The Meme of the Internet Index will be the new normal to analyze and predict facts and sensations which go around the Internet.
https://www.bigdataspain.org/2017/talk/meme-index-analyzing-fads-and-sensations-on-the-internet
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Bayesian inference and big data: are we there yet? by Jose Luis Hidalgo at Bi...Big Data Spain
Bayesian inference has a reputation of being complex and only applicable to fairly small datasets. Very recent developments may be chaining that, and we are starting to see some successful real world application of Bayesian inference to very large datasets, and some tools to make it accessible to many more data science practitioners.
https://www.bigdataspain.org/2017/talk/bayesian-inference-and-big-data-are-we-there-yet
Big Data Spain 2017
Come diventare data scientist - Si ringrazie per le slide Paolo Pellegrini, Senior Consultant presso P4I (Partners4Innovation) e referente di tutte le progettualità relative alle tematiche Data Science e Big Data Analytics. Owner del primo gruppo in Italia dedicato dai Data Scientist.
Artificial Intelligence and Data-centric businesses by Óscar Méndez at Big Da...Big Data Spain
Artificial Intelligence and Data-centric businesses.
https://www.bigdataspain.org/2017/talk/tbc
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Bayesian inference and big data: are we there yet? by Jose Luis Hidalgo at Bi...Big Data Spain
Bayesian inference has a reputation of being complex and only applicable to fairly small datasets. Very recent developments may be chaining that, and we are starting to see some successful real world application of Bayesian inference to very large datasets, and some tools to make it accessible to many more data science practitioners.
https://www.bigdataspain.org/2017/talk/bayesian-inference-and-big-data-are-we-there-yet
Big Data Spain 2017
Come diventare data scientist - Si ringrazie per le slide Paolo Pellegrini, Senior Consultant presso P4I (Partners4Innovation) e referente di tutte le progettualità relative alle tematiche Data Science e Big Data Analytics. Owner del primo gruppo in Italia dedicato dai Data Scientist.
Artificial Intelligence and Data-centric businesses by Óscar Méndez at Big Da...Big Data Spain
Artificial Intelligence and Data-centric businesses.
https://www.bigdataspain.org/2017/talk/tbc
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Andrea Pietracaprina - In this talk, we will overview some popular computing frameworks (e.g., MapReduce, Spark) which are widely used to unleash the computational potential of the cloud for big-data applications. For concreteness, we will describe efficient implementations of some key tools used in data analysis (e.g, clustering, diversity maximization).
FUTURE OF DATA SCIENCE IN INDIA
DATA SCIENCE
It is a tool that uses all kinds of data, algorithms and scientific methods. It is a very important tool as it combines two of the most important things in technology and modern science that is mathematics and computer science together. Organizing, data delivery and packaging are the three most important components involved in data science. Data Science handles data works on them and makes conclusion based on the data.
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
Big data characteristics, value chain and challengesMusfiqur Rahman
Abstract—Recently the world is experiencing an deluge of
data from different domains such as telecom, healthcare
and supply chain systems. This growth of data has led to
an explosion, coining the term Big Data. In addition to the
growth in volume, Big Data also exhibits other unique
characteristics, such as velocity and variety. This large
volume, rapidly increasing and verities of data is becoming
the key basis of completion, underpinning new waves of
productivity growth, innovation and customer surplus. Big
Data is about to offer tremendous insight to the
organizations, but the traditional data analysis
architecture is not capable to handle Big Data. Therefore,
it calls for a sophisticated value chain and proper analytics
to unearth the opportunity it holds. This research
identifies the characteristics of Big Data and presents a
sophisticated Big Data value chain as finding of this
research. It also describes the typical challenges of Big
Data, which are required to be solved. As a part of this
research twenty experts from different industries and
academies of Finland were interviewed.
Data and Analytics Career Paths, Presented at IEEE LYC'19.
About Speaker:
Ahmed Amr is a Data/Analytics Engineer at Rubikal, where he leads, develops, and creates daily data/analytics operations, which includes data ingestion , data streaming, data warehousing, and analytical dashboards. Ahmed is graduated from Computer Engineering Department, Alexandria University; and he is currently pursuing his MSc degree in Computer Science, AAST. Professionally, Ahmed worked with Egyptian/US startups such as (Badr, Incorta, WhoKnows) to develop their data/analytics projects. Academically, Ahmed worked as a Teaching Assistant in CS department, AAST. Ahmed helps software companies to develop robust data engineering infrastructure, and powerful analytical insights.
References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
2) Analytics: The real-world use of big data, IBM, Executive Report
Graph applications were once considered “exotic” and expensive. Until recently, few software engineers had much experience putting graphs to work. However, the use cases are now becoming more commonplace.
This talk explores a practical use case, one which addresses key issues of data governance and reproducible research, and depends on sophisticated use of graph technology.
Consider: some academic disciplines such as astronomy enjoy a wealth of data — mostly open data. Popular machine learning algorithms, open source Python libraries, and distributed systems all owe much to those disciplines and their history of big data.
Other disciplines require strong guarantees for privacy and security. Datasets used in social science research involve confidential details about human subjects: medical histories, wages, home addresses for family members, police records, etc.
Those cannot be shared openly, which impedes researchers from learning about related work by others. Reproducibility of research and the pace of science in general are limited. Nonetheless, social science research is vital for civil governance, especially for evidence-based policymaking (US federal law since 2018).
Even when data may be too sensitive to share openly, often the metadata can be shared. Constructing knowledge graphs of metadata about datasets — along with metadata about authors, their published research, methods used, data providers, data stewards, and so on — that provides effective means to tackle hard problems in data governance.
Knowledge graph work supports use cases such as entity linking, discovery and recommendations, axioms to infer about compliance, etc. This talk reviews the Rich Context AI competition and the related ADRF framework used now by more than 15 federal agencies in the US.
We’ll explore knowledge graph use cases, use of open standards and open source, and how this enhances reproducible research. Social science research for the public sector has much in common with data use in industry.
Issues of privacy, security, and compliance overlap, pointing toward what will be required of banks, media channels, etc., and what technologies apply. We’ll look at comparable work emerging in other parts of industry: open source projects, open standards emerging, and in particular a new set of features in Project Jupyter that support knowledge graphs about data governance.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
NLP Community Conference - Dr. Catherine Havasi (ConceptNet/MIT Media Lab/Lum...Maryam Farooq
Dr. Catherine Havasi's keynote talk from the AI Community Conference on Natural Language Processing (by NYAI.co) on Thurs, Jun 27th 2019 at Moody's Analytics.
Sponsored by Moody's Analytics, NYU Tandon Future Lab, NYAI.co
For more information & the full talk video, please visit nyai.co
Andrea Pietracaprina - In this talk, we will overview some popular computing frameworks (e.g., MapReduce, Spark) which are widely used to unleash the computational potential of the cloud for big-data applications. For concreteness, we will describe efficient implementations of some key tools used in data analysis (e.g, clustering, diversity maximization).
FUTURE OF DATA SCIENCE IN INDIA
DATA SCIENCE
It is a tool that uses all kinds of data, algorithms and scientific methods. It is a very important tool as it combines two of the most important things in technology and modern science that is mathematics and computer science together. Organizing, data delivery and packaging are the three most important components involved in data science. Data Science handles data works on them and makes conclusion based on the data.
This video includes:
Purpose of Data Science, Role of Data Scientist, Skills required for Data Scientist, Job roles for Data Scientist, Applications of Data Science, Career in Data Science.
Data Science Innovations : Democratisation of Data and Data Science suresh sood
Data Science Innovations : Democratisation of Data and Data Science covers the opportunity of citizen data science lying at the convergence of natural language generation and discoveries in data made by the professions, not data scientists.
Big data characteristics, value chain and challengesMusfiqur Rahman
Abstract—Recently the world is experiencing an deluge of
data from different domains such as telecom, healthcare
and supply chain systems. This growth of data has led to
an explosion, coining the term Big Data. In addition to the
growth in volume, Big Data also exhibits other unique
characteristics, such as velocity and variety. This large
volume, rapidly increasing and verities of data is becoming
the key basis of completion, underpinning new waves of
productivity growth, innovation and customer surplus. Big
Data is about to offer tremendous insight to the
organizations, but the traditional data analysis
architecture is not capable to handle Big Data. Therefore,
it calls for a sophisticated value chain and proper analytics
to unearth the opportunity it holds. This research
identifies the characteristics of Big Data and presents a
sophisticated Big Data value chain as finding of this
research. It also describes the typical challenges of Big
Data, which are required to be solved. As a part of this
research twenty experts from different industries and
academies of Finland were interviewed.
Data and Analytics Career Paths, Presented at IEEE LYC'19.
About Speaker:
Ahmed Amr is a Data/Analytics Engineer at Rubikal, where he leads, develops, and creates daily data/analytics operations, which includes data ingestion , data streaming, data warehousing, and analytical dashboards. Ahmed is graduated from Computer Engineering Department, Alexandria University; and he is currently pursuing his MSc degree in Computer Science, AAST. Professionally, Ahmed worked with Egyptian/US startups such as (Badr, Incorta, WhoKnows) to develop their data/analytics projects. Academically, Ahmed worked as a Teaching Assistant in CS department, AAST. Ahmed helps software companies to develop robust data engineering infrastructure, and powerful analytical insights.
References:
1) https://www.datacamp.com/community/tutorials/data-science-industry-infographic
2) Analytics: The real-world use of big data, IBM, Executive Report
Graph applications were once considered “exotic” and expensive. Until recently, few software engineers had much experience putting graphs to work. However, the use cases are now becoming more commonplace.
This talk explores a practical use case, one which addresses key issues of data governance and reproducible research, and depends on sophisticated use of graph technology.
Consider: some academic disciplines such as astronomy enjoy a wealth of data — mostly open data. Popular machine learning algorithms, open source Python libraries, and distributed systems all owe much to those disciplines and their history of big data.
Other disciplines require strong guarantees for privacy and security. Datasets used in social science research involve confidential details about human subjects: medical histories, wages, home addresses for family members, police records, etc.
Those cannot be shared openly, which impedes researchers from learning about related work by others. Reproducibility of research and the pace of science in general are limited. Nonetheless, social science research is vital for civil governance, especially for evidence-based policymaking (US federal law since 2018).
Even when data may be too sensitive to share openly, often the metadata can be shared. Constructing knowledge graphs of metadata about datasets — along with metadata about authors, their published research, methods used, data providers, data stewards, and so on — that provides effective means to tackle hard problems in data governance.
Knowledge graph work supports use cases such as entity linking, discovery and recommendations, axioms to infer about compliance, etc. This talk reviews the Rich Context AI competition and the related ADRF framework used now by more than 15 federal agencies in the US.
We’ll explore knowledge graph use cases, use of open standards and open source, and how this enhances reproducible research. Social science research for the public sector has much in common with data use in industry.
Issues of privacy, security, and compliance overlap, pointing toward what will be required of banks, media channels, etc., and what technologies apply. We’ll look at comparable work emerging in other parts of industry: open source projects, open standards emerging, and in particular a new set of features in Project Jupyter that support knowledge graphs about data governance.
My class presentation at USC. It gives an introduction about what is data science, machine learning, applications, recommendation system and infrastructure.
Being able to make data driven decisions is a crucial skill for any company. The requirements are growing tougher - the volume of collected data keeps increasing in orders of magnitude and the insights must be smarter and faster. Come learn more about why data science is important and what challenges the data teams need to face.
NLP Community Conference - Dr. Catherine Havasi (ConceptNet/MIT Media Lab/Lum...Maryam Farooq
Dr. Catherine Havasi's keynote talk from the AI Community Conference on Natural Language Processing (by NYAI.co) on Thurs, Jun 27th 2019 at Moody's Analytics.
Sponsored by Moody's Analytics, NYU Tandon Future Lab, NYAI.co
For more information & the full talk video, please visit nyai.co
NLP & Machine Learning - An Introductory Talk Vijay Ganti
An Introductory talk with the goal of getting people started on the NLP/ML journey. A practitioner's perspective. Code that makes it real and accessible.
Bridging the Gap Between Data Science & Engineer: Building High-Performance T...ryanorban
Data scientists, data engineers, and data businesspeople are critical to leveraging data in any organization. A common complaint from data science managers is that data scientists invest time prototyping algorithms, and throw them over a proverbial fence to engineers to implement, only to find the algorithms must be rebuilt from scratch to scale. This is a symptom of a broader ailment -- that data teams are often designed as functional silos without proper communication and planning.
This talk outlines a framework to build and organize a data team that produces better results, minimizes wasted effort among team members, and ships great data products.
You've heard the news, Data Science is the cool new career opportunity sweeping the world. Come learn from Thinkful Mentors all about this new and exciting industry.
No more BITS - Blind Insignificant Technologies ands Systems by Roger Roberts...ACTUONDA
No more BITS - Blind Insignificant Technologies ands Systems by Roger Roberts of RTBF TITAN
Primer encuentro BIG MEDIA
Conectando Media, Audiencia y Publicidad con Datos
24 de junio 2014, Madrid
• Sponsor Platinum : Perfect Memory
• Sponsor Gold : Stratio, Paradigma
• Con el apoyo de : Big Data Spain, Medios On
• Socio tecnológico : Agora News
• Organizadores : Actuonda y Cátedra Big Data UAM-IBM
• Contacto : Nicolas Moulard (Actuonda) moulard@actuonda.com @Radio_20
www.bigmediaconnect.es
DataScientist Job : Between Myths and Reality.pdfJedha Bootcamp
Swipe through the smoke and mirrors and learn about the "sexiest job of the 21st century" with Nicola, Machine Learning Scientist @ Bumble
✨ Artificial Intelligence? Business Intelligence? Data Science? What do these terms sound like when put into action at one of the world's most forefront dating platforms? Jedha is proud to host an evening with Nicola Ghio, Senior Machine Learning Scientist at Bumble, who will give us a "peek behind the curtain" into what this enviable job title looks like in practice.
😎 Nicola will share some of his experiences working at Bumble. 🎯 Hear first-hand about Bumble's harassment and toxic imaging detector as well as the real skills required to work in the industry. We also look forward to hearing about Nicola's personal story, his background and his advice for those that want to dive deeper into the world of tech.
Meet Jedha 😍 Your Data and Cyber Security Bootcamp, ranked #1 in Europe (Switch Up). Our mission is to demystify the world of tech and to make its skills accessible to anyone who desires to learn. We have courses suited to all ambitions and skill levels: From beginners who have never typed a line of code in their lives right through to skilled tech professionals who want to achieve mastery. Our methods and teachers help to unlock human potential in the unlimited world of tech.
The Role of Data Wrangling in Driving Hadoop AdoptionInside Analysis
The Briefing Room with Mark Madsen and Trifacta
Live Webcast September 1, 2015
Watch the archive: https://bloorgroup.webex.com/bloorgroup/onstage/g.php?MTID=eb655874d04ba7d560be87a9d906dd2fd
Like all enterprise software solutions, Hadoop must deliver business value in order to be a success. Much of the innovation around the big data industry these days therefore addresses usability. While there will always be a technical side to the Hadoop equation, the need for user-friendly tools to manage the data will continue to focus on business users. That’s why self-service data preparation or "data wrangling" is a serious and growing trend, one which promises to move Hadoop beyond the early adopter phase and more into the mainstream of business.
Register for this episode of The Briefing Room to hear veteran Analyst Mark Madsen of Third Nature explain why business users will play an increasingly important role in the evolution of big data. He’ll be briefed by Trifacta's Will Davis and Alon Bartur, who will demonstrate how Trifacta's solution empowers business users to “wrangle" data of all shapes and sizes faster and easier than ever before. They’ll discuss why a new approach to accessing and preparing diverse data is required and how it can accelerate and broaden the use of big data within organizations.
Visit InsideAnalysis.com for more information.
Fairness, Transparency, and Privacy in AI @LinkedInC4Media
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2V9zW73.
Krishnaram Kenthapadi talks about privacy breaches, algorithmic bias/discrimination issues observed in the Internet industry, regulations & laws, and techniques for achieving privacy and fairness in data-driven systems. He focusses on the application of privacy-preserving data mining and fairness-aware ML techniques in practice, by presenting case studies spanning different LinkedIn applications. Filmed at qconsf.com.
Krishnaram Kenthapadi is part of the AI team at LinkedIn, where he leads the transparency and privacy modeling efforts across different applications. He is LinkedIn's representative in Microsoft's AI and Ethics in Engineering & Research Committee. He shaped the technical roadmap for LinkedIn Salary product, and served as the relevance lead for the LinkedIn Careers & Talent Solutions Relevance team.
Toward a System Building Agenda for Data Integration(and Dat.docxjuliennehar
Toward a System Building Agenda for Data Integration
(and Data Science)
AnHai Doan, Pradap Konda, Paul Suganthan G.C., Adel Ardalan, Jeffrey R. Ballard, Sanjib Das,
Yash Govind, Han Li, Philip Martinkus, Sidharth Mudgal, Erik Paulson, Haojun Zhang
University of Wisconsin-Madison
Abstract
We argue that the data integration (DI) community should devote far more effort to building systems,
in order to truly advance the field. We discuss the limitations of current DI systems, and point out that
there is already an existing popular DI “system” out there, which is PyData, the open-source ecosystem
of 138,000+ interoperable Python packages. We argue that rather than building isolated monolithic DI
systems, we should consider extending this PyData “system”, by developing more Python packages that
solve DI problems for the users of PyData. We discuss how extending PyData enables us to pursue an
integrated agenda of research, system development, education, and outreach in DI, which in turn can
position our community to become a key player in data science. Finally, we discuss ongoing work at
Wisconsin, which suggests that this agenda is highly promising and raises many interesting challenges.
1 Introduction
In this paper we focus on data integration (DI), broadly interpreted as covering all major data preparation steps
such as data extraction, exploration, profiling, cleaning, matching, and merging [10]. This topic is also known
as data wrangling, munging, curation, unification, fusion, preparation, and more. Over the past few decades, DI
has received much attention (e.g., [37, 29, 31, 20, 34, 33, 6, 17, 39, 22, 23, 5, 8, 36, 15, 35, 4, 25, 38, 26, 32, 19,
2, 12, 11, 16, 2, 3]). Today, as data science grows, DI is receiving even more attention. This is because many
data science applications must first perform DI to combine the raw data from multiple sources, before analysis
can be carried out to extract insights.
Yet despite all this attention, today we do not really know whether the field is making good progress. The
vast majority of DI works (with the exception of efforts such as Tamr and Trifacta [36, 15]) have focused on
developing algorithmic solutions. But we know very little about whether these (ever-more-complex) algorithms
are indeed useful in practice. The field has also built mostly isolated system prototypes, which are hard to use and
combine, and are often not powerful enough for real-world applications. This makes it difficult to decide what
to teach in DI classes. Teaching complex DI algorithms and asking students to do projects using our prototype
systems can train them well for doing DI research, but are not likely to train them well for solving real-world DI
problems in later jobs. Similarly, outreach to real users (e.g., domain scientists) is difficult. Given that we have
Copyright 0000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for
advertising or promotional purpose ...
Analyzing Big Data's Weakest Link (hint: it might be you)HPCC Systems
Tim Menzies, NC State University, presents at the 2015 HPCC Systems Engineering Summit Community Day.
For Big Data applications, there is a lack of any gold standards for "good analysis" or methods to assess our certification programs. Hence, we are still in the dark about whether or not our human analysts are making the best use possible of the tools of Big Data. While much progress has been made in the systems aspects of Big Data, certain critical human-centered aspects remain an open issue. Regardless of the sophistication of the analysis tools and environment, all that architecture can still be used incorrectly by users. If this issue was confined to a small number of inexperienced users, then it could be addressed via process improvements such as better training. But is it? What do we know about our analysts? Where are the studies that mine the people doing the data miners?
This presentation offers some preliminary results on tools that combine ECL with other methods that recognize the code generated by experienced or inexperienced developers. While the results are preliminary, they do raise the possibility that we can better characterize what it means to be experienced (or inexperienced) at Big Data applications.
Big Data, Big Quality? by Irene Gonzálvez at Big Data Spain 2017Big Data Spain
Insights can only be as good as the data. The data quality domain is enormously large, so you need to understand your company pain points to know what to focus on first.
https://www.bigdataspain.org/2017/talk/big-data-big-quality
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Scaling a backend for a big data and blockchain environment by Rafael Ríos at...Big Data Spain
2gether is a financial platform based on Blockchain, Big Data and Artificial Intelligence that allows interaction between users and third-party services in a single interface.
https://www.bigdataspain.org/2017/talk/scaling-a-backend-for-a-big-data-and-blockchain-environment
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Disaster Recovery for Big Data by Carlos Izquierdo at Big Data Spain 2017Big Data Spain
All modern Big Data solutions, like Hadoop, Kafka or the rest of the ecosystem tools, are designed as distributed processes and as such include some sort of redundancy for High Availability.
https://www.bigdataspain.org/2017/talk/disaster-recovery-for-big-data
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Presentation: Boost Hadoop and Spark with in-memory technologies by Akmal Cha...Big Data Spain
In this presentation, attendees will see how to speed up existing Hadoop and Spark deployments by just making Apache Ignite responsible for RAM utilization. No code modifications, no new architecture from scratch!
https://www.bigdataspain.org/2017/talk/boost-hadoop-and-spark-with-in-memory-technologies
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Data science for lazy people, Automated Machine Learning by Diego Hueltes at ...Big Data Spain
The power of this new set of tools for Data Science. Is really easy to start applying these technics in your current workflow.
https://www.bigdataspain.org/2017/talk/data-science-for-lazy-people-automated-machine-learning
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Training Deep Learning Models on Multiple GPUs in the Cloud by Enrique Otero ...Big Data Spain
GPUs on the cloud as Infrastructure as a Service (IaaS) seem a commodity. However to efficiently distribute deep learning tasks on several GPUs is challenging.
https://www.bigdataspain.org/2017/talk/training-deep-learning-models-on-multiple-gpus-in-the-cloud
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Unbalanced data: Same algorithms different techniques by Eric Martín at Big D...Big Data Spain
Unbalanced data is a specific data configuration that appears commonly in nature. Applying machine learning techniques to this kind of data is a difficult process, usually addressed by unbalanced reduction techniques.
https://www.bigdataspain.org/2017/talk/unbalanced-data-same-algorithms-different-techniques
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
State of the art time-series analysis with deep learning by Javier Ordóñez at...Big Data Spain
Time series related problems have traditionally been solved using engineered features obtained by heuristic processes.
https://www.bigdataspain.org/2017/talk/state-of-the-art-time-series-analysis-with-deep-learning
Big Data Spain 2017
November 16th - 17th
Trading at market speed with the latest Kafka features by Iñigo González at B...Big Data Spain
Not long ago only banks and hedge funds could afford doing automated and High Frequency Trading, that is, the ability to send buy commodities in microseconds intervals.
https://www.bigdataspain.org/2017/talk/trading-at-market-speed-with-the-latest-kafka-features
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Unified Stream Processing at Scale with Apache Samza by Jake Maes at Big Data...Big Data Spain
The shift to stream processing at LinkedIn has accelerated over the past few years. We now have over 200 Samza applications in production processing more than 260B events per day.
https://www.bigdataspain.org/2017/talk/apache-samza-jake-maes
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
The Analytic Platform behind IBM’s Watson Data Platform by Luciano Resende a...Big Data Spain
IBM has built a “Data Science Experience” cloud service that exposes Notebook services at web scale.
https://www.bigdataspain.org/2017/talk/the-analytic-platform-behind-ibms-watson-data-platform
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Why big data didn’t end causal inference by Totte Harinen at Big Data Spain 2017Big Data Spain
Ten years ago there were rumours of the death of causal inference. Big data was supposed to enable us to rely on purely correlational data to predict and control the world.
https://www.bigdataspain.org/2017/talk/why-big-data-didnt-end-causal-inference
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Vehicle Big Data that Drives Smart City Advancement by Mike Branch at Big Dat...Big Data Spain
Geotab is a leader in the expanding world of Internet of Things (IoT) and telematics industry with Big Data.
https://www.bigdataspain.org/2017/talk/vehicle-big-data-that-drives-smart-city-advancement
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
End of the Myth: Ultra-Scalable Transactional Management by Ricardo Jiménez-P...Big Data Spain
The talk will focus on explaining why operational databases do not scale due to limitations in legacy transactional management.
https://www.bigdataspain.org/2017/talk/end-of-the-myth-ultra-scalable-transactional-management
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Attacking Machine Learning used in AntiVirus with Reinforcement by Rubén Mart...Big Data Spain
In recent years Machine Learning (ML) and especially Deep Learning (DL) have achieved great success in many areas such as visual recognition, NLP or even aiding in medical research.
https://www.bigdataspain.org/2017/talk/attacking-machine-learning-used-in-antivirus-with-reinforcement
Big Data Spain 2017
16th - 17th Kinépolis Madrid
More people, less banking: Blockchain by Salvador Casquero at Big Data Spain ...Big Data Spain
Primary function of banking sector is promoting economic activity; which means “commerce”, exchanging what someone produces-has for something that someone consumes-desires.
https://www.bigdataspain.org/2017/talk/more-people-less-banking-blockchain
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Make the elephant fly, once again by Sourygna Luangsay at Big Data Spain 2017Big Data Spain
Bol.com has been an early Hadoop user: since 2008 where it was first built for a recommendation algorithm.
https://www.bigdataspain.org/2017/talk/make-the-elephant-fly-once-again
Big Data Spain 2017
16th - 17th Kinépolis Madrid
Feature selection for Big Data: advances and challenges by Verónica Bolón-Can...Big Data Spain
In an era of growing data complexity and volume and the advent of Big Data, feature selection has a key role to play in helping reduce high-dimensionality in machine learning problems.
https://www.bigdataspain.org/2017/talk/feature-selection-for-big-data-advances-and-challenges
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
Deep reinforcement learning : Starcraft learning environment by Gema Parreño ...Big Data Spain
A theorical description of reinforcement learning principles and a deep dive into DeepMind Research environment .
https://www.bigdataspain.org/2017/talk/reinforced-learning-deepmind-starcraft-learning-environment
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
End-to-End “Exactly Once” with Heron & Pulsar by Ivan Kelly at Big Data Spain...Big Data Spain
Heron is an open-source streaming engine, employed by Twitter, Microsoft and Google, to process billions of events every day.
https://www.bigdataspain.org/2017/talk/end-to-end-exactly-once-with-heron-pulsar
Big Data Spain 2017
November 16th - 17th Kinépolis Madrid
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.
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.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
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/
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
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.
Search and Society: Reimagining Information Access for Radical FuturesBhaskar Mitra
The field of Information retrieval (IR) is currently undergoing a transformative shift, at least partly due to the emerging applications of generative AI to information access. In this talk, we will deliberate on the sociotechnical implications of generative AI for information access. We will argue that there is both a critical necessity and an exciting opportunity for the IR community to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy of trying to mitigate potential social harms from emerging technologies, the community should aim to proactively set the research agenda for the kinds of systems we should build inspired by diverse explicitly stated sociotechnical imaginaries. The sociotechnical imaginaries that underpin the design and development of information access technologies needs to be explicitly articulated, and we need to develop theories of change in context of these diverse perspectives. Our guiding future imaginaries must be informed by other academic fields, such as democratic theory and critical theory, and should be co-developed with social science scholars, legal scholars, civil rights and social justice activists, and artists, among others.
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
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
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.
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.
3. Miguel Romero [short bio]
@donkelito
• Working in SDG Group.
• My professional experience.
• 7 years as Data Engineer
• 6 years as Business Developer and
Solution Architect in BIG Data &
Advanced Analytics practice
• 1 year as Big Data Head
• Executive MBA
• Not working on my PhD
• Big Data Master Professor
• Member of M team.
• Agile mode in each instant
• Currently: Developing my
speaker facet! #BDS15 #BDS17
4. W TF is a m em e?
• Big Data technology has brought an unprecedented explosion in
unstructured data.
• A meme (neologism modeled on gene) that is a shortening of
mimeme (greek) that means imitated thing, is an attempt to
explain the way cultural information spreads in terms of
evolutionary principles between humans;
• Internet memes are
– a subset of this general meme concept specific to the
culture and environment of the Internet.
– a piece of art which spreads from Person to Person via the
Internet which carries an additional property that
ordinary memes do not (the media through which they
propagate that renders them traceable and analyzable)
24. The Big M em e
IndexAnalyzing fads and sensations
on the Internet
25. tw itter
D ata
Sources
A PIs # m em e
Sites
D ata
Collect
D ata Fast A nalytics
Stream M edia
Processor
R D D
D Stream
FA Stream Media Collector
[http source]
FA Stream Media Collector
[http source]
M em e R epository
R D D
{ photo:’distracted-boyfriend’,
sense:‘opinionchange’,
w ords:[{w :‘H IV E’,p:1},
{w :’D S’,p:2},
{w :’SPA R K ’,p:3}]}
# spark
# ds
# hive
Enrich R epository
R D D
D Stream
D ata
Services
microservices
What do you have?
What do you like?
Breadth of the Influencers
according to trend memes
Cultural VS Education
VS Trending
Stream M edia
B uffer
26. Template matching is a technique for finding areas of an image
that match (are similar) to a template image (patch).
• Use the OpenCV function matchTemplate to search for matches between an image patch and an input
image
• Use the OpenCV function minMaxLoc to find the maximum and minimum values (as well as their
positions) in a given array
Tem plate m atching
28. tw itter
D ata
Sources
A PIs # m em e
Sites
D ata
Collect
D ata Fast A nalytics
Stream M edia
Processor
R D D
D Stream
FA Stream Media Collector
[http source]
FA Stream Media Collector
[http source]
M em e R epository
R D D
{ photo:’distracted-boyfriend’,
sense:‘opinionchange’,
w ords:[{w :‘H IV E’,p:1},
{w :’D S’,p:2},
{w :’SPA R K ’,p:3}]}
# spark
# ds
# hive
Enrich R epository
R D D
D Stream
D ata
Services
microservices
What do you have?
What do you like?
Breadth of the Influencers
according to trend memes
Cultural VS Education
VS Trending
Stream M edia
B uffer
29. Takeaw ay points
• Memes are a vehicle whose information
could be used as advisory
• Memes are unstructured data and until
now was not easy to analyze
• With Big Data and Advance Analytics
technology an architecture to analyze
memes (image + text) in NRT or even
video on live can be built.
30. B ig D ata Spain 2018
idea!W hat is there in com m on am ong
the m usic that data-lovers listen
to?