This document provides an overview of the major components of the USGS ScienceBase including:
- Data file inputs that can be ingested like shapefiles, NetCDF, GeoTIFFs, and other files.
- Primary output interfaces for accessing the data like HTML, JSON, ISO metadata standards and CSV.
- The ScienceBase technical stack using technologies like GeoServer, GeoTools, ArcGIS Server, and THREDDS for storing, serving and indexing the data.
- Interfaces for ingesting and accessing the data through APIs, OGC standards, and direct downloads.
- Downstream uses of the data and services through tools like Python, R, Drupal and ArcGIS Desktop.
This document provides an overview and agenda for a presentation on Google BigQuery. It includes sections on BigQuery internals and architecture, use cases, and demos. The presentation will cover an overview of Google Cloud Platform and BigQuery, their internals including storage, execution engines, and compute resources, common use cases, and end with a demo in Google Cloud Platform console and BigQuery. It is presented by an expert in BI, analytics, and cloud platforms.
Big Data with hadoop, Spark and BigQuery (Google cloud next Extended 2017 Kar...Imam Raza
Google Next Extended (https://cloudnext.withgoogle.com/) is an annual Google event focusing on Google cloud technologies. This presentation is from tech talk held in Google Next Extended 2017 Karachi event
This document provides an overview of BigQuery and how to get started with it. BigQuery is a fully managed data warehouse offered by Google Cloud Platform. It offers a serverless, fast, and scalable solution for data analysis. The document discusses what BigQuery is, its key features and concepts like datasets, tables, and billing. It also covers best practices for query performance and cost optimization. The presenter provides their contact details and links to their website and YouTube channel for additional resources.
FIWARE Global Summit - QuantumLeap: Time-series and Geographic QueriesFIWARE
This document describes QuantumLeap, an open source software that stores and queries spatial-temporal IoT data from NGSI entities. It converts NGSI entities to a tabular format and stores them in time series and geo-spatial databases for efficient querying over space and time. QuantumLeap can be easily deployed using Docker containers on platforms like Kubernetes and supports multiple database backends. It provides a REST API and Grafana integration for querying and visualizing IoT data.
PhD Projects in CoAP Research GuidancePhD Services
The document discusses potential PhD project topics in CoAP (Constrained Application Protocol). It lists several topics for CoAP projects, including IoT smart cities, dynamic resource caching, and traffic load balancing between brokers. It also discusses current subject areas like wireless hart & Zigbee long-range WPAN, security in CoAP body area networks, and IoT applications in intelligent transportation systems and low powered wireless sensor networks. The document provides contact information for an organization that offers research assistance for PhD and MS scholars.
This document lists 2016 IEEE projects related to cloud computing and data mining/knowledge engineering. It includes 15 projects on secure and efficient cloud computing technologies such as keyword search, data sharing, auditing, and access control schemes. It also includes 8 projects on location-aware keyword queries, sentiment analysis, spatial keyword search, privacy-preserving location services, and approximate nearest neighbor queries over graph databases.
This document provides an overview of the major components of the USGS ScienceBase including:
- Data file inputs that can be ingested like shapefiles, NetCDF, GeoTIFFs, and other files.
- Primary output interfaces for accessing the data like HTML, JSON, ISO metadata standards and CSV.
- The ScienceBase technical stack using technologies like GeoServer, GeoTools, ArcGIS Server, and THREDDS for storing, serving and indexing the data.
- Interfaces for ingesting and accessing the data through APIs, OGC standards, and direct downloads.
- Downstream uses of the data and services through tools like Python, R, Drupal and ArcGIS Desktop.
This document provides an overview and agenda for a presentation on Google BigQuery. It includes sections on BigQuery internals and architecture, use cases, and demos. The presentation will cover an overview of Google Cloud Platform and BigQuery, their internals including storage, execution engines, and compute resources, common use cases, and end with a demo in Google Cloud Platform console and BigQuery. It is presented by an expert in BI, analytics, and cloud platforms.
Big Data with hadoop, Spark and BigQuery (Google cloud next Extended 2017 Kar...Imam Raza
Google Next Extended (https://cloudnext.withgoogle.com/) is an annual Google event focusing on Google cloud technologies. This presentation is from tech talk held in Google Next Extended 2017 Karachi event
This document provides an overview of BigQuery and how to get started with it. BigQuery is a fully managed data warehouse offered by Google Cloud Platform. It offers a serverless, fast, and scalable solution for data analysis. The document discusses what BigQuery is, its key features and concepts like datasets, tables, and billing. It also covers best practices for query performance and cost optimization. The presenter provides their contact details and links to their website and YouTube channel for additional resources.
FIWARE Global Summit - QuantumLeap: Time-series and Geographic QueriesFIWARE
This document describes QuantumLeap, an open source software that stores and queries spatial-temporal IoT data from NGSI entities. It converts NGSI entities to a tabular format and stores them in time series and geo-spatial databases for efficient querying over space and time. QuantumLeap can be easily deployed using Docker containers on platforms like Kubernetes and supports multiple database backends. It provides a REST API and Grafana integration for querying and visualizing IoT data.
PhD Projects in CoAP Research GuidancePhD Services
The document discusses potential PhD project topics in CoAP (Constrained Application Protocol). It lists several topics for CoAP projects, including IoT smart cities, dynamic resource caching, and traffic load balancing between brokers. It also discusses current subject areas like wireless hart & Zigbee long-range WPAN, security in CoAP body area networks, and IoT applications in intelligent transportation systems and low powered wireless sensor networks. The document provides contact information for an organization that offers research assistance for PhD and MS scholars.
This document lists 2016 IEEE projects related to cloud computing and data mining/knowledge engineering. It includes 15 projects on secure and efficient cloud computing technologies such as keyword search, data sharing, auditing, and access control schemes. It also includes 8 projects on location-aware keyword queries, sentiment analysis, spatial keyword search, privacy-preserving location services, and approximate nearest neighbor queries over graph databases.
Microsoft Azure is a leader in 4 Gartner Magic Quadrants and has the largest global footprint of any cloud platform. It provides infrastructure as a service (IaaS), platform as a service (PaaS) and cloud services across computing, networking, storage, data, analytics and more. Azure allows developers to build, deploy and manage applications through tools and services like Visual Studio, SQL Database, Web Apps, and Machine Learning.
Evangelos Kapsalakis, Partner Specialist at Microsoft, provides valuable insights on Microsoft Azure and its flexibility when it comes to migration deployment. From Cloud Migration Through Automation: Next Level Flexibility virtual event, hosted on September 30, 2020
Data Con LA 2022 - What's new with MongoDB 6.0 and AtlasData Con LA
Sig Narvaez, Executive Solution Architect at MongoDB
MongoDB is now a Developer Data Platform. Come learn what�s new in the 6.0 release and Atlas following all the recent announcements made at MongoDB World 2022. Topics will include
- Atlas Search which combines 3 systems into one (database, search engine, and sync mechanisms) letting you focus on your product's differentiation.
- Atlas Data Federation to seamlessly query, transform, and aggregate data from one or more MongoDB Atlas databases, Atlas Data Lake and AWS S3 buckets
- Queryable Encryption lets you run expressive queries on fully randomized encrypted data to meet the most stringent security requirements
- Relational Migrator which analyzes your existing relational schemas and helps you design a new MongoDB schema.
- And more!
The document discusses Microsoft's Windows Azure cloud computing platform. It provides an overview of the platform's infrastructure, services, and pricing models. The key points are:
1. Windows Azure provides infrastructure and services for building applications and storing data in the cloud. It offers compute, storage, database, and connectivity services.
2. The platform's infrastructure includes globally distributed data centers housing servers in shipping containers for high density.
3. Services include SQL Azure, storage, content delivery, queues, and an app development platform. Pricing models are consumption-based or via subscriptions.
- Study the architecture and design
- Compare Old & New Technology stack
- Analyze evolution of architecture and scalability
- Lessons learned over time
Open Source für den geschäftskritischen EinsatzMariaDB plc
The document summarizes MariaDB's 2017 roadshow. It discusses that MariaDB is building an easy to use, extendable database that can be deployed on premise, in the cloud, or in a hybrid environment. It also notes that MariaDB provides enterprise features like high availability, performance, and security while being fully open source and encouraging community collaboration. Finally, it provides information on where MariaDB is used and how organizations can get support from MariaDB.
Dotted Eyes - Open Software, Standards and DataDotted Eyes
Dotted Eyes is a UK-based spatial solutions provider with over 20 years of experience. They take a solution-led approach, focusing on open software, standards and data to provide tailored solutions that best meet customers' requirements. Case studies show how open solutions can help keep transport maps up to date for events and provide a cost-effective hosted application for contractor data analysis.
The document discusses several technology topics including:
1. SOA and its benefits such as facilitating interoperability and promoting technology reuse.
2. Cloud computing and common questions around it such as what cloud computing is, how many clouds there will be, and what's new in cloud computing.
3. An example scenario of a company called FredsList gradually adopting more cloud capabilities for their listings website, from basic storage to search, photos, analytics and performance optimization.
Open Source für den geschäftskritischen EinsatzMariaDB plc
The document summarizes MariaDB's Roadshow Bonn 2017 event. It discusses MariaDB's goals of building an easy to use, extensible, and deployable database. It outlines how MariaDB provides enterprise features like high availability and performance while also enabling open source innovation through community collaboration. Examples of large customers and their MariaDB implementations are provided, showing MariaDB's adoption across industries. Resources for learning more about MariaDB products and getting started with MariaDB are listed at the end.
Understanding the Windows Azure Platform - Dec 2010DavidGristwood
This document provides an overview of the Windows Azure platform. It describes Windows Azure as a platform as a service (PaaS) that provides scalable compute and storage services in the cloud. It outlines the core services of Windows Azure including compute, storage, networking and tools for development, deployment and management. It also discusses key advantages like scalability, reliability, flexibility and the pay-as-you-go business model.
Machine Learning on Google Cloud with H2OSri Ambati
This document provides an overview of H2O.ai, a leading AI platform company. It discusses that H2O.ai was founded in 2012, is funded with $75 million, and has products including its open source H2O machine learning platform and its Driverless AI automated machine learning product. It also describes H2O.ai's leadership in the machine learning platform market according to Gartner, its team of 90 AI experts, and its global presence across several offices. Finally, it outlines H2O.ai's machine learning capabilities and how customers can use its platform and products.
Microsoft Azure DocumentDB is a NoSQL document database service that is part of Microsoft Azure. It allows for the storage and querying of JSON documents and offers rich query capabilities over schema-free data using SQL and JavaScript. DocumentDB provides scalability, availability, and predictable performance for cloud applications.
Open source databases like MySQL, PostgreSQL, and Berkeley DB are flexible alternatives to proprietary databases. PostGIS extends PostgreSQL with spatial database capabilities for storing and querying geographic data and objects. It implements OpenGIS standards and provides functions for spatial indexing, analysis, and data access. PostGIS allows integrating geographic data into web and desktop applications and is used successfully in various real-world GIS projects and systems.
A talk about the OSGeo Live project; covering 43 projects that are available in a live DVD format (for you to run without installing). The project is much improved with OGC documentation and a description of many of the projects. New this year (thanks to some sponsorship) is quickstarts for several of the projects.
This document discusses Microsoft Azure cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It provides examples of Azure services like virtual machines, web apps, SQL databases, and machine learning. It also demonstrates how to perform tasks like uploading files to Azure blob storage using different programming languages.
This document provides an overview of the Microsoft Azure cloud platform, including its capabilities and services. It discusses Microsoft's experience in the cloud with services like Outlook.com, Xbox Live, and Bing. It also provides examples of how companies have used Azure, such as a vehicle data website using it for overflow capacity, a call center leveraging web services on Azure, and an online education tool saving on development costs. Overall, the document promotes Azure as a scalable and cost-effective cloud platform for developing and hosting a variety of application types and models.
How to Radically Simplify Your Business Data ManagementClusterpoint
Relational databases were designed for tabular data storage model. It requires complex software: schemas, encoded data, inflexible relations, sophisticated indexes. Complexity of your IT systems increases over your database life-time many-fold. Your costs too. Yet, we have a solution for this.
Keynote: Open Source für den geschäftskritischen EinsatzMariaDB plc
The document summarizes MariaDB's 2017 roadshow, including what they are doing and where they are going. It discusses how MariaDB is building an easy to use, deploy, and extend database and how it aims to be the new leader in the changing database market. It then outlines the enterprise capabilities MariaDB provides and why organizations should consider MariaDB due to benefits like annual subscriptions, cloud infrastructure, and reduced costs.
Microsoft Azure is a leader in 4 Gartner Magic Quadrants and has the largest global footprint of any cloud platform. It provides infrastructure as a service (IaaS), platform as a service (PaaS) and cloud services across computing, networking, storage, data, analytics and more. Azure allows developers to build, deploy and manage applications through tools and services like Visual Studio, SQL Database, Web Apps, and Machine Learning.
Evangelos Kapsalakis, Partner Specialist at Microsoft, provides valuable insights on Microsoft Azure and its flexibility when it comes to migration deployment. From Cloud Migration Through Automation: Next Level Flexibility virtual event, hosted on September 30, 2020
Data Con LA 2022 - What's new with MongoDB 6.0 and AtlasData Con LA
Sig Narvaez, Executive Solution Architect at MongoDB
MongoDB is now a Developer Data Platform. Come learn what�s new in the 6.0 release and Atlas following all the recent announcements made at MongoDB World 2022. Topics will include
- Atlas Search which combines 3 systems into one (database, search engine, and sync mechanisms) letting you focus on your product's differentiation.
- Atlas Data Federation to seamlessly query, transform, and aggregate data from one or more MongoDB Atlas databases, Atlas Data Lake and AWS S3 buckets
- Queryable Encryption lets you run expressive queries on fully randomized encrypted data to meet the most stringent security requirements
- Relational Migrator which analyzes your existing relational schemas and helps you design a new MongoDB schema.
- And more!
The document discusses Microsoft's Windows Azure cloud computing platform. It provides an overview of the platform's infrastructure, services, and pricing models. The key points are:
1. Windows Azure provides infrastructure and services for building applications and storing data in the cloud. It offers compute, storage, database, and connectivity services.
2. The platform's infrastructure includes globally distributed data centers housing servers in shipping containers for high density.
3. Services include SQL Azure, storage, content delivery, queues, and an app development platform. Pricing models are consumption-based or via subscriptions.
- Study the architecture and design
- Compare Old & New Technology stack
- Analyze evolution of architecture and scalability
- Lessons learned over time
Open Source für den geschäftskritischen EinsatzMariaDB plc
The document summarizes MariaDB's 2017 roadshow. It discusses that MariaDB is building an easy to use, extendable database that can be deployed on premise, in the cloud, or in a hybrid environment. It also notes that MariaDB provides enterprise features like high availability, performance, and security while being fully open source and encouraging community collaboration. Finally, it provides information on where MariaDB is used and how organizations can get support from MariaDB.
Dotted Eyes - Open Software, Standards and DataDotted Eyes
Dotted Eyes is a UK-based spatial solutions provider with over 20 years of experience. They take a solution-led approach, focusing on open software, standards and data to provide tailored solutions that best meet customers' requirements. Case studies show how open solutions can help keep transport maps up to date for events and provide a cost-effective hosted application for contractor data analysis.
The document discusses several technology topics including:
1. SOA and its benefits such as facilitating interoperability and promoting technology reuse.
2. Cloud computing and common questions around it such as what cloud computing is, how many clouds there will be, and what's new in cloud computing.
3. An example scenario of a company called FredsList gradually adopting more cloud capabilities for their listings website, from basic storage to search, photos, analytics and performance optimization.
Open Source für den geschäftskritischen EinsatzMariaDB plc
The document summarizes MariaDB's Roadshow Bonn 2017 event. It discusses MariaDB's goals of building an easy to use, extensible, and deployable database. It outlines how MariaDB provides enterprise features like high availability and performance while also enabling open source innovation through community collaboration. Examples of large customers and their MariaDB implementations are provided, showing MariaDB's adoption across industries. Resources for learning more about MariaDB products and getting started with MariaDB are listed at the end.
Understanding the Windows Azure Platform - Dec 2010DavidGristwood
This document provides an overview of the Windows Azure platform. It describes Windows Azure as a platform as a service (PaaS) that provides scalable compute and storage services in the cloud. It outlines the core services of Windows Azure including compute, storage, networking and tools for development, deployment and management. It also discusses key advantages like scalability, reliability, flexibility and the pay-as-you-go business model.
Machine Learning on Google Cloud with H2OSri Ambati
This document provides an overview of H2O.ai, a leading AI platform company. It discusses that H2O.ai was founded in 2012, is funded with $75 million, and has products including its open source H2O machine learning platform and its Driverless AI automated machine learning product. It also describes H2O.ai's leadership in the machine learning platform market according to Gartner, its team of 90 AI experts, and its global presence across several offices. Finally, it outlines H2O.ai's machine learning capabilities and how customers can use its platform and products.
Microsoft Azure DocumentDB is a NoSQL document database service that is part of Microsoft Azure. It allows for the storage and querying of JSON documents and offers rich query capabilities over schema-free data using SQL and JavaScript. DocumentDB provides scalability, availability, and predictable performance for cloud applications.
Open source databases like MySQL, PostgreSQL, and Berkeley DB are flexible alternatives to proprietary databases. PostGIS extends PostgreSQL with spatial database capabilities for storing and querying geographic data and objects. It implements OpenGIS standards and provides functions for spatial indexing, analysis, and data access. PostGIS allows integrating geographic data into web and desktop applications and is used successfully in various real-world GIS projects and systems.
A talk about the OSGeo Live project; covering 43 projects that are available in a live DVD format (for you to run without installing). The project is much improved with OGC documentation and a description of many of the projects. New this year (thanks to some sponsorship) is quickstarts for several of the projects.
This document discusses Microsoft Azure cloud services including Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). It provides examples of Azure services like virtual machines, web apps, SQL databases, and machine learning. It also demonstrates how to perform tasks like uploading files to Azure blob storage using different programming languages.
This document provides an overview of the Microsoft Azure cloud platform, including its capabilities and services. It discusses Microsoft's experience in the cloud with services like Outlook.com, Xbox Live, and Bing. It also provides examples of how companies have used Azure, such as a vehicle data website using it for overflow capacity, a call center leveraging web services on Azure, and an online education tool saving on development costs. Overall, the document promotes Azure as a scalable and cost-effective cloud platform for developing and hosting a variety of application types and models.
How to Radically Simplify Your Business Data ManagementClusterpoint
Relational databases were designed for tabular data storage model. It requires complex software: schemas, encoded data, inflexible relations, sophisticated indexes. Complexity of your IT systems increases over your database life-time many-fold. Your costs too. Yet, we have a solution for this.
Keynote: Open Source für den geschäftskritischen EinsatzMariaDB plc
The document summarizes MariaDB's 2017 roadshow, including what they are doing and where they are going. It discusses how MariaDB is building an easy to use, deploy, and extend database and how it aims to be the new leader in the changing database market. It then outlines the enterprise capabilities MariaDB provides and why organizations should consider MariaDB due to benefits like annual subscriptions, cloud infrastructure, and reduced costs.
代表的なオープンソース空間データサーバの1つであるGeoServerは、多くの強力な機能を提供します。 特に、さまざまなデータソースからの空間データへの接続とパブリッシングをサポートします。 GeoServerはOpen Geospatial Consortiumによって地理空間フィーチャデータを要求するために設定された標準プロトコルであるWeb Feature Service(WFS)もサポートしています。 しかしながら、GeoServerは2次元ジオメトリのための関数しか提供しないため、3D空間データを処理する関数はほとんどありません。 GeoServerの重要なコンポーネントであるJTS Topology Suiteは3D空間操作をサポートしていないため、ソリッドジオメトリもサポートしていません。 この講演では、3D空間データを扱うために私たちが実装したGeoServerの拡張モジュールを紹介します。
GeoServer, one of the representative open source spatial data servers, provides many powerful features. In particular, it supports connecting to and publishing spatial data from a variety of data sources. GeoServer also supports Web Feature Service (WFS), which is a standard protocol established by the Open Geospatial Consortium to request geospatial feature data. However, GeoServer provides functions only for two-dimensional geometry, so it provides few functions for handling 3D spatial data. Because JTS Topology Suite, which is an important component of GeoServer, does not support 3D spatial operations, it also does not support solid geometries. In this talk, I will introduce extension modules of GeoServer that we have implemented to handle 3D spatial data.
FOSS4G 2017 KYOTO.KANSAI The OSGeo foundation new initiatives and challengesOSgeo Japan
Open Source Software, Open Data and Open Standards are the three vital pillars for facilitating implementation/deployment of interoperable, scalable and sustainable geospatial infrastructure. Over the last decade, Free and Open Source Solutions for Geoinformatics (FOSS4G) has supported a variety of societal needs and gained worldwide acceptance. The Open Source Geospatial Foundation (OSGeo) has been spearheading the collaborative development of geospatial software and promote its widespread use through several initiatives and outreach activities.
This talk present about the evolution of OSGeo Foundation, its current accomplishments and avenues for future improvements. Some recent developments and opportunities for leveraging the OSGeo ecosystem for geospatial innovation will be discussed.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Project Management Semester Long Project - Acuityjpupo2018
Acuity is an innovative learning app designed to transform the way you engage with knowledge. Powered by AI technology, Acuity takes complex topics and distills them into concise, interactive summaries that are easy to read & understand. Whether you're exploring the depths of quantum mechanics or seeking insight into historical events, Acuity provides the key information you need without the burden of lengthy texts.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
OpenID AuthZEN Interop Read Out - AuthorizationDavid Brossard
During Identiverse 2024 and EIC 2024, members of the OpenID AuthZEN WG got together and demoed their authorization endpoints conforming to the AuthZEN API
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Salesforce Integration for Bonterra Impact Management (fka Social Solutions A...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 integration of Salesforce with Bonterra Impact Management.
Interested in deploying an integration with Salesforce for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Your One-Stop Shop for Python Success: Top 10 US Python Development Providersakankshawande
Simplify your search for a reliable Python development partner! This list presents the top 10 trusted US providers offering comprehensive Python development services, ensuring your project's success from conception to completion.
Digital Marketing Trends in 2024 | Guide for Staying AheadWask
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Main news related to the CCS TSI 2023 (2023/1695)Jakub Marek
An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
The original Czech 🇨🇿 version of the presentation can be found here: https://www.slideshare.net/slideshow/hlavni-novinky-souvisejici-s-ccs-tsi-2023-2023-1695/269688092 .
The videorecording (in Czech) from the presentation is available here: https://youtu.be/WzjJWm4IyPk?si=SImb06tuXGb30BEH .
Before delving into what was accomplished this year just a brief introduction to what GeoServer is for those of us who have never seen or heard of GeoServer before. If you asked three different people what is GeoServer you would get three different answers. But basically GeoServer is a web server for geo content. It reads spatial data in a variety for formats, and pushes that data onto the web. Just like a web server reads other types of content and pushes it out onto the web. You will often hear GeoServer described in terms of the protocols and standards its supports. I am sure most people here are familiar with the terms WMS, WFS, and many of the other Geospatial web service standards that are defined by the OGC. GeoServer supports a number of these standards including WMS, or the Web Map Service. Which means GeoServer is a map rendering engine, it takes some data, applies some pre-defined styling rules, and spits out a map. GeoServer is also a WFS, or Web Feature Service, which does not something that produces a map or something to look at, but delivers the data behind the map. Similar in nature to WCS, Web Coverage Service, which serves the same purpose as WFS, but for raster data rather than vector data. GeoServer has also been tied closely to the open standards movement. The original goal of GeoServer back in 2001 was to be an open source implementation of the WFS specification. It did so and actually became the reference implementation of wfs 1.0. And from there on GeoServer evolved to adopt new standards such as WMS, which was added around 2003. WCS was added in 2005. And currently being worked on is an implementation of WPS (Web Processing Service) which is something geoserver developers are very excited about. As of today GeoServer Is the reference implementation of WFS 1.0 and 1.1, as well as WCS 1.1. And GeoServer is fully cite compliant with the WMS 1.1 and WCS 1.0 specifications. And past open standards is the idea of GeoServer as a gateway to make geo information available to a wider “geoweb”. Supporting other not necessarily OGC defined formats and protocols, in an effort to make geospatial information as widely available as possible.
So again to sum up, GeoServer is an engine that reads in spatial data in a wide variety of formats from file based formats like Shapefile, to spatial databases like PostGIS and Oracle, to raster formats like GeoTiff, etc… and pushes that data onto the web via well defined protocols, to a wide variety of clients which consume those services, from desktop clients like udig, gvSig, qgis to web based clients like openlayers, etc…
So in preparation for this presentation I went through release notes in order to come up with a feature timeline for the past year. But then I found myself going back further in time so I decided to keep going back to the very beginning. This diagram shows GeoServer back from the very start. The project was started in 2001, and as you can see the first few releases were quite basic in terms of what GeoServer is today. It started as just a basic WFS with support for a few formats like Shapefile and PostGIS. Full transactional capabilities were added for the 1.0 release, so from 1.0 GeoServer has been a complete WFS implementation. However, the utility of a WFS all by its self was somewhat limited. More and more people realized that they needed a way to visualize data and at the time there was no WFS client to do the visualization. So after getting tired of staring at GML in the browser all day developers added a WMS component to GeoServer whose purpose was not necessarily to be a full WMS, but really just a way to visualize WFS. So am integrated WMS was added for the 1.1 release by Gabriel Roldan working on contract for the Spanish government. 1.2 saw a heap of functionality added by Refractions Research in Victoria, British Columbia Canada, who added the web administration interface and a feature validation engine. The 1.3 branch of GeoServer began to focus more on the WMS component. With the uptake of WMS being much greater than the uptake of WFS, and the continued lack of a good WFS client, it was becoming more and more evident that there was a lot of utility to be had in a general WMS. So the 1.3 release really focused on the aspects of WMS like rendering, and support for SLD allowing users to control styling.
Compared to other branches the 1.4 release was relatively short lived and was really not much of a feature oriented release, but was more centered around work to the core of GeoServer. In 1.4 GeoServer moved to the spring framework and was broken apart into a modular architecture. The 1.5 branch marked the inclusion of the great work from GeoSolutions in Italy to bring raster capabilities to GeoServer. Which was really what has made GeoServer a complete solution for serving spatial data. Support for raster formats came with the addition of the Web Coverage Service as well. The 1.6 branch really began to see a feature explosion. WFS 1.1 support was added and with it the ability to do reprojection with WFS. A security subsystem as added enabling the ability to secure data and services. Much work was done to better the performance of the GeoSever WMS. Much of the 1.6 branch focused on supporting more “geoweb” friendly formats like GeoRSS, and GeoJSON, as well as improving existing KML support adding templates. This feature push continued onto into the early stages of the 1.7 branch, which saw further improvements to the security system enabling finer grained access control at the dataset level, rather than at the service level. Support for a much wider array of raster formats such as ECW and MrSID was added again courtesy of GeoSolutions, who leverage the existing functionality of the GDAL library.
Which brings us to the past year of development. As you can see it has been quite a busy one.
There has been a lot of work further continuing to improve KML support. Improvements to super overlay output which allow KML produced by geoserver to be broken up into tiles or regions and allows for the nice streaming affect that you get when you view native layers on google earth. I will be showing an example of this in a few minutes. KML extrudes were added giving the ability to produce KML with extrudes or height information attached giving a 3D effect. This works with the existing GeoServer template system. So if you have a dataset that has some notion of height associated it you can write a simple template file that specifies the attribute and that information will be passed through to google earth. The example I have actually shows extrudes in conjunctions with super overlays. Supporting Googles GeoSearch has been something focused on and partly funded by Google. Much of the work has centered around working with data partners to start pushing some of their datasets out via GeoServer KML and getting that data indexed by the Google Crawler.
Past KML, a RESTful configuration api has been added. This allows clients to modify GeoServer configuration programmatically in REST-like, without having to use the user interface or without having to hack configuration files. I say REST-like because I don’t want to aggravate the REST purists in the audience today. While for the most part it does follow REST best practices there are places where it deviates and performs some no nos. One of the clients to note consuming this new API is the “Styler” extension, which is based on the GeoExt library. It allows one to configure styles directly in the browser with an integrated map panel which immediately renders the style changes. I will be showing this shortly.
There has been much work to improve rendering quality in GeoServer, and really key features when it comes to good cartography. Things like labeling along curves for linear features like roads, and wrapping labels for polygonal features. I hope to show a few of these features off. Also on the rendering front has been the ability to have more control over the resources that get used during a rendering job. Unfortunately there are various ways to really grind GeoServer to a halt with a single request. For example requesting an image that is huge in size and forcing the server to allocate a ton of memory to render it. There are also cases when a connection to a database goes haywire in the middle of a rendering job leaving the job hanging and holding own to server resources. Thankfully now there are some sensible limitations in place that can limit the amount of time a rendering job takes, as well as the amount of memory it can use up. And these settings are configurable so that the server admin can tweak them on a site by site basis.
ArcSDE support is something I am happy to say has gotten a lot better over this past year. Both on the vector side and the raster side of the fence. We see a much wider range of raster support including pretty much all flavors. And scalability across the board has been improved drastically. I will talk a bit more about the specifics in a later slide.
Much work has gone into the database support over the year, utilizing some key work at the geotools level to improve the architecture and the implementation of database datastore drivers. With it we have seen improved datastore drivers for oracle, db2, and postgis, as well as the addition of a new sql server driver. The new architecture has also allowed us to include things like JNDI defined connections, which allow datastores to share global connection pools, or connection pools defined by the application server etc…
And as usually there has been a number of new output formats added to GeoServer. An output for WFS based on OGR was developed that allows one to configure Geoserver to point at an OGR installation on the system. This then allows GeoServer to output vector data in any of the formats the OGR installation is configured for. Also contributed as a WFS output format is the ability to CSV or directly as an Excel spreadsheet. On the WMS side we saw the addition of the HTML ImageMap format, currently available as a GeoServer extension. When activates data is output as an HTML image map, and allows one to easily attach events to various features or geometries in a dataset. For example it would allow one to implement google maps like popups quite easily with the imagemap output and a bit of javascript.
And there have been various other interesting extensions over the year. The charting extension allows one to dynamically render charts and diagrams directly onto a map using a google charts like api facility. The JDBC mosaic extension allows one to store tiles and pyramids for large raster files in a database. The vector pyramid extension allows one to set up datasets with multiple level of generalization in a pyramid with appropriate levels of generalization attached to appropriate scales. This can drastically improve the rendering performance of vector data. And more… I apologize to those whose contributions I have overlooked… too many to keep track of.
So looking back at a year in Review. About 1100 bugs fixed, 1700 commits made, and 7 major releases. I should also point out that this is just three quarters of the year and missing the months of October, November and December. When I gathered these numbers I thought it would be interesting to see how these stacked up against previous years of the project. So for fun I decided to gather some stats from previous years as well.
The next stat I gathered was number of bugs fixed per year going back to 2004. No real surprises here but I am not sure how to interpret the graph. Either we are being more productive and closing more issues as time goes by or just introducing more bugs as time goes by. Maybe a bit of both.
Now this one is a bit more interesting. It’s the number of commits made into version control. The big spike in 2004 is the work done by Refractions for the 1.2 release adding the new user interface and the validation engine. I actually kind of surprised when I analyzed the number of commits made. Equally as interesting is the sudden drop off in 2005. This one actually baffles me umm… because their should be more commits logged. My guess is that some of the logs were lost during the changover in subversion repositories to the codehaus repository we use now. But from there on we see a general upward trend yet still not able to achieve the huge numbers of 2004.
And the rest of the graphs are pretty straight forward. Here we see downloads over time. I should also mention that the numbers gathered in these stats are not exactly accurate. For instance these numbers come from the sourceforge download stats. I suspect the tracker is a bit on the generous side when it comes to the numbers but I could be wrong. That said the interesting part of the analysis are the relative differences between subsequent years.
And finally one last stat is traffic on the users mailing list. Thankfully we see this going upward as well which means the user base is broadening. That or the users we have are becoming vocal.
While on the topic of mailing lists one very exiting occurrence of this year has been a burst in the wider international GeoServer community. The Chinese, Italian and Spanish mailing lists are all new this year, and continuing to grow. The Brazlian geoserver users list reached a milestone of 300 members this month. And the original English list continues to grow as well.
While on the topic of mailing lists one very exiting occurrence of this year has been a burst in the wider international GeoServer community. The Chinese, Italian and Spanish mailing lists are all new this year, and continuing to grow. The Brazlian geoserver users list reached a milestone of 300 members this month. And the original English list continues to grow as well.
So with a brief overview of the year out of the way I want to go into a bit more detail into some of the major features developed over the year. Starting with the major focus of GeoServer 2.0, the new web user interface. Basically the web administration console has been totally rewritten from scratch with a newer web development framework called Wicket. With Wicket one can build rich ajaxified web applications, with out actually having to do Ajax or Javascript. This sort of makes it a very natural fit for GeoServer since most of us old Java developers aren’t as hip to that new web stuff. And with it we have been able to create a new interface that is much more dynamic and convenient than the old version, may it rest in peace. However, while we have rewritten the user interface from scratch we have tried to maintain the same basic workflows and functionality of the old user interface. The first milestone release of 2.0 had the goal to have the new user interface cover all the features of the old user interface. While this perhaps did not allow us to work on exciting new features, it did allow us to create something that would not be totally foreign to users of the old interface. And also to ensure that users switching did not feel like they were losing any functionality by upgrading. However, one thing was intentionally lost with the new design. The Submit – Apply – Save workflow. Anyone who has used the old web interface has probably found it weird and tedious to have to click three buttons for every single change one wants to make. So this has been done away with. Now when you submit a form on a page, that change is persisted, no need to explicitly apply or save. We are hoping that this is something that users wont miss. Time permitting I will show off a bit of the new interface a little later. And one last thing to mention is that the new interface has been developed in an extensible manner. Which means that people wanting to develop extensions for GeoServer can now build a user interface for their extensions in a modular way. This was also one of the driving decisions behind going with Wicket as the web framework, as it allows you do this.
Which has already lead to the first major web extensions, the security user interface which allows you to configure the GeoServer security sub system. Before 2.0 the only way to add users, roles, and set up access control rules was to hack on property files. But now with the security ui this can all be done from the user interface. The extension was contributed by the geoSDI group, working for the Italian Department of Civil Protection. Special thanks goes out to them and Francesco Izzi, who is the project lead for geoSDI, and the maintainer of the extension.
Moving on… once again this year KML has been a primary focus. The main bit of work being around super overlays and regionation. Historically the way GeoServer has served KML has been to send a single kml stream that matches the current camera viewport. And to continually update so that when the camera moves the kml stream updates. The downside of this is that there is continually a split second in which the view is updating. This leads to a bit of an undesirable visualization affect. However, KML supports the notion of regions. A region being more or less a tile, a square section of the screen. By grouping KML into regions allows google earth to dynamically turn certain regions on and off as a region comes into and out of view. Also regions can be grouped into a hierarchy or pyramid the same way tiles are. And the same rule applies, as the camera zooms in regions or tiles lower in the hierarchy get activated and come into view. Now.. The upshot of all of this is the nice streaming affect that occurs as one zooms and pans around and as different regions come into view they get phased in and as regions come out of view they get phased out. A much more visually pleasing affect than the constant updating of the entire kml stream that results in a lot of flicker. We are all used to seeing this with the regular layers in google earth. But now one can achieve this same affect with layers coming from GeoServer. Also of much focus this past year was the GeoSearch. This feature allows GeoServer to publish KML in such a way that it can be crawled and index by Google. The end result being the ability to search for your geospatial data right in google maps. And I will try to show this in action if time permits. KML extrudes allow one to attach height or extrude information to a KML feature. Now GeoServer allows one to specify an attribute or some expression based on an attribute, which will be used as the value of an extrude. This allows 3Dish visualization in google earth. Again I will try to show this off if time permits.
Moving past KML but sticking with the visualization theme, much work has been done to improve cartographic output in GeoServer, with various rendering features implemented. And pretty much all of this is courtesy of the hard work of Andrea Aime, one of the lead developers on the project. Improvements to GeoServer’s labeling capabilities were implemented. This work was funded by Trimet, the transport authority for the city of Portland, in Oregon USA. But GeoServer is now able to label along linear features such as roads or streams. This is something that other map servers have been able to do for some time, and something fairly fundamental so it is nice to have this feature finally implemented. Other improvements having to do with the labeling of linear features are the ability to repeat labels on a single curve, and specify the distance between repeating labels. Along with curved labels for linear features is the ability wrap labels for polygonal features, so that labels stay contained within the bounds of a containing building for park for instance. Also implemented this year were the notion of dynamic symbolizers. The idea behind dynamic symbolizers Is to be able to do advanced symbology with SLD, advanced meaning going on what is strictly specified in the SLD standard, but still staying within the bounds of the SLD language itself. With this feature one can do things like hatched fills, or cross hatches along lines, and various other interesting effects. And finally, while not much a visual feature but a very important one nonetheless is the ability to set resource limits on GeoServer rendering jobs. Unfortunately before this feature was present it was quite easy to bring a GeoServer to a grinding halt with a single request. For instance requesting a huge image causing GeoServer to allocate a ton of memory. Or requesting that millions of features be rendered to a small image causing GeoServer to start a very expensive rendering job. And then there is always the problem of misbehaved clients. That is clients that start a rendering job but then close the connection before the job is finished. Unfortunately GeoServer can not know that it should stop rendering so this can lead to a lot of pointless rendering jobs piling up and bogging down the server. So basically what this allows for is the ability to set limiits on such things. For instance setting the maximum amount of memory any single job can have access to. And setting a timeout on a rendering job so that after a certain time the job is canceled instead of continuing on and on consuming resources pointlessly. Things very very important when running GeoServer in a production system.
A feature that has become very popular among users is the RESTful configuration api which allows for the configuration of a GeoServer instance via a RESTful interface. This gives people the ability to configure GEoServer from a variety of scripting and programming environments. And more importantly it allows for the easy automation of configuring a GeoServer instance. Before the hacks involved in a programmatically configuring GeoServer were quite ugly. This feature is very important for organizations that need to batch configure GeoServer with many layers of data, or that need to add new layers to a GeoServer at frequent intervals. For instance think about continually capturing imagery from a satellite and needing to publish that imagery on the fly. With the REST api it is possible to add new Shapefiles, PostGIS tables, GeoTIFF file, and more. It is also possible to add and configure styles with the interface. Which is where the Styler application comes in. Styler is a web application that provides a style editor directly in the browser. It uses the REST api to edit and publish GeoServer styles. So at this point I am going to cut out to a quick demo to showcase the last few features I have discussed.
Ok, so back the year in review, next up is ArcSDE. A whole lot of work has been done around SDE support in GeoServer. First and foremost is SDE raster support. Until recently GeoServer supported a very limited flavor of SDE rasters. Basically of the top of my head I think 3 and 4 band 8 bit unsigned without any color map support. But now Geoserver should support basically all pixel depths signed and unsigned, with color maps. And as well raster catalogs of any type. This work was funded by MassGIS, the office of Geographical and Environmental information for the state of massachussets. MassGIS is one of the biggest and earliest users of GEoServer in production. Most of the development on sde support in GEoServer has been carried out directly or indirectly by MassGIS. So special thanks to them and also special thanks to Gabriel Roldan for actually carying out the raster work.
However massgis is not the only ones to thank. A number of ArcSDe features were implemented this year on behalf of, and now I know I am going to butcher this name, but Rijkswaterstaat (RWS). The Ministry of Transport, Public Work, and Water Management in the Netherlands. They funded support for multi version tables, and basically publishing a snapshot of a version table. Also support for tables without a geometric value. And finally support for connecting to an SDE instance via a JNDI defined connection. So special thanks to Thijs van Menen who is the Coordinate GeoServices for setting that funding up. And also to Bart van den Eijnden for helping with the implementation and testing.
which formats? * mosaics: - making missing tiles transparent - specifying pixel values for transparency * coverage rendering?
OSGeo - apache of geospatial - big tent to allow all projects to gather, promote the sector as a whole, very grass roots and volunteer based, runs the foss4g conference OpenGeo - social enterprise, like a company, employs core developers of a small set of OSGeo projects. Doing an open source business model, but reinvesting profits back in to the software. 2 OpenLayers core committers, main geowebcache developer, 3 GeoExt committers, 5 GeoServer committers, 5 GeoTools committers. All OpenGeo projects are OSGeo, incubating or intend to. OGC - standards organization, consortium of companies that agree on interfaces. All OpenGeo projects implement, and most OSGeo projects do as well. We know it’s confusing, and we apologize, but open and geo is what we are most about
Founded by Mark Gorton in 2001 Makes his money with Hedge Funds, Brokerage Firm, Limewire Wants to improve New York City with better urban planning In to technology and open source (limewire was released as OS after he read Cathedral and Bazaar)
Mark's first idea to help improve NYC was 'open source traffic models'. Build software that would let people design streets differently and run models to challenge the assumptions about transportation by the New York City Department of Transportation. He wanted to show that New York would be better without cars. But the problem was there's no data available, you need the real data to do modeling. So started GeoServer, initial focus on getting real data, the 'source code' of the map, not just the image Soon found the OGC and their WFS specification, became the reference implementation.
Up until this point GeoServer had just been one of TOPP's projects, kept up since people were using it, it was doing good in the world. There were three people working on it, and since TOPP had the core developers they would be contracted by people who wanted to improve the software. My goal was to be completely self sustaining by doing contracts Near the middle of 2007 we got really close, and told the good news to our funder. Who responded: 'Great! You should hire more people.' I protested: 'But I'm just about to reach my goal of sustainability, more people will cost more'. Mark responded: 'Well it will be more sustainable in the long run'. To which I could say nothing.
At this point we could have just hired ten more developers to work on GeoServer and have it be a huge package solution. This would be awesome for GeoServer, but would likely kill the growing community, by sucking all the air towards TOPP. But we decided the best way was to leverage existing Open Source communities to improve the whole story around GeoServer. Hiring key developers in great projects to help steer them towards where we wanted to go. Our philosophy is to keep each piece architecturally independent, using open standards, to grow the maximum community around it. That way our investment can go much further than if we tried to grow communities on our own.
Around this time OpenLayers had emerged as clearly the best of the web mapping clients clients. And GeoServer could use a facelift - servers without nice clients are just a bunch of XML. Hired Tim Schaub, one of the top three contributors to the library. Focused on improving WFS editing and versioning Offering professional services on OpenLayers, contract OpenGeo instead of just Tim. We now have multiple developers contributing.
Next we needed something to accelerate our maps, dividing in to tiles. Started just recommending TileCache, but hard to install with GeoServer. Jython was just too slow. Built GeoWebCache on JtileCache, a Google Summer of Code Project Have reached 1.0.1, and has a growing community. Ships with GeoServer, it's integral to our Google Earth performance. Next improvments will be to cache dynamic maps But we keep it architectually independent, and it has its own community, people use it with MapServer and others.
The final major piece came at the beginning of the year, with Paul Ramsey joining OpenGeo. PostGIS was always our preferred database, as it's clearly the gold standard in the Open Source Geo world. We only want to provide support if we are actively contribute and employ at least one core developer. Paul has already pushed out a number of great PostGIS improvements.
While OpenLayers provided the map, we wanted to extend the interface to give a rich desktop type feel A number of OL developers had found Ext.js, and made rich mapping applications with it. We decided to help grow a community, forming GeoExt with Camp to Camp and others Our goal is to be able to provide rich GIS functionality, but in applications that anyone can use. No need to buy a GIS and then train someone on it when you can just make a web application that has the five GIS features they need in an intuitive interface * No need for a ‘buffer’ operation when you can have a ‘show all radio towers within 5 miles of this stream’
What we've done is assemble what we believe are the best of breed components for web-based geospatial work. We provide support and improvements on them, and we invest heavily in making each of them the best, and in making the whole work seamlessly. It's an open suite, interoperating with Open Standards, and working great with proprietary solutions as well. We don't believe in locking you in, we want it to work with your existing system.
Our funding has been primarily an investment from Mark Gorton, who continues to put money in as he sees the potential for us to reach a point of sustainability - recovering all our costs. We are set up in a not for profit structure, so he will never see financial returns. But by pushing us towards sustainability his money goes much farther in the world. Once we reach profitability the investment will go back to TOPP, to start other initiatives to help the world
As a mission driven organization, our goal is to play a key role in creating the Open Geospatial Web We feel the best way to accomplish the most impact for the least amount of investment is to create a hybrid organization, combining the best of the for profit and non-profit worlds Our software is built with values of openness We emphasize the latest advances, google earth, etc, but always stay rooted in the standards. Focus on data and users, not metadata, building the infrastructure bottom up instead of top down as most traditional SDIs have The core goal of OpenGeo is not to make profit, but to make great software to advance the Open Geospatial Web. We just compete in the market providing services so that we can build more software.
To get to full cost recovery, our next step is to transition from a services / consulting company to a 'product' company. We plan to compete head to head with expensive proprietary solutions. Creating a premium brand, turning the open source assumptions on their head by focusing on the fact that you get applications tailored to your needs, and direct contact with core developers. Hiring traditional biz dev and sales, doing marketing and PR, to sell a product that we define and improve
First step in that transition has been 'GeoServer Enterprise', a support package providing unlimited bug fixes, web and telephone support, and bundled hours. Four reference clients - TriMet, MassGIS, WHO and Finnish Ministry of Agriculture We are only taking contract work that advances the product directions we are pushing. Which works out well, as clients can get a guaranteed time of delivery, instead of just waiting for when we get to releasing it, as our priorities can shift with other clients TriMet funded curved labeling and other rendering improvements, MassGIS ArcSDE raster, Finns improvements to Oracle datastore, WHO GeoExt components and reverse proxy
Next step is to launch OpenGeo Enterprise, a full solution that large enterprises can purchase to support their full deployments It will be based completely on open source software, contributing back as much as possible. The benefit to them will be a package solution, we will custom tailor the packaging to their needs, provide a professional face to the OS world. Our aim is to leverage Mark's investment to grow to an organization size where we can complete a 'win-win-win' situation for all involved OS Software wins, as developers are employed full time to work on improving the code Institutional customers win, with access to a one stop shop to support their deployments Open Source Users win, as there is a sustainable funding stream Core developer win, by getting to do what they love.
Once we get to full cost recovery, our plan is to spin off OpenGeo as it's own company, fully owned by The Open Planning Project. This will include profit sharing for employees, so they can share in the success of what they built And for practical reasons, we don't want our best employees going to do a start up on the same software because the organization is making tons of money and they aren't OpenGeo will also then be in a position to 'incubate' other similar types of organizations, for example transit or land use planning, that will start as OpenGeo projects, but then spin off to have their own control, making money in the market to support themselves. TOPP will take any profit and start other, new social ventures that are structured similarly. A long term goal of mine is to make this type of structure a new type of entity in the world. Drawing inspiration from Open Source Software, applying the principles to running a business Should have a higher level of transparency Open Books, you can read all our team coordination lists Any profit made by a dot-org has to be invested in other dot-orgs Using capital like the GPL used copyright law - to create a 'logic of the wedge', where any success is encoded to engender future success. I believe this type of structure is more appropriate to creating and maintaining Open Source software than a for profit company or a non-profit foundation - employ the core developers and find a business model that works for them.