This document discusses big data and intensive data processing. It defines big data and compares it to traditional analytics. It discusses technologies used for big data like Hadoop, MapReduce, and machine learning. It also discusses frameworks for analyzing big data like Apache Mahout and how Mahout is moving away from MapReduce to platforms like Apache Spark.
Memory Management in BigData: A Perpective Viewijtsrd
The requirement to perform complicated statistic analysis of big data by institutions of engineering, scientific research, health care, commerce, banking and computer research is immense. However, the limitations of the widely used current desktop software like R, excel, minitab and spss gives a researcher limitation to deal with big data and big data analytic tools like IBM BigInsight, HP Vertica, SAP HANA & Pentaho come at an overpriced license. Apache Hadoop is an open source distributed computing framework that uses commodity hardware. With this project, I intend to collaborate Apache Hadoop and R software to develop an analytic platform that stores big data (using open source Apache Hadoop) and perform statistical analysis (using open source R software).Due to the limitations of vertical scaling of computer unit, data storage is handled by several machines and so analysis becomes distributed over all these machines. Apache Hadoop is what comes handy in this environment. To store massive quantities of data as required by researchers, we could use commodity hardware and perform analysis in distributed environment. Bhavna Bharti | Prof. Avinash Sharma"Memory Management in BigData: A Perpective View" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14436.pdf http://www.ijtsrd.com/engineering/computer-engineering/14436/memory-management-in-bigdata-a-perpective-view/bhavna-bharti
A very categorized presentation about big data analytics Various topics like Introduction to Big Data,Hadoop,HDFS Map Reduce, Mahout,K-means Algorithm,H-Base are explained very clearly in simple language for everyone to understand easily.
Big Data Analysis Patterns with Hadoop, Mahout and Solrboorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
Memory Management in BigData: A Perpective Viewijtsrd
The requirement to perform complicated statistic analysis of big data by institutions of engineering, scientific research, health care, commerce, banking and computer research is immense. However, the limitations of the widely used current desktop software like R, excel, minitab and spss gives a researcher limitation to deal with big data and big data analytic tools like IBM BigInsight, HP Vertica, SAP HANA & Pentaho come at an overpriced license. Apache Hadoop is an open source distributed computing framework that uses commodity hardware. With this project, I intend to collaborate Apache Hadoop and R software to develop an analytic platform that stores big data (using open source Apache Hadoop) and perform statistical analysis (using open source R software).Due to the limitations of vertical scaling of computer unit, data storage is handled by several machines and so analysis becomes distributed over all these machines. Apache Hadoop is what comes handy in this environment. To store massive quantities of data as required by researchers, we could use commodity hardware and perform analysis in distributed environment. Bhavna Bharti | Prof. Avinash Sharma"Memory Management in BigData: A Perpective View" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-4 , June 2018, URL: http://www.ijtsrd.com/papers/ijtsrd14436.pdf http://www.ijtsrd.com/engineering/computer-engineering/14436/memory-management-in-bigdata-a-perpective-view/bhavna-bharti
A very categorized presentation about big data analytics Various topics like Introduction to Big Data,Hadoop,HDFS Map Reduce, Mahout,K-means Algorithm,H-Base are explained very clearly in simple language for everyone to understand easily.
Big Data Analysis Patterns with Hadoop, Mahout and Solrboorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
Data has been increasing at an exponential rate and organizations are either struggling to cope up or rushing to take advantage by analyzing it. Hadoop is an excellent open source framework, which addresses this big data problem.
I have used Hadoop within the financial sector for the last few years but could not find any resource or book that explains the usage of Hadoop for finance use cases. The best books I have ever found are again on Hadoop, Hive, or some MapReduce patterns, with examples on counting words or Twitter messages in all possible ways.
I have written this book with the objective of explaining the basic usage of Hadoop and other products to tackle big data for finance use cases. I have touched base on the majority of use cases, providing a very practical approach.
The book sold on:
http://www.amazon.co.uk/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.com/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.in/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
Big data analytics is the process of examining large data sets containing a variety of data types i.e., big data to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits. Enterprises are increasingly looking to find actionable insights into their data. Many big data projects originate from the need to answer specific business questions. With the right big data analytics platforms in place, an enterprise can boost sales, increase efficiency, and improve operations, customer service and risk management. Notably, the business area getting the most attention relates to increasing efficiencies and optimizing operations. By using big data analytics you can extract only the relevant information from terabytes, petabytes and exabytes, and analyse it to transform your business decisions for the future. Becoming proactive with big data analytics isn't a one-time endeavour, it is more of a culture change – a new way of gaining ground.
Keywords: business, analytics, exabytes, efficiency, data sets
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being co-relational to Hadoop is false; focus needs to be given on business value as well. Data Warehousing, Hadoop and stream processing complement each other very well. In this paper, we have tried reviewing a few frameworks and products
which use real time data streaming by providing modifications to Hadoop.
Introducing Big Data concepts & Hadoop to those who wish to begin their journey in the future of Information Technology. It is certain that data is going to play a major role in days to come, from our daily lives to the biggest of the venture we might undertake.And hence, knowing about Big data and technologies to work with the same is going to be essential for IT professionals.
We will start with some simple presentations and then will go on building upon it. For more intense, focused introduction & training on Big Data and related technologies, visit our website or write to us.
Fully featured, commercially supported machine learning suites that can build Decision Trees in Hadoop are few and far between. Addressing this gap, Revolution Analytics recently enhanced its entire scalable analytics suite to run in Hadoop. In this talk, I will explain how our Decision Tree implementation exploits recent research reducing the computational complexity of decision tree estimation, allowing linear scalability with data size and number of nodes. This streaming algorithm processes data in chunks, allowing scaling unconstrained by aggregate cluster memory. The implementation supports both classification and regression and is fully integrated with the R statistical language and the rest of our advanced analytics and machine learning algorithms, as well as our interactive Decision Tree visualizer.
Big Data Meets HPC - Exploiting HPC Technologies for Accelerating Big Data Pr...inside-BigData.com
DK Panda from Ohio State University presented this deck at the Switzerland HPC Conference.
"This talk will provide an overview of challenges in accelerating Hadoop, Spark and Mem- cached on modern HPC clusters. An overview of RDMA-based designs for multiple com- ponents of Hadoop (HDFS, MapReduce, RPC and HBase), Spark, and Memcached will be presented. Enhanced designs for these components to exploit in-memory technology and parallel file systems (such as Lustre) will be presented. Benefits of these designs on various cluster configurations using the publicly available RDMA-enabled packages from the OSU HiBD project (http://hibd.cse.ohio-state.edu) will be shown."
Watch the video presentation: https://www.youtube.com/watch?v=glf2KITDdVs
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Inovação e equipes geograficamente distribuídas - Palestrante: Maíra GattiRio Info
Rio Info 2013
Seminário Tecnologia da Informação & Recursos Humanos
17 de setembro - 14h às 18h
Inovação e equipes geograficamente distribuídas
Palestrante: Maíra Gatti
Data has been increasing at an exponential rate and organizations are either struggling to cope up or rushing to take advantage by analyzing it. Hadoop is an excellent open source framework, which addresses this big data problem.
I have used Hadoop within the financial sector for the last few years but could not find any resource or book that explains the usage of Hadoop for finance use cases. The best books I have ever found are again on Hadoop, Hive, or some MapReduce patterns, with examples on counting words or Twitter messages in all possible ways.
I have written this book with the objective of explaining the basic usage of Hadoop and other products to tackle big data for finance use cases. I have touched base on the majority of use cases, providing a very practical approach.
The book sold on:
http://www.amazon.co.uk/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.com/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
http://www.amazon.in/381/dp/B00X3TVGJY/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=&qid=
Big data analytics is the process of examining large data sets containing a variety of data types i.e., big data to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information. The analytical findings can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, competitive advantages over rival organizations and other business benefits. Enterprises are increasingly looking to find actionable insights into their data. Many big data projects originate from the need to answer specific business questions. With the right big data analytics platforms in place, an enterprise can boost sales, increase efficiency, and improve operations, customer service and risk management. Notably, the business area getting the most attention relates to increasing efficiencies and optimizing operations. By using big data analytics you can extract only the relevant information from terabytes, petabytes and exabytes, and analyse it to transform your business decisions for the future. Becoming proactive with big data analytics isn't a one-time endeavour, it is more of a culture change – a new way of gaining ground.
Keywords: business, analytics, exabytes, efficiency, data sets
Big Data Analysis Patterns - TriHUG 6/27/2013boorad
Big Data Analysis Patterns: Tying real world use cases to strategies for analysis using big data technologies and tools.
Big data is ushering in a new era for analytics with large scale data and relatively simple algorithms driving results rather than relying on complex models that use sample data. When you are ready to extract benefits from your data, how do you decide what approach, what algorithm, what tool to use? The answer is simpler than you think.
This session tackles big data analysis with a practical description of strategies for several classes of application types, identified concretely with use cases. Topics include new approaches to search and recommendation using scalable technologies such as Hadoop, Mahout, Storm, Solr, & Titan.
On a business level, everyone wants to get hold of the business value and other organizational advantages that big data has to offer. Analytics has arisen as the primitive path to business value from big data. Hadoop is not just a storage platform for big data; it’s also a computational and processing platform for business analytics. Hadoop is, however, unsuccessful in fulfilling business requirements when it comes to live data streaming. The initial architecture of Apache Hadoop did not solve the problem of live stream data mining. In summary, the traditional approach of big data being co-relational to Hadoop is false; focus needs to be given on business value as well. Data Warehousing, Hadoop and stream processing complement each other very well. In this paper, we have tried reviewing a few frameworks and products
which use real time data streaming by providing modifications to Hadoop.
Introducing Big Data concepts & Hadoop to those who wish to begin their journey in the future of Information Technology. It is certain that data is going to play a major role in days to come, from our daily lives to the biggest of the venture we might undertake.And hence, knowing about Big data and technologies to work with the same is going to be essential for IT professionals.
We will start with some simple presentations and then will go on building upon it. For more intense, focused introduction & training on Big Data and related technologies, visit our website or write to us.
Fully featured, commercially supported machine learning suites that can build Decision Trees in Hadoop are few and far between. Addressing this gap, Revolution Analytics recently enhanced its entire scalable analytics suite to run in Hadoop. In this talk, I will explain how our Decision Tree implementation exploits recent research reducing the computational complexity of decision tree estimation, allowing linear scalability with data size and number of nodes. This streaming algorithm processes data in chunks, allowing scaling unconstrained by aggregate cluster memory. The implementation supports both classification and regression and is fully integrated with the R statistical language and the rest of our advanced analytics and machine learning algorithms, as well as our interactive Decision Tree visualizer.
Big Data Meets HPC - Exploiting HPC Technologies for Accelerating Big Data Pr...inside-BigData.com
DK Panda from Ohio State University presented this deck at the Switzerland HPC Conference.
"This talk will provide an overview of challenges in accelerating Hadoop, Spark and Mem- cached on modern HPC clusters. An overview of RDMA-based designs for multiple com- ponents of Hadoop (HDFS, MapReduce, RPC and HBase), Spark, and Memcached will be presented. Enhanced designs for these components to exploit in-memory technology and parallel file systems (such as Lustre) will be presented. Benefits of these designs on various cluster configurations using the publicly available RDMA-enabled packages from the OSU HiBD project (http://hibd.cse.ohio-state.edu) will be shown."
Watch the video presentation: https://www.youtube.com/watch?v=glf2KITDdVs
See more talks in the Swiss Conference Video Gallery: http://insidehpc.com/2016-swiss-hpc-conference/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
Inovação e equipes geograficamente distribuídas - Palestrante: Maíra GattiRio Info
Rio Info 2013
Seminário Tecnologia da Informação & Recursos Humanos
17 de setembro - 14h às 18h
Inovação e equipes geograficamente distribuídas
Palestrante: Maíra Gatti
Big data is data that, by virtue of its velocity, volume, or variety (the three Vs), cannot be easily stored or analyzed with traditional methods. Hadoop is an open-source software framework for storing data and running applications on clusters of commodity hardware.
Anusua Trivedi, Data Scientist at Texas Advanced Computing Center (TACC), UT ...MLconf
Building a Recommender System for Publications using Vector Space Model and Python:In recent years, it has become very common that we have access to large number of publications on similar or related topics. Recommendation systems for publications are needed to locate appropriate published articles from a large number of publications on the same topic or on similar topics. In this talk, I will describe a recommender system framework for PubMed articles. PubMed is a free search engine that primarily accesses the MEDLINE database of references and abstracts on life-sciences and biomedical topics. The proposed recommender system produces two types of recommendations – i) content-based recommendation and (ii) recommendations based on similarities with other users’ search profiles. The first type of recommendation, viz., content-based recommendation, can efficiently search for material that is similar in context or topic to the input publication. The second mechanism generates recommendations using the search history of users whose search profiles match the current user. The content-based recommendation system uses a Vector Space model in ranking PubMed articles based on the similarity of content items. To implement the second recommendation mechanism, we use python libraries and frameworks. For the second method, we find the profile similarity of users, and recommend additional publications based on the history of the most similar user. In the talk I will present the background and motivation for these recommendation systems, and discuss the implementations of this PubMed recommendation system with example.
This talk will cover, via live demo & code walk-through, the key lessons we’ve learned while building such real-world software systems over the past few years. We’ll incrementally build a hybrid machine learned model for fraud detection, combining features from natural language processing, topic modeling, time series analysis, link analysis, heuristic rules & anomaly detection. We’ll be looking for fraud signals in public email datasets, using Python & popular open-source libraries for data science and Apache Spark as the compute engine for scalable parallel processing.
A short overview of Bigdata along with its popularity, ups and downs from past to present. We had a look of its needs, challenges and risks too. Architectures involved in it. Vendors associated with it.
User can run queries via MicroStrategy’s visual interface without the need to write unfamiliar HiveQL or MapReduce scripts. In essence, any user, without programming skill in Hadoop, can ask questions against vast volumes of structured and unstructured data to gain valuable business insights.
A short presentation on big data and the technologies available for managing Big Data. and it also contains a brief description of the Apache Hadoop Framework
Mankind has stored more than 295 billion gigabytes (or 295 Exabyte) of data since 1986, as per a report by the University of Southern California. Storing and monitoring this data in widely distributed environments for 24/7 is a huge task for global service organizations. These datasets require high processing power which can’t be offered by traditional databases as they are stored in an unstructured format. Although one can use Map Reduce paradigm to solve this problem using java based Hadoop, it cannot provide us with maximum functionality. Drawbacks can be overcome using Hadoop-streaming techniques that allow users to define non-java executable for processing this datasets. This paper proposes a THESAURUS model which allows a faster and easier version of business analysis.
Big Data Tools: A Deep Dive into Essential ToolsFredReynolds2
Today, practically every firm uses big data to gain a competitive advantage in the market. With this in mind, freely available big data tools for analysis and processing are a cost-effective and beneficial choice for enterprises. Hadoop is the sector’s leading open-source initiative and big data tidal roller. Moreover, this is not the final chapter! Numerous other businesses pursue Hadoop’s free and open-source path.
The technique of extracting usable information from data is known as data science. This is the procedure for collecting, modelling and analysing, data in order to address real-world issues. Data Science tools have been developed as a result of the vast range of applications and rising demand. The following section goes through the greatest Data Science tools in detail.The most notable attribute of these tools is that they do not require the usage of programming languages to implement Data Science.
Read More: https://bit.ly/3rbp1Lb
For Enquiry:
India: +91 91769 66446
UK: +44 7537144372
Email: info@phdassistance.com
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
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
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
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/
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Empowering NextGen Mobility via Large Action Model Infrastructure (LAMI): pav...
Big data: Descoberta de conhecimento em ambientes de big data e computação na nuvem - Nelson Favilla
1. Processamento Intensivo de
Dados
Intensive Data Processing
(Big Data)
Nelson F. F.
Ebecken
NTT/COPPE/UFRJ
Your Big Data Is Worthless if You Don’t Bring It Into the Real World
http://www.wired.com/2014/04/your-big-data-is-worthless-if-you-dont-bring-it-into-the real--world/
2. Big Data
Big Data refers to data that is too big to fit on a
single server, too unstructured to fit into a
row-and-column database, or too
continuously flowing to fit into a static data
warehouse (Thomas H. Davenport)
3. Big Data and traditional analytics
Type of data
Volume of Data
Big Data
Unstructured formats
100 terabytes to petabytes
Traditional analytics
Formated in rows and
columns
Tens of terabytes or less
Flow of Data
Analysis methods
Constant flow of data
Machine Learning
Static pool of data
Hypothesis-based
Primary purpose Data-based products Internal decision support
and services
4. A menu of big data possibilities
Style of data Source of data Industry affected Function affected
Large volume Online Financial services Marketing
Unstructured Video Health care Supply chain
Continuous flow Sensor Manufacturing Human resources
Multiple formats Genomic Travel/transport Finance
5. Terminology for using and analyzing data
Term Time frame
Decision support 1970-1985
Executive support 1980-1990
Online analytical
processing OLAP
1990-2000
Business intelligence 1989-2005
Analytics 2005-2010
Big Data 2010-present
Specific meaning
Use of data analysis to
support decision making
Focus on data analysis for
decisions by senior
executives
Software for analysing
multidimensional data
tables
Tools to support data-driven
decisions, with
emphasis on reporting
Focus on ststistical and
mathematical analysis for
decisions
Focus on very large,
unstructured, fast moving
data
6. How important is Big Data to You and Your Organization ?
Has your management team considered some of the new types of data
that may affect your business and industry, both now and in the next
several years ?
Have you discussed the term big data and wether it’s a good description of
what your organization is doing with data and analytics ?
Are you beggining to change your decision-making processes toward a
more continuos approach driven by the continuos availability of data ?
Has your organization adopted faster and more agile approaches to
analyzing and acting on important data and analysis ?
Are you beggining to focus more on external information about business
and makets enviroments ?
Have you made a big bet on big data ?
7. Big data is going to reshape a lot of different
businesses and industries
Every industry that moves things
Every industry that sells to consumers
Every industry that emplys machinery
Every industry that sells or uses content
Every industry that provides service
Every industry that has physical facilities
Every industry that involves money
8. Responsability locus for big data projects
Cost savings
Faster decisions
Better decisions
Product/service innovation
Discovery
IT innovation group
Business unit or function
analytics group
Business unit or function
analytics group
R&D or product
development group
Production
IT architecture and
operations
Business unit or function
executive
Business unit or function
executive
Product development or
product management
9. Overview of technologies for big data
Technology
Hadoop
Definition
Open source software for processing
big data across multiple parallel servers
MapReduce
Scripting languages
Machine learning
Visual analytics
Natural language processing NLP
In-memory analytics
The architectural framework on which
Hadoop is based
Programming languages that work well
with big data (Python, Pig, Hive...)
Algorithms for rapidly finding the model
that best fits a data set
Display of analytical results in visual or
graphic formats
Algorithms for analyzing text, frequencies,
meanings,...
Processing big data in computer memory
for greater speed
10. MapReduce
MapReduce is a programming model for expressing
distributed computations on massive amounts of data and
an execution framework for large-scale data processing on
clusters of commodity servers.
It was originally developed by Google
In 2003, Google's distributed file system, called GFS
In 2004, Google published the paper that introduced
MapReduce
MapReduce has since enjoyed widespread adoption via
an open-source implementation called Hadoop, whose
development was led by Yahoo (an Apache project).
11. Programming Model
Input & Output: each a set of key/value pairs
Programmer specifies two functions:
Processes input key/value pair
Produces set of intermediate pairs
'map (in_key, in_value) -> list(out_key,
intermediate_value)I
• Produces a set of merged output values (usually just one)
'reduce (out_key, list(intermediate_value)) -> list(out_value)I
12. Map-Reduce
. Parallel programming for large masses of data
Map/Combine/Partition Shuffle Sort/Reduce
key/val key/val
key/val key/val
key/val key/val
Reduce output
Reduce output
Reduce output
input Map
input Map
input Map
14
13. Why learn models in MapReduce?
High data throughput
Stream about 100 Tb per hour using 500 mappers
Framework provides fault tolerance
Monitors mappers and reducers and re-starts tasks on
other machines should one of the machines fail
Excels in counting patterns over data records
Built on relatively cheap, commodity hardware
No special purpose computing hardware
Large volumes of data are being increasingly
stored on Grid clusters running MapReduce
Especially in the internet domain
14. Why learn models in MapReduce?
• Learning can become limited by computation
time and not data volume
With large enough data and number of machines
Reduces the need to down-sample data
More accurate parameter estimates compared to
learning on a single machine for the same amount of time
15. Learning models in MapReduce
A primer for learning models in MapReduce (MR)
Illustrate techniques for distributing the learning algorithm in a
MapReduce framework
Focus on the mapper and reducer computations
Data parallel algorithms are most appropriate for
MapReduce implementations
Not necessarily the most optimal implementation for a
specific algorithm
Other specialized non-MapReduce implementations exist for
some algorithms, which may be better
MR may not be the appropriate framework for exact
solutions of non data parallel/sequential algorithms
Approximate solutions using MapReduce may be good enough
16. Types of learning in MapReduce
• Three common types of learning models using
MapReduce framework
1. Parallel training of multiple models
– Train either in mappers or reducers
2. Ensemble training methods
– Train multiple models and combine them
3. Distributed learning algorithms
– Learn using both mappers and reducers
Use the Grid as a
large cluster
of independent
machines
(with fault
tolerance)
17. Parallel training of multiple models
Train multiple models simultaneously using a learning
algorithm that can be learnt in memory
Useful when individual models are trained using a
subset, filtered or modification of raw data
Can train 1000`s of models simultaneously
Essentially, treat Grid as a large cluster of machines
– Leverage fault tolerance of Hadoop
Train 1 model in each reducer
– Map:
Input: All data
Filters subset of data relevant for each model training
Output: <model_index, subset of data for training this model>
– Reduce
Train model on data corresponding to that model_index
18. Apache Mahout
Scalable to large data sets. Our core algorithms for clustering, classification and
collaborative filtering are implemented on top of scalable, distributed systems.
However, contributions that run on a single machine are welcome as well.
Scalable to support your business case. Mahout is distributed under a
commercially friendly Apache Software license.
Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse
community to facilitate discussions not only on the project itself but also on potential
use cases. Come to the mailing lists to find out more.
Currently Mahout supports mainly three use cases: Recommendation mining takes
users' behavior and from that tries to find items users might like. Clustering takes
e.g. text documents and groups them into groups of topically related documents.
Classification learns from existing categorized documents what documents of a
specific category look like and is able to assign unlabelled documents to the
(hopefully) correct category.
25 April 2014 - Goodbye MapReduce
The Mahout community decided to move its codebase onto modern data processing systems that offer a richer
programming model and more efficient execution than Hadoop MapReduce. Mahout will therefore reject new
MapReduce algorithm implementations from now on. We will however keep our widely used MapReduce
algorithms in the codebase and maintain them.
We are building our future implementations on top of a DSL for linear algebraic operations which has been
developed over the last months. Programs written in this DSL are automatically optimized and executed in
parallel on Apache Spark.
Furthermore, there is an experimental contribution undergoing which aims to integrate the h20 platform into
Mahout.
Apache Spark™ is a fast and general engine for large-scale data processing.
H2O is the open source in memory solution from 0xdata for predictive analytics on big data.
19. Matrix
Methods
Slides with bit.ly/10SIe1A
Code github.com/dgleich/matrix-Hadoop hadoop-tutorial
DAVID F.
GLEICH ASSISTANT PROFESSOR
COMPUTER SCIENCE
PURDUE UNIVERSITY
David Gleich á Purdue bit.ly/10SIe1A
1
21. ACM KDD 2014
24-27/08
New environments: Microsoft Azure ML Studio, Google
Prediction API,…
2 Research Sessions + Industry & Government
Statistical Techniques for Big Data
Scaling-up Methods for Big Data
Topic Modeling
22. Big data & machine learning
This is a huge field, growing very fast
Many algorithms and techniques:
can be seen as a giant toolbox with wide-ranging applications
Ranging from the very simple to the extremely sophisticated
Difficult to see the big picture
Huge range of applications
Math skills are crucial