Using multifactor-analysis to efficiently analyse large amounts of data in order to find a cluster of closely related projects for business opportunities, partnerships, competitor analysis and collaboration
Presented by Michelle Hirsch, Head of MATLAB Product Management, MathWorks on 28th April in Bangalore in joint languages meetup @Walmart.
Companies are scrambling to get insight from the massive quantities of data they collect but are struggling to find employees who combine the deep expertise in computer science, statistics and machine learning, and the domain expertise to truly understand the data. In this talk, Dr. Hirsch discusses how MATLAB enables engineers and scientists to apply their domain expertise to big data analytics.
Highlights:
* Accessing data in large text files, databases, or from the Hadoop Distributed File System (HDFS)
* Using virtual “tall” arrays to process out-of-core data with natural mathematical syntax
Developing machine learning models
* Integrating MATLAB analytics into production systems
About the speaker: Michelle Hirsch, Ph.D. is responsible for driving strategy and direction for MATLAB, the leading programming platform for engineers and scientists. Based outside of Boston, Massachusetts, Michelle is joining our meetup during a trip to meet with MATLAB users across India.
Supporting data: https://www.slideshare.net/CodeOps/flight-test-analysis-final
Presented by Michelle Hirsch, Head of MATLAB Product Management, MathWorks on 28th April in Bangalore in joint languages meetup @Walmart.
Companies are scrambling to get insight from the massive quantities of data they collect but are struggling to find employees who combine the deep expertise in computer science, statistics and machine learning, and the domain expertise to truly understand the data. In this talk, Dr. Hirsch discusses how MATLAB enables engineers and scientists to apply their domain expertise to big data analytics.
Highlights:
* Accessing data in large text files, databases, or from the Hadoop Distributed File System (HDFS)
* Using virtual “tall” arrays to process out-of-core data with natural mathematical syntax
Developing machine learning models
* Integrating MATLAB analytics into production systems
About the speaker: Michelle Hirsch, Ph.D. is responsible for driving strategy and direction for MATLAB, the leading programming platform for engineers and scientists. Based outside of Boston, Massachusetts, Michelle is joining our meetup during a trip to meet with MATLAB users across India.
Supporting data: https://www.slideshare.net/CodeOps/flight-test-analysis-final
Tools using AI will affect and, in many cases, redefine most areas of societal impact such as medical practice and intervention, autonomous transportation and law enforcement. While so far, most of the focus and time is invested into optimizing models’ performance, whenever a single wrong prediction has big implications in terms of value or life, accuracy becomes less important than explainability.
In this talk, we will learn about explainable AI and we will see how to apply some of the available tools to answer the question ‘’what did my system consider in order to output a specific prediction’.
Building a robust machine learning model is not an easy task. After all, most POCs don't make it into production. And even if they make it into production, you still need to monitor its performance.
How can you build performant, tolerant, stable, predictive models that have known and fair biases? How can you make sure your models yield their value over time and stay performant after your team has deployed them? What are the current practices of model validation (or lack of), how are they flawed, and how could we improve them?
Simon Dagenais from Snitch AI will go through the reasons behind using an efficient validation framework that goes beyond the common metrics used by ML practitioners and why these tests matter when building high-quality models.
Agenda:
-----------
3:45pm - 4:00pm: Arrival & Networking
4:00pm - 4:15pm: News & Intro
4:15pm - 5:15pm: How to QA your ML models
5:15pm - 5:30pm: Virtual Snack & Networking
About the main speaker:
---------------------------------
Simon Dagenais is the Lead Data Scientist at Snitch AI, a machine learning validation tool. Before working on Snitch AI, Simon was a data scientist consultant at Moov AI, the parent company of Snitch AI. During his time as a consultant, he built and deployed custom ML solutions to solve business needs at companies like DRW, Société de Transport de Montréal and Cogeco. He now aspires to solve problems that data science teams will encounter during the course of a ML project cycle. Simon obtained an M.Sc. in economics from HEC Montreal. He frequently speaks in conferences, panels and meetups.
The Future is Big Graphs: A Community View on Graph Processing SystemsNeo4j
Alexandru Iosup, Full Professor, Vrije Universiteit Amsterdam (VU Amsterdam)
Angela Bonifati, Full Professor of Computer Science, Université de Lyon
Hannes Voigt, Software Engineer, Neo4j
DN18 | The Evolution and Future of Graph Technology: Intelligent Systems | Ax...Dataconomy Media
Abstract of the Prersentation:
The field of graph technology has developed rapidly in recent years and established itself as an independent technology sector that will probably even receive its own query language standard (GQL). As almost any business benefits from graph platforms it is no wonder that adoption is broad and fast. There must be good reasons for that. In his talk Axel will give an overview of the evolution of technology and products in the Graph Space from the early beginnings up to current developments in machine learning and artificial intelligence. He will also give some examples and explain why graph technology is so well suited for most use cases and to build intelligent systems.
About the Author:
Axel Morgner started Structr in 2010 to create the next-gen CMS. Previously, he worked for Oracle and founded an ECM company. Axel loves Open Source. As CEO, he’s responsible for the company behind Structr and the project itself, with focus on the front end.
Can we use data to train Machine Learning models, perform statistical analysis, yet without putting private data on risk? There are tools and techniques such as Federated Learning, Differential Privacy or Homomorphic Encryption enabling safer work on the data.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
There is almost no CEO or CIO who hasn't already heard this statement or similar when analysing the root cause of incidents, data leaks or other "cloud gates". It is time for a clear, lightweight yet complete IT service management framework that leaves no room for potentially critical misunderstandings and "It's no one's fault" attitudes.
"And bob's your uncle." - Navigating the sea of Cloud offerings to business o...Michel Drescher
If one thing is clear, then it is the widespread adoption of Cloud Computing in all its aspects and variants. However, with a thousand flowers are blooming it becomes increasingly difficult to find the flowers matching your individual needs. The CloudWATCH project and its successor (naturally called CloudWATCH 2) developed a methodology, and a supplementing online service that allow any user to easily and quickly find Cloud projects and activities, providers, technology providers, and service consumers with a similar cloud characteristic profile for matchmaking and business opportunities.
كتبوا،، فقرأنا مع الكاتب فائز السعدون - الصدى نتEl Sada الصدى نت
الصدى نت - زوايا - كتبوا،، فقرأنا
زاوية كتبوا.. فقرأنا .. عرض لكتاب او فصل من كتاب او دراسة ذات قيمة علمية مما تصدره مؤسسات التفكير العالمية
مما يخص الشؤون الدولية وشؤون الشرق الأوسط بشكل خاص من الإصدارات الحديثة والقديمة عندما تكون ذات صلة بتطورات او احداث معاصرة
Tools using AI will affect and, in many cases, redefine most areas of societal impact such as medical practice and intervention, autonomous transportation and law enforcement. While so far, most of the focus and time is invested into optimizing models’ performance, whenever a single wrong prediction has big implications in terms of value or life, accuracy becomes less important than explainability.
In this talk, we will learn about explainable AI and we will see how to apply some of the available tools to answer the question ‘’what did my system consider in order to output a specific prediction’.
Building a robust machine learning model is not an easy task. After all, most POCs don't make it into production. And even if they make it into production, you still need to monitor its performance.
How can you build performant, tolerant, stable, predictive models that have known and fair biases? How can you make sure your models yield their value over time and stay performant after your team has deployed them? What are the current practices of model validation (or lack of), how are they flawed, and how could we improve them?
Simon Dagenais from Snitch AI will go through the reasons behind using an efficient validation framework that goes beyond the common metrics used by ML practitioners and why these tests matter when building high-quality models.
Agenda:
-----------
3:45pm - 4:00pm: Arrival & Networking
4:00pm - 4:15pm: News & Intro
4:15pm - 5:15pm: How to QA your ML models
5:15pm - 5:30pm: Virtual Snack & Networking
About the main speaker:
---------------------------------
Simon Dagenais is the Lead Data Scientist at Snitch AI, a machine learning validation tool. Before working on Snitch AI, Simon was a data scientist consultant at Moov AI, the parent company of Snitch AI. During his time as a consultant, he built and deployed custom ML solutions to solve business needs at companies like DRW, Société de Transport de Montréal and Cogeco. He now aspires to solve problems that data science teams will encounter during the course of a ML project cycle. Simon obtained an M.Sc. in economics from HEC Montreal. He frequently speaks in conferences, panels and meetups.
The Future is Big Graphs: A Community View on Graph Processing SystemsNeo4j
Alexandru Iosup, Full Professor, Vrije Universiteit Amsterdam (VU Amsterdam)
Angela Bonifati, Full Professor of Computer Science, Université de Lyon
Hannes Voigt, Software Engineer, Neo4j
DN18 | The Evolution and Future of Graph Technology: Intelligent Systems | Ax...Dataconomy Media
Abstract of the Prersentation:
The field of graph technology has developed rapidly in recent years and established itself as an independent technology sector that will probably even receive its own query language standard (GQL). As almost any business benefits from graph platforms it is no wonder that adoption is broad and fast. There must be good reasons for that. In his talk Axel will give an overview of the evolution of technology and products in the Graph Space from the early beginnings up to current developments in machine learning and artificial intelligence. He will also give some examples and explain why graph technology is so well suited for most use cases and to build intelligent systems.
About the Author:
Axel Morgner started Structr in 2010 to create the next-gen CMS. Previously, he worked for Oracle and founded an ECM company. Axel loves Open Source. As CEO, he’s responsible for the company behind Structr and the project itself, with focus on the front end.
Can we use data to train Machine Learning models, perform statistical analysis, yet without putting private data on risk? There are tools and techniques such as Federated Learning, Differential Privacy or Homomorphic Encryption enabling safer work on the data.
For further details contact:
N.RAJASEKARAN B.E M.S 9841091117,9840103301.
IMPULSE TECHNOLOGIES,
Old No 251, New No 304,
2nd Floor,
Arcot road ,
Vadapalani ,
Chennai-26.
www.impulse.net.in
Email: ieeeprojects@yahoo.com/ imbpulse@gmail.com
There is almost no CEO or CIO who hasn't already heard this statement or similar when analysing the root cause of incidents, data leaks or other "cloud gates". It is time for a clear, lightweight yet complete IT service management framework that leaves no room for potentially critical misunderstandings and "It's no one's fault" attitudes.
"And bob's your uncle." - Navigating the sea of Cloud offerings to business o...Michel Drescher
If one thing is clear, then it is the widespread adoption of Cloud Computing in all its aspects and variants. However, with a thousand flowers are blooming it becomes increasingly difficult to find the flowers matching your individual needs. The CloudWATCH project and its successor (naturally called CloudWATCH 2) developed a methodology, and a supplementing online service that allow any user to easily and quickly find Cloud projects and activities, providers, technology providers, and service consumers with a similar cloud characteristic profile for matchmaking and business opportunities.
كتبوا،، فقرأنا مع الكاتب فائز السعدون - الصدى نتEl Sada الصدى نت
الصدى نت - زوايا - كتبوا،، فقرأنا
زاوية كتبوا.. فقرأنا .. عرض لكتاب او فصل من كتاب او دراسة ذات قيمة علمية مما تصدره مؤسسات التفكير العالمية
مما يخص الشؤون الدولية وشؤون الشرق الأوسط بشكل خاص من الإصدارات الحديثة والقديمة عندما تكون ذات صلة بتطورات او احداث معاصرة
A Tale of Ice and Fire, or: The Cloud And The StandardsMichel Drescher
Presentation at the International Industry-Academia Workshop on Cloud Reliability and Resilience. 7-8 November 2016, Berlin, Germany.
Organized by EIT Digital and Huawei GRC, Germany.
Twitter: @CloudRR2016
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian explains data science, steps in a data science workflow and show some experiments in AzureML. He also mentions about big data issues in a data science project and solutions to them.
Data Science as a Service: Intersection of Cloud Computing and Data SciencePouria Amirian
Dr. Pouria Amirian from the University of Oxford explains Data Science and its relationship with Big Data and Cloud Computing. Then he illustrates using AzureML to perform a simple data science analytics.
With approximately 1.x years of delay to the US, the term "Data Science" is also gaining speed in Europe. We see more and more job openings for- and business cards of data scientists, new events dedicated to the topic and an increased demand in related education literally every month. In response to this trend, Zurich University of Applied Sciences founded the ZHAW Data Science Laboratory (Datalab) last year.
This talk is to give an updated overview of Data Science in Europe by the example of the Datalab's activities in Switzerland. After a definition and classification of the field, a presentation of real technical projects sets the stage for what Data Science looks like here, offside of internet behemoths and big data clichés. Then, conclusions on the state of the art at least in Switzerland are drawn from evaluating the recent "1st Swiss Workshop on Data Science" event and ZHAW's professional education programme "DAS in Data Science".
With the help of the audience during the subsequent discussion, these results can eventually be extrapolated to the wider European community.
CloudLightning - Project and Architecture OverviewCloudLightning
This is a PowerPoint presentation delivered by Prof John Morrison (UCC) on 9 December 2016 at the IC4 and Host in Ireland Workshop: Data Centres in Ireland.
Structuring Big Data results to create new information: Smart Data. These Smart Data can be used to advance knowledge and support decision-making processes.
A close cooperation between industry and science creates better conditions for cutting-edge research in Data Engineering/Smart Data.
Platform for Big Data Analytics and Visual Analytics: CSIRO use cases. Februa...Tomasz Bednarz
Presented at the ACEMS workshop at QUT in February 2015.
Credits: whole project team (names listed in the first slide).
Approved by CSIRO to be shared externally.
Bridging the Last Mile: Getting Data to the People Who Need ItDenodo
Watch full webinar here: https://bit.ly/3cUA0Qi
Many organizations are embarking on strategically important journeys to embrace data and analytics. The goal can be to improve internal efficiencies, improve the customer experience, drive new business models and revenue streams, or – in the public sector – provide better services. All of these goals require empowering employees to act on data and analytics and to make data-driven decisions. However, getting data – the right data at the right time – to these employees is a huge challenge and traditional technologies and data architectures are simply not up to this task. This webinar will look at how organizations are using Data Virtualization to quickly and efficiently get data to the people that need it.
Attend this session to learn:
- The challenges organizations face when trying to get data to the business users in a timely manner
- How Data Virtualization can accelerate time-to-value for an organization’s data assets
- Examples of leading companies that used data virtualization to get the right data to the users at the right time
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
Automate your Data Science pipeline with Ansible, Python and Kubernetes - ODSC Talk
What is Data Science and the Data Science Landscape
Process and Flow
Understanding Data
The Data Science Toolkit
The Big Data Challenge
Cloud Computing Solutions
The rise of DevOps in Data Science
Automate your data pipeline with Ansible
Introductory talk given to PhD students starting research at NUS PhD open day 2020. Covers research in Computer Science, and some experience in research on trustworthy software systems.
RECAP at ETSI Experiential Network Intelligence (ENI) MeetingRECAP Project
This presentation was delivered by Johan Forsman (Tieto), Jörg Domaschka (UULM) and Paolo Casari (IMDEA Networks) at the ETSI Experiential Network Intelligence (ENI) Meeting in Warsaw, Poland, on April 12th, 2019. ETSI Experiential Networked Industry Specification Group (ENI ISG) work on defining a Cognitive Network Management architecture using Artificial Intelligence (AI) techniques and context-aware policies to adjust offered services based on changes in user needs, environmental conditions and business goals. The intention is that the use of Artificial Intelligence techniques in the network management system should solve some of the problems of future network deployment and operations. For more information, see https://www.etsi.org/technologies/experiential-networked-intelligence.
Rabobank - There is something about DataBigDataExpo
Technologische mogelijkheden en GDPR, een continue clash? En hoe staat het met de het ethisch (her)gebruik van data? Leer in deze sessie van Rabobank’s Big Data journey en krijg inzicht in: organisatorische keuzes, data Lab technologie visie & data strategie, als enabler en accelerator van digitale innovatie en transformatie.
ATMOSPHERE was invited to be a speaker at Think Milano event, on 6th June from 14.30 to 17.30, to join a panel discussion called “L’infrastruttura cloud ready protagonista del future” on how cloud infrastructures are important for different market sectors.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Show drafts
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
From a sea of projects to collaboration opportunities within seconds
1. From a sea of projects to collaboration
opportunities within seconds
Michel Drescher,
Cloud computing standards specialist
OeRC, University of Oxford, UK
3. This is your project among H2020.
3
8400+ projects currently funded
under H2020.
Source: European Commission
You’ve got a problem…
Hello, project!
How will you ever find
potential collaboration
projects effectively and
efficiently?
6. The 1-slide explanation
6
38 responses, scoring the
importance of NIST Cloud
characteristics for them selves
Out of these, your
collaboration opportunities lie
within this cluster!
(Had you provided your
scores…)
12. A bit of statistics
Principal Component Analysis (Karl Pierson 1901, Harold Hotelling 1930)
Multi-variance analysis
Emphasises variance and patterns in data
Data transformation for dimension reduction in analysis
Hierarchical clustering using Euclidian distance
Form clusters of “similar” respondents, starting with 1-member
clusters
“Similarity” (i.e., distance) calculated using Euclidian distance function
Distance between clusters uses “weighed pair-group centroid”
performs well with large variance in cluster sizes
12
14. Yes you can.
But first, you must submit your scores!
SESA – 10 of 19 projects responded
ICEI – 4 of 13 projects responded
NATRES – 11 of 20 projects responded
DPSP – 3 of 24 projects responded
14
https://tethys.oerc.ox.ac.uk:8443/cluster/index.xhtml
