PostGIS WKT Raster is an open source alternative to Oracle GeoRaster that allows raster data to be stored and analyzed in a PostgreSQL/PostGIS database. It uses the Well-Known Text (WKT) format to represent raster data as a single data type, while Oracle GeoRaster uses two related data types. PostGIS WKT Raster supports indexing and analysis of both raster and vector data, unlike Oracle GeoRaster which only allows analysis of raster data using vector extents.
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
This presentation will dive into a development team’s use case for choosing MongoDB as their spatially enabled NoSQL solution. The talk will also cover how the integration of GeoServer can expand the accessibility of your data. GeoServer is the open source implementation of Open Geospatial Consortium (OGC) standards and a core component of the Geospatial Web.
LocationTech is an Eclipse Foundation industry working group for location aware technologies. This presentation introduces LocationTech, looks at what it means for our industry and the participating projects.
Libraries: JTS Topology Suite is the rocket science of GIS providing an implementation of Geometry. Mobile Map Tools provides a C++ foundation that is translated into Java and Javascript for maps on iOS, Andriod and WebGL. GeoMesa is a distributed key/value store based on Accumulo. Spatial4j integrates with JTS to provide Geometry on curved surface.
Process: GeoTrellis real-time distributed processing used scala, akka and spark. GeoJinni mixes spatial data/indexing with Hadoop.
Applications: GEOFF offers OpenLayers 3 as a SWT component. GeoGit distributed revision control for feature data. GeoScipt brings spatial data to Groovy, JavaScript, Python and Scala. uDig offers an eclipse based desktop GIS solution.
Attend this presentation if want to know what LocationTech is about, are interested in these projects or curious about what projects will be next.
This presentation will dive into a development team’s use case for choosing MongoDB as their spatially enabled NoSQL solution. The talk will also cover how the integration of GeoServer can expand the accessibility of your data. GeoServer is the open source implementation of Open Geospatial Consortium (OGC) standards and a core component of the Geospatial Web.
Slides from my Introduction to PostGIS workshop at the FOSS4G conference in 2009. The material is available at http://revenant.ca/www/postgis/workshop/
Java Image Processing for Geospatial CommunityJody Garnett
The Java Advanced Imaging is a powerful Java image processing engine underlines our popular OSGeo open source projects - including GeoTools, GeoServer, GeoNetwork, and GeoNode, and more! Tragically there has been one problem with this, the JAI library is not open source!
The library originated at Sun Microsystem as a core component of the Java Runtime Environment, but was not included as part of OpenJDK collaboration.
This talk explores:
* Capabilities that make JAI attractive for GeoSpatial work
* How JAI has been used in our community
* The exciting JAI-EXT project by GeoSolutions
One of the reasons our community has been so addicted to this library is its power. It explored concepts like parallel processing, and distributed parallel processing in 1999, well ahead of the curve. It is an excellent example of engineering and software design.
Importantly we will cover the search for an open source alternative, and are the exciting progress in producing an open source alternative.
Come see how our this foundational library is being propelled into an open source future by our community.
Accumulo Collections is a lightweight library that dramatically simplifies development of fast NoSQL applications by encapsulating many powerful, distributed features of Accumulo in the familiar Java Collections interface. Accumulo is a giant sorted map with rich server-side functionality, and our AccumuloSortedMap is a robust java SortedMap implementation that is backed by an Accumulo table. It handles serialization and foreign keys, and provides extensive server-side features like entry timeout, aggregates, filtering, efficient one-to-many mapping, partitioning and sampling. Users can define custom server-side transformations and aggregates with Accumulo iterators.
More information on this project can be found on github at: https://github.com/isentropy/accumulo-collections/wiki
– Speaker –
Jonathan Wolff
Founder, Director of Engineering, Isentropy LLC
Jonathan is an ex-physicist who operates a consultancy specializing in big data and data science project work. He worked for Bloomberg last year and built their Accumulo File System, which was presented as 2015 Accumulo Summit's keynote speech. He's also done distributed computing project work for Yahoo! in Pig.
Jonathan holds a BA in Physics (Harvard, magna cum laude 2001) and an MS in Mechanical Engineering (Columbia, 2003), and has been avidly programming since the 1980's.
— More Information —
For more information see http://www.accumulosummit.com/
State of GeoServer provides an update on our community and reviews the new and noteworthy features for 2018. GeoServer is a web service for publishing your geospatial data. using industry standards for vector, raster and mapping.
We have an active community and a lot to cover for 2.12 and 2.13 release, as well what is cooking in September’s 2.14 release.
Each release provides exciting new features, this talk covers diverse improvements across GeoServer:
* OGC compliance work for WFS 2.0 and WMTS 1.0, WFS 3.0 support
* improvements for cloud deployments
* cascade WMTS services
* progress in NetCDF support
* getting ready for the Java 18.9 roadmap
* And much more…
Attend this talk for a cheerful update on what is happening with this popular OSGeo project. Whether you are an expert user, a developer, or simply curious what GeoServer can do for you.
Vector Tiles with GeoServer and OpenLayersJody Garnett
The latest release of GeoServer adds support for creating Vector Tiles in GeoJSON, TopoJSON, and MapBox Vector Tiles format through its WMS service for all the vector data formats it supports. These tiles can be cached using GeoWebCache (built into GeoServer), and served with the various tiling protocols (TMS, WMTS, and WMS-C). Thanks to very recent OpenLayers 3 development, these Vector Tiles can be easily and efficiently styled on a map.
This technical talk will look at how GeoServer makes Vector Tiles accessible through standard OGC services and how they differ from normal WMS and WFS usage. It will also look at how OpenLayers 3 - as a simple-to-use vector tiles client - interacts with GeoServer to retrieve tiles and effectively manage and style them. OpenLayer 3’s extensive style infrastructure will be investigated.
This presentation was given by Pavan Naik in Open Source India (OSI) 2014 even held in Nimans Convention Centre, Bangalore. It talks about GIS features in MySQL 5.7.
Presentation covers following topics :
1. Introduction to GIS
2. Common Terms and Concepts
3. What's new in MySQL 5.7
4. A Real World Example
5. What's next for MySQL GIS
GeoMesa on Apache Spark SQL with Anthony FoxDatabricks
GeoMesa is an open-source toolkit for processing and analyzing spatio-temporal data, such as IoT and sensor-produced observations, at scale. It provides a consistent API for querying and analyzing data on top of distributed databases (e.g. HBase, Accumulo, Bigtable, Cassandra) and messaging networks (e.g. Kafka) to handle batch analysis of historical archives of data and low-latency processing of data in-stream.
GeoMesa has deep integration with Spark SQL. It has added spatial types (e.g. Point, LineString, Polygons), spatial predicates (st_contains, st_intersects, etc.), and geometry processing functions (e.g. st_buffer, st_convexHull, etc.) to Spark SQL. It also optimizes the processing of these extensions by integrating with the Catalyst SQL optimizer to intercept SQL statements with spatial predicates and provision RDDs based on the underlying spatial index.
This session will describe the implementation of the GeoMesa Spark SQL integration, illustrate its application in production systems and demonstrate spatial aggregations and analytics using map-based visualizations.
Apache Spark - Key-Value RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sewz2m
This CloudxLab Key-Value RDD tutorial helps you to understand Key-Value RDD in detail. Below are the topics covered in this tutorial:
1) Spark Key-Value RDD
2) Creating Key-Value Pair RDDs
3) Transformations on Pair RDDs - reduceByKey(func)
4) Count Word Frequency in a File using Spark
GeoServer is the start of a great open source success story. This talk introduces the core GeoServer application and explores the ecosystem that has developed around this beloved OSGeo application.
This talks draws on the GeoServer ecosystem for use-cases and examples of how the application has been used successfully by a wide range of organizations.
Andrea Amie from GeoSolutions is on hand to share success stories highlighting GeoServer use in managing vulnerable ecosystems, agriculture information management, and marine data management.
Jody Garnett will look at how GeoServer enables Boundless products including Boundless Server and Boundless Server Enterprise.
We will look at GeoServer use at OSGeo with both GeoNetwork and GeoNode making use of the technology.
LocationTech is not ignored with the “big data” players in the form of GeoMesa and GeoWave bridging to cloud data sources of epic proportion
We use each use-case to highlights a capability of GeoServer providing an overview of the application drawn from practical examples.
Attend this talk for inspiration on what is possible with GeoServer and open source.
Slides for a talk given by Fede Fernández & Fran Pérez. It describes some common tips to improve a Kafka and Spark application, going through improving table joins, operational parameters as blockIntervalTime or number of partitions, serializations or how byKey operations work under the scenes.
Stockage, manipulation et analyse de données matricielles avec PostGIS RasterACSG Section Montréal
La plus importantes nouveautés de la base de données spatiale open source PostgreSQL/PostGIS 2.0 est le support pour les données raster. PostGIS Raster comprend un outil d’importation similaire à shp2pgsql basé sur GDAL et une série d’opérateurs SQL pour la manipulation et l'analyse des données matricielles. Le nouveau type RASTER est géoréférencé, multi-résolutions et multi-bandes et il supporte une valeur nulle (nodata) et un type de valeur de pixel par bande. PostGIS raster s’inspire de la simplicité de l’expérience vecteur offerte par PostGIS pour rendre toutes les opérations raster aussi simples que possible. Comme pour une couverture vecteur, une couverture raster est divisée en un ensemble d’enregistrements (une ligne = une tuile) stockés dans une seule table (contrairement à Oracle Spatial qui utilise deux types et donc deux tables ou plus). Il est possible d’importer une couverture complète et de la retuiler en une seule commande avec l’outil d’importation et de multiples résolutions de la même couverture peuvent être importées dans des tables adjacentes. Les propriétés des objets raster et de chacune des bandes peuvent être consultées et modifiées ainsi que les valeurs des pixels. Des fonctions existent pour obtenir le minimum, le maximum, la somme, la moyenne, la déviation standard, l’histogramme d’une tuile ou d’une couverture complète. Les fonctions ST_Intersection() et ST_Intersects() fonctionnent pratiquement de manière transparente entre des données raster et vecteur et une série de fonctions pour l’algèbre matricielle (ST_MapAlgebra()) permet de faire de l’analyse de type raster. Il est possible de reclasser les bandes et de les convertir en n’importe quel format d’écriture GDAL. Des fonctions pour générer des rasters et des bandes existent également pour du développement PL/pgSQL. Un driver GDAL pour convertir les couvertures raster en fichiers images est en développement et des plugins pour QGIS et svSIG existent déjà pour les visualiser.
Slides from my Introduction to PostGIS workshop at the FOSS4G conference in 2009. The material is available at http://revenant.ca/www/postgis/workshop/
Java Image Processing for Geospatial CommunityJody Garnett
The Java Advanced Imaging is a powerful Java image processing engine underlines our popular OSGeo open source projects - including GeoTools, GeoServer, GeoNetwork, and GeoNode, and more! Tragically there has been one problem with this, the JAI library is not open source!
The library originated at Sun Microsystem as a core component of the Java Runtime Environment, but was not included as part of OpenJDK collaboration.
This talk explores:
* Capabilities that make JAI attractive for GeoSpatial work
* How JAI has been used in our community
* The exciting JAI-EXT project by GeoSolutions
One of the reasons our community has been so addicted to this library is its power. It explored concepts like parallel processing, and distributed parallel processing in 1999, well ahead of the curve. It is an excellent example of engineering and software design.
Importantly we will cover the search for an open source alternative, and are the exciting progress in producing an open source alternative.
Come see how our this foundational library is being propelled into an open source future by our community.
Accumulo Collections is a lightweight library that dramatically simplifies development of fast NoSQL applications by encapsulating many powerful, distributed features of Accumulo in the familiar Java Collections interface. Accumulo is a giant sorted map with rich server-side functionality, and our AccumuloSortedMap is a robust java SortedMap implementation that is backed by an Accumulo table. It handles serialization and foreign keys, and provides extensive server-side features like entry timeout, aggregates, filtering, efficient one-to-many mapping, partitioning and sampling. Users can define custom server-side transformations and aggregates with Accumulo iterators.
More information on this project can be found on github at: https://github.com/isentropy/accumulo-collections/wiki
– Speaker –
Jonathan Wolff
Founder, Director of Engineering, Isentropy LLC
Jonathan is an ex-physicist who operates a consultancy specializing in big data and data science project work. He worked for Bloomberg last year and built their Accumulo File System, which was presented as 2015 Accumulo Summit's keynote speech. He's also done distributed computing project work for Yahoo! in Pig.
Jonathan holds a BA in Physics (Harvard, magna cum laude 2001) and an MS in Mechanical Engineering (Columbia, 2003), and has been avidly programming since the 1980's.
— More Information —
For more information see http://www.accumulosummit.com/
State of GeoServer provides an update on our community and reviews the new and noteworthy features for 2018. GeoServer is a web service for publishing your geospatial data. using industry standards for vector, raster and mapping.
We have an active community and a lot to cover for 2.12 and 2.13 release, as well what is cooking in September’s 2.14 release.
Each release provides exciting new features, this talk covers diverse improvements across GeoServer:
* OGC compliance work for WFS 2.0 and WMTS 1.0, WFS 3.0 support
* improvements for cloud deployments
* cascade WMTS services
* progress in NetCDF support
* getting ready for the Java 18.9 roadmap
* And much more…
Attend this talk for a cheerful update on what is happening with this popular OSGeo project. Whether you are an expert user, a developer, or simply curious what GeoServer can do for you.
Vector Tiles with GeoServer and OpenLayersJody Garnett
The latest release of GeoServer adds support for creating Vector Tiles in GeoJSON, TopoJSON, and MapBox Vector Tiles format through its WMS service for all the vector data formats it supports. These tiles can be cached using GeoWebCache (built into GeoServer), and served with the various tiling protocols (TMS, WMTS, and WMS-C). Thanks to very recent OpenLayers 3 development, these Vector Tiles can be easily and efficiently styled on a map.
This technical talk will look at how GeoServer makes Vector Tiles accessible through standard OGC services and how they differ from normal WMS and WFS usage. It will also look at how OpenLayers 3 - as a simple-to-use vector tiles client - interacts with GeoServer to retrieve tiles and effectively manage and style them. OpenLayer 3’s extensive style infrastructure will be investigated.
This presentation was given by Pavan Naik in Open Source India (OSI) 2014 even held in Nimans Convention Centre, Bangalore. It talks about GIS features in MySQL 5.7.
Presentation covers following topics :
1. Introduction to GIS
2. Common Terms and Concepts
3. What's new in MySQL 5.7
4. A Real World Example
5. What's next for MySQL GIS
GeoMesa on Apache Spark SQL with Anthony FoxDatabricks
GeoMesa is an open-source toolkit for processing and analyzing spatio-temporal data, such as IoT and sensor-produced observations, at scale. It provides a consistent API for querying and analyzing data on top of distributed databases (e.g. HBase, Accumulo, Bigtable, Cassandra) and messaging networks (e.g. Kafka) to handle batch analysis of historical archives of data and low-latency processing of data in-stream.
GeoMesa has deep integration with Spark SQL. It has added spatial types (e.g. Point, LineString, Polygons), spatial predicates (st_contains, st_intersects, etc.), and geometry processing functions (e.g. st_buffer, st_convexHull, etc.) to Spark SQL. It also optimizes the processing of these extensions by integrating with the Catalyst SQL optimizer to intercept SQL statements with spatial predicates and provision RDDs based on the underlying spatial index.
This session will describe the implementation of the GeoMesa Spark SQL integration, illustrate its application in production systems and demonstrate spatial aggregations and analytics using map-based visualizations.
Apache Spark - Key-Value RDD | Big Data Hadoop Spark Tutorial | CloudxLabCloudxLab
Big Data with Hadoop & Spark Training: http://bit.ly/2sewz2m
This CloudxLab Key-Value RDD tutorial helps you to understand Key-Value RDD in detail. Below are the topics covered in this tutorial:
1) Spark Key-Value RDD
2) Creating Key-Value Pair RDDs
3) Transformations on Pair RDDs - reduceByKey(func)
4) Count Word Frequency in a File using Spark
GeoServer is the start of a great open source success story. This talk introduces the core GeoServer application and explores the ecosystem that has developed around this beloved OSGeo application.
This talks draws on the GeoServer ecosystem for use-cases and examples of how the application has been used successfully by a wide range of organizations.
Andrea Amie from GeoSolutions is on hand to share success stories highlighting GeoServer use in managing vulnerable ecosystems, agriculture information management, and marine data management.
Jody Garnett will look at how GeoServer enables Boundless products including Boundless Server and Boundless Server Enterprise.
We will look at GeoServer use at OSGeo with both GeoNetwork and GeoNode making use of the technology.
LocationTech is not ignored with the “big data” players in the form of GeoMesa and GeoWave bridging to cloud data sources of epic proportion
We use each use-case to highlights a capability of GeoServer providing an overview of the application drawn from practical examples.
Attend this talk for inspiration on what is possible with GeoServer and open source.
Slides for a talk given by Fede Fernández & Fran Pérez. It describes some common tips to improve a Kafka and Spark application, going through improving table joins, operational parameters as blockIntervalTime or number of partitions, serializations or how byKey operations work under the scenes.
Stockage, manipulation et analyse de données matricielles avec PostGIS RasterACSG Section Montréal
La plus importantes nouveautés de la base de données spatiale open source PostgreSQL/PostGIS 2.0 est le support pour les données raster. PostGIS Raster comprend un outil d’importation similaire à shp2pgsql basé sur GDAL et une série d’opérateurs SQL pour la manipulation et l'analyse des données matricielles. Le nouveau type RASTER est géoréférencé, multi-résolutions et multi-bandes et il supporte une valeur nulle (nodata) et un type de valeur de pixel par bande. PostGIS raster s’inspire de la simplicité de l’expérience vecteur offerte par PostGIS pour rendre toutes les opérations raster aussi simples que possible. Comme pour une couverture vecteur, une couverture raster est divisée en un ensemble d’enregistrements (une ligne = une tuile) stockés dans une seule table (contrairement à Oracle Spatial qui utilise deux types et donc deux tables ou plus). Il est possible d’importer une couverture complète et de la retuiler en une seule commande avec l’outil d’importation et de multiples résolutions de la même couverture peuvent être importées dans des tables adjacentes. Les propriétés des objets raster et de chacune des bandes peuvent être consultées et modifiées ainsi que les valeurs des pixels. Des fonctions existent pour obtenir le minimum, le maximum, la somme, la moyenne, la déviation standard, l’histogramme d’une tuile ou d’une couverture complète. Les fonctions ST_Intersection() et ST_Intersects() fonctionnent pratiquement de manière transparente entre des données raster et vecteur et une série de fonctions pour l’algèbre matricielle (ST_MapAlgebra()) permet de faire de l’analyse de type raster. Il est possible de reclasser les bandes et de les convertir en n’importe quel format d’écriture GDAL. Des fonctions pour générer des rasters et des bandes existent également pour du développement PL/pgSQL. Un driver GDAL pour convertir les couvertures raster en fichiers images est en développement et des plugins pour QGIS et svSIG existent déjà pour les visualiser.
GeoServer, GeoNetwork and INSPIRE: where we are and what is missingGeoSolutions
GeoSolutions' presentation for the PTA Interreg 2012 wrkshop in La Salle, Aosta (ITALY) about GeoServer, GeoNetwork and the status for INSPIRE Compliancy
Postgres Vision 2018: PostGIS and Spatial ExtensionsEDB
The extensibility of PostgreSQL has enabled the combination of PostgreSQL and the geospatial extension PostGIS, creating what many say is the most powerful SQL/MM compliant database for location-based applications. This presentation, delivered at Postgres Vision 2018 by Regina Obe, Co-founder of Paragon Corporation and PostGIS Project Steering Committee member, and Leo Hsu, Co-founder of Paragon Corporation, examines PostgreSQL spatial extensions that work with PostGIS.
Thrid part of the Course "Java Open Source GIS Development - From the building blocks to extending an existing GIS application." held at the University of Potsdam in August 2011
Raster Data In GeoServer And GeoTools: Achievements, Issues And Future Develo...GeoSolutions
The purpose of this presentation is to discuss the developments during last years in raster data support in GeoTools and GeoServer, and also to introduce and discuss future development directions.
SF Big Analytics 20191112: How to performance-tune Spark applications in larg...Chester Chen
Uber developed an new Spark ingestion system, Marmaray, for data ingestion from various sources. It’s designed to ingest billions of Kafka messages every 30 minutes. The amount of data handled by the pipeline is of the order hundreds of TBs. Omar details how to tackle such scale and insights into the optimizations techniques. Some key highlights are how to understand bottlenecks in Spark applications, to cache or not to cache your Spark DAG to avoid rereading your input data, how to effectively use accumulators to avoid unnecessary Spark actions, how to inspect your heap and nonheap memory usage across hundreds of executors, how you can change the layout of data to save long-term storage cost, how to effectively use serializers and compression to save network and disk traffic, and how to reduce amortize the cost of your application by multiplexing your jobs, different techniques for reducing memory footprint, runtime, and on-disk usage. CGI was able to significantly (~10%–40%) reduce memory footprint, runtime, and disk usage.
Speaker: Omkar Joshi (Uber)
Omkar Joshi is a senior software engineer on Uber’s Hadoop platform team, where he’s architecting Marmaray. Previously, he led object store and NFS solutions at Hedvig and was an initial contributor to Hadoop’s YARN scheduler.
Getting Started with Geospatial Data in MongoDBMongoDB
MongoDB supports geospatial data and specialized indexes that make building location-aware applications easy and scalable.
In this session, you will learn the fundamentals of working with geospatial data in MongoDB. We will explore how to store and index geospatial data and best practices for using geospatial query operators and methods. By the end of this session, you should be able to implement basic geolocation functionality in an application.
In this webinar, you will learn:
- Getting geospatial data into MongoDB and how to build geospatial indexes.
- The fundamentals of MongoDB's geospatial query operators and how to design queries that meet the needs of your application.
- Advanced geospatial capabilities with Java geospatial libraries and MongoDB.
mago3D: Let's integrate BIM and 3D GIS on top of FOSS4GSANGHEE SHIN
This presentation was given at FOSS4G Europe 2017 which was held at ENSG, France.
***
Let's integrate BIM and 3D GIS on top of FOSS4G!
Sanghee Shin, Sungdo Son, BJ Jang, JeongDae Cheon, Hakjoon Kim
Although there has been numerous attempts to integrate indoor and outdoor space on a single geospatial platform, the outcome of these attempts are not so satisfactory till to date. Difference of data model, massive number of data to be rendered, big volume of file size are among those major technical barriers that hindered seamless integration of indoor and outdoor space. This talk will introduce a brand-new FOSS4G project called Mago3D that could seamlessly integrate BIM(Building Information Model) and popular 3D GIS model in web browser using Cesium or Web World Wind. Mago3D project aims at developing a JavaScript plug-in for existing WebGL Globe to expand WebGL Globe's functionality and usability even to indoor space and architectural(BIM) areas. To do this, Mago3D.js has been designed and developed as a WebGL independent JavaScript. Mago3D.js is composed of 6 main components like follows:
Mago3D Connector that interacts with WebGL Globe such as Cesium
Mago3D Renderer that shades and renders 3D data
Mago3D Accelerator that carries out performance enhancing algorithms such frustum & occlusion culling, indexing, LOD(Level Of Detail) handing
Mago3D Data Container that contains and manages 3D data
Mago3D Process Manager that manages whole process from data receiving to rendering
Mago3D REST API that provides API for 3D data sending and receiving
By plug in Mago3D.js to Cesium, Web World Wind, users can expand those WebGL functionality and usability to indoor space. One of big hurdle to integrate indoor and outdoor space simultaneously is handling and visualisation of massive 3D data. To overcome this hurdle, new format called F4D has been devised adopting block reference concept. Also a format converter that converts popular 3D format to F4D has been developed. Currently industry standard IFC(Industry Foundation Classes), JT(Jupiter Tessellation), and popular 3D formats such as OBJ, 3DS, COLLADA DAE can be converted to F4D format. F4D format coupled with Mago3D.js has proven that it can increase memory management efficiency and rendering speed drastically. MAGO3D can now visualise massive 3D data including indoor objects, at least 100k objects, in a single scene seamlessly with traditional outdoor 3D GIS objects. Users can now manage and handle almost every geospatial object from bolt & nut level to whole globe level with MAGO3D. This project will evolve to manage and service more dynamic data such as IoT (Internet of Things), point clouds, climate & weather data, and transportation..
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
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During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
3. Oracle GeoRaster roadmap 10gR1 (2003) First version Interleaving Georeferencing Pyramids Raster loader, viewer and exporter 10gR2 (2005) Raster compression/decompression GeoRaster objects in other schemas Enhaced GeoRaster tools 11gR1 (2007) Automatic DML trigger creation SDO_GEOR_ADMIN Bitmap masks NODATA ranges Empty raster blocks Random blocking size New functions, procedures and other features 11gR2 (2009) Java API GCP Support Raster reprojections Optimized blocking Grid interpolations Polygon-based clipping in queries New functions, procedures and other features
7. Main characteristics: Indexing Oracle GeoRaster: Creates a spatial index (R-Tree index) on the spatial extent of the GeoRaster object. PostGIS WKT Raster: Creates a GiST index on the raster column, using convex hull .
8. Main characteristics: Pyramids Oracle GeoRaster: Reduced-resolution versions of rasters can be generated using 5 resampling techniques. The pyramids are stored in the same raster data table as the GeoRaster object, with the same SRS than the original one. PostGIS WKT Raster: GDAL-provided pyramids are generated on loading time at desired levels. The pyramids are stored in different tables than the original raster.
9. Main characteristics: Metadata Oracle GeoRaster: Metadata are part of the SDO_GEORASTER object, and follow a XML schema. PostGIS WKT Raster: The metadata is packed with the raster data, like the georeference information. Only basic metadata is stored (upper left corner, width, height, pixel size, skew, srid and numbands)
10. Main characteristics: Open specs Oracle GeoRaster : The specs for SDO_GEORASTER and SDO_RASTER objects are open. This is really important, to allow third party tools to provide capabilities not implemented in the server, like spatial analysis . PostGIS WKT Raster : Uses WKT/WKB format for representing data. Is a open specification too.
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12. Java loader. Few formats accepted (TIFF, GeoTIFF, JPEG, BMP, GIF, PNG and JP2 for images. ESRI World Files, GeoTIFF and Digital Globe RPC files for georef)
30. Simple example with Oracle GeoRaster (I) Example: Compute pixel value statistics on areas delimited by vector polygons (http://gis4free.wordpress.com/2010/09/01/oracle-georaster-part-ii/). Step 1: Load vector data (points distribution). Oracle Spatial only accept SDO format for input geometry data. We have to convert our shapefiles to SDO format using sdo2shp Tools used: sdo2shp + sqlplus + sqlldr (possible to use only ogr2ogr )
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32. Simple example with Oracle GeoRaster (III) Step 3: Create buffers around points Tools used: PL/SQL API create table cariboupoint_buffers_wgs AS select t.id, sdo_geom.sdo_mbr(sdo_geom.sdo_buffer(sdo_cs.transform(t.geom, 4326), 1000, 1)) geom from cariboupoints t;
33. Simple example with Oracle GeoRaster (IV) Step 4: Create indexes Tools used: PL/SQL API First, we must update metadata DELETE FROM user_sdo_geom_metadata WHERE table_name = 'spain_images' AND column_name = 'IMAGE.SPATIALEXTENT'; INSERT INTO user_sdo_geom_metadata VALUES ('spain_images', 'IMAGE.SPATIALEXTENT', SDO_DIM_ARRAY(SDO_DIM_ELEMENT('X', -180, 180, .00000005), SDO_DIM_ELEMENT('Y', -90, 90, .00000005)), 4326); Now, create the index DROP INDEX spain_images_sidx; CREATE INDEX spain_images_sidx ON spain_images(image.spatialExtent) INDEXTYPE IS mdsys.spatial_index; Same operation with vector buffers DELETE FROM user_sdo_geom_metadata WHERE table_name = 'cariboupoint_buffers_wgs' AND column_name = 'geom'; INSERT INTO user_sdo_geom_metadata VALUES ('cariboupoints_buffers_wgs', 'geom', SDO_DIM_ARRAY(SDO_DIM_ELEMENT('X', -180, 180, .00000005), SDO_DIM_ELEMENT('Y', -90, 90, .00000005)), 4326); DROP INDEX spain_images_sidx; CREATE INDEX cariboupoints_buffers_wgs_gidx ON cariboupoints_buffers_wgs(geom) INDEXTYPE IS mdsys.spatial_index;
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35. We compute raster statistics by SDO_GEOR.generateStatistics , using as sampling window the intersecting buffers.
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37. The intersection process was really fast, because we don't intersect vector with real data, but with MBR of the data. Why? Because Oracle GeoRaster was created primarily for raster data storage, not for raster data analysis.
38. Simple example with PostGIS WKT Raster (I) The same example ( http://trac.osgeo.org/postgis/wiki/WKTRasterTutorial01 ) Step 1: Load vector data (points distribution). PostGIS only accept shapefiles as input data. We use them Tools used: shp2pgsql (possible to use ogr2ogr )
39. Simple example with PostGIS WKT Raster (II) Step 2: Load raster data Tools used: gdal2wktraster, psql Step 3: Create buffers around points Tools used: PgSQL API Note: The buffers are round, not rectangular. This is because Oracle GeoRaster only accepts rectangular sampling windows. But now, it's not necessary.
40. Simple example with PostGIS WKT Raster (III) Step 4: Create indexes Not needed! Created when loading data. Step 5: Compute statistics. The mean elevation of the raster in areas intersected by vector buffers. Tools used: pgSQL API Time: About 10 mins. Note: We really intersect raster data with vector data. And the raster data is first polygonized to be intersected with buffers.
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42. Evaluation Matrix Requirements Oracle GeoRaster PostGIS WKT Raster Specific Data Type SDO_GEORASTER WKT Raster Multidimensional Support Up to 3 Up to 3 Georeferencing Fullfilled Fullfilled Image pyramids Fullfilled Fullfilled Partitions Only regular Only regular Raster compression Fullfilled Fullfilled Scan order Not Fullfilled Not fullfilled Analysis capability Not fullfilled Fullfilled (+ r&v) Slicing Only get 1 layer Only get 1 layer, planned Subsetting Fullfilled Not Fullfilled (planned) Content-based search Using vector MBR Partially (topological planned) Spatial Indexing Fullfilled (over MBR) Fullfilled (over cells) Open specification Fullfilled Fullfilled
43. Credits Screenshots & tutorial: Pierre Racine Evaluation Matrix: Damon Riga Noktula (“ Server-based Raster Operations for Spatio-temporal Application in Raster Database using Oracle GeoRaster ”), based on Peter Bauman's & others criteria.
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Editor's Notes
El formato de las diapositivas a utilizar para la exposición es flexible, siempre que se siga la plantilla proporcionada. El tiempo de exposición por tarea debe ser, como máximo, de 15 minutos, incluyendo los tiempos de cambio de ponente entre tarea. Para los AEI, el tiempo disponible será de 20 minutos como máximo. Es importante no excederse.