This document discusses how FME (Feature Manipulation Engine) software has been used by a company to manage spatial data and solve problems related to mapping energy generation locations. It describes how FME was used to georeference non-spatial data from a System Operator to map millions of records for medium generation and electric vehicle connections at a more granular level. This improved the System Operator's view of the data and enabled more accurate and efficient decision making.
Trees and Airplanes don't play nicely together. Many airports face the constant struggle of ensuring that obstacles, like trees, do not threaten the safe operation of arriving and departing aircraft. How does YYJ, located on the west coast of Canada, in area known for its especially large trees, deal with this issue? LiDAR is often used to capture the height of obstacles around the airport, but what do you do with all that data?
This presentation will show how FME is used by YYJ to manipulate the LiDAR data, develop cost estimates for removal and develop layers for an ArcGIS Online management tool.
FME Around the World (FME Trek Part 1): Ken Bragg - Safe Software FME World T...IMGS
Aim: "To seek out innovative FME users
throughout the galaxy, sharing
their stories and ideas to inspire
you to take your data where no
data has gone before."
Streamlining Processes Towards High-Quality CartographySafe Software
Cartography is both the science and the art of visually representing geographical data. It promotes a better understanding of our data, and ultimately, data-driven business decisions.
GIS tools produce excellent maps. But taking your cartography to the next level requires a vector graphics editor - even if it’s just for a few finishing touches - and that can be a tedious, manual effort. Using FME and Avenza as part of the process, you can eliminate the struggles of high-quality map creation. You’ll save many hours of work, leaving you more time for the fun, creative aspects of your cartography.
Join us and special guest speakers Hans van der Maarel from Red Geographics and Nick Burchell from Avenza to learn how FME shines as part of a high-end map production workflow. During the hour, we’ll walk you through:
- An overview of MAPublisher’s extensive cartographic capabilities
- An overview of FME and its basic cartographic capabilities, including the Illustrator format writer
- How Avenza’s MAPublisher integrates FME with Adobe Illustrator directly
- The power of automation to achieve faster map production
Don’t miss out on the chance to learn how you can make your maps with more precision, speed, and style than ever. See you there!
Trees and Airplanes don't play nicely together. Many airports face the constant struggle of ensuring that obstacles, like trees, do not threaten the safe operation of arriving and departing aircraft. How does YYJ, located on the west coast of Canada, in area known for its especially large trees, deal with this issue? LiDAR is often used to capture the height of obstacles around the airport, but what do you do with all that data?
This presentation will show how FME is used by YYJ to manipulate the LiDAR data, develop cost estimates for removal and develop layers for an ArcGIS Online management tool.
FME Around the World (FME Trek Part 1): Ken Bragg - Safe Software FME World T...IMGS
Aim: "To seek out innovative FME users
throughout the galaxy, sharing
their stories and ideas to inspire
you to take your data where no
data has gone before."
Streamlining Processes Towards High-Quality CartographySafe Software
Cartography is both the science and the art of visually representing geographical data. It promotes a better understanding of our data, and ultimately, data-driven business decisions.
GIS tools produce excellent maps. But taking your cartography to the next level requires a vector graphics editor - even if it’s just for a few finishing touches - and that can be a tedious, manual effort. Using FME and Avenza as part of the process, you can eliminate the struggles of high-quality map creation. You’ll save many hours of work, leaving you more time for the fun, creative aspects of your cartography.
Join us and special guest speakers Hans van der Maarel from Red Geographics and Nick Burchell from Avenza to learn how FME shines as part of a high-end map production workflow. During the hour, we’ll walk you through:
- An overview of MAPublisher’s extensive cartographic capabilities
- An overview of FME and its basic cartographic capabilities, including the Illustrator format writer
- How Avenza’s MAPublisher integrates FME with Adobe Illustrator directly
- The power of automation to achieve faster map production
Don’t miss out on the chance to learn how you can make your maps with more precision, speed, and style than ever. See you there!
FME, The Tool to Use When Standing Up a New Fiber UtilitySafe Software
We will cover the use of FME technology to interface with survey data in judging pre-enrollment of prospective fiber utility clients, logging and acknowledging enrollment and translation to marketing efforts for a complete picture of take rate. We will also show how we use FME to translate data to our very complex fiber map schema for use in inspection, construction of the utility, as-built's and final homes passed records of clients after they take service. Overall, FME has been integral in standing up our new utility from start-up to construction, as-built changes all the way to billing our customers. We have multiple FME workbenches as well as FME server processes to show.
Common Uses:
CAD <-> GIS
Format and Data Model Conversion
Data Migration
Data Reprojection
Data Homogenization and Integration
Quality Assurance, Validation, and Cleaning
Data Mining
Extract, Transform and Load
Join us on a tour of the FME Platform, with live demos that will showcase the latest and greatest features. First, we’ll build an app that connects to an API and helps us make intelligent choices based on that data. Next, we’ll build an app that uses machine learning tools to analyze images and make decisions based on what the images contain. Get tips and tricks from our experts and see what’s new in FME 2020.
FME Server - In the Cloud and on the Ground!Safe Software
A look at how FME Server in the cloud was used by Sterling Geo to deliver a flexible and efficient solution for a time-sensitive inspection project in the energy sector. FME Cloud was integral in data management, billing and data delivery.
FME, The Tool to Use When Standing Up a New Fiber UtilitySafe Software
We will cover the use of FME technology to interface with survey data in judging pre-enrollment of prospective fiber utility clients, logging and acknowledging enrollment and translation to marketing efforts for a complete picture of take rate. We will also show how we use FME to translate data to our very complex fiber map schema for use in inspection, construction of the utility, as-built's and final homes passed records of clients after they take service. Overall, FME has been integral in standing up our new utility from start-up to construction, as-built changes all the way to billing our customers. We have multiple FME workbenches as well as FME server processes to show.
Common Uses:
CAD <-> GIS
Format and Data Model Conversion
Data Migration
Data Reprojection
Data Homogenization and Integration
Quality Assurance, Validation, and Cleaning
Data Mining
Extract, Transform and Load
Join us on a tour of the FME Platform, with live demos that will showcase the latest and greatest features. First, we’ll build an app that connects to an API and helps us make intelligent choices based on that data. Next, we’ll build an app that uses machine learning tools to analyze images and make decisions based on what the images contain. Get tips and tricks from our experts and see what’s new in FME 2020.
FME Server - In the Cloud and on the Ground!Safe Software
A look at how FME Server in the cloud was used by Sterling Geo to deliver a flexible and efficient solution for a time-sensitive inspection project in the energy sector. FME Cloud was integral in data management, billing and data delivery.
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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.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
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.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Mapping Future Energy
Adrian Moisey
3. 3
Why FME?
In 2008 moved from OS LandLine to OS MasterMap
FME Desktop used to manage OS MasterMap updates.
Quickly discovered how powerful FME was.
Rolled out FME Server in 2013
5. 5
What else have we used FME for?
Merging spatial and non-spatial data.
Spatially representing priority work on the Network.
Generating Tiff images from OS OnDemand.
Handling file updates received from Ordnance Survey.
Running queries and generating outputs for the
business.
7. 7
What was the problem?
Millions of records for medium generation and EV
connections.
Manually associated with a general location.
Had to use A0 map of the UK.
Data was not granular enough for customers.
8. 8
How did we solve the problem?
SO had been working with Oxford University to
generate the operation zones.
Available data was not spatial.
Using FME the data was georefernced.
13. 13
How has this improved things
SO now have a more accurate view
Much more detailed information.
Improved decision making.
More efficient process.
I am Adrian Moisey, I am the GIS Officer for National Grid Electric Transmission, I have been working for NG now for 20 years and have been working with GIS systems for the last 10 years and FME for the last 7 years.
In 2008 we moved from using the old OS LandLine product to OS MasterMap, as part of that transition we were given an FME workbench by Intergraph that managed part of the process for loading the data and change only updates into our Oracle database. Shortly after starting to use FME for this we discovered it was capable of doing much more than just helping us load OSMM data and could be a very powerful tool to the team, allowing us to automate tasks we undertook on a daily basis, as well allowing us to preform queries for the business in a quick and efficient manner. In 2013 we put together a business case to purchase FME Server, this has enabled us to share the power of FME to the wider business without the necessity of training people how to use FME Desktop, using FME server has improved efficiency within the GIS team and has also allowed the business to self-server data without having to come to our team for it.
Besides the original reason for purchasing FME it is now one of our key tools within the team. Some of the things we have used it for are merging spatial and non-spatial data sets together (something I will talk about in a bit more detail later on).
Spatially representing priority work on our Electricity Transmission network, we are currently trying to increase the throughput of work on the network and being able to see the work on a map has allowed the business to make better decisions on what work can be grouped together to improve efficiency.
We use FME to generate Tiff images using OS OnDemand, when we moved to Vector Map Local OS gave us a workbench to rasterise the VML data, however it wasn’t perfect and performance was not great so I looked at using FME to do the job for us.
We also use FME for handling file updates received from OS, this includes things like copying the files and moving them to the desired location as well as updating the data in our Oracle database.
We also use FME for creating queries and output for the business, if it is something the business wants to perform on a regular basis then we also make this available on FME Server so they can self-serve the data without coming to us every time the need it updating.
SO had millions of records for Solar PV (on peoples roofs), Electric Vehicle charging points as well as wind and solar farm locations.
These would be assigned a general location using a very adhoc method based on a number of assumptions, for instance the residential model they used was based on wealth and house size. Prior to this work they had to use an A0 map of the UK to get a general idea of the location.
The data was not granular enough for their customers needs, the customers needed the data at a higher level of detail down to smaller operation zones, this just was not available to business.
SO had been working with Oxford University to generate the smaller operational zones required, this work had come to an end and needed to be taken over by ourselves.
The data supplied for the locations of Solar PV/wind farms etc was all spreadsheet based and contained no coordinates for us to easily georeference against, the only data supplied we could use was a post code or partial post code, we therefore used this to create a spatial reference for each row of data.
I used FME to match the data provided to a postcode and then generated a spatial data set from this, through running through several iterations and scenarios in the workbench I was able to spatially reference most of the data.
The resulting data sets were then output to a spreadsheet for easy reference outside the GIS and also made available to users within the business via our GIS system.
This is one of the workbenches I created to spatially reference the data sets SO provided me, you can see a number of scenarios being stepped through to attempt to match as much of the data as possible. I could possibly have created a custom transformer in here to do the matching, maybe something I can do to improve the workbench in the future.
Here is an example of the outputs I generated for SO. The blue polygons you can see are the smaller operation zones they needed to map the data against, this is the level of detail their customers required but they could not provide previously. The other data shown here is the location of plugin hybrid car charging points, this was provided as a text file from the DVLA.
Here we can see where the solar PV installations are around the country, the results of creating this data set were actually a surprise for SO, they were expecting the locations to be more dispersed around the country, where as the data visualised on a map shows it is more concentrated around the south of the country.
This slide shows us the locations of Hydro generation, again the data set when visualized against a map surprised SO, they had based their models on them being located more in the north of the country as opposed to being more widely dispersed as this shows.
SO now have a much more accurate view of where these features are located, as I mentioned previously, instances of solar installations are heavily concentrated towards the south of the country than their models had indicated and the hydro generations is much more dispersed than they thought.
The information they hold on these is now much more detailed, the locations of these features allows the business to make much better informed decisions and has helped improve their processes.