Big Data is creating large amounts of metadata from users' smartphones and online activities. While this data is now being collected, enterprises still struggle to effectively analyze it and develop useful algorithms from the poor mining of Big Data. As more resources are devoted to analyzing metadata, automated tasks will be able to make better use of Big Data. However, the rapid growth of Big Data outpaces what most enterprises can currently handle from a technology and personnel standpoint.
Enabling Big Data with Data-Level Security:The Cloud Analytics Reference Arch...Booz Allen Hamilton
; Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
IABE Big Data information paper - An actuarial perspectiveMateusz Maj
We look closely on the insurance value chain and assess the impact of Big Data on underwriting, pricing and claims reserving. We examine the ethics of Big Data including data privacy, customer identification, data ownership and the legal aspects. We also discuss new frontiers for insurance and its impact on the actuarial profession. Will actuaries will be able to leverage Big Data, create sophisticated risk models and more personalized insurance offers, and bring new wave of innovation to the market?
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
Enabling Big Data with Data-Level Security:The Cloud Analytics Reference Arch...Booz Allen Hamilton
; Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
IABE Big Data information paper - An actuarial perspectiveMateusz Maj
We look closely on the insurance value chain and assess the impact of Big Data on underwriting, pricing and claims reserving. We examine the ethics of Big Data including data privacy, customer identification, data ownership and the legal aspects. We also discuss new frontiers for insurance and its impact on the actuarial profession. Will actuaries will be able to leverage Big Data, create sophisticated risk models and more personalized insurance offers, and bring new wave of innovation to the market?
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
Data Lake-based Approaches to Regulatory-Driven Technology ChallengesBooz Allen Hamilton
Booz Allen Hamilton has found that a data lake-based approach to CA3 requirements is scalable, extensible, and improves the range and sophistication of analyses that can be supported while providing higher levels of data control and security.
The objective of this module is to provide an overview of what the future impacts of big data are likely to be.
Upon completion of this module you will:
Gain valuable insight into the predictions for the future of Big Data
Be better placed to recognise some of the trends that are emerging
Acquire an overview of the possible opportunities your business can have with Big Data
Understand some of the start up challenges you might have with Big Data
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...Geoffrey Fox
Motivating Introduction to MOOC on Big Data from an applications point of view https://bigdatacoursespring2014.appspot.com/course
Course says:
Geoffrey motivates the study of X-informatics by describing data science and clouds. He starts with striking examples of the data deluge with examples from research, business and the consumer. The growing number of jobs in data science is highlighted. He describes industry trend in both clouds and big data.
He introduces the cloud computing model developed at amazing speed by industry. The 4 paradigms of scientific research are described with growing importance of data oriented version. He covers 3 major X-informatics areas: Physics, e-Commerce and Web Search followed by a broad discussion of cloud applications. Parallel computing in general and particular features of MapReduce are described. He comments on a data science education and the benefits of using MOOC's.
IBM Smarter Analytics takes a look at Big Data and Insurance: uncovering the key area's and impact that insurers need to consider as volumes of data (both structured and unstructured) continue to increase..
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
Efficient data filtering algorithm for Big Data technology Telecommunication is a concept aimed at effectively filtering desired information for preventive purposes, the challenges posed by unprecedented rise in volume, variety and velocity of information has necessitated the need for exploring various methods Big Data which is simply a data sets that are so large and complex that traditional data processing tools and technologies cannot cope with is been considered. The process of examining such data to uncover hidden patterns in them was evolved, this was achieved by coming up with an Algorithm comprising of various stages like Artificial neural Network, Backtracking Algorithm, Depth First Search, Branch and Bound and dynamic programming and error check. The algorithm developed gave rise to the flowchart, with each line of block representing a sub-algorithm.
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
Hvilke teknologier forventer IBM får størst betydning fremover?
Få indblik i hvordan det er gået med IBM's tidligere forudsigelser og få et bud på, hvad fremtiden bringer fra IBM Research.
Anders Quitzau, Chief Technologist, IBM
Data Science Courses - BigData VS Data ScienceDataMites
Go through the slides to know what is Big Data and what is Data Science and Know the difference between Big Data and Data Science.
DataMites is a global institute, providing industry-aligned courses in Data Science, Machine Learning, and
Artificial Intelligence.
The Certified Data Scientist certification offered by DataMites covers all the important aspects of data science knowledge. The course is designed based on the accepted standards which demonstrates the quality of knowledge of a data science professional.
For more details please visit: https://datamites.com/data-science-course-training-chennai/
Data Lake-based Approaches to Regulatory-Driven Technology ChallengesBooz Allen Hamilton
Booz Allen Hamilton has found that a data lake-based approach to CA3 requirements is scalable, extensible, and improves the range and sophistication of analyses that can be supported while providing higher levels of data control and security.
The objective of this module is to provide an overview of what the future impacts of big data are likely to be.
Upon completion of this module you will:
Gain valuable insight into the predictions for the future of Big Data
Be better placed to recognise some of the trends that are emerging
Acquire an overview of the possible opportunities your business can have with Big Data
Understand some of the start up challenges you might have with Big Data
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Applications & Analytics Motivation: Big Data and the Cloud; Centerp...Geoffrey Fox
Motivating Introduction to MOOC on Big Data from an applications point of view https://bigdatacoursespring2014.appspot.com/course
Course says:
Geoffrey motivates the study of X-informatics by describing data science and clouds. He starts with striking examples of the data deluge with examples from research, business and the consumer. The growing number of jobs in data science is highlighted. He describes industry trend in both clouds and big data.
He introduces the cloud computing model developed at amazing speed by industry. The 4 paradigms of scientific research are described with growing importance of data oriented version. He covers 3 major X-informatics areas: Physics, e-Commerce and Web Search followed by a broad discussion of cloud applications. Parallel computing in general and particular features of MapReduce are described. He comments on a data science education and the benefits of using MOOC's.
IBM Smarter Analytics takes a look at Big Data and Insurance: uncovering the key area's and impact that insurers need to consider as volumes of data (both structured and unstructured) continue to increase..
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
Efficient data filtering algorithm for Big Data technology Telecommunication is a concept aimed at effectively filtering desired information for preventive purposes, the challenges posed by unprecedented rise in volume, variety and velocity of information has necessitated the need for exploring various methods Big Data which is simply a data sets that are so large and complex that traditional data processing tools and technologies cannot cope with is been considered. The process of examining such data to uncover hidden patterns in them was evolved, this was achieved by coming up with an Algorithm comprising of various stages like Artificial neural Network, Backtracking Algorithm, Depth First Search, Branch and Bound and dynamic programming and error check. The algorithm developed gave rise to the flowchart, with each line of block representing a sub-algorithm.
Big Data Analytics : Understanding for Research ActivityAndry Alamsyah
Big Data Analytics Presentation at International Workshop Colloquium Exploring Research Opportunity. School of Business and Management (SBM) - ITB. Bandung, 8 August 2019.
Hvilke teknologier forventer IBM får størst betydning fremover?
Få indblik i hvordan det er gået med IBM's tidligere forudsigelser og få et bud på, hvad fremtiden bringer fra IBM Research.
Anders Quitzau, Chief Technologist, IBM
Data Science Courses - BigData VS Data ScienceDataMites
Go through the slides to know what is Big Data and what is Data Science and Know the difference between Big Data and Data Science.
DataMites is a global institute, providing industry-aligned courses in Data Science, Machine Learning, and
Artificial Intelligence.
The Certified Data Scientist certification offered by DataMites covers all the important aspects of data science knowledge. The course is designed based on the accepted standards which demonstrates the quality of knowledge of a data science professional.
For more details please visit: https://datamites.com/data-science-course-training-chennai/
The REAL Impact of Big Data on PrivacyClaudiu Popa
The awesome promise of Big Data is tempered by the need to protect personal information. Data scientists must expertly navigate the legislative waters and acquire the skills to protect privacy and security. This talk provides enterprise leaders with answers and suggests questions to ask when the time comes to consider the vast opportunities offered by big data.
Global Data Management: Governance, Security and Usefulness in a Hybrid WorldNeil Raden
With Global Data Management methodology and tools, all of your data can be accessed and used no matter where it is or where it is from: on-premises, private cloud, public cloud(s), hybrid cloud, open source, third-party data and any combination of the these, with security, privacy and governance applied as if they were a single entity. Ingenious software products and the economics of computing make it economical to do this. Not free, but feasible.
Big Data and the Future of Journalism (Futurist Keynote Speaker Gerd Leonhard...Gerd Leonhard
This is a slightly edited version of my slides presented in London on June 7, 2013 and the Reuters Institute see https://reutersinstitute.politics.ox.ac.uk/research/conferences/forthcoming-conferences/big-data-big-ideas-for-media.html
BTW: You can download ALL of my slideshows, free books and other stuff at http://futuristgerd.com/downloads/
"Data stockpiles are growing exponentially...consumer profiles, media content usage patterns, Twitter and Facebook posts, online purchases, public records, real-time media user behavior and much more. The Big Ideas conference speakers will inspire tactics and strategies to harness these data.
The media industry's leading edge experts from journalism and business disciplines will detail their own case studies, outlining their challenges and triumphs using tools to understand complex data sets. They will outline how these experiences have paved the way to prize-winning journalism, audience insights and growing revenues..."
All product and company names mentioned herein are for identification and educational purposes only and are the property of, and may be trademarks of, their respective owners.
What exactly is big data? What exactly is big data? .pptxTusharSengar6
big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.” Put simply, big data is larger, more complex data sets, especially from new data sources.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
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
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...
Big Data: 8 facts and 8 fictions
1. Click through for eight facts and eight fictions regarding Big Data, as identified by
ThreatTrack Security.
1
2. Fact #1: You are Big Data. Much of the world’s Big Data is
created as metadata from users’ smartphones and GPS traffic.
Every day you create metadata with smartphones that enable GPS location services.
Every picture you take, every website you visit, every route you map creates
metadata that is stored and available for analysis. With more than five billion mobile
phones in use, including more than one billion smartphones in 2012, according to
research firm Strategy Analytics, it’s no wonder that many enterprises and
government organizations are interested in gleaning valuable content from the
information.
2
3. Fact #2: Big Data tends to be mined poorly to build
ineffective threat analysis algorithms.
With all the metadata that exists, we are only now figuring out how to make sense of
it and how to cultivate beneficial data from it. For one, enterprises traditionally
haven’t had the resources in place to analyze metadata. As those investments
increase, the mining for trends and useful analysis will increase as well.
3
4. Fact #3: Big Data is automating tasks that used to
involve tedious manual labor.
Software companies are developing better business intelligence tools that can not
only analyze metadata, but also automate tasks to more quickly make use of that
data to their advantage. This allows companies to be more flexible and also make the
analysis of Big Data much less costly than in the past.
4
5. Fact #4: Big Data is being used to categorize and classify malware more
effectively, grouping bad files the same way Google ranks pages.
As more information is gleaned about malware and more analysis picks up on trends, algorithms for categorizing
and classifying malware are being developed to help security providers. We at ThreatTrack Security use Big Data
in four ways: first, to discuss CART (Classification and Regression Trees) for predictive classification of event
modifiers; second, to make use of Shewhart Control Charts for outlier threat detection; third, we use Splines for
non-linear exploratory modeling; lastly, we apply the Goodness of fit principle to check for stability of historical
threat data and constructing a parsimonious model for APTs.
Our case study works by using a closed loop system beginning with identifying a file/URL, correlating the
information and finding where the file initially came from, where it was downloaded from, how it entered the
company's data space, what it downloaded, what it installed, its current payload and so on.
5
6. Fact #5: Big Data theory is moving faster than the reality of what an
enterprise is capable of from both a technology and manpower
standpoint.
Since much of Big Data is derived from user-centric behavior and usage, it moves a lot faster than what an enterprise typically
generates from its application systems. About 70 percent of the digital universe has been created by individuals, not
corporations.
The primary reason why the Big Data theory is moving faster than its practice is to address the solution for managing such
humongous amounts of data. Oceans of data will be created between now and the year 2020, resulting in a 4,300 percent
increase in annual data generation as the macro drivers of user-generated data, along with the shift from analog to digital
media, propel us to the next frontier.
The Big Data tsunami is also causing technologies to be modernized. What used to be stored in conventional RDBMS and later in
NoSQL databases are insufficient and cannot be accessed by direct record access methods. The current technology of choice is
not conventional RDBMS but a map-reduced database like Hadoop that operates off distributed hardware substrate.
6
7. Fact #6: Big Data will create a major shift in
visualization of threats within the next three years.
Visualization of objects in excess of a few million in quantity requires thinking differently. For
instance, imagine the complexity of modeling huge data sets that are increasingly being
gathered by ubiquitous information-sensing mobile devices, aerial sensory
technologies, software logs, cameras, microphones, radio-frequency identification readers
and wireless sensor networks. Right now, the largest memory requirements for visualizing Big
Data working sets can’t be addressed by conventional computing models. That’s why the
science of visualization will have to be re-imagined and re-visited in the next three years.
7
8. Fact #7: Yesterday's antivirus endpoints are becoming
tomorrow's real threat-track vectors.
First, antivirus is a well-understood and mature market where users have a
reasonable idea of what spam is, what viruses are and how to remediate them using
up-to-date antivirus software. Second, the conventional endpoint of corporations is
the edge of the network. Both these paradigms are outdated now. First, antivirus is
not enough to protect against Advanced Persistent Threats. Enterprises need an antithreat type of software to combat sophisticated attacks that find their way in through
endpoints in new and creative ways.
8
9. Fact #8: Yesterday's endpoints of the brick-and-mortar enterprise have
shifted to the users, with the proliferation of the BYOD paradigm where
user devices are the real endpoints.
This extends the point made in Fact #7. With the advent of BYOD becoming the norm
in the corporate environment, the real vulnerable endpoint of enterprises has turned
out to be handheld smartphones. As more smartphones connect to corporate
networks and data, it increases the vulnerabilities organizations face trying to secure
all those additional points of entry.
9
10. Fiction #1: Security companies are equipped to handle
the volume and velocity of Big Data.
Like many enterprises, security companies are also learning to wrap their hands
around Big Data, and the theory of Big Data for that matter, eliminating potential
vulnerabilities to ensure that the data remains clean for analysis and production. As
the concept of Big Data grows and evolves, security companies must perpetually
grow and evolve too.
10
11. Fiction #2: Security developers are easily extracting
value from collected data.
The old saying “you don’t know what you don’t know” applies to security developers.
Without proper analysis tools in place, security companies aren’t able to extract
valuable content from the collected data. Only with those analysis tools, algorithms
and applications can developers truly garner valuable insight from collected data.
11
12. Fiction #3: Analytics technology is ready-made for
security.
From the phrase “finding a needle in the haystack,” analytics is useless in haystacks of
data where there are no needles to begin with. The hype has caused us to create
massive data stacks with poor references (or indices) around those stacks. Any data
analyst will attest to the fact that a better index of smaller data sets yields better
analytics than a larger data set with lame indices.
12
13. Fiction #4: Leveraging Big Data in a security context is as
simple as using it for any generalized purpose.
Successfully leveraging Big Data first must address the point in Fiction #3, that analytics is ready made for security.
Second, establishing a security context is the next problem. Security context can be established connecting the
relationships (after map reducing the data itself) between data sets to reveal valuable insights in the patterns that
were previously not correlated or compared. Mining for trends requires that data be managed coherently at first.
Similarly, mining for relationships requires that trends be understood. Only after you have the data map
reduced, and the trends in it understood, can you then mine for relationships among the trends of the mapreduced data farms. Only after all of these prerequisites are achievable, can you establish the big security context
of Big Data.
Think of security context as the metadata fabric of relationships, which is a lot more powerful and useful for
visualizing risks, threats and predictive analytics.
13
14. Fiction #5: Big Data will cause major change in the
security industry within the next year.
Big Data won’t cause major change in the security industry. Instead, the major change
will be in identifying anomalies that can be identified as advanced security attacks.
And both concepts will join together and work in concert to realize value for
enterprises.
14
15. Fiction #6: There is a widespread belief that Big Data sets offer a higher
form of intelligence that can generate insights that were previously
impossible.
That’s not true by itself. We need more algorithms that can offer more
intelligence, not bigger data sets. The two kinds of algorithms are: Bayesian
algorithms, which deal with prior occurrences, and predictive analytics, which is
forward facing. Looking at the future, big context in security is going to be more
innovative than Big Data in security.
15
16. Fiction #7: Big Data searched with dumb algorithms fails to yield what
little data can yield using smarter algorithms.
The concept of Big Data should be about the algorithms and not about the data itself.
Better precision and better searching techniques will trap the breaches. Better
algorithms and lesser data stacks will provide more value than lesser algorithms and
bigger data stacks. The better net will catch better stuff.
16
17. Fiction #8: Most data scientists have experience with Big Data. This isn’t true
because much of Big Data isn’t directly used; rather, it is summarized or “map
reduced” before being analyzed, which is often not very big.
Data science is a new branch of study. Most data scientists fall into two groups:
statisticians turned programmers, and programmers turned statisticians that compete
for data scientist jobs. While they used to work with data sets and map-reduced data
sets, they’re not as used to working with user profile data, which is what Big Data
primarily consists of.
17