
Technologies, and Applications
Hugh J. Watson
University of Georgia, [email protected]
Follow this and additional works at: http://aisel.aisnet.org/cais
This material is brought to you by the Journals at AIS Electronic Library (AISeL). It has been accepted for inclusion in Communications of the
Association for Information Systems by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact
[email protected]
Recommended Citation
Watson, Hugh J. (2014) "Tutorial: Big Data Analytics: Concepts, Technologies, and Applications," Communications of the Association
for Information Systems: Vol. 34, Article 65.
Available at: http://aisel.aisnet.org/cais/vol34/iss1/65
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mailto:[email protected]>
Volume 34 Article 65
Tutorial: Big Data Analytics: Concepts, Technologies, and Applications
Hugh J. Watson
Department of MIS, University of Georgia
[email protected]
We have entered the big data era. Organizations are capturing, storing, and analyzing data that has high volume,
velocity, and variety and comes from a variety of new sources, including social media, machines, log files, video,
text, image, RFID, and GPS. These sources have strained the capabilities of traditional relational database
management systems and spawned a host of new technologies, approaches, and platforms. The potential value of
big data analytics is great and is clearly established by a growing number of studies. The keys to success with big
data analytics include a clear business need, strong committed sponsorship, alignment between the business and
IT strategies, a fact-based decision-making culture, a strong data infrastructure, the right analytical tools, and people
skilled in the use of analytics. Because of the paradigm shift in the kinds of data being analyzed and how this data is
used, big data can be considered to be a new, fourth generation of decision support data management. Though the
business value from big data is great, especially for online companies like Google and Facebook, how it is being
used is raising significant privacy concerns.
Keywords: big data, analytics, benefits, architecture, platforms, privacy
Volume 34, .
Big Data (This paper has some minor issues with the refere.docxhartrobert670
Big Data
(This paper has some minor issues with the references at the end but is otherwise good)
Introduction
Information is one of the most important resources that companies have available to them; this
information allows decisions to be made to determine what the company is going to do for the next day,
the next month, and the next year. The core component of this important resource is data, and with a
little data, companies can have a little bit information to plan future operations. That same company
with large amounts of data, or big data as it is known, can much more accurately find trends, become
more efficient, increase productivity, and in turn be more profitable. What separates data from big data,
what defining characteristics does it have, how can such a massive resource be fully utilized, and why
should businesses, especially smaller businesses, even bother with such an undertaking.
To understand what big data is first one must start at what came before this big data revolution
that some big companies are just now at the cusp of. Before the advent of big data when companies
gathered data, first it was fairly cost prohibitive due to issue with storage of larger amounts of data and
since computers processing power was not equal to what most businesses are working with today what
those companies were trying to accomplish could end up taking larger or not being possible by the
equipment or techniques being used. Since the first reason has become less burdensome for companies
it has become easier to collect larger amounts of data and store larger amounts of data, which has
allowed some companies to use old data for things outside the original intended purpose. When a
business collects data it normally is towards a goal or trying to gain an understanding but after the
meaning from the data gathered had been extracted not much else would be done with the data and
typically thrown away. With it no longer being as cost prohibitive companies like Google were able to
reuse old data for other purposes and glean additional insight beyond what the initial set of data had
revealed. This is the idea behind big data and what companies hope to gain is more information beyond
the explicit information within very large sets of data.
Key information
How is data any different than big data; at what point does the size of this raw information
change how it’s labeled. Actually this is misleading because it is not just the size of the data, but three
defining characteristics that help to identify what big data is. According to the web site Gartner.com
(Laney, 2001), the focus area of data management were related to volume, variety, and velocity. Volume
specifies the actual size of the data being stored, and as such since overtime data storage has become
more efficient the for where big data starts is something that has changed with better technology.
Even with all of the advances in storage architecture and data ...
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Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
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Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
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A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
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- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
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8. Big Data
– “large data sets so big that commonly-used software tools are unable to capture,
curate, manage, and process the data within a tolerable elapsed time.”
Hadoop Dominates Big Data market
– Used widely by some of the world's largest websites,
such as Facebook, eBay, Amazon and Yahoo
– Moving into the enterprise
– Invented by developers at Yahoo!
/ page 8
What is Big Data?
Apache Hadoop
10. / page 10
Characteristics of Big Data
Component Parts
Big Data is facilitated by Data Science
Data Science is facilitated by Machine Learning
Machine Learning is a confluence of disciplines: computer science,
mathematical statistics, probability theory, visualization, etc.
What is the “New” Part of Big Data
“Big” is new, more data to manage than ever before
Traditional data content is now coupled with internal and external sources of
unstructured data via social media
New forms of analysis such as sentiment and credibility analysis
Bubble Brewing?
Circa 2000 and the Internet bubble event. Will it occur again?
A bubble may occur, but not because of Big Data
11. / page 11
Applications for Big Data
Smarter Healthcare
Multi-channel sales
Financial Services
Log Analysis
Homeland Security
Traffic Control
Telecom
Search Quality
Manufacturing
Trading Analytics
Fraud and Risk
Retail: Churn
“Big Data is the definitive source of
competitive advantage across all
industries. For those organizations
that understand and embrace the new
reality of Big Data, the possibilities
for new innovation, improved agility,
and increased profitability are nearly
endless.”
Source: Wikibon 2012