Why we need open data and how should we provide it. FMI provides the same data in its own open data portal but also in AWS public dataset program. Different use cases require different services and channels.
The Finnish Meteorological Institute (FMI) opened its data in 2013, making basically all of its data with property rights publicly available in machine-readable formats. This includes near real-time and historical weather and climate data. The data is distributed through FMI's Open Data Portal, which follows INSPIRE requirements, as well as on Amazon Web Services (AWS) cloud platform through Amazon S3 buckets as part of a two-year pilot project to increase access and use of weather data. The AWS buckets contain HIRLAM surface and pressure level weather model data for Europe.
F5 announced enhancements to its data solutions portfolio including support for cloud storage, a new storage management API, and a virtual edition of its ARX file virtualization product. These additions provide seamless integration with cloud storage, enable partners and customers to leverage F5's strategic points of control over data traffic, and extend the reach of F5's intelligence to new hybrid solutions. The enhancements help customers meet challenges of data growth and change by providing a more agile storage infrastructure.
Search Joins with the Web - ICDT2014 Invited LectureChris Bizer
The talk will discuss the concept of Search Joins. A Search Join is a join operation which extends a local table with additional attributes based on the large corpus of structured data that is published on the Web in various formats. The challenge for Search Joins is to decide which Web tables to join with the local table in order to deliver high-quality results. Search joins are useful in various application scenarios. They allow for example a local table about cities to be extended with an attribute containing the average temperature of each city for manual inspection. They also allow tables to be extended with large sets of additional attributes as a basis for data mining, for instance to identify factors that might explain why the inhabitants of one city claim to be happier than the inhabitants of another.
In the talk, Christian Bizer will draw a theoretical framework for Search Joins and will highlight how recent developments in the context of Linked Data, RDFa and Microdata publishing, public data repositories as well as crowd-sourcing integration knowledge contribute to the feasibility of Search Joins in an increasing number of topical domains.
The document discusses several projects related to open metadata and linked data including:
1. The AIM25 project which aggregates archive descriptions from 123 partners and aims to test the value of linked data.
2. The COMET project which is releasing a large subset of bibliographic records under an open license and working to convert them to linked open data.
3. The Jerome project which harvests and unifies data from several library systems, supplements it with open data, and provides fast search APIs.
The document describes an XCRI-CAP aggregator that collects course data from different providers into a unified repository. It indexes and updates changes in the core aggregation database. It includes APIs for third parties to embed or use the course data. The overall architecture includes a feed manager, discovery interface, REST APIs, and submission interface. The feed manager allows uploading and scheduling collection of XCRI-CAP documents. The discovery interface allows searching across feeds and viewing results. The REST APIs provide programmatic access to the course data. The submission interface allows directly pushing XCRI documents into the aggregator.
Conference: 15th International
Conference on Industrial Informatics
(INDIN2017). Emden, Germany – July
24-26, 2017
Title of the paper: Development of a
Mobile Application for the C2NET Supply
Chain Cloud–based Platform
Authors: Enbo Chen, Wael M.
Mohammed, Borja Ramis Ferrer, Jose L.
Martinez Lastra
If you would like to receive a reprint of
the original paper, please contact us
The document describes the XCRI-CAP Aggregator API and tooling. The aggregator collects course data from any provider into a unified repository by periodically pulling and indexing XCRI-CAP documents. It includes a feed manager for uploading documents, a discovery interface for searching, and APIs for third party use and direct submission. The overall architecture includes interfaces for managing feeds, searching harvested data, and directly accessing the underlying Elasticsearch index or triplestore. Future work includes expanding the APIs, improving search capabilities, and releasing client libraries and code.
The Finnish Meteorological Institute (FMI) opened its data in 2013, making basically all of its data with property rights publicly available in machine-readable formats. This includes near real-time and historical weather and climate data. The data is distributed through FMI's Open Data Portal, which follows INSPIRE requirements, as well as on Amazon Web Services (AWS) cloud platform through Amazon S3 buckets as part of a two-year pilot project to increase access and use of weather data. The AWS buckets contain HIRLAM surface and pressure level weather model data for Europe.
F5 announced enhancements to its data solutions portfolio including support for cloud storage, a new storage management API, and a virtual edition of its ARX file virtualization product. These additions provide seamless integration with cloud storage, enable partners and customers to leverage F5's strategic points of control over data traffic, and extend the reach of F5's intelligence to new hybrid solutions. The enhancements help customers meet challenges of data growth and change by providing a more agile storage infrastructure.
Search Joins with the Web - ICDT2014 Invited LectureChris Bizer
The talk will discuss the concept of Search Joins. A Search Join is a join operation which extends a local table with additional attributes based on the large corpus of structured data that is published on the Web in various formats. The challenge for Search Joins is to decide which Web tables to join with the local table in order to deliver high-quality results. Search joins are useful in various application scenarios. They allow for example a local table about cities to be extended with an attribute containing the average temperature of each city for manual inspection. They also allow tables to be extended with large sets of additional attributes as a basis for data mining, for instance to identify factors that might explain why the inhabitants of one city claim to be happier than the inhabitants of another.
In the talk, Christian Bizer will draw a theoretical framework for Search Joins and will highlight how recent developments in the context of Linked Data, RDFa and Microdata publishing, public data repositories as well as crowd-sourcing integration knowledge contribute to the feasibility of Search Joins in an increasing number of topical domains.
The document discusses several projects related to open metadata and linked data including:
1. The AIM25 project which aggregates archive descriptions from 123 partners and aims to test the value of linked data.
2. The COMET project which is releasing a large subset of bibliographic records under an open license and working to convert them to linked open data.
3. The Jerome project which harvests and unifies data from several library systems, supplements it with open data, and provides fast search APIs.
The document describes an XCRI-CAP aggregator that collects course data from different providers into a unified repository. It indexes and updates changes in the core aggregation database. It includes APIs for third parties to embed or use the course data. The overall architecture includes a feed manager, discovery interface, REST APIs, and submission interface. The feed manager allows uploading and scheduling collection of XCRI-CAP documents. The discovery interface allows searching across feeds and viewing results. The REST APIs provide programmatic access to the course data. The submission interface allows directly pushing XCRI documents into the aggregator.
Conference: 15th International
Conference on Industrial Informatics
(INDIN2017). Emden, Germany – July
24-26, 2017
Title of the paper: Development of a
Mobile Application for the C2NET Supply
Chain Cloud–based Platform
Authors: Enbo Chen, Wael M.
Mohammed, Borja Ramis Ferrer, Jose L.
Martinez Lastra
If you would like to receive a reprint of
the original paper, please contact us
The document describes the XCRI-CAP Aggregator API and tooling. The aggregator collects course data from any provider into a unified repository by periodically pulling and indexing XCRI-CAP documents. It includes a feed manager for uploading documents, a discovery interface for searching, and APIs for third party use and direct submission. The overall architecture includes interfaces for managing feeds, searching harvested data, and directly accessing the underlying Elasticsearch index or triplestore. Future work includes expanding the APIs, improving search capabilities, and releasing client libraries and code.
Why we need open data and how should we provide it. FMI provides the same data in its own open data portal but also in AWS public dataset program. Different use cases require different services and channels. Presentation kept in AWS pop-up loft in Stockholm 2018.
Neo4j is a native graph database platform company that provides graph databases, tools, and services. In this document:
- Neo4j announced new features for version 3.4 including 70% faster queries, native string indexes for 5x faster writes, and geospatial search.
- A new product, Neo4j Bloom, was introduced as a graph visualization and exploration tool targeting business users.
- Neo4j also discussed its Graph Query Language initiative to develop a standard query language for graphs across platforms and vendors.
ATMOSPHERE at HPC2018 – Fogbow: Middleware for the Federation of IaaS Cloud P...ATMOSPHERE .
ATMOSPHERE was invited to be a speaker at HPC2018 workshop. Francisco Brasileiro, Brazilian Coordinator of ATMOSPHERE and Professor at Federal University of Campina Grande, will present a talk on “Fogbow: A Middleware for the Federation of IaaS Cloud Providers”.
Francisco Brasileiro presented the design and implementation of a middleware that allows the fast and non-intrusive deployment of very large federations of IaaS cloud providers. The use of the middleware in production systems will be also discussed, providing concrete evidences of its suitability
WEBINAR: Open Research Data in Horizon 2020OpenAIRE
The document discusses a webinar about open research data in the Horizon 2020 program. It provides information on the European Commission's open research data pilot, including details on the flexible nature of the pilot, which areas are participating, and how projects can partially or fully opt-out. It also covers what a data management plan is, how to write one, and what it should include regarding FAIR data principles and making data findable, accessible, interoperable and reusable. Recommendations are provided on issues like metadata, file formats, storage, and where to deposit data for long-term preservation and access.
FMI Open Data on AWS Public dataset programRoope Tervo
The Finnish Meteorological Institute (FMI) has begun distributing some of its open data via Amazon Web Services (AWS) cloud platform. This includes weather data from the HIRLAM model covering Europe, as well as air quality data from the global SILAM model. FMI data on AWS is freely accessible through Amazon S3 buckets and includes near-real time and forecast data. Making FMI data available on AWS expands its potential audience and allows certain users like those needing full model datasets to more conveniently access and analyze the data.
The massive growth in road traffic and subsequence
generation of traffic related data insisting the researcher to
proceed for the analytical research on the traffic prediction.
However the gigantic size of the data and chances of storage
failure may cause the purpose inefficient.The advancement in
technologies and high demand for fault tolerant storage solutions
most of the cloud based commercial storage service providers
are now equipped with Erasure based Reed – Solomon fault
tolerance mechanism. However the additional cost for replication
is still an overhead for service providers and customers. In this
work, we propose a novel erasure based code and further
optimization as shortening the proposedcode also for the digital
storage formats. The work also results into a comparative study
of cost analysis for commercial cloud based storage service
providers. Finally the work demonstrates the improvement in
code shortening and making the performance higher.
InfoSphere Streams is an advanced computing platform that can quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources.
IT professionals are being asked to do more with less and highly skilled resources are in demand. As streaming applications play a growing role in critical applications so does the need for simplicity. InfoSphere Streams empowers IT users of all types and skill levels to have deeper insights into operations and performance. In today’s engaged world, a five minute delay means business goes elsewhere. A new administration console, a Java Management Extensions (JMX) management and monitoring application programming interface (API), simpler security and adoption of Apache Zookeeper are now available in InfoSphere Streams
OpenAIRE webinar. Open Research Data in H2020OpenAIRE
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This use case demonstrates how Airbus is using Tuleap after one year of deployment and on how Tuleap will be the masterpiece of Airbus’ DevOps strategy.
Data Acquisition Automation for NiFi in a Hybrid Cloud environment – the Path...DataWorks Summit
Liberty Global is one of the world’s largest international TV and broadband company, operating in multiple European countries, and with tens of millions of TV, broadband internet, telephony and mobile subscribers.
The Data Solutions team's journey started last year with a strategic project that aimed to implement a state of the art Hybrid Cloud Big Data platform. In this talk, the Manager and the Platform Architect are presenting the team’s data acquisition journey which begins with implementing NiFi flows with simple Get-Put pattern and, in its the final iteration, produces a solution capable of generating complex flows automatically, leading the path to the DataOps way of working.
Cloud computing security issues .what is cloud computing, cloud clients, disadvantages of clouds, security issues, value of data, threat model and solutions.
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This document discusses using IBM Spectrum Scale to provide a colder storage tier for Hadoop & Spark workloads using IBM Elastic Storage Server (ESS) and HDFS transparency. Some key points discussed include:
- Using Spectrum Scale to federate ESS with existing HDFS or Spectrum Scale filesystems, allowing data to be seamlessly accessed even if moved to the ESS tier.
- Extending HDFS across multiple HDFS and Spectrum Scale clusters without needing to move data using Spectrum Scale's HDFS transparency connector.
- Integrating ESS tier with Spectrum Protect for backup and Spectrum Archive for archiving to take advantage of their policy engines and automation.
- Examples of using the unified storage for analytics workflows, life
The HDF Group provides support for NPP/NPOESS in a number of ways, including development and maintenance of software capabilities in HDF5 libraries and tools that help NPP/NPOESS data producers and users, software testing on platforms of importance to NPP/NPOESS, high quality rapid response user support for NPP/NPOESS, and performance of special projects. The purposes of this presentation are to apprise attendees of the areas of emphasis for FY 2010, and to solicit ideas and opinions that will help the project understand how best to use its resources in order to best serve the needs of NPP/NPOESS.
End To End Machine Learning With Google Cloud Tu Pham
This document discusses end-to-end machine learning with Google Cloud. It outlines an 8-step process for collecting raw data, converting it to Apache Parquet files, uploading it to Cloud Storage, exploring it in Datalab, developing models in TensorFlow/Scikit-learn, training models at scale on Cloud ML Engine, deploying models via APIs on Compute Engine, and exposing APIs with Load Balancing. Key principles discussed are keeping it simple, avoiding repetition, and focusing on scalability, performance, and cost optimization. The presenter encourages planning systems with single responsibilities, separating real-time and batch flows, and saving on networking, instance, and storage costs through monitoring.
1 open power foundation_japan meetup - v1Yutaka Kawai
The document discusses the OpenPOWER Foundation, which aims to drive innovation through an open ecosystem approach. It summarizes that OpenPOWER has grown to include over 340 members across 35 countries working on innovations across all layers of the computing stack. Major goals include open source software, partner software, optimized libraries, and accelerator roadmaps to drive performance/$ and deployment via cloud models.
The ARCHIVER project aims to develop long-term data preservation and archiving services for scientific data in the petabyte range using commercial cloud services. It has a budget of 3.4M euro and runs from 2019 to 2022. The project will procure these services through an open tender process and integrate them into the European Open Science Cloud catalogue. It is seeking feedback on draft specifications from suppliers and will co-design test plans with suppliers to evaluate solutions across different scientific domains and deployment scenarios dealing with data volumes and ingest rates ranging from gigabytes to petabytes.
Finnish Meteorological Institute conducted the impact assesment of its open data. The survey was employed by Spatineo. FMI open data portal gets over 10 data requests each second and the open data have remarkable affect on Finnish society.
The document provides information about the Finnish Meteorological Institute (FMI) including:
- FMI has roughly 650 full-time employees split between research and operational services related to public safety, commercial, and other sectors.
- FMI's software development team consists of 40-50 developers working across several units, with all new development done as open source.
- In 2013, FMI began openly providing its data in machine-readable formats through an open data portal.
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Why we need open data and how should we provide it. FMI provides the same data in its own open data portal but also in AWS public dataset program. Different use cases require different services and channels. Presentation kept in AWS pop-up loft in Stockholm 2018.
Neo4j is a native graph database platform company that provides graph databases, tools, and services. In this document:
- Neo4j announced new features for version 3.4 including 70% faster queries, native string indexes for 5x faster writes, and geospatial search.
- A new product, Neo4j Bloom, was introduced as a graph visualization and exploration tool targeting business users.
- Neo4j also discussed its Graph Query Language initiative to develop a standard query language for graphs across platforms and vendors.
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The document discusses a webinar about open research data in the Horizon 2020 program. It provides information on the European Commission's open research data pilot, including details on the flexible nature of the pilot, which areas are participating, and how projects can partially or fully opt-out. It also covers what a data management plan is, how to write one, and what it should include regarding FAIR data principles and making data findable, accessible, interoperable and reusable. Recommendations are provided on issues like metadata, file formats, storage, and where to deposit data for long-term preservation and access.
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The Finnish Meteorological Institute (FMI) has begun distributing some of its open data via Amazon Web Services (AWS) cloud platform. This includes weather data from the HIRLAM model covering Europe, as well as air quality data from the global SILAM model. FMI data on AWS is freely accessible through Amazon S3 buckets and includes near-real time and forecast data. Making FMI data available on AWS expands its potential audience and allows certain users like those needing full model datasets to more conveniently access and analyze the data.
The massive growth in road traffic and subsequence
generation of traffic related data insisting the researcher to
proceed for the analytical research on the traffic prediction.
However the gigantic size of the data and chances of storage
failure may cause the purpose inefficient.The advancement in
technologies and high demand for fault tolerant storage solutions
most of the cloud based commercial storage service providers
are now equipped with Erasure based Reed – Solomon fault
tolerance mechanism. However the additional cost for replication
is still an overhead for service providers and customers. In this
work, we propose a novel erasure based code and further
optimization as shortening the proposedcode also for the digital
storage formats. The work also results into a comparative study
of cost analysis for commercial cloud based storage service
providers. Finally the work demonstrates the improvement in
code shortening and making the performance higher.
InfoSphere Streams is an advanced computing platform that can quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources.
IT professionals are being asked to do more with less and highly skilled resources are in demand. As streaming applications play a growing role in critical applications so does the need for simplicity. InfoSphere Streams empowers IT users of all types and skill levels to have deeper insights into operations and performance. In today’s engaged world, a five minute delay means business goes elsewhere. A new administration console, a Java Management Extensions (JMX) management and monitoring application programming interface (API), simpler security and adoption of Apache Zookeeper are now available in InfoSphere Streams
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This use case demonstrates how Airbus is using Tuleap after one year of deployment and on how Tuleap will be the masterpiece of Airbus’ DevOps strategy.
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Liberty Global is one of the world’s largest international TV and broadband company, operating in multiple European countries, and with tens of millions of TV, broadband internet, telephony and mobile subscribers.
The Data Solutions team's journey started last year with a strategic project that aimed to implement a state of the art Hybrid Cloud Big Data platform. In this talk, the Manager and the Platform Architect are presenting the team’s data acquisition journey which begins with implementing NiFi flows with simple Get-Put pattern and, in its the final iteration, produces a solution capable of generating complex flows automatically, leading the path to the DataOps way of working.
Cloud computing security issues .what is cloud computing, cloud clients, disadvantages of clouds, security issues, value of data, threat model and solutions.
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An overview of the first geospatial project for Intergraph UK that deployed a hosted service on the Amazon Cloud. The presentation will cover the business and IT considerations that underpinned the clients (Transport for London) decision to move from an internal IT provided system to SaaS hosted solution. It will share some of the lessons learnt from the project and an update on benefits realised by the client.
Cloud computing is shared pools of configurable computer system resources and higher-level services that can be rapidly provisioned with minimal management effort, often over the Internet. Cloud computing relies on sharing of resources to achieve coherence and economies of scale, similar to a public utility.
Hadoop and Spark Analytics over Better StorageSandeep Patil
This document discusses using IBM Spectrum Scale to provide a colder storage tier for Hadoop & Spark workloads using IBM Elastic Storage Server (ESS) and HDFS transparency. Some key points discussed include:
- Using Spectrum Scale to federate ESS with existing HDFS or Spectrum Scale filesystems, allowing data to be seamlessly accessed even if moved to the ESS tier.
- Extending HDFS across multiple HDFS and Spectrum Scale clusters without needing to move data using Spectrum Scale's HDFS transparency connector.
- Integrating ESS tier with Spectrum Protect for backup and Spectrum Archive for archiving to take advantage of their policy engines and automation.
- Examples of using the unified storage for analytics workflows, life
The HDF Group provides support for NPP/NPOESS in a number of ways, including development and maintenance of software capabilities in HDF5 libraries and tools that help NPP/NPOESS data producers and users, software testing on platforms of importance to NPP/NPOESS, high quality rapid response user support for NPP/NPOESS, and performance of special projects. The purposes of this presentation are to apprise attendees of the areas of emphasis for FY 2010, and to solicit ideas and opinions that will help the project understand how best to use its resources in order to best serve the needs of NPP/NPOESS.
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Why Mobile App Regression Testing is Critical for Sustained Success_ A Detail...kalichargn70th171
A dynamic process unfolds in the intricate realm of software development, dedicated to crafting and sustaining products that effortlessly address user needs. Amidst vital stages like market analysis and requirement assessments, the heart of software development lies in the meticulous creation and upkeep of source code. Code alterations are inherent, challenging code quality, particularly under stringent deadlines.
Graspan: A Big Data System for Big Code AnalysisAftab Hussain
We built a disk-based parallel graph system, Graspan, that uses a novel edge-pair centric computation model to compute dynamic transitive closures on very large program graphs.
We implement context-sensitive pointer/alias and dataflow analyses on Graspan. An evaluation of these analyses on large codebases such as Linux shows that their Graspan implementations scale to millions of lines of code and are much simpler than their original implementations.
These analyses were used to augment the existing checkers; these augmented checkers found 132 new NULL pointer bugs and 1308 unnecessary NULL tests in Linux 4.4.0-rc5, PostgreSQL 8.3.9, and Apache httpd 2.2.18.
- Accepted in ASPLOS ‘17, Xi’an, China.
- Featured in the tutorial, Systemized Program Analyses: A Big Data Perspective on Static Analysis Scalability, ASPLOS ‘17.
- Invited for presentation at SoCal PLS ‘16.
- Invited for poster presentation at PLDI SRC ‘16.
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
A Study of Variable-Role-based Feature Enrichment in Neural Models of Code
Why we need open data? FMI Open Data on AWS
1. Open data distributed on Amazon’s cloud service
Roope Tervo, Mikko Visa
Finnish Meteorological Institute
2. Motivation
The world is changing
A role of NMHS is also changing
Challenges:
Ensure authoritative voice in warnings
More efficiency in development and operations
Ensure the impact of produced information
9/4/2018
Open Source Software @ Finnish Meteorological
Institute | Roope Tervo, Harri Pietarila, Mikko
Rauhala
3
3. Motivation
Streamline collaboration with partners
• Open data and open source software can boost the development
• Open data lower usability barrier and thus increase data usage
• Collaboration is open and easy
• No long and burden negotiations – just evaluate, use, develop
• Using Open Source Software prevents from vendor locks
9/4/2018
Open Source Software @ Finnish Meteorological
Institute | Roope Tervo, Harri Pietarila, Mikko
Rauhala
4
ApplicationsObservations
Collaboration and OSS Proprietary software
4. Motivation
Increase appropriability of
weather and climate data
• Maximal coverage requires
several different channels
and services
• One organization can’t handle
them all
9/4/2018
Open Source Software @ Finnish Meteorological
Institute | Roope Tervo, Harri Pietarila, Mikko
Rauhala
5
5. Motivation
Support Research
• Open data and open source empowers research as
well
• Easy and open methods to access and analyze the data
• Methods are repeatable when anyone can access the tools
9/4/2018
Open Source Software @ Finnish Meteorological
Institute | Roope Tervo, Harri Pietarila, Mikko
Rauhala
6
6. • Finnish Meteorological Institute
opened its data in 2013.
• Basically everything that FMI has
property rights was opened.
• Both (near) real-time and historical
and climatological data.
• Data is provided in freely in machine
readable format.
9/4/2018 7
FMI Open Data
https://en.ilmatieteenlaitos.fi/open-data
Open data distributed on Amazon’s cloud service
7. FMI Open Data Portal follows INSPIRE requirements.
FMI Open Data
Data Portal
Meta data
Services
The very same data portal works as Open Data and
INSPIRE portal.
9/4/2018 8
ISO19115 WFS WMS
CSW
Grid Series
Observations
Time Series
Observations
Data
Models O&M
Simple
Feature
GRIB
NetCDF GeoTiff
Open data distributed on Amazon’s cloud service
8. FMI Open Data
Registration
o Registration is required to use View and Download
Services
o Working email address is the only mandatory
information
o After registration the user gets an API key which have to
be added into all requests
o GET parameter fmi-apikey=…&
o Header fmi-apikey; …
o Part of url http://wms.fmi.fi/fmi-apikey/…/wms?
o One can create several API keys with one email
9/4/2018 9Open data distributed on Amazon’s cloud service
9. And a little over
830 000 data
downloads
per day
(9,6 req/s)
At the moment
about 11 700
registered users
Some Experiences
9/4/2018 10Open data distributed on Amazon’s cloud service
10. Motivation
Increase appropriability of
weather and climate data
• Data is valuable only when it
reach relevant audience
• Maximal coverage requires
several different channels
and services
FMI joins Amazon’s Public
Datasets
9/4/2018
Open Source Software @ Finnish Meteorological
Institute | Roope Tervo, Harri Pietarila, Mikko
Rauhala
11
11. FMI OpenData on AWS
• FMI OpenData is also distributed on
Amazon Web Services (AWS) Cloud platform
• 2-years pilot
• Started in May 2017
• Hirlam surface and pressure levels in the first stage
• The objective is to
• increase the utility and effective use of weather and climate data
• support public-private-partnership
• Specially convenient for users who need the whole model data
• i.e for post-processing or generating map visualizations
• Licence: CC BY 4.0
9/4/2018 Open data distributed on Amazon’s cloud service 12
12. FMI OpenData on AWS
• Content
• Hirlam Surface
• Hirlam Pressure Levels
• Coverage: Europe
• Grid resolution: 7,5 km
• Updates: 4 times a day
• Time range: 54 hours (from model
run start)
• Time step: 1 hour
• Archive kept during the pilot
• Parameters:
http://en.ilmatieteenlaitos.fi/open-
data-on-aws-s3
9/4/2018 Open data distributed on Amazon’s cloud service 13
13. FMI OpenData on AWS
• Access through buckets:
• Surface data: fmi-opendata-rcrhirlam-surface-grib
• Pressure level data: fmi-opendata-rcrhirlam-pressure-grib
• Browse bucket content:
• http://fmi-opendata-rcrhirlam-surface-grib.s3-website-eu-west-
1.amazonaws.com/
• http://fmi-opendata-rcrhirlam-pressure-grib.s3-website-eu-west-
1.amazonaws.com/
• Public Amazon SNS topics are available for every new object added to
the Amazon S3:
• arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-rcrhirlam-surface-grib
• arn:aws:sns:eu-west-1:916174725480:new-fmi-opendata-rcrhirlam-pressure-
grib
9/4/2018 Open data distributed on Amazon’s cloud service 14
14. FMI OpenData on AWS
See documentation:
http://en.ilmatieteenlaitos.fi/open-data-on-aws-s3
9/4/2018 Open data distributed on Amazon’s cloud service 15
15. Requests by Data Format
in FMI Open Data Portal
9/4/2018 Open data distributed on Amazon’s cloud service 16
16. Hirlam Requests in binary
format by Channel
9/4/2018 Open data distributed on Amazon’s cloud service 17
17. Would mean
200 – 300 users who
fetch whole data
operatively
At the moment
6000 - 46000
downloads per day
Some Experiences
9/4/2018 18Open data distributed on Amazon’s cloud service
18. Point requests are
still by far the most
popular type
For binary data
downloaders S3 is
the most popular
channel
Some Experiences
9/4/2018 19Open data distributed on Amazon’s cloud service
Using Open Source Software prevents from vendor locks
Collaboration is open and easy
No long and burden negotiations – just evaluate, use, develop
If you need any changes or modifications, you can do them yourself or order them from 3rd party
Even just finding users to the software boost development
Open data helps, but..
Weather and climate data is complicated to handle
Huge volumes, complex formats, complicated domain
Providing tools to handle and analyze the data empowers 3rd party users to correctly utilize it
Proper tools ensures consistency between information regardless of channel and service providers
- Registration is not very convenient for users and is against open data principles but it provides us a useful information about the usage and makes it easier for us to prevent misusage of the portal
We have done well but this is not enough
weather forecast model
Covers europe
Updates 4 times a day
Two days ahead
Archive is provided
The pilot have been going on for 3 months now
How well has it succeed?
Before showing actual numbers I want to show some context
This chart show how requests are divided between different data models
FMI provides the same data in different formats and data models
Three columns on the right side of the graphs are point data requests and the invisible column on the left side is grid data requests (in binaray format)
So: most of the requests are point data requests
But there are still almost 40 thousand grid data requests from 259 different ip addresses during July
This chart shows how Hirlam weather forecast data is divided between FMI data portal and S3
The blue chart shows number of requests, based on that S3 is far more popular
But data in S3 is divided in a way that every parameter (like temperature or precipitation) is in separate files and from FMI data portal one can download everything with one request
If we assume that every S3 user downloads everything, we can compare a popularity of these channels /shown in red column)
and we can see that we get about 13 thousand requests more on S3 than on FMI data portal
Pressure levels 9900 req/day
Would mean 309 operative users.
Surface data 6000-46000 req/day
Mean 15 000 req/day
Would mean 220 operative users