This document provides an overview of MATLAB and its toolboxes for technical computing, modeling, and working with geospatial and scientific data formats like HDF. MATLAB is a technical computing environment used for data analysis, algorithm development, and custom application building. It includes toolboxes for tasks like image processing, mapping, and working with HDF file formats. The Mapping Toolbox allows users to access, visualize, and analyze geospatial data. The Distributed Computing Toolbox enables running MATLAB applications on multiple computers to accelerate processing.
A Reshmi is seeking a position to utilize her knowledge and skills to accomplish organizational goals. She has over 1 year of experience working with Cognizant Technology Solutions in Coimbatore, India on projects involving MicroStrategy, SSIS, and SSAS. She has a BE in Electrical and Electronics Engineering from Sri Ramakrishna Engineering College with strong communication, learning, and problem-solving skills.
The document describes HDF-EOS5, an extension of HDF used by NASA for Earth science data. HDF-EOS5 is based on HDF5 and contains standardized structures for gridded, swath, point, and zonal average data. It provides a library for reading, writing, and manipulating these data structures and their associated metadata. The library contains functions prefixed with "HE5_" for accessing, defining, input/output, inquiry, and subsetting HDF-EOS5 data.
Este documento introduz o MATLAB® e suas aplicações em engenharia elétrica. Ele descreve as janelas e tipos de variáveis do MATLAB®, além de operações básicas com vetores e matrizes. Também apresenta comandos de programação, importação e exportação de dados, plotagem gráfica e a ferramenta Simulink para simulação de sistemas.
Meetup 21/9/2017 - Image Recogonition: onmisbaar voor een slimme stad?Digipolis Antwerpen
1) Image recognition and computer vision technologies can enable various smart city applications like crowd behavior analysis, traffic analysis, and thermal signature tracking.
2) Autonomous systems that use computer vision and machine learning can perceive their environment and act independently to help during disasters by providing survivors and emergency personnel with locating information.
3) MATLAB provides tools for computer vision, machine learning, and deep learning that can help develop prototypes and applications for smart cities from idea to product.
This document outlines the course content for a Tableau certification training program. The 13-module course covers topics such as Tableau architecture, dashboards, data visualization, data blending, mapping, calculations, parameters, and integrating Tableau with R. Students will learn various chart types, data preparation techniques, and how to build interactive dashboards and stories. Hands-on exercises are included to help students practice the skills learned. There are no prerequisites for taking the course.
MATLAB, short for Matrix Laboratory, is a powerful software platform and programming language developed by MathWorks. It offers a wide range of features and capabilities that make it an indispensable tool for researchers, students, and professionals in science, engineering, and beyond. With its intuitive syntax, extensive library of functions, and interactive data analysis environment, MATLAB enables users to perform numerical computations, visualize data, and develop algorithms and models with ease. Its applications span across engineering, data analysis, research and development, and education, making it a versatile tool for innovation and problem-solving. MATLAB's impact lies in its ability to accelerate development cycles, facilitate data analysis and simulation, and empower interdisciplinary collaborations, ultimately driving advancements in various fields.
Ratan Mohapatra- Computer Systems Administrator, Computer Systems AnalystRatan Mohapatra
I am a diversified IT professional experienced in multi-platform computing (Windows . Linux . Macintosh . Unix), network security and programming (PowerShell . Visual Basic . C), looking for relevant opportunities and professional collaborations. Highlights of my career (based in Canada, Germany, India, and U.K.) include over 15 years' experience in building innovative analytical solutions to address complex professional problems by project development and management. I have authored over 20 critically acclaimed technical articles by critical analysis of project results and interpretation in light of a “bigger picture”.
I am a multi-faceted creative expressionist who has been voted among the top 5 web designers in Ottawa. Web Development (HTML . PHP . MySQL . LAMP . WAMP . XAMPP . WordPress . Drupal), digital graphic designing (Adobe Creative Suite) and photojournalism are my creative activities.
My ideal career is one that inspires me to “thinking outside the box” to address routine and exceptional professional challenges and provide me an opportunity to explore and learn new possibilities.
---------------------------
Server Administration and Development (Windows Server up to 2012R2, Linux: Ubuntu and Suse), PowerShell, Excel (VBA), C, C#, PHP, MySQL, Technical Writing and Publication, Mass Spectrometry . R+D, Photo Journalism . Digital Graphic Designing . Web Development
Developing and deploying AI solutions on the cloud using Team Data Science Pr...Debraj GuhaThakurta
Presented at: Global Big AI Conference, Santa Clara, Jan 2018 Developing and deploying AI solutions on the cloud using Team Data Science Process (TDSP) and Azure Machine Learning (AML)
A Reshmi is seeking a position to utilize her knowledge and skills to accomplish organizational goals. She has over 1 year of experience working with Cognizant Technology Solutions in Coimbatore, India on projects involving MicroStrategy, SSIS, and SSAS. She has a BE in Electrical and Electronics Engineering from Sri Ramakrishna Engineering College with strong communication, learning, and problem-solving skills.
The document describes HDF-EOS5, an extension of HDF used by NASA for Earth science data. HDF-EOS5 is based on HDF5 and contains standardized structures for gridded, swath, point, and zonal average data. It provides a library for reading, writing, and manipulating these data structures and their associated metadata. The library contains functions prefixed with "HE5_" for accessing, defining, input/output, inquiry, and subsetting HDF-EOS5 data.
Este documento introduz o MATLAB® e suas aplicações em engenharia elétrica. Ele descreve as janelas e tipos de variáveis do MATLAB®, além de operações básicas com vetores e matrizes. Também apresenta comandos de programação, importação e exportação de dados, plotagem gráfica e a ferramenta Simulink para simulação de sistemas.
Meetup 21/9/2017 - Image Recogonition: onmisbaar voor een slimme stad?Digipolis Antwerpen
1) Image recognition and computer vision technologies can enable various smart city applications like crowd behavior analysis, traffic analysis, and thermal signature tracking.
2) Autonomous systems that use computer vision and machine learning can perceive their environment and act independently to help during disasters by providing survivors and emergency personnel with locating information.
3) MATLAB provides tools for computer vision, machine learning, and deep learning that can help develop prototypes and applications for smart cities from idea to product.
This document outlines the course content for a Tableau certification training program. The 13-module course covers topics such as Tableau architecture, dashboards, data visualization, data blending, mapping, calculations, parameters, and integrating Tableau with R. Students will learn various chart types, data preparation techniques, and how to build interactive dashboards and stories. Hands-on exercises are included to help students practice the skills learned. There are no prerequisites for taking the course.
MATLAB, short for Matrix Laboratory, is a powerful software platform and programming language developed by MathWorks. It offers a wide range of features and capabilities that make it an indispensable tool for researchers, students, and professionals in science, engineering, and beyond. With its intuitive syntax, extensive library of functions, and interactive data analysis environment, MATLAB enables users to perform numerical computations, visualize data, and develop algorithms and models with ease. Its applications span across engineering, data analysis, research and development, and education, making it a versatile tool for innovation and problem-solving. MATLAB's impact lies in its ability to accelerate development cycles, facilitate data analysis and simulation, and empower interdisciplinary collaborations, ultimately driving advancements in various fields.
Ratan Mohapatra- Computer Systems Administrator, Computer Systems AnalystRatan Mohapatra
I am a diversified IT professional experienced in multi-platform computing (Windows . Linux . Macintosh . Unix), network security and programming (PowerShell . Visual Basic . C), looking for relevant opportunities and professional collaborations. Highlights of my career (based in Canada, Germany, India, and U.K.) include over 15 years' experience in building innovative analytical solutions to address complex professional problems by project development and management. I have authored over 20 critically acclaimed technical articles by critical analysis of project results and interpretation in light of a “bigger picture”.
I am a multi-faceted creative expressionist who has been voted among the top 5 web designers in Ottawa. Web Development (HTML . PHP . MySQL . LAMP . WAMP . XAMPP . WordPress . Drupal), digital graphic designing (Adobe Creative Suite) and photojournalism are my creative activities.
My ideal career is one that inspires me to “thinking outside the box” to address routine and exceptional professional challenges and provide me an opportunity to explore and learn new possibilities.
---------------------------
Server Administration and Development (Windows Server up to 2012R2, Linux: Ubuntu and Suse), PowerShell, Excel (VBA), C, C#, PHP, MySQL, Technical Writing and Publication, Mass Spectrometry . R+D, Photo Journalism . Digital Graphic Designing . Web Development
Developing and deploying AI solutions on the cloud using Team Data Science Pr...Debraj GuhaThakurta
Presented at: Global Big AI Conference, Santa Clara, Jan 2018 Developing and deploying AI solutions on the cloud using Team Data Science Process (TDSP) and Azure Machine Learning (AML)
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.
This document summarizes a GIS project to create an online mapping and data portal for Pitkin County. It outlines the project goals of providing easy-to-use GIS data and mapping tools, the timeline and vendor selection process, preparation of maps and data, development of site functions using Geocortex software, and outreach efforts. It concludes with an analysis of the project benefits, including time savings, improved data access, and leveraging of technology, and proposes next steps such as developing department-specific sites and new mapping capabilities.
Neo4j GraphTalk Basel - Building intelligent Software with GraphsNeo4j
The document discusses using graphs and Neo4j to build intelligent solutions. It outlines Neo4j's professional services which include training, solution delivery, and packaged services. Typical technical requirements and a methodology for delivering solutions from use case to implementation are presented. Examples of graph-based solutions and how machine learning can be integrated are provided. Finally, a case study of Adobe migrating from Cassandra to Neo4j is summarized, reducing infrastructure costs significantly.
Neo4j GraphTalk Düsseldorf - Building intelligent solutions with GraphsNeo4j
This document discusses using graphs and machine learning to build intelligent solutions. It describes Neo4j services including professional services, training, and managed services. It also outlines innovation labs that help generate graph-based use cases. The document reviews using graph analytics and machine learning together, and provides examples of how Neo4j can be leveraged throughout the machine learning life cycle from data integration to model deployment. Real-world customer examples are also presented.
The document describes a MATLAB workshop proposal from M-LABS aimed at university students. The 4-module workshop covers basic MATLAB programming, digital image processing, digital signal processing, and communication systems. It provides hands-on experience and training in MATLAB toolbox applications for research. Participants will gain comprehensive MATLAB knowledge and skills to solve problems in signals, images, and communications. The workshop includes materials, projects, career guidance, and competitions with prizes.
Getting started with Matlab by Hannah Dotson, Vikram Kodibagkar laboratorySairam Geethanath
These slides are put together by Hannah Dotson, a STARS program intern at the Kodibagkar laboratory at UTSW. Folks new to Matlab and its usage at MIRC can find this tutorial material handy. Thanks Hannah!
Matthew Kitching is a data scientist with over 15 years of experience in artificial intelligence, machine learning, and data science. He holds a Ph.D. in Computer Science from the University of Toronto specializing in artificial intelligence. He has worked as a data scientist at Bell Canada and Apption, developing predictive models and data strategies. He has extensive experience in Python, R, Spark, and Hadoop.
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
- The document discusses automating data science pipelines with DevOps tools like Ansible, Packer, and Kubernetes.
- It covers obtaining data, exploring and modeling data, and how to automate infrastructure setup and deployment with tools like Packer to build machine images and Ansible for configuration management.
- The rise of DevOps and its cultural aspects are discussed as well as how tools like Packer, Ansible, Kubernetes can help automate infrastructure and deploy machine learning models at scale in production environments.
MapInfo Professional 12.5 and Discover3D 2014 - A brief overviewPrakher Hajela Saxena
MapInfo Professional and Discover3D is a complete suite of software specifically designed for geoscientists, environmentalists, and geochemists.
The software is being used in various industries today like, environment, mining, exploration, hydrology, etc.
Data Science Introduction: Concepts, lifecycle, applications.pptxsumitkumar600840
This document provides an introduction to the subject of data visualization using R programming and Power BI. It discusses key concepts in data science including the data science lifecycle, components of data science like statistics and machine learning, and applications of data science such as image recognition. The document also outlines some advantages and disadvantages of using data science.
This document provides an overview of how to build your own personalized search and discovery tool like Microsoft Delve by combining machine learning, big data, and SharePoint. It discusses the Office Graph and how signals across Office 365 are used to populate insights. It also covers big data concepts like Hadoop and machine learning algorithms. Finally, it proposes a high-level architectural concept for building a Delve-like tool using Azure SQL Database, Azure Storage, Azure Machine Learning, and presenting insights.
How to build your own Delve: combining machine learning, big data and SharePointJoris Poelmans
You are experiencing the benefits of machine learning everyday through product recommendations on Amazon & Bol.com, credit card fraud prevention, etc… So how can we leverage machine learning together with SharePoint and Yammer. We will first look into the fundamentals of machine learning and big data solutions and next we will explore how we can combine tools such as Windows Azure HDInsight, R, Azure Machine Learning to extend and support collaboration and content management scenarios within your organization.
This document discusses data science and machine learning concepts and tools. It introduces the IBM Data Science Experience (DSX) and Watson Machine Learning (WML) products, which provide environments for data scientists and developers to build machine learning models. DSX offers notebooks, IDEs and collaboration tools, while WML focuses on visual model creation, access to algorithms, full ML workflows and APIs. It then demonstrates these products.
Introduction to Decision Intelligence using DataKaren Lim
This document outlines the modules in the Data for Decision Intelligence programme at Ngee Ann Polytechnic. The 4 modules are: 1) Data Wrangling and Statistics, which teaches data analysis using R and DataCamp; 2) Visualization of Data with R & Tableau, which teaches data visualization in R and Tableau; 3) Machine Learning Modelling, which covers regression, trees and other techniques; and 4) Design Thinking for Data Science, which teaches integrating human insights with machine learning and building data science projects.
Create a Data Science Lab with Microsoft and Open Source toolsMarcel Franke
This document provides an overview of creating a data science lab using Microsoft and open source tools. It discusses what data science is, provides a brief history of its use in gambling and weather forecasting, and examines current applications in areas like social media, customer analysis, and predictive maintenance. The document advocates learning from nature by taking an evolutionary approach of variation and selection to complex problems. It then describes setting up an efficient lab for experimentation using tools like Power BI, SQL Server, and open source software R, and scaling solutions using technologies like Revolution Analytics, Hadoop, and cloud services.
1. The document discusses Neo4j, the world's most popular graph database. It highlights Neo4j's customers in top retail, financial, and software firms and its presence in Silicon Valley and global offices.
2. Neo4j is used both on-premises and in the cloud as a database-as-a-service. The document also discusses Neo4j's graph data science capabilities and its rise in popularity from 2010 to 2020.
3. Going forward, Neo4j is focusing on cloud services and positioning developers at the center of its strategy and products like Neo4j Aura and the Graph Data Science Library.
Apache Spark for Machine Learning with High Dimensional Labels: Spark Summit ...Spark Summit
This talk will cover the tools we used, the hurdles we faced and the work arounds we developed with the help from Databricks support in our attempt to build a custom machine learning model and use it to predict the TV ratings for different networks and demographics.
The Apache Spark machine learning and dataframe APIs make it incredibly easy to produce a machine learning pipeline to solve an archetypal supervised learning problem. In our applications at Cadent, we face a challenge with high dimensional labels and relatively low dimensional features; at first pass such a problem is all but intractable but thanks to a large number of historical records and the tools available in Apache Spark, we were able to construct a multi-stage model capable of forecasting with sufficient accuracy to drive the business application.
Over the course of our work we have come across many tools that made our lives easier, and others that forced work around. In this talk we will review our custom multi-stage methodology, review the challenges we faced and walk through the key steps that made our project successful.
2.DATAMANAGEMENT-DIGITAL TRANSFORMATION AND STRATEGYGeorgeDiamandis11
The document discusses digitalization in logistics and analytics of key performance indicators. It covers several topics related to data management, including business intelligence, data warehousing, big data, and analytics tools. Case studies are provided on how various organizations have optimized operations, increased speed, and created new services using big data analytics techniques. Examples include detecting fraud, anticipating demand, optimizing inventory, scenario simulation, improving health outcomes, and customizing education.
This document discusses how to optimize HDF5 files for efficient access in cloud object stores. Key optimizations include using large dataset chunk sizes of 1-4 MiB, consolidating internal file metadata, and minimizing variable-length datatypes. The document recommends creating files with paged aggregation and storing file content information in the user block to enable fast discovery of file contents when stored in object stores.
This document provides an overview of HSDS (Highly Scalable Data Service), which is a REST-based service that allows accessing HDF5 data stored in the cloud. It discusses how HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects to optimize performance. The document also describes how HSDS was used to improve access performance for NASA ICESat-2 HDF5 data on AWS S3 by hyper-chunking datasets into larger chunks spanning multiple original HDF5 chunks. Benchmark results showed that accessing the data through HSDS provided over 2x faster performance than other methods like ROS3 or S3FS that directly access the cloud storage.
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.
This document summarizes a GIS project to create an online mapping and data portal for Pitkin County. It outlines the project goals of providing easy-to-use GIS data and mapping tools, the timeline and vendor selection process, preparation of maps and data, development of site functions using Geocortex software, and outreach efforts. It concludes with an analysis of the project benefits, including time savings, improved data access, and leveraging of technology, and proposes next steps such as developing department-specific sites and new mapping capabilities.
Neo4j GraphTalk Basel - Building intelligent Software with GraphsNeo4j
The document discusses using graphs and Neo4j to build intelligent solutions. It outlines Neo4j's professional services which include training, solution delivery, and packaged services. Typical technical requirements and a methodology for delivering solutions from use case to implementation are presented. Examples of graph-based solutions and how machine learning can be integrated are provided. Finally, a case study of Adobe migrating from Cassandra to Neo4j is summarized, reducing infrastructure costs significantly.
Neo4j GraphTalk Düsseldorf - Building intelligent solutions with GraphsNeo4j
This document discusses using graphs and machine learning to build intelligent solutions. It describes Neo4j services including professional services, training, and managed services. It also outlines innovation labs that help generate graph-based use cases. The document reviews using graph analytics and machine learning together, and provides examples of how Neo4j can be leveraged throughout the machine learning life cycle from data integration to model deployment. Real-world customer examples are also presented.
The document describes a MATLAB workshop proposal from M-LABS aimed at university students. The 4-module workshop covers basic MATLAB programming, digital image processing, digital signal processing, and communication systems. It provides hands-on experience and training in MATLAB toolbox applications for research. Participants will gain comprehensive MATLAB knowledge and skills to solve problems in signals, images, and communications. The workshop includes materials, projects, career guidance, and competitions with prizes.
Getting started with Matlab by Hannah Dotson, Vikram Kodibagkar laboratorySairam Geethanath
These slides are put together by Hannah Dotson, a STARS program intern at the Kodibagkar laboratory at UTSW. Folks new to Matlab and its usage at MIRC can find this tutorial material handy. Thanks Hannah!
Matthew Kitching is a data scientist with over 15 years of experience in artificial intelligence, machine learning, and data science. He holds a Ph.D. in Computer Science from the University of Toronto specializing in artificial intelligence. He has worked as a data scientist at Bell Canada and Apption, developing predictive models and data strategies. He has extensive experience in Python, R, Spark, and Hadoop.
DevOps for Data Engineers - Automate Your Data Science Pipeline with Ansible,...Mihai Criveti
- The document discusses automating data science pipelines with DevOps tools like Ansible, Packer, and Kubernetes.
- It covers obtaining data, exploring and modeling data, and how to automate infrastructure setup and deployment with tools like Packer to build machine images and Ansible for configuration management.
- The rise of DevOps and its cultural aspects are discussed as well as how tools like Packer, Ansible, Kubernetes can help automate infrastructure and deploy machine learning models at scale in production environments.
MapInfo Professional 12.5 and Discover3D 2014 - A brief overviewPrakher Hajela Saxena
MapInfo Professional and Discover3D is a complete suite of software specifically designed for geoscientists, environmentalists, and geochemists.
The software is being used in various industries today like, environment, mining, exploration, hydrology, etc.
Data Science Introduction: Concepts, lifecycle, applications.pptxsumitkumar600840
This document provides an introduction to the subject of data visualization using R programming and Power BI. It discusses key concepts in data science including the data science lifecycle, components of data science like statistics and machine learning, and applications of data science such as image recognition. The document also outlines some advantages and disadvantages of using data science.
This document provides an overview of how to build your own personalized search and discovery tool like Microsoft Delve by combining machine learning, big data, and SharePoint. It discusses the Office Graph and how signals across Office 365 are used to populate insights. It also covers big data concepts like Hadoop and machine learning algorithms. Finally, it proposes a high-level architectural concept for building a Delve-like tool using Azure SQL Database, Azure Storage, Azure Machine Learning, and presenting insights.
How to build your own Delve: combining machine learning, big data and SharePointJoris Poelmans
You are experiencing the benefits of machine learning everyday through product recommendations on Amazon & Bol.com, credit card fraud prevention, etc… So how can we leverage machine learning together with SharePoint and Yammer. We will first look into the fundamentals of machine learning and big data solutions and next we will explore how we can combine tools such as Windows Azure HDInsight, R, Azure Machine Learning to extend and support collaboration and content management scenarios within your organization.
This document discusses data science and machine learning concepts and tools. It introduces the IBM Data Science Experience (DSX) and Watson Machine Learning (WML) products, which provide environments for data scientists and developers to build machine learning models. DSX offers notebooks, IDEs and collaboration tools, while WML focuses on visual model creation, access to algorithms, full ML workflows and APIs. It then demonstrates these products.
Introduction to Decision Intelligence using DataKaren Lim
This document outlines the modules in the Data for Decision Intelligence programme at Ngee Ann Polytechnic. The 4 modules are: 1) Data Wrangling and Statistics, which teaches data analysis using R and DataCamp; 2) Visualization of Data with R & Tableau, which teaches data visualization in R and Tableau; 3) Machine Learning Modelling, which covers regression, trees and other techniques; and 4) Design Thinking for Data Science, which teaches integrating human insights with machine learning and building data science projects.
Create a Data Science Lab with Microsoft and Open Source toolsMarcel Franke
This document provides an overview of creating a data science lab using Microsoft and open source tools. It discusses what data science is, provides a brief history of its use in gambling and weather forecasting, and examines current applications in areas like social media, customer analysis, and predictive maintenance. The document advocates learning from nature by taking an evolutionary approach of variation and selection to complex problems. It then describes setting up an efficient lab for experimentation using tools like Power BI, SQL Server, and open source software R, and scaling solutions using technologies like Revolution Analytics, Hadoop, and cloud services.
1. The document discusses Neo4j, the world's most popular graph database. It highlights Neo4j's customers in top retail, financial, and software firms and its presence in Silicon Valley and global offices.
2. Neo4j is used both on-premises and in the cloud as a database-as-a-service. The document also discusses Neo4j's graph data science capabilities and its rise in popularity from 2010 to 2020.
3. Going forward, Neo4j is focusing on cloud services and positioning developers at the center of its strategy and products like Neo4j Aura and the Graph Data Science Library.
Apache Spark for Machine Learning with High Dimensional Labels: Spark Summit ...Spark Summit
This talk will cover the tools we used, the hurdles we faced and the work arounds we developed with the help from Databricks support in our attempt to build a custom machine learning model and use it to predict the TV ratings for different networks and demographics.
The Apache Spark machine learning and dataframe APIs make it incredibly easy to produce a machine learning pipeline to solve an archetypal supervised learning problem. In our applications at Cadent, we face a challenge with high dimensional labels and relatively low dimensional features; at first pass such a problem is all but intractable but thanks to a large number of historical records and the tools available in Apache Spark, we were able to construct a multi-stage model capable of forecasting with sufficient accuracy to drive the business application.
Over the course of our work we have come across many tools that made our lives easier, and others that forced work around. In this talk we will review our custom multi-stage methodology, review the challenges we faced and walk through the key steps that made our project successful.
2.DATAMANAGEMENT-DIGITAL TRANSFORMATION AND STRATEGYGeorgeDiamandis11
The document discusses digitalization in logistics and analytics of key performance indicators. It covers several topics related to data management, including business intelligence, data warehousing, big data, and analytics tools. Case studies are provided on how various organizations have optimized operations, increased speed, and created new services using big data analytics techniques. Examples include detecting fraud, anticipating demand, optimizing inventory, scenario simulation, improving health outcomes, and customizing education.
This document discusses how to optimize HDF5 files for efficient access in cloud object stores. Key optimizations include using large dataset chunk sizes of 1-4 MiB, consolidating internal file metadata, and minimizing variable-length datatypes. The document recommends creating files with paged aggregation and storing file content information in the user block to enable fast discovery of file contents when stored in object stores.
This document provides an overview of HSDS (Highly Scalable Data Service), which is a REST-based service that allows accessing HDF5 data stored in the cloud. It discusses how HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects to optimize performance. The document also describes how HSDS was used to improve access performance for NASA ICESat-2 HDF5 data on AWS S3 by hyper-chunking datasets into larger chunks spanning multiple original HDF5 chunks. Benchmark results showed that accessing the data through HSDS provided over 2x faster performance than other methods like ROS3 or S3FS that directly access the cloud storage.
This document summarizes the current status and focus of the HDF Group. It discusses that the HDF Group is located in Champaign, IL and is a non-profit organization focused on developing and maintaining HDF software and data formats. It provides an overview of recent HDF5, HDF4 and HDFView releases and notes areas of focus for software quality improvements, increased transparency, strengthening the community, and modernizing HDF products. It invites support and participation in upcoming user group meetings.
This document provides an overview of HSDS (HDF Server and Data Service), which allows HDF5 files to be stored and accessed from the cloud. Key points include:
- HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects for scalability and parallelism.
- Features include streaming support, fancy indexing for complex queries, and caching for improved performance.
- HSDS can be deployed on Docker, Kubernetes, or AWS Lambda depending on needs.
- Case studies show HSDS is used by organizations like NREL and NSF to make petabytes of scientific data publicly accessible in the cloud.
This document discusses creating cloud-optimized HDF5 files by rearranging internal structures for more efficient data access in cloud object stores. It describes cloud-native and cloud-optimized storage formats, with the latter involving storing the entire HDF5 file as a single object. The benefits of cloud-optimized HDF5 include fast scanning and using the HDF5 library. Key aspects covered include using optimal chunk sizes, compression, and minimizing variable-length datatypes.
This document discusses updates and performance improvements to the HDF5 OPeNDAP data handler. It provides a history of the handler since 2001 and describes recent updates including supporting DAP4, new data types, and NetCDF data models. A performance study showed that passing compressed HDF5 data through the handler without decompressing/recompressing led to speedups of around 17-30x by leveraging HDF5 direct I/O APIs. This allows outputting HDF5 files as NetCDF files much faster through the handler.
This document provides instructions for using the Hyrax software to serve scientific data files stored on Amazon S3 using the OPeNDAP data access protocol. It describes how to generate ancillary metadata files called DMR++ files using the get_dmrpp tool that provide information about the data file structure and locations. The document explains how to run get_dmrpp inside a Docker container to process data files on S3 and generate customized DMR++ files that the Hyrax server can use to serve the files to clients.
This document provides an overview and examples of accessing cloud data and services using the Earthdata Login (EDL), Pydap, and MATLAB. It discusses some common problems users encounter, such as being unable to access HDF5 data on AWS S3 using MATLAB or read data from OPeNDAP servers using Pydap. Solutions presented include using EDL to get temporary AWS tokens for S3 access in MATLAB and providing code examples on the HDFEOS website to help users access S3 data and OPeNDAP services. The document also notes some limitations, such as tokens being valid for only 1 hour, and workarounds like requesting new tokens or using the MATLAB HDF5 API instead of the netCDF API.
The HDF5 Roadmap and New Features document outlines upcoming changes and improvements to the HDF5 library. Key points include:
- HDF5 1.13.x releases will include new features like selection I/O, the Onion VFD for versioned files, improved VFD SWMR for single-writer multiple-reader access, and subfiling for parallel I/O.
- The Virtual Object Layer allows customizing HDF5 object storage and introduces terminal and pass-through connectors.
- The Onion VFD stores versions of HDF5 files in a separate onion file for versioned access.
- VFD SWMR improves on legacy SWMR by implementing single-writer multiple-reader capabilities
This document discusses user analysis of the HDFEOS.org website and plans for future improvements. It finds that the majority of the site's 100 daily users are "quiet", not posting on forums or other interactive elements. The main user types are locators, who search for examples or data; mergers, who combine or mosaic datasets; and converters, who change file formats. The document outlines recent updates focused on these user types, like adding Python examples for subsetting and calculating latitude and longitude. It proposes future work on artificial intelligence/machine learning uses of HDF files and examples for processing HDF data in the cloud.
This document summarizes a presentation about the current status and future directions of the Hierarchical Data Format (HDF) software. It provides updates on recent HDF5 releases, development efforts including new compression methods and ways to access HDF5 data, and outreach resources. It concludes by inviting the audience to share wishes for future HDF development.
The document describes H5Coro, a new C++ library for reading HDF5 files from cloud storage. H5Coro was created to optimize HDF5 reading for cloud environments by minimizing I/O operations through caching and efficient HTTP requests. Performance tests showed H5Coro was 77-132x faster than the previous HDF5 library at reading HDF5 data from Amazon S3 for NASA's SlideRule project. H5Coro supports common HDF5 elements but does not support writing or some complex HDF5 data types and messages to focus on optimized read-only performance for time series data stored sequentially in memory.
This document summarizes MathWorks' work to modernize MATLAB's support for HDF5. Key points include:
1) MATLAB now supports HDF5 1.10.7 features like single-writer/multiple-reader access and virtual datasets through new and updated low-level functions.
2) Performance benchmarks show some improvements but also regressions compared to the previous HDF5 version, and work continues to optimize code and support future versions.
3) There are compatibility considerations for Linux filter plugins, but interim solutions are provided until MathWorks can ship a single HDF5 version.
HSDS provides HDF as a service through a REST API that can scale across nodes. New releases will enable serverless operation using AWS Lambda or direct client access without a server. This allows HDF data to be accessed remotely without managing servers. HSDS stores each HDF object separately, making it compatible with cloud object storage. Performance on AWS Lambda is slower than a dedicated server but has no management overhead. Direct client access has better performance but limits collaboration between clients.
HDF5 and Zarr are data formats that can be used to store and access scientific data. This presentation discusses approaches to translating between the two formats. It describes how HDF5 files were translated to the Zarr format by creating a separate Zarr store to hold HDF5 file chunks, and storing chunk location metadata. It also discusses an implementation that translates Zarr data to the HDF5 format by using a special chunking layout and storing chunk information in an HDF5 compound dataset. Limitations of the translations include lack of support for some HDF5 dataset properties in Zarr, and lack of support for some Zarr compression methods in the HDF5 implementation.
The document discusses HDF for the cloud, including new features of the HDF Server and what's next. Key points:
- HDF Server uses a "sharded schema" that maps HDF5 objects to individual storage objects, allowing parallel access and updates without transferring entire files.
- Implementations include HSDS software that uses the sharded schema with an API and SDKs for different languages like h5pyd for Python.
- New features of HSDS 0.6 include support for POSIX, Azure, AWS Lambda, and role-based access control.
- Future work includes direct access to storage without a server intermediary for some use cases.
This document compares different methods for accessing HDF and netCDF files stored on Amazon S3, including Apache Drill, THREDDS Data Server (TDS), and HDF5 Virtual File Driver (VFD). A benchmark test of accessing a 24GB HDF5/netCDF-4 file on S3 from Amazon EC2 found that TDS performed the best, responding within 2 minutes, while Apache Drill failed after 7 minutes. The document concludes that TDS 5.0 is the clear winner based on performance and support for role-based access control and HDF4 files, but the best solution depends on use case and software.
This document discusses STARE-PODS, a proposal to NASA/ACCESS-19 to develop a scalable data store for earth science data using the SpatioTemporal Adaptive Resolution Encoding (STARE) indexing scheme. STARE allows diverse earth science data to be unified and indexed, enabling the data to be partitioned and stored in a Parallel Optimized Data Store (PODS) for efficient analysis. The HDF Virtual Object Layer and Virtual Data Set technologies can then provide interfaces to access the data in STARE-PODS in a familiar way. The goal is for STARE-PODS to organize diverse data for alignment and parallel/distributed storage and processing to enable integrative analysis at scale.
This document provides an overview and update on HDF5 and its ecosystem. Key points include:
- HDF5 1.12.0 was recently released with new features like the Virtual Object Layer and external references.
- The HDF5 library now supports accessing data in the cloud using connectors like S3 VFD and REST VOL without needing to modify applications.
- Projects like HDFql and H5CPP provide additional interfaces for querying and working with HDF5 files from languages like SQL, C++, and Python.
- The HDF5 community is moving development to GitHub and improving documentation resources on the HDF wiki site.
This document summarizes new features in HDF5 1.12.0, including support for storing references to objects and attributes across files, new storage backends using a virtual object layer (VOL), and virtual file drivers (VFDs) for Amazon S3 and HDFS. It outlines the HDF5 roadmap for 2019-2022, which includes continued support for HDF5 1.8 and 1.10, and new features in future 1.12.x releases like querying, indexing, and provenance tracking.
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2. The MathWorks at a Glance
Headquarters:
Natick, Massachusetts USA
USA:
California, Michigan,
Washington DC, Texas
Europe:
UK, France, Germany,
Switzerland, Italy,
Spain, Benelux, Nordic
Asia-Pacific:
Korea
Worldwide training
and consulting
Earth’s topography on an
equidistant cylindrical projection,
created with the MATLAB® Mapping
Toolbox
Distributors in 20 countries
2
3. Core MathWorks Products
The leading environment for technical
computing
–
–
Explore, analyze and visualize data
Develop algorithms, interactive graphics, and
custom deployable tools
The leading environment for ModelBased Design
–
Model, simulate, analyze and implement
dynamic, multidomain systems
3
4. Go Further with MATLAB Toolboxes
Signal Processing
Statistics Toolbox
Database Toolbox
Mapping Toolbox
Toolbox
Image Processing
Toolbox
Image Acquisition
Toolbox
MATLAB Compiler
4
5. Image Processing Toolbox 5.0
Perform image processing, analysis,
visualization and algorithm
development
Image enhancement
Image analysis
Morphology and segmentation
Graphical tools
Spatial transformations
Image registration
Support for multidimensional images
5
6. Mapping Toolbox 2.0
Access, visualize, and analyze geospatial data
Geospatial data access
Manipulation of map data
Map projections
2-D and 3-D map displays
Analysis functions
6
7. Geospatial Data Access
Standard file formats
–
Gridded terrain and bathymetry
–
ESRI shapefiles, Arc Grid ASCII,
GeoTIFF, TIFF/JPEG/PNG with
world file, SDTS raster profile,
HDF/HDF-EOS and more
USGS DEM, NIMA DTED,
GTOPO30, Smith and Sandwell
grid and more
Vector map products
–
VMAP0, DCW, TIGER, GSHHS
7
13. Distributed Computing with
MATLAB and Simulink
MATLAB Distributed
Computing Engine
Client Machine
Task
Result
CPU
Worker
Task
Job
Toolboxes
Distributed
Computing
Toolbox
Result
Result
Job
Manager
CPU
Worker
Task
Result
CPU
Worker
Task
Blocksets
Functionality:
• Create jobs
• Create tasks
• Pass data
• Retrieve results
Result
Functionality:
Queue jobs
Dynamically license workers
Evaluate tasks
CPU
Worker
13
14. Key Features
1.
Distributed execution of coarse-grained MATLAB and
Simulink applications on remote MATLAB sessions
2.
Access to single or multiple clusters by single or multiple
users
3.
Distributed processing on both homogeneous and
heterogeneous platforms
4.
Control of the distributed computing process via a functionbased or object-based interface
5.
Dynamic licensing
14
Editor's Notes
The MathWorks corporate headquarters are located in Natick, Massachusetts, just outside of Boston.
In the US, we have field personnel in Detroit to serve our automotive customers, and in California, Washington, and Texas serving customers in aerospace and defense.
The MathWorks also has offices throughout Europe, and in Korea.
From these locations, The MathWorks offers training and consulting throughout the world
Elsewhere, marked with the gray icons, The MathWorks is represented by distributors that represent and support our products in their regions.
Note: When appropriate, mention the capabilities of the local representative or one that’s important for the audience (such as Cybernet in Japan for the automotive market) and their close and long-term relationship with The MathWorks.
Background: The graphic shows topology (elevation) data, rendered in this map projection using MATLAB and the Mapping Toolbox.
Our company vision through the use of our tools, is to help engineers and researchers spend less time thinking
about the actual programming of their designs allowing them more time to accelerate innovation and creativity in their work
We have two flagship products that help with this
-MATLAB, flexible programming environment, similar to C
-Simulink, graphical user environment for modeling dynamic systems
Note to presenter: Use this slide to show that we have a number of toolboxes that extend the capabilities of MATLAB and a number of them are useful for Image Processing applications. You do not need to describe all of these toolboxes.
Image Acquisition Toolbox
Capture images and video from hardware, control devices within MATLAB
Database ToolboxExchange data with relational databases
Statistics Toolbox
Perform statistical analysis, like Principle Components Analysis and K-means clustering
Signal Processing Toolbox
Analyze one dimensional signals and create filtering kernels that can be used for image processing
Mapping Toolbox
Analyze geospatial data and place images on map displays using related coordinate data usually found with satellite image files.
MATLAB Compiler
Deploy components for larger C/C++ projects or deploy stand-alone desktop applications.
Image data has become a significant part of many applications in scientific fields and engineering activities. Images are captured on a variety of devices at different costs levels from space telescopes and medical imaging systems to webcams and inexpensive digital cameras. As image data becomes more available and useful, it will be involved in more scientific and engineering tasks.
The Image Processing Toolbox from the MathWorks provides a comprehensive set of reference-standard algorithms and graphical tools that will help you analyze, process, and visualize image data. Let’s a take a few minutes to explore the different areas of capabilities within the toolbox.
Note: “Geospatial” is a term that refers to any type of data that is referenced to the Earth. Examples are maps, satellite images, altimetry, topographic maps, and sea surface temperature data.
With the Mapping Toolbox and MATLAB, you have an ideal environment in which to perform original research and develop innovative analysis techniques. The Mapping Toolbox provides key functionality to access geospatial data, create 2-D and 3-D map displays, and perform geographic analysis. These capabilities enable you to use geospatial data in MATLAB and take advantage of its well-known capabilities for numerical computation, analysis, visualization, algorithm development and deployment.
Recently, we release the first major upgrade to the Mapping Toolbox, which offers improved capabilities in geospatial data access and visualization. The toolbox now supports a broader range of data types, including vector maps, georeferenced imagery and gridded data. In particular, many of you will find it useful that we now support ESRI shapefiles. In the area of visualization, we have completely revamped our map display functionality to incorporate the broader support of data types as well as a brand new interactive mapviewer.
Why this is important:
Within the past few years, there have been an increasing number of sources for such data. Numerous satellites and airborne systems have come online to generate terrabytes of data. This enormous base of geospatial data is used in a wide range of applications that are not considered “remote sensing” or “mapping.” For example, radar systems engineers are using terrain data to develop better ways of handling ground clutter. Reinsurance companies are using it to model tropical storm risk for asset loss. Earth Scientists are using it to model ocean circulation in the Gulf of Mexico. The toolbox can support a wide range of applications for geospatial data in fields such as defense, intelligence, and homeland security applications, as well as in oceanography, geophysics, and other earth and planetary sciences.
Pitch: You may not have thought of using geospatial data to help solve your problem, but if it is related to a specific place on Earth, it could probably help you. The Mapping Toolbox enables you to access this data and use it in the analysis and visualization environment of MATLAB to which you are accustomed.
As you can see here, we support a wide assortment of geospatial data types. This data falls into three basic categories. The first category is vector map data, which is supported by ESRI shapefiles, Arc ASCII Grid, and a number of well-known data products (I.e. proprietary formats for pre-assembled map data). The second category is georeferenced imagery, which is supported by GEoTIFF and TIFF, JPEG or PNG files with associated world files. The third category is gridded terrain data and bathymetry.
MATLAB itself supports several standard geospatial file formats: HDF, HDF5, HDF-EOS, CDF, FITS, and band-interleaved data (note: that last one will be interesting to folks)
In addition, the Mapping Toolbox provides built-in atlas and almanac data. This is useful when you need to build a base map to determine your area of interest. It is also useful when political boundaries or coastlines provide improved visualization to your geospatial data. As an example, take a look at the picture on this slide, which overlayed sea surface temperature data (source: MODIS) of the Red Sea on top of a political boundary map of Egypt, Israel, Saudi Arabia, etc.
For source data, go to www.matlabcentral.com and search on “hdf-eos”
So, how do we do distributed Computing with ML and SL??
The bottom line is that the distributed computing tools lets you coordinate and execute MATLAB operations on a cluster of computers.
A job is a large operation that you need to perform in your ML/SL session. A job is broken down into segments called tasks. You decide how the job is divided up into tasks. A typical job is usually divided into identical tasks, but this is not the only way to define tasks.
The MATLAB session in which the job and its tasks are defined is called the client session. Often, this is on the machine where you sit and program MATLAB.
IN ML…, in SL… (meaning of a job)
The job manager is the software that coordinates the execution of jobs and the evaluation of their tasks. The job manager distributes the tasks for evaluation to remote MATLAB sessions that run in the cluster nodes called workers.
The workers execute tasks by calling the function specified by a task, passing the appropriate input data to the function, and then producing a result. The result is then made available for retrieval.
Once all tasks for a running job have been assigned to workers, the job manager starts running the next job.
Multiple users can send jobs to the same job manager. Each worker is associated with only one job manager
This slide summarizes the key features of the distributed computing products products
Distributed execution of coarse-grained MATLAB and Simulink applications on remote MATLAB sessions
See slide #10
Access to single or multiple clusters by single or multiple users
See slide #11
Distributed processing on both homogeneous and heterogeneous platforms
See slide #12
Support for both synchronous and asynchronous operations
Once a user submits a job to a cluster, he/she can wait for the results of the distributed computation (synchronous operation) or can continue working in MATLAB or Simulink (asynchronous operations). While working in asynchronous mode, the user has full control of the toolboxes and blocksets licenses.