Restructuring and transforming attributes is often an important part of data preparation. Here are tips for managing and validating attributes, plus examples of new functionality that makes it easier than ever to work with date and time.
Karnataka Geospatial Experience FME World Tour 2017 IndiaRaghavendran S
The document describes the Karnataka State Council for Science and Technology (KSCST), which was established in 2012 to advise the government of Karnataka on science and technology matters. It details the organizational structure of KSCST, including its council, executive committee, and secretariat. It also discusses KSCST's linkages with other government departments and institutions, and its activities related to surveys, project implementation, awareness programs, and more.
With information available in more systems than ever, how do we make sense of it all? Here are a few examples of how people have blended large amounts of data across the web and enterprise, and turned it into something useful and visually pleasing.
The views expressed in this presentation are that of the author and is not endorsed by PIXEL SOFTEK or any other companies or their representatives referenced in this presentation
Blending Enterprise Data with FME ServerSafe Software
GIS has become the foundation of our organization’s asset management program. Many of our business systems rely on our GIS services for authoritative spatial data and map services. Other business systems (SunGard ERP, Cityworks CMMS, and legacy WinCan CCTV inspection system) contain critical non-spatial data that must be linked to spatial asset information. In this presentation I’ll share how we are using FME Server to “blend” spatial and non-spatial data together for the ArcGIS Server map services that power our Geocortex applications, our Cityworks CMMS, and our new ITpipes CCTV inspection system.
The document discusses the new features and improvements in QGIS 2.0, including enhanced atlas/mapbook generation, composer improvements, new symbology options, an improved attribute table, integration of SEXTANTE processing capabilities, and a redesigned user interface. It also provides an overview of adoption of QGIS, the current release plan, and a call for help finding and fixing bugs prior to the June public release.
Tools for Visualizing Geospatial Data in a Web BrowserSafe Software
Learn what libraries and web services are available for visualizing your 2D and 3D geospatial data in a web browser. We’ll demo how to use FME to connect to data from any source and prepare it for three.js, Leaflet, Mapbox, glTF, I3S, and Cesium.
This document discusses 5 ways to improve LiDAR workflows using FME software. It begins with an overview of LiDAR and point clouds before addressing each of the 5 ways: 1) simplifying point cloud transformations with FME transformers, 2) preparing data, 3) automating surface model creation, 4) visualizing solutions through 3D city modeling, and 5) expanding tools with FME Hub and third party tools like LAStools. The presentation concludes by emphasizing how FME can help simplify and scale LiDAR workflows.
Leveraging Autodesk Products with FME: AutoCAD to GIS is Only the BeginningSafe Software
Go beyond AutoCAD to GIS and achieve more with FME. In this webinar you’ll learn how FME can be used to leverage several Autodesk solutions including AutoCAD Civil 3D, Revit and A360 with other applications in your organization.
Karnataka Geospatial Experience FME World Tour 2017 IndiaRaghavendran S
The document describes the Karnataka State Council for Science and Technology (KSCST), which was established in 2012 to advise the government of Karnataka on science and technology matters. It details the organizational structure of KSCST, including its council, executive committee, and secretariat. It also discusses KSCST's linkages with other government departments and institutions, and its activities related to surveys, project implementation, awareness programs, and more.
With information available in more systems than ever, how do we make sense of it all? Here are a few examples of how people have blended large amounts of data across the web and enterprise, and turned it into something useful and visually pleasing.
The views expressed in this presentation are that of the author and is not endorsed by PIXEL SOFTEK or any other companies or their representatives referenced in this presentation
Blending Enterprise Data with FME ServerSafe Software
GIS has become the foundation of our organization’s asset management program. Many of our business systems rely on our GIS services for authoritative spatial data and map services. Other business systems (SunGard ERP, Cityworks CMMS, and legacy WinCan CCTV inspection system) contain critical non-spatial data that must be linked to spatial asset information. In this presentation I’ll share how we are using FME Server to “blend” spatial and non-spatial data together for the ArcGIS Server map services that power our Geocortex applications, our Cityworks CMMS, and our new ITpipes CCTV inspection system.
The document discusses the new features and improvements in QGIS 2.0, including enhanced atlas/mapbook generation, composer improvements, new symbology options, an improved attribute table, integration of SEXTANTE processing capabilities, and a redesigned user interface. It also provides an overview of adoption of QGIS, the current release plan, and a call for help finding and fixing bugs prior to the June public release.
Tools for Visualizing Geospatial Data in a Web BrowserSafe Software
Learn what libraries and web services are available for visualizing your 2D and 3D geospatial data in a web browser. We’ll demo how to use FME to connect to data from any source and prepare it for three.js, Leaflet, Mapbox, glTF, I3S, and Cesium.
This document discusses 5 ways to improve LiDAR workflows using FME software. It begins with an overview of LiDAR and point clouds before addressing each of the 5 ways: 1) simplifying point cloud transformations with FME transformers, 2) preparing data, 3) automating surface model creation, 4) visualizing solutions through 3D city modeling, and 5) expanding tools with FME Hub and third party tools like LAStools. The presentation concludes by emphasizing how FME can help simplify and scale LiDAR workflows.
Leveraging Autodesk Products with FME: AutoCAD to GIS is Only the BeginningSafe Software
Go beyond AutoCAD to GIS and achieve more with FME. In this webinar you’ll learn how FME can be used to leverage several Autodesk solutions including AutoCAD Civil 3D, Revit and A360 with other applications in your organization.
Spatial decision support and analytics on a campus scale: bringing GIS, CAD, ...Safe Software
1. The document discusses how spatial decision support and analytics can be applied at the campus scale by integrating various data sources such as GIS, CAD, BIM, and Tableau.
2. A key challenge is that a campus is a complex system with different processes and specialized data silos. The presentation explores using GIS as an enabling technology to create a comprehensive spatial model and dissolve these silos.
3. Examples of spatial decision problems on campus include optimal space assignment and indoor routing. Solutions involve building spatial databases and networks from CAD floor plans to support optimization and scenario analysis.
Generating Pipeline Alignment Sheets Using FMESafe Software
Presented by Jerrod Stutzman & Kyle Brock of Devon Energy
Abstract: The standard workflow for converting pipeline survey data into alignment sheets is a tedious and repetitive process. Using Data Interoperability (FME) via ArcGIS Server geoprocessing, we can automate the entire process, saving many man hours while also retiring a 3rd party application.
Is This Thing On? A Well State Model for the PeopleDatabricks
The document discusses using machine learning models to determine well production state (on vs off) from sensor data. It presents an existing data architecture and issues with data quality. A supervised learning model is proposed using a decision tree trained on labeled rod pump production data. The modeling workflow includes data preprocessing, feature engineering, hyperparameter tuning and grid search. Decision trees are chosen for their interpretability but the document notes larger models may perform better. Overall production state modeling could help optimize operations and outperform existing controllers.
Geoprocessing(Building Your Own Tool) and Geostatistical Analysis(An Introdu...Nepal Flying Labs
Its a presentation slide prepared by me and my team for a workshop at my college.Don't hesitate to mail me at utmpudasaini@hotmail.com or utmpudasaini@gmail.com if you want to know more or details regarding the demos.
1Spatial Australia: Introduction and getting started with fme 20171Spatial
This document introduces new features in FME 2017 including over 20 new data formats that can be read and written, more than 10 new transformers, updates to existing transformers, improved user interface features for workflows, expanded web services and file system capabilities, an updated data inspector, and new automation capabilities for running workflows on demand or on a schedule. The overall goal of FME is to allow data to flow freely between systems and applications while enabling users to spend more time making decisions rather than struggling with data integration tasks.
MicroStation DGN: How to Integrate CAD and GISSafe Software
This document discusses converting CAD data to GIS formats and some of the challenges involved. It describes problems with representing parcel/block boundaries and attributes when converting CAD data to GIS and shows the workflow and outputs. It also details issues with converting elevation points and lines from CAD formats where the elevation is stored as text not linked to the features. The document proposes solutions like representing the data as 3D points and lines in GIS and meeting specification requirements. Later sections discuss converting GIS data to CAD formats and blending MicroStation and lidar datasets to model 3D buildings.
Taming Our Case Management Database and GIS with FMESafe Software
With a strong economy and a rapidly growing urban area, Austin attracts considerable development interest. Some of the key indicators of future changes in the built environment include the applications for site plans, subdivisions, and zoning changes which are filed with the City of Austin for approval. Processing this information is vital in understanding changes in land use, and developing strategies to plan for the impacts of incoming development. This presentation will look at how the City of Austin leveraged FME to create a process to automate the creation and publication of geospatial and non-geospatial datasets for applications for site plans, subdivisions and zoning changes to disseminate information to the public as well as other city departments and organizations.
Enabling Spatial Decision Support and Analytics on a Campus Scale with FME Te...Safe Software
University and College Campuses are complex environments. The campus comprises many physical sub-systems, such as buildings, outdoor spaces, utilities, transportation, which are maintained by several divisions using multiple IT tools and different formats. Making campus-wide analytics requires bringing all these data elements and different formats (CAD, GIS, BIM) together to create a comprehensive common operating picture. In this presentation we will demonstrate that FME Technology is a key and crucial component in data automation and integration on Campus scale.
The DBMS market trends focused on the Graph DBMS. The benefit of the Graph Database and its forecasted the growth rate. The Advice from the renowned market research institute.
5 Tips for Integrating CAD Data with Esri ArcGISSafe Software
Converting between CAD and GIS can be a contorted, convoluted task. But it doesn’t have to be. Learn how you can integrate your CAD data with ArcGIS while preserving rich CAD symbology and GIS attribution details. Plus, see how you can automate conversion and validation processes.
Tips & Tricks for Using FME for Business IntelligenceSafe Software
Business intelligence platforms like Tableau, Qlik and Power BI enable users to visually identify patterns in their data. When the data to be analyzed is in disparate formats and structures it requires some blending and/or remodelling prior to being loaded into the BI system. Although this may be done manually, this carries a risk of introducing error, and can also be tedious. This webinar will demonstrate the ways FME can be used to prepare data automatically to save time and errors.
This document summarizes the new features in FME 2017 including over 30 new data formats that can be read and written, more than 10 new transformers, updates to existing transformers, and improvements to usability such as additional options for inspecting data and managing transformer parameters. It also describes enhancements to FME Server including new instance types for running workspaces in the cloud and lower pricing for most users. The goal of FME is to allow data to flow freely between systems and formats while simplifying complex data integration tasks.
The document provides an introduction to the ArcGIS Pipeline Data Model (APDM), which is a standardized data model for storing pipeline geospatial data. It describes the core components of a geographic information system (GIS) and how the APDM implements these components using ESRI's geodatabase tools. This includes discussions of feature classes, object classes, attributes, relationships, and how the pipeline data is structured and related within the APDM schema.
Slides from the Big Data Gurus meetup at Samsung R&D, August 14, 2013
This presentation covers the high level architecture of the Netflix Data Platform with a deep dive into the architecture, implementation, use cases, and future of Lipstick (https://github.com/Netflix/Lipstick) - our open source tool for graphically analyzing and monitoring the execution of Apache Pig scripts.
Netflix uses Apache Pig to express many complex data manipulation and analytics workflows. While Pig provides a great level of abstraction between MapReduce and data flow logic, once scripts reach a sufficient level of complexity, it becomes very difficult to understand how data is being transformed and manipulated across MapReduce jobs. To address this problem, we created (and open sourced) a tool named Lipstick that visualizes and monitors the progress and performance of Pig scripts.
The document describes Curriculum Associates' journey to develop a real-time application architecture to provide teachers and students with real-time feedback. They started with batch ETL to a data warehouse and migrated to an in-memory database. They added Kafka message queues to ingest real-time event data and integrated a data lake. Now their system uses MemSQL, Kafka, and a data lake to provide real-time and batch processed data to users.
Continuous Evaluation of Deployed Models in Production Many high-tech industr...Databricks
Many high-tech industries rely on machine-learning systems in production environments to automatically classify and respond to vast amounts of incoming data. Despite their critical roles, these systems are often not actively monitored. When a problem first arises, it may go unnoticed for some time. Once it is noticed, investigating its underlying cause is a time-consuming, manual process. Wouldn’t it be great if the model’s output were automatically monitored? If they could be visualized, sliced by different dimensions? If the system could automatically detect performance degradation and trigger alerts? In this presentation, we describe our experience from building such a core machine-learning services: Model Evaluation.
Our service provides automated, continuous evaluation of the performance of a deployed model over commonly-used metrics like the area-under-the-curve (AUC), root-mean-square-error (RMSE) etc. In addition, summary statistics about the model’s output, their distributions are also computed. The service also provides a dashboard to visualize the performance metrics, summary statistics and distributions of a model over time along with REST APIs to retrieve these metrics programmatically.
These metrics can be sliced by input features (e.g. Geography, Product type) to provide insights into model performance over different segments. The talk will describe various components that are required in building such a service and metrics of interest. Our system has a backend component built with spark on Azure Databricks. The backend can scale to analyze TBs of data to generate model evaluation metrics.
We will talk about how we modified Spark MLLib for computing AUC sliced by different dimensions and other optimizations in Spark to improve compute and performance. Our front-end and middle-tier, built with Docker and Azure Webapp provides visuals and REST APIs to retrieve the above metrics. This talk will cover various aspects of building, deploying and using the above system.
GraphX: Graph Analytics in Apache Spark (AMPCamp 5, 2014-11-20)Ankur Dave
GraphX is a graph processing framework built into Apache Spark. This talk introduces GraphX, describes key features of its API, and gives an update on its status.
FME, and Throwing Off the Spatial BlindersSafe Software
If you've got a spatial ETL task, most certainly hitch the plow to your horse 'FME' and get the job done.
But what if you were bold enough to instead throw a saddle on your trusty steed? Where could it take you and your data? You already know what's on the other side of the field, but what's on the horizon? Saddle up and ride, FME can take you there. At Grant PUD we grew tired of traversing the countryside on foot, dragging our rapidly aging languages and code along with us. It wasn't until we were faced with a timeline that traditional coding couldn't meet that we took a gamble and jumped on FME's back, we haven't looked back since.
We'll take a brief look at how Grant PUD has, and continues to replace many of its business critical interfaces with FME, and the benefits gained.
Double double, less toil and trouble… FME has always had tricks for optimizing your workflows, and FME 2017 has a few more up its sleeve. Learn about faster reading and filtering of tabular data, better ways to preserve system resources, pulling data directly from a web page, and more.
Spatial decision support and analytics on a campus scale: bringing GIS, CAD, ...Safe Software
1. The document discusses how spatial decision support and analytics can be applied at the campus scale by integrating various data sources such as GIS, CAD, BIM, and Tableau.
2. A key challenge is that a campus is a complex system with different processes and specialized data silos. The presentation explores using GIS as an enabling technology to create a comprehensive spatial model and dissolve these silos.
3. Examples of spatial decision problems on campus include optimal space assignment and indoor routing. Solutions involve building spatial databases and networks from CAD floor plans to support optimization and scenario analysis.
Generating Pipeline Alignment Sheets Using FMESafe Software
Presented by Jerrod Stutzman & Kyle Brock of Devon Energy
Abstract: The standard workflow for converting pipeline survey data into alignment sheets is a tedious and repetitive process. Using Data Interoperability (FME) via ArcGIS Server geoprocessing, we can automate the entire process, saving many man hours while also retiring a 3rd party application.
Is This Thing On? A Well State Model for the PeopleDatabricks
The document discusses using machine learning models to determine well production state (on vs off) from sensor data. It presents an existing data architecture and issues with data quality. A supervised learning model is proposed using a decision tree trained on labeled rod pump production data. The modeling workflow includes data preprocessing, feature engineering, hyperparameter tuning and grid search. Decision trees are chosen for their interpretability but the document notes larger models may perform better. Overall production state modeling could help optimize operations and outperform existing controllers.
Geoprocessing(Building Your Own Tool) and Geostatistical Analysis(An Introdu...Nepal Flying Labs
Its a presentation slide prepared by me and my team for a workshop at my college.Don't hesitate to mail me at utmpudasaini@hotmail.com or utmpudasaini@gmail.com if you want to know more or details regarding the demos.
1Spatial Australia: Introduction and getting started with fme 20171Spatial
This document introduces new features in FME 2017 including over 20 new data formats that can be read and written, more than 10 new transformers, updates to existing transformers, improved user interface features for workflows, expanded web services and file system capabilities, an updated data inspector, and new automation capabilities for running workflows on demand or on a schedule. The overall goal of FME is to allow data to flow freely between systems and applications while enabling users to spend more time making decisions rather than struggling with data integration tasks.
MicroStation DGN: How to Integrate CAD and GISSafe Software
This document discusses converting CAD data to GIS formats and some of the challenges involved. It describes problems with representing parcel/block boundaries and attributes when converting CAD data to GIS and shows the workflow and outputs. It also details issues with converting elevation points and lines from CAD formats where the elevation is stored as text not linked to the features. The document proposes solutions like representing the data as 3D points and lines in GIS and meeting specification requirements. Later sections discuss converting GIS data to CAD formats and blending MicroStation and lidar datasets to model 3D buildings.
Taming Our Case Management Database and GIS with FMESafe Software
With a strong economy and a rapidly growing urban area, Austin attracts considerable development interest. Some of the key indicators of future changes in the built environment include the applications for site plans, subdivisions, and zoning changes which are filed with the City of Austin for approval. Processing this information is vital in understanding changes in land use, and developing strategies to plan for the impacts of incoming development. This presentation will look at how the City of Austin leveraged FME to create a process to automate the creation and publication of geospatial and non-geospatial datasets for applications for site plans, subdivisions and zoning changes to disseminate information to the public as well as other city departments and organizations.
Enabling Spatial Decision Support and Analytics on a Campus Scale with FME Te...Safe Software
University and College Campuses are complex environments. The campus comprises many physical sub-systems, such as buildings, outdoor spaces, utilities, transportation, which are maintained by several divisions using multiple IT tools and different formats. Making campus-wide analytics requires bringing all these data elements and different formats (CAD, GIS, BIM) together to create a comprehensive common operating picture. In this presentation we will demonstrate that FME Technology is a key and crucial component in data automation and integration on Campus scale.
The DBMS market trends focused on the Graph DBMS. The benefit of the Graph Database and its forecasted the growth rate. The Advice from the renowned market research institute.
5 Tips for Integrating CAD Data with Esri ArcGISSafe Software
Converting between CAD and GIS can be a contorted, convoluted task. But it doesn’t have to be. Learn how you can integrate your CAD data with ArcGIS while preserving rich CAD symbology and GIS attribution details. Plus, see how you can automate conversion and validation processes.
Tips & Tricks for Using FME for Business IntelligenceSafe Software
Business intelligence platforms like Tableau, Qlik and Power BI enable users to visually identify patterns in their data. When the data to be analyzed is in disparate formats and structures it requires some blending and/or remodelling prior to being loaded into the BI system. Although this may be done manually, this carries a risk of introducing error, and can also be tedious. This webinar will demonstrate the ways FME can be used to prepare data automatically to save time and errors.
This document summarizes the new features in FME 2017 including over 30 new data formats that can be read and written, more than 10 new transformers, updates to existing transformers, and improvements to usability such as additional options for inspecting data and managing transformer parameters. It also describes enhancements to FME Server including new instance types for running workspaces in the cloud and lower pricing for most users. The goal of FME is to allow data to flow freely between systems and formats while simplifying complex data integration tasks.
The document provides an introduction to the ArcGIS Pipeline Data Model (APDM), which is a standardized data model for storing pipeline geospatial data. It describes the core components of a geographic information system (GIS) and how the APDM implements these components using ESRI's geodatabase tools. This includes discussions of feature classes, object classes, attributes, relationships, and how the pipeline data is structured and related within the APDM schema.
Slides from the Big Data Gurus meetup at Samsung R&D, August 14, 2013
This presentation covers the high level architecture of the Netflix Data Platform with a deep dive into the architecture, implementation, use cases, and future of Lipstick (https://github.com/Netflix/Lipstick) - our open source tool for graphically analyzing and monitoring the execution of Apache Pig scripts.
Netflix uses Apache Pig to express many complex data manipulation and analytics workflows. While Pig provides a great level of abstraction between MapReduce and data flow logic, once scripts reach a sufficient level of complexity, it becomes very difficult to understand how data is being transformed and manipulated across MapReduce jobs. To address this problem, we created (and open sourced) a tool named Lipstick that visualizes and monitors the progress and performance of Pig scripts.
The document describes Curriculum Associates' journey to develop a real-time application architecture to provide teachers and students with real-time feedback. They started with batch ETL to a data warehouse and migrated to an in-memory database. They added Kafka message queues to ingest real-time event data and integrated a data lake. Now their system uses MemSQL, Kafka, and a data lake to provide real-time and batch processed data to users.
Continuous Evaluation of Deployed Models in Production Many high-tech industr...Databricks
Many high-tech industries rely on machine-learning systems in production environments to automatically classify and respond to vast amounts of incoming data. Despite their critical roles, these systems are often not actively monitored. When a problem first arises, it may go unnoticed for some time. Once it is noticed, investigating its underlying cause is a time-consuming, manual process. Wouldn’t it be great if the model’s output were automatically monitored? If they could be visualized, sliced by different dimensions? If the system could automatically detect performance degradation and trigger alerts? In this presentation, we describe our experience from building such a core machine-learning services: Model Evaluation.
Our service provides automated, continuous evaluation of the performance of a deployed model over commonly-used metrics like the area-under-the-curve (AUC), root-mean-square-error (RMSE) etc. In addition, summary statistics about the model’s output, their distributions are also computed. The service also provides a dashboard to visualize the performance metrics, summary statistics and distributions of a model over time along with REST APIs to retrieve these metrics programmatically.
These metrics can be sliced by input features (e.g. Geography, Product type) to provide insights into model performance over different segments. The talk will describe various components that are required in building such a service and metrics of interest. Our system has a backend component built with spark on Azure Databricks. The backend can scale to analyze TBs of data to generate model evaluation metrics.
We will talk about how we modified Spark MLLib for computing AUC sliced by different dimensions and other optimizations in Spark to improve compute and performance. Our front-end and middle-tier, built with Docker and Azure Webapp provides visuals and REST APIs to retrieve the above metrics. This talk will cover various aspects of building, deploying and using the above system.
GraphX: Graph Analytics in Apache Spark (AMPCamp 5, 2014-11-20)Ankur Dave
GraphX is a graph processing framework built into Apache Spark. This talk introduces GraphX, describes key features of its API, and gives an update on its status.
FME, and Throwing Off the Spatial BlindersSafe Software
If you've got a spatial ETL task, most certainly hitch the plow to your horse 'FME' and get the job done.
But what if you were bold enough to instead throw a saddle on your trusty steed? Where could it take you and your data? You already know what's on the other side of the field, but what's on the horizon? Saddle up and ride, FME can take you there. At Grant PUD we grew tired of traversing the countryside on foot, dragging our rapidly aging languages and code along with us. It wasn't until we were faced with a timeline that traditional coding couldn't meet that we took a gamble and jumped on FME's back, we haven't looked back since.
We'll take a brief look at how Grant PUD has, and continues to replace many of its business critical interfaces with FME, and the benefits gained.
Double double, less toil and trouble… FME has always had tricks for optimizing your workflows, and FME 2017 has a few more up its sleeve. Learn about faster reading and filtering of tabular data, better ways to preserve system resources, pulling data directly from a web page, and more.
Introduction and Getting Started with FME 2017Safe Software
This document provides an overview of new features in FME 2017 including more formats supported for reading and writing data, new and updated transformers for performing powerful transformations, improvements to the data inspector for inspecting data, enhancements for automating workflows on FME Server, and updates to FME Cloud including new pricing and instance types. The document demonstrates turning raw satellite imagery into a 3D model and highlights time-saving features for everyday use of FME like adding formats quickly and copying transformer parameters.
El documento describe un ejercicio sobre un despacho de arquitectura que necesita almacenar información sobre sus arquitectos, obras y supervisores. Se deben crear tablas para arquitectos, obras y supervisores y relacionarlas mediante una relación de uno a muchos entre arquitectos y obras, y una relación de muchos a muchos entre obras y supervisores.
Magical Methods for Batch Data ProcessingSafe Software
Here’s what you need to know about applying the same processing to a large number of similar files. We’ll look at a few examples, what’s working for people, and the pros and cons of each approach.
Remote Sensing Data — Instant Home Delivery!Safe Software
Satellites are gathering new information every second — and you have access to it. The question: What will you do with it? Here’s how to pull in remote sensed data from several sources, plus a real example of this in action.
See FME Desktop 2017 in action. Learn how you can take advantage of the top new features, formats, and transformers to solve more data challenges even faster.
Take advantage of FME Server’s capabilities for real-time integration and change data capture. Learn about workflows for monitoring and updating your data as it changes. We’ll look at what data sources/systems are monitored out-of-the-box and how you can enable change data capture for other data sources/systems.
This document provides information about purchasing a 3Com 3CGBIC92-OEM product from Launch 3 Telecom. It details that the estimated time of arrival for the product is 7-10 days, and provides contact information for purchasing. It also gives information about payment options, same-day shipping and order tracking, warranty, and additional services offered by Launch 3 Telecom like repairs, maintenance contracts, and de-installation.
This document analyzes and compares the digipak designs of albums by Coldplay, Rihanna, Ed Sheeran, and Little Mix. Some key points made in the analysis include:
- Coldplay's design uses bright colors and graffiti images to appeal to younger audiences. Rihanna's design focuses more on her image and words like "victory" and "fearless" to appeal to women.
- Ed Sheeran's design stands out for its simplicity, using just black and neon green colors along with a large "X" symbol.
- Little Mix's design prominently features the group's image on the front cover and uses the color red throughout in a "gir
This document provides information about purchasing a 3Com 3C37601 16-port fast Ethernet card from Launch 3 Telecom. It describes Launch 3 Telecom as a supplier of telecom hardware and 3Com replacement parts since 2003. It outlines payment and shipping options for purchasing the 3C37601, and notes that orders placed before 3PM receive same-day shipping. A warranty and return policy is also described. Additional services offered by Launch 3 Telecom like repairs, maintenance contracts, and de-installation are listed.
This document provides information about purchasing a 3Com 000606-0 Dual T1 Card Set, including how to buy, payment options, shipping details, warranty, and additional services from Launch 3 Telecom such as repairs, de-installation, and asset recovery. Launch 3 Telecom is an authorized reseller and service provider for 3Com networking equipment.
La Unión Europea ha acordado un paquete de sanciones contra Rusia por su invasión de Ucrania. Las sanciones incluyen restricciones a las transacciones con bancos rusos clave y la prohibición de la venta de aviones y equipos a Rusia. Los líderes de la UE esperan que las sanciones aumenten la presión económica sobre Rusia y la disuadan de continuar su agresión contra Ucrania.
Ultrassom Point of Care - Aula da Residência S J Campos-SPAlexandre Francisco
O documento apresenta protocolos de ultrassom point-of-care em UTI, incluindo o protocolo BLUE para avaliação pulmonar, o protocolo E-FAST para detecção de derrame abdominal e tamponamento cardíaco em trauma, e o protocolo FALLS para avaliação hemodinâmica em choque.
This document provides information about purchasing a 3Com 150A0055-02 product. It describes the product, lists contact information for purchasing, and details payment and shipping options. It also outlines the warranty and additional services offered by Launch 3 Telecom, such as repairs, maintenance contracts, de-installation, and recycling.
1Spatial: Cardiff FME World Tour: Time machines and attribute alchemy1Spatial
1. Attribute transformation is a key part of ETL work like data migration and involves techniques for parsing, formatting, and extracting attribute values.
2. Date/time attributes require special handling using datetime transformers and functions to deal with inconsistent formats, time zones, and calculations. Regular expressions and string functions help match patterns and extract substrings from attributes.
3. The document provides examples of using regular expressions and string functions in FME to clean up attribute values by extracting parts of strings, validating addresses, and finding postal codes.
Back to FME School - Day 1: Your Data and FMESafe Software
It’s that time of year. The season is changing and FME ‘school’ is now in session! Join us for a series of 9 mini-talks to learn the latest tips for data transformation, see live demos, and get your FME questions answered. Registration gives you access for all three days — sign up now to tune in to the talks you’re most interested in.
Course Schedule – Day 1: Your Data and FME
8:00am – FME Workbench Performance Tips & Tricks
8:40am – A Database for Every Occasion
9:20am – Working with Attributes in FME
FSI201 FINRA’s Managed Data Lake – Next Gen Analytics in the CloudAmazon Web Services
FINRA’s Data Lake unlocks the value in its data to accelerate analytics and machine learning at scale. FINRA's Technology group has changed its customer's relationship with data by creating a Managed Data Lake that enables discovery on Petabytes of capital markets data, while saving time and money over traditional analytics solutions. FINRA’s Managed Data Lake includes a centralized data catalog and separates storage from compute, allowing users to query from petabytes of data in seconds. Learn how FINRA uses Spot instances and services such as Amazon S3, Amazon EMR, Amazon Redshift, and AWS Lambda to provide the 'right tool for the right job' at each step in the data processing pipeline. All of this is done while meeting FINRA’s security and compliance responsibilities as a financial regulator.
This document provides an overview and agenda for a presentation on Amazon DynamoDB. It discusses key features of DynamoDB including its data model, scaling capabilities, and data modeling best practices. It also provides examples of how to model and query common data scenarios like event logging, product catalogs, messaging apps, and multiplayer gaming.
MongoDB World 2018: Overnight to 60 Seconds: An IOT ETL Performance Case StudyMongoDB
The document summarizes an IOT ETL performance case study where the author collected water and electric meter data and loaded it into a database. The initial load of over 90 million documents from a 10GB file into a MongoDB database took over 4 hours. The author then redesigned the data schema, splitting it into hourly documents to improve query performance. This reduced the processing time to just 3 minutes and the data size to 13MB. The key lessons were that changing the data schema and using batch writes with multiple workers can dramatically improve ETL and query performance.
The document provides an overview of new transformers in FME that help optimize GIS workflows by simplifying attribute and data validation tasks. The AttributeManager allows consolidated handling of attribute tasks like creation, renaming, copying, and validation. The AttributeValidator performs validation tests on attributes and outputs validation messages. The FeatureWriter enables writing features in workflows to avoid chaining workspaces and support post-processing like notifications and automation.
Convert and Migrate Your NoSQL Database or Data Warehouse to AWS - May 2017 A...Amazon Web Services
Learning Objectives:
- Understand the use cases for migrating or replicating databases to the cloud
- Learn about the benefits of cloud-native databases for performance and costs reduction
- See how AWS Database Migration Service helps with your migration
- See how AWS Schema Conversion Tool makes conversions simple and quick
Moving or replicating your databases to the cloud should be simple and inexpensive. AWS has recently enhanced the AWS Database Migration Service and the AWS Schema Conversion Tool with new data sources to increase your migration options. You can now export from MongoDB databases and Greenplum, IBM Netezza, HPE Vertica, Teradata, Oracle DW and Microsoft SQL Server data warehouses to AWS. Learn how to export and migrate your data and procedural code with minimal downtime to the cloud database of your choice, including cloud-native offerings such as Amazon Aurora, Amazon DynamoDB and Amazon Redshift.
Large volume data analysis on the Typesafe Reactive Platform - Big Data Scala...Martin Zapletal
The document discusses distributed machine learning and data processing. It covers several topics including reasons for using distributed machine learning, different distributed computing architectures and primitives, distributed data stores and analytics tools like Spark, streaming architectures like Lambda and Kappa, and challenges around distributed state management and fault tolerance. It provides examples of failures in distributed databases and suggestions to choose the appropriate tools based on the use case and understand their internals.
This document provides an overview of the R programming language. It describes that R can handle numeric and textual data, perform matrix algebra and statistical functions. While R is not a database, it can connect to external databases. It also summarizes that R has no graphical user interface but can connect to other languages for visualization, and its interpreter can be slow but users can call optimized C/C++ code. The document also contrasts the differences between using R and commercial packages.
Amazon DynamoDB is a fast and flexible NoSQL database service for applications that need consistent, single-digit millisecond latency at any scale. It is a fully managed cloud database and supports both document and key-value store models. Its flexible data model and reliable performance make it a great fit for mobile, web, gaming, ad tech, IoT, and many other applications.
Learning Objectives:
Understand the differences between relational and non-relational databases
Learn about common use cases for DynamoDB across gaming, ad tech, IoT, and more
See how DynamoDB helps customers handle spikes in traffic and save development time for new feature launches
Who Should Attend:
Developers, IT Decision Makers, and Executives interested in learning more about Amazon Web Services’ serverless NoSQL service to scale mobile, web, IoT, ad tech, and gaming apps
Convert and Migrate Your NoSQL Database or Data Warehouse to AWS - July 2017Amazon Web Services
Learning Objectives:
- Understand the use cases for migrating or replicating databases to the cloud
- Learn about the benefits of cloud-native databases for performance and costs reduction
- See how AWS Database Migration Service helps with your migration and how AWS Schema Conversion Tool makes conversions simple and quick
Moving or replicating your databases to the cloud should be simple and inexpensive. AWS has recently enhanced the AWS Database Migration Service and the AWS Schema Conversion Tool with new data sources to increase your migration options. You can now export from MongoDB databases and Greenplum, IBM Netezza, HPE Vertica, Teradata, Oracle DW and Microsoft SQL Server data warehouses to AWS. Learn how to export and migrate your data and procedural code with minimal downtime to the cloud database of your choice, including cloud-native offerings such as Amazon Aurora, Amazon DynamoDB and Amazon Redshift.
1. Kusto (Azure Data Explorer) is a fast and flexible data exploration service for analyzing security and application logs, performance counters, and other streaming data.
2. A Data Engineer's role is evolving to focus more on real-time analysis using Kusto as opposed to traditional SQL. Understanding how to use Kusto's query engine and data ingestion capabilities is key.
3. Techniques like using materialized views, partitioning data, and leader-follower databases can help distribute workloads and improve query performance at scale in Kusto. However, Kusto has limitations around concurrency, memory usage, and result set sizes that need to be considered.
The document describes Hadoop MapReduce and its key concepts. It discusses how MapReduce allows for parallel processing of large datasets across clusters of computers using a simple programming model. It provides details on the MapReduce architecture, including the JobTracker master and TaskTracker slaves. It also gives examples of common MapReduce algorithms and patterns like counting, sorting, joins and iterative processing.
Netflix Machine Learning Infra for Recommendations - 2018Karthik Murugesan
Faisal Siddiqi presented on machine learning infrastructure for recommendations. He outlined Boson and AlgoCommons, two major ML infra components. Boson focuses on offline training for both ad-hoc exploration and production. It provides utilities for data transfer, feature schema, stratification, and feature transformers. AlgoCommons provides common abstractions and building blocks for ML like data access, feature encoders, predictors, and metrics. It aims for composability, portability, and avoiding training-serving skew.
ML Infra for Netflix Recommendations - AI NEXTCon talkFaisal Siddiqi
Faisal Siddiqi presented on machine learning infrastructure for recommendations. He outlined Boson and AlgoCommons, two major ML infra components. Boson focuses on offline training for both ad-hoc exploration and production. It provides utilities for data preparation, feature engineering, training, metrics, and visualization. AlgoCommons provides common abstractions and building blocks for ML like data access, feature encoders, predictors, and metrics. It aims for composability, portability, and avoiding training-serving skew.
[db tech showcase Tokyo 2017] C34: Replacing Oracle Database at DBS Bank ~Ora...Insight Technology, Inc.
Migrating Oracle based applications to MariaDB has become easier and economically advantageous with the feature set of MariaDB 10.2 and the upcoming 10.3 release. We’ll present details of the features that led DBS Bank to migrate mission critical applications to MariaDB.
Sql server 2016: System Databases, data types, DML, json, and built-in functionsSeyed Ibrahim
SQL Server 2016 Slides for the Newbies. Prepared for a session. Covers SQL 2016 JSON support, Built-in Functions, Data Types & Pre-built system databases
A closer look at the fast, fully managed data warehouse that makes it simple and cost-effective to analyze all your data using standard SQL and your existing Business Intelligence (BI) tools. We'll show how to run complex analytic queries against petabytes of structured data, using sophisticated query optimization, columnar storage on high-performance local disks, and massively parallel query execution.
Level: Beginner
Speakers:
Jay Formosa - Solutions Architect, AWS
Aser Moustafa - Data Warehouse Specialist Solutions Architect, AWS
Data Warehousing with Amazon Redshift: Data Analytics Week SFAmazon Web Services
The document discusses Amazon Redshift, a data warehousing service from AWS. It provides the following key points:
- Redshift uses a massively parallel processing (MPP) architecture with columnar data storage for high performance analytics on large datasets.
- It consists of leader and compute nodes that store metadata and execute queries in parallel respectively. Data is distributed across slices for parallel query processing.
- Redshift utilizes various techniques like compression, zone maps, and sorting to optimize storage and improve query performance by reducing I/O. Best practices for these techniques are also covered.
Similar to Time Machines and Attribute Alchemy (20)
Essentials of Automations: Exploring Attributes & Automation ParametersSafe Software
Building automations in FME Flow can save time, money, and help businesses scale by eliminating data silos and providing data to stakeholders in real-time. One essential component to orchestrating complex automations is the use of attributes & automation parameters (both formerly known as “keys”). In fact, it’s unlikely you’ll ever build an Automation without using these components, but what exactly are they?
Attributes & automation parameters enable the automation author to pass data values from one automation component to the next. During this webinar, our FME Flow Specialists will cover leveraging the three types of these output attributes & parameters in FME Flow: Event, Custom, and Automation. As a bonus, they’ll also be making use of the Split-Merge Block functionality.
You’ll leave this webinar with a better understanding of how to maximize the potential of automations by making use of attributes & automation parameters, with the ultimate goal of setting your enterprise integration workflows up on autopilot.
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!
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
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.
The Zero-ETL Approach: Enhancing Data Agility and InsightSafe Software
In the ever-evolving landscape of data management, Zero-ETL is an approach that is reshaping how businesses handle and integrate their data. This webinar explores Zero-ETL, a paradigm shift from the traditional Extract, Transform, Load (ETL) process, offering a more streamlined, efficient, and real-time data integration method.
We will begin with an introduction to the concept of Zero-ETL, including how it allows direct access to data in its native environment and real-time data transformation, providing up-to-date information with significantly reduced data redundancy.
Next, we'll take you through several demonstrations showing how Zero-ETL can deliver real-time data and enable the free movement of data between systems. We will also discuss the various tools that support all aspects of Zero-ETL, providing attendees with an understanding of how they can adopt this innovative approach in their organizations.
Lastly, the session will conclude with an interactive Q&A segment, allowing participants to gain deeper insights into how Zero-ETL can be tailored to their specific business needs and how they can get started today.
Join us to discover how Zero-ETL can elevate your organization's data strategy.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
Following the popularity of “Cloud Revolution: Exploring the New Wave of Serverless Spatial Data,” we’re thrilled to announce this much-anticipated encore webinar.
In this sequel, we’ll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR.
Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios.
Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects.
Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you’re building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
Following the popularity of "Cloud Revolution: Exploring the New Wave of Serverless Spatial Data," we're thrilled to announce this much-anticipated encore webinar.
In this sequel, we'll dive deeper into the Cloud-Native realm by uncovering practical applications and FME support for these new formats, including COGs, COPC, FlatGeoBuf, GeoParquet, STAC, and ZARR.
Building on the foundation laid by industry leaders Michelle Roby of Radiant Earth and Chris Holmes of Planet in the first webinar, this second part offers an in-depth look at the real-world application and behind-the-scenes dynamics of these cutting-edge formats. We will spotlight specific use-cases and workflows, showcasing their efficiency and relevance in practical scenarios.
Discover the vast possibilities each format holds, highlighted through detailed discussions and demonstrations. Our expert speakers will dissect the key aspects and provide critical takeaways for effective use, ensuring attendees leave with a thorough understanding of how to apply these formats in their own projects.
Elevate your understanding of how FME supports these cutting-edge technologies, enhancing your ability to manage, share, and analyze spatial data. Whether you're building on knowledge from our initial session or are new to the serverless spatial data landscape, this webinar is your gateway to mastering cloud-native formats in your workflows.
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization’s performance. The power of real-time data automation through FME can turn this vision into reality.
Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We’ll explore:
FME’s role in real-time event processing, from data intake and analysis to transformation and reporting
An overview of leveraging streams vs. automations
FME’s impact across various industries highlighted by real-life case studies
Live demonstrations on setting up FME workflows for real-time data
Practical advice on getting started, best practices, and tips for effective implementation
Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.
From Event to Action: Accelerate Your Decision Making with Real-Time AutomationSafe Software
Imagine a world where information flows as swiftly as thought itself, making decision-making as fluid as the data driving it. Every moment is critical, and the right tools can significantly boost your organization's performance. The power of real-time data automation through FME can turn this vision into reality.
Aimed at professionals eager to leverage real-time data for enhanced decision-making and efficiency, this webinar will cover the essentials of real-time data and its significance. We'll explore:
FME's role in real-time event processing, from data intake and analysis to transformation and reporting
An overview of leveraging streams vs. automations
FME's impact across various industries highlighted by real-life case studies
Live demonstrations on setting up FME workflows for real-time data
Practical advice on getting started, best practices, and tips for effective implementation
Join us to enhance your skills in real-time data automation with FME, and take your operational capabilities to the next level.
Beyond Boundaries: Leveraging No-Code Solutions for Industry InnovationSafe Software
Hiring and retaining software development talent is next to impossible for AEC firms and other industries alike.
Join us and guest speakers from HOK, a leader in the AEC industry, as they share their success in navigating the tight talent market through the use of no-code solutions and FME.
Discover how HOK approached the process of building a custom tool to automate the creation of projects and user management for Trimble Connect and ProjectSight.
Using a mix of traditional and no-code in FME, our guest speakers will reveal how the team bridged the resource gap and used the available talent pool, producing the mission-critical web app “Trajectory”.
They will also dive into details, illustrating first-hand how JSON data was used as a “glue” between two development groups.
Learn how embracing FME as a no-code solution can unlock potential within your teams, foster collaboration, and drive efficiency.
Powering Real-Time Decisions with Continuous Data StreamsSafe Software
In an era where making swift, data-driven decisions can set industry leaders apart, understanding the world of data streaming and stream processing is crucial. During this webinar, we'll explore:
Stream Processing Overview: Dive into what stream processing entails and the value it brings organizations.
Stream vs. Batch Processing: Learn the key differences and benefits of stream processing compared to traditional batch processing, highlighting the efficiency of real-time data handling.
Mastering Data Volumes: Discover strategies for effectively managing both high and low volume data streams, ensuring optimal performance.
Boosting Operational Excellence: Explore how adopting data streaming can enhance your organization's operational workflows and productivity.
Spatial Data's Role in Streams: Understand the importance of spatial data in stream processing for more informed decision-making.
Interactive Demos: Watch practical demos, from dynamic geofencing to group-based processing.
Plus, we’ll show you how you can do it without coding! Register now to take the first step towards more informed, timely, and precise decision-making for your organization.
The Critical Role of Spatial Data in Today's Data EcosystemSafe Software
In today's data-driven landscape, integrating spatial data is becoming increasingly crucial for organizations aiming to harness the full potential of their data. Spatial data offers unique insights based on location, making it a fundamental component for addressing various challenges across different sectors, including urban planning, environmental sustainability, public health, and logistics.
Our webinar delves into the indispensable role of spatial data in data management and analysis. We'll showcase how omitting spatial data from your data strategy not only weakens your data infrastructure, but also limits the depth of your insights. Through real-world case studies, we'll highlight the transformative impact of spatial data, demonstrating its ability to uncover complex patterns, trends, and relationships.
Join us for this introductory-level webinar as we explore the critical importance of spatial data integration in driving strategic decision-making processes. By the end of the webinar, you'll gain a renewed perspective on how spatial data is essential for confronting and overcoming challenges across various domains.
Cloud Revolution: Exploring the New Wave of Serverless Spatial DataSafe Software
Once in a while, there really is something new under the sun. The rise of cloud-hosted data has fueled innovation in spatial data storage, enabling a brand new serverless architectural approach to spatial data sharing. Join us in our upcoming webinar to learn all about these new ways to organize your data, and leverage data shared by others. Explore the potential of Cloud Native Geospatial Formats in your workflows with FME, as we introduce five new formats: COGs, COPC, FlatGeoBuf, GeoParquet, STAC and ZARR.
Learn from industry experts Michelle Roby from Radiant Earth and Chris Holmes from Planet about these cloud-native geospatial data formats and how they can make data easier to manage, share, and analyze. To get us started, they’ll explain the goals of the Cloud-Native Geospatial Foundation and provide overviews of cloud-native technologies including the Cloud-Optimized GeoTIFF (COG), SpatioTemporal Asset Catalogs (STAC), and GeoParquet.
Following this, our seasoned FME team will guide you through practical demonstrations, showcasing how to leverage each format to its fullest potential. Learn strategic approaches for seamless integration and transition, along with valuable tips to enhance performance using these formats in FME.
Discover how these formats are reshaping geospatial data handling and how you can seamlessly integrate them into your FME workflows and harness the explosion of cloud-hosted data.
Igniting Next Level Productivity with AI-Infused Data Integration WorkflowsSafe Software
Learn where FME meets AI in this upcoming webinar to offer you incredible time savings. This webinar is tailored to ignite imaginations and offer solutions to your data integration challenges. As the new digital era sets sail on the winds of AI, the tangibility of its integration in our daily schema is unfolding.
Segment 1, titled “AI: The Good, the Bad and the FME” by Darren Fergus of Locus, navigates through the realms of AI, scrutinizing its pervasive impact while underscoring the symbiotic potential of FME and AI. Join in an engaging demonstration as FME and ChatGPT collaboratively orchestrate a PowerPoint narrative, epitomizing the alliance of AI with human ingenuity.
In Segment 2, “Integrating GeoAI Models in FME” by Dennis Wilhelm and Dr. Christopher Britsch of con terra GmbH, the spotlight veers towards operationalizing AI in our daily tasks through FME. A practical approach to embedding GeoAI Models into FME Workspaces is unveiled, showcasing the ease of incorporating AI-driven methodologies into your FME workflows, skyrocketing productivity levels.
To follow, Segment 3, "Unleash generative AI on your terms!" by Oliver Morris of Avineon-Tensing. While the prospects of Generative AI are thrilling, security and IT reservations, especially with 'phone home' tools, are genuine concerns. However, with open-source tools, you can locally harness large language models. In this demo, we'll unravel the magic of local AI deployment and its seamless integration into an FME workspace.
Bonus! Dmitri will join us for a fourth segment to tie us off, showcasing what he has been up to this week, including using OpenAI API for texturing in FME, amoung other projects.
Join us to explore the synergy of FME and AI: opening portals to a realm of revolutionized productivity and enriched user experiences.
The Zero-ETL Approach: Enhancing Data Agility and InsightSafe Software
In the ever-evolving landscape of data management, Zero-ETL is an approach that is reshaping how businesses handle and integrate their data. This webinar explores Zero-ETL, a paradigm shift from the traditional Extract, Transform, Load (ETL) process, offering a more streamlined, efficient, and real-time data integration method.
We will begin with an introduction to the concept of Zero-ETL, including how it allows direct access to data in its native environment and real-time data transformation, providing up-to-date information with significantly reduced data redundancy.
Next, we'll take you through several demonstrations showing how Zero-ETL can deliver real-time data and enable the free movement of data between systems. We will also discuss the various tools that support all aspects of Zero-ETL, providing attendees with an understanding of how they can adopt this innovative approach in their organizations.
Lastly, the session will conclude with an interactive Q&A segment, allowing participants to gain deeper insights into how Zero-ETL can be tailored to their specific business needs and how they can get started today.
Join us to discover how Zero-ETL can elevate your organization's data strategy.
Mastering MicroStation DGN: How to Integrate CAD and GISSafe Software
Dive deep into the world of CAD-GIS integration with our expert-led webinar. Discover how to seamlessly transfer data between Bentley MicroStation and leading GIS platforms, such as Esri ArcGIS. This session goes beyond mere CAD/GIS conversion, showcasing techniques to precisely transform MicroStation elements including cells, text, lines, and symbology. We’ll walk you through tags versus item types, and understanding how to leverage both. You’ll also learn how to reproject to any coordinate system. Finally, explore cutting-edge automated methods for managing database links, and delve into innovative strategies for enabling self-serve data collection and validation services.
Join us to overcome the common hurdles in CAD and GIS integration and enhance the efficiency of your workflows. This session is perfect for professionals, both new to FME and seasoned users, seeking to streamline their processes and leverage the full potential of their CAD and GIS systems.
Geospatial Synergy: Amplifying Efficiency with FME & EsriSafe Software
Dive deep into the world of geospatial data management and transformation in our upcoming webinar focusing on the powerful integration of FME and Esri technologies. This insightful session comprises two compelling segments aimed at enhancing your geospatial workflows, while minimizing operational hurdles.
In the first segment, guest speaker Jan Roggisch from Locus unveils how Auckland Council triumphed over the challenges of handling large, frequent data updates on ArcGIS Online using FME. Discover the journey from manual data handling to an automated, streamlined process that reduced server downtime from minutes to seconds: setting a new standard for local government organizations.
The second segment, led by James Botterill from 1Spatial, unveils the magic of incorporating ArcPy into your FME workflows. Delve into real-world scenarios where ArcGIS geoprocessing is harmoniously orchestrated within FME using the PythonCaller. Gain insights into raster-vector data conversion, spatial analysis, and a host of practical tips and tricks that empower you to leverage the combined capabilities of FME and Esri for efficient data manipulation and conversion.
Join us to explore the remarkable possibilities that open up when FME and Esri technologies converge – enhancing your ability to manage and transform geospatial data with unprecedented efficiency.
Introducing the New FME Community Webinar - Feb 21, 2024 (2).pdfSafe Software
Join us at Safe Software as we unveil the exciting new FME Community platform.
Picture yourself entering a vibrant, interconnected world, where every click brings you closer to a fellow FME enthusiast, a new idea, or a solution that could revolutionize your workflow.
Since its inception, the FME Community has been a dynamic hub for knowledge sharing, where thousands of users converge to exchange insights, engage in stimulating discussions, and collaboratively solve challenges. Now, envision this community reimagined - retaining the features you know and love, but infused with new, cutting-edge functionalities designed to make your experience even more enriching and effortless. The Community is also planned to soon act as a central hub for all FME community acticity across the web.
This webinar is your personal tour through this enhanced FME Community landscape. Whether you're an experienced user familiar with every nook and cranny of the old platform, or you're setting foot in this community for the first time, our webinar will ensure you navigate the new terrain with ease and confidence. Discover how to maximize your engagement, tap into the wealth of resources available, and contribute to the growing tapestry of FME innovation.
Join us in celebrating the future of FME collaboration, where your next breakthrough idea, insightful article, or spirited discussion awaits. Don't miss this opportunity to be a part of the evolution of the FME Community!
Breaking Barriers & Leveraging the Latest Developments in AI TechnologySafe Software
Explore how to best leverage the latest of AI technology in our upcoming webinar, where we delve into advancements and trends in the field since our previous AI webinars in 2023. Join us for a session filled with fresh insights and practical knowledge. We're stitching together the final threads of this presentation as we speak, keeping pace with AI's breakneck speed. Expect a session brimming with the freshest insights, releases and breakthroughs in AI – right up to the minute! A spotlight of this session is set to include Dmitri Bagh’s exploration of innovative AI integrations with FME, ranging from generating 3D features for augmented reality using Dall-E, to enhancing urban planning with orthoimagery completion, and showcasing the power of AI in workspace analysis and geoart creation.
Whether you're new to AI or an experienced practitioner, this webinar is tailored to keep you at the forefront of AI innovation. Get ready for a session that is as informative as it is inspiring, equipping you with the tools to excel in the dynamic world of artificial intelligence.
"NATO Hackathon Winner: AI-Powered Drug Search", Taras KlobaFwdays
This is a session that details how PostgreSQL's features and Azure AI Services can be effectively used to significantly enhance the search functionality in any application.
In this session, we'll share insights on how we used PostgreSQL to facilitate precise searches across multiple fields in our mobile application. The techniques include using LIKE and ILIKE operators and integrating a trigram-based search to handle potential misspellings, thereby increasing the search accuracy.
We'll also discuss how the azure_ai extension on PostgreSQL databases in Azure and Azure AI Services were utilized to create vectors from user input, a feature beneficial when users wish to find specific items based on text prompts. While our application's case study involves a drug search, the techniques and principles shared in this session can be adapted to improve search functionality in a wide range of applications. Join us to learn how PostgreSQL and Azure AI can be harnessed to enhance your application's search capability.
Getting the Most Out of ScyllaDB Monitoring: ShareChat's TipsScyllaDB
ScyllaDB monitoring provides a lot of useful information. But sometimes it’s not easy to find the root of the problem if something is wrong or even estimate the remaining capacity by the load on the cluster. This talk shares our team's practical tips on: 1) How to find the root of the problem by metrics if ScyllaDB is slow 2) How to interpret the load and plan capacity for the future 3) Compaction strategies and how to choose the right one 4) Important metrics which aren’t available in the default monitoring setup.
Conversational agents, or chatbots, are increasingly used to access all sorts of services using natural language. While open-domain chatbots - like ChatGPT - can converse on any topic, task-oriented chatbots - the focus of this paper - are designed for specific tasks, like booking a flight, obtaining customer support, or setting an appointment. Like any other software, task-oriented chatbots need to be properly tested, usually by defining and executing test scenarios (i.e., sequences of user-chatbot interactions). However, there is currently a lack of methods to quantify the completeness and strength of such test scenarios, which can lead to low-quality tests, and hence to buggy chatbots.
To fill this gap, we propose adapting mutation testing (MuT) for task-oriented chatbots. To this end, we introduce a set of mutation operators that emulate faults in chatbot designs, an architecture that enables MuT on chatbots built using heterogeneous technologies, and a practical realisation as an Eclipse plugin. Moreover, we evaluate the applicability, effectiveness and efficiency of our approach on open-source chatbots, with promising results.
How information systems are built or acquired puts information, which is what they should be about, in a secondary place. Our language adapted accordingly, and we no longer talk about information systems but applications. Applications evolved in a way to break data into diverse fragments, tightly coupled with applications and expensive to integrate. The result is technical debt, which is re-paid by taking even bigger "loans", resulting in an ever-increasing technical debt. Software engineering and procurement practices work in sync with market forces to maintain this trend. This talk demonstrates how natural this situation is. The question is: can something be done to reverse the trend?
Discover the Unseen: Tailored Recommendation of Unwatched ContentScyllaDB
The session shares how JioCinema approaches ""watch discounting."" This capability ensures that if a user watched a certain amount of a show/movie, the platform no longer recommends that particular content to the user. Flawless operation of this feature promotes the discover of new content, improving the overall user experience.
JioCinema is an Indian over-the-top media streaming service owned by Viacom18.
GlobalLogic Java Community Webinar #18 “How to Improve Web Application Perfor...GlobalLogic Ukraine
Під час доповіді відповімо на питання, навіщо потрібно підвищувати продуктивність аплікації і які є найефективніші способи для цього. А також поговоримо про те, що таке кеш, які його види бувають та, основне — як знайти performance bottleneck?
Відео та деталі заходу: https://bit.ly/45tILxj
"What does it really mean for your system to be available, or how to define w...Fwdays
We will talk about system monitoring from a few different angles. We will start by covering the basics, then discuss SLOs, how to define them, and why understanding the business well is crucial for success in this exercise.
Introducing BoxLang : A new JVM language for productivity and modularity!Ortus Solutions, Corp
Just like life, our code must adapt to the ever changing world we live in. From one day coding for the web, to the next for our tablets or APIs or for running serverless applications. Multi-runtime development is the future of coding, the future is to be dynamic. Let us introduce you to BoxLang.
Dynamic. Modular. Productive.
BoxLang redefines development with its dynamic nature, empowering developers to craft expressive and functional code effortlessly. Its modular architecture prioritizes flexibility, allowing for seamless integration into existing ecosystems.
Interoperability at its Core
With 100% interoperability with Java, BoxLang seamlessly bridges the gap between traditional and modern development paradigms, unlocking new possibilities for innovation and collaboration.
Multi-Runtime
From the tiny 2m operating system binary to running on our pure Java web server, CommandBox, Jakarta EE, AWS Lambda, Microsoft Functions, Web Assembly, Android and more. BoxLang has been designed to enhance and adapt according to it's runnable runtime.
The Fusion of Modernity and Tradition
Experience the fusion of modern features inspired by CFML, Node, Ruby, Kotlin, Java, and Clojure, combined with the familiarity of Java bytecode compilation, making BoxLang a language of choice for forward-thinking developers.
Empowering Transition with Transpiler Support
Transitioning from CFML to BoxLang is seamless with our JIT transpiler, facilitating smooth migration and preserving existing code investments.
Unlocking Creativity with IDE Tools
Unleash your creativity with powerful IDE tools tailored for BoxLang, providing an intuitive development experience and streamlining your workflow. Join us as we embark on a journey to redefine JVM development. Welcome to the era of BoxLang.
The Microsoft 365 Migration Tutorial For Beginner.pptxoperationspcvita
This presentation will help you understand the power of Microsoft 365. However, we have mentioned every productivity app included in Office 365. Additionally, we have suggested the migration situation related to Office 365 and how we can help you.
You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
inQuba Webinar Mastering Customer Journey Management with Dr Graham HillLizaNolte
HERE IS YOUR WEBINAR CONTENT! 'Mastering Customer Journey Management with Dr. Graham Hill'. We hope you find the webinar recording both insightful and enjoyable.
In this webinar, we explored essential aspects of Customer Journey Management and personalization. Here’s a summary of the key insights and topics discussed:
Key Takeaways:
Understanding the Customer Journey: Dr. Hill emphasized the importance of mapping and understanding the complete customer journey to identify touchpoints and opportunities for improvement.
Personalization Strategies: We discussed how to leverage data and insights to create personalized experiences that resonate with customers.
Technology Integration: Insights were shared on how inQuba’s advanced technology can streamline customer interactions and drive operational efficiency.
This talk will cover ScyllaDB Architecture from the cluster-level view and zoom in on data distribution and internal node architecture. In the process, we will learn the secret sauce used to get ScyllaDB's high availability and superior performance. We will also touch on the upcoming changes to ScyllaDB architecture, moving to strongly consistent metadata and tablets.
What is an RPA CoE? Session 2 – CoE RolesDianaGray10
In this session, we will review the players involved in the CoE and how each role impacts opportunities.
Topics covered:
• What roles are essential?
• What place in the automation journey does each role play?
Speaker:
Chris Bolin, Senior Intelligent Automation Architect Anika Systems
Dandelion Hashtable: beyond billion requests per second on a commodity serverAntonios Katsarakis
This slide deck presents DLHT, a concurrent in-memory hashtable. Despite efforts to optimize hashtables, that go as far as sacrificing core functionality, state-of-the-art designs still incur multiple memory accesses per request and block request processing in three cases. First, most hashtables block while waiting for data to be retrieved from memory. Second, open-addressing designs, which represent the current state-of-the-art, either cannot free index slots on deletes or must block all requests to do so. Third, index resizes block every request until all objects are copied to the new index. Defying folklore wisdom, DLHT forgoes open-addressing and adopts a fully-featured and memory-aware closed-addressing design based on bounded cache-line-chaining. This design offers lock-free index operations and deletes that free slots instantly, (2) completes most requests with a single memory access, (3) utilizes software prefetching to hide memory latencies, and (4) employs a novel non-blocking and parallel resizing. In a commodity server and a memory-resident workload, DLHT surpasses 1.6B requests per second and provides 3.5x (12x) the throughput of the state-of-the-art closed-addressing (open-addressing) resizable hashtable on Gets (Deletes).
From Natural Language to Structured Solr Queries using LLMsSease
This talk draws on experimentation to enable AI applications with Solr. One important use case is to use AI for better accessibility and discoverability of the data: while User eXperience techniques, lexical search improvements, and data harmonization can take organizations to a good level of accessibility, a structural (or “cognitive” gap) remains between the data user needs and the data producer constraints.
That is where AI – and most importantly, Natural Language Processing and Large Language Model techniques – could make a difference. This natural language, conversational engine could facilitate access and usage of the data leveraging the semantics of any data source.
The objective of the presentation is to propose a technical approach and a way forward to achieve this goal.
The key concept is to enable users to express their search queries in natural language, which the LLM then enriches, interprets, and translates into structured queries based on the Solr index’s metadata.
This approach leverages the LLM’s ability to understand the nuances of natural language and the structure of documents within Apache Solr.
The LLM acts as an intermediary agent, offering a transparent experience to users automatically and potentially uncovering relevant documents that conventional search methods might overlook. The presentation will include the results of this experimental work, lessons learned, best practices, and the scope of future work that should improve the approach and make it production-ready.
Session 1 - Intro to Robotic Process Automation.pdfUiPathCommunity
👉 Check out our full 'Africa Series - Automation Student Developers (EN)' page to register for the full program:
https://bit.ly/Automation_Student_Kickstart
In this session, we shall introduce you to the world of automation, the UiPath Platform, and guide you on how to install and setup UiPath Studio on your Windows PC.
📕 Detailed agenda:
What is RPA? Benefits of RPA?
RPA Applications
The UiPath End-to-End Automation Platform
UiPath Studio CE Installation and Setup
💻 Extra training through UiPath Academy:
Introduction to Automation
UiPath Business Automation Platform
Explore automation development with UiPath Studio
👉 Register here for our upcoming Session 2 on June 20: Introduction to UiPath Studio Fundamentals: https://community.uipath.com/events/details/uipath-lagos-presents-session-2-introduction-to-uipath-studio-fundamentals/
4. RCMP E Division
Heidi Lee | Robert Shultz
Goal: Load GPS records into ArcGIS.
Problems:
➔ Inconsistent date formats
➔ Time zones
➔ Daylight saving
Dates and times are
complicated.
5. Formatting?
● YYMMDD, HHMMSS, UTC
● Jun 2016
● ‘on Saturday, Jan 9th 2016, 01:00 am’ & ‘+0530’
● 2016-12-07 12:20:07.785403-05
● 20160313020000.000 (March 13 - Daylight Saving)
● <d v="2016-12-13T00:00:00"/> (Excel)
● YYYY-MM-DD hh:mm:ss[.nnnnnnn] (SQL Server
‘datetime2’ value)
Calculations?
● Date2-Date1 = How many days?
13. Southern Company
Jeff DeWitt
HOK Inc.
David Baldacchino
Goals:
➔ Test for patterns in attribute values
➔ Extract substrings from attribute values
➔ Validate strings
2. Finding patterns
NGI Belgium
Jan Beyen
RCMP E Div.
Heidi Lee
14. Southern Company
Problem: Attribute value cleanup
- MONTANA * or Sales/Other (1)
HOK Inc.
Problem: Extract Sheet numbers from file names
- MyProject - Sheet - A512 - PARTITION TYPES & …
- G001 - GENERAL NOTES, ABBREVIATIONS, SYMBOLS, ...
NGI
Problem: Validate address strings
- Rue Achille Masset 52A
15. Sheet number extraction
MyProject - Sheet - A512 - PARTITION TYPES & …
G001 - GENERAL NOTES, ABBREVIATIONS, SYMBOLS, ...
^[Ss]*?[-]?[ ]?([A-Z0-9]{1,5})[ ]+[-]+[Ss]*$
Address validation
Rue Achille Masset 52A
^((([a-zA-Z]+) )+)([0-9]+)([a-zA-Z]*)$
Wave the wand of regex
18. Regex vs. String Functions Example
Code ABD3705337067
Regular Expression: ([A-Z]{3})([0-9]+)
String Functions:
Attribute String Function
alpha @Left(@Value(Code),3)
beta @Substring(@Value(Code),3,-1)
19. TRC Inc.
Peter Veensta
Goals:
➔ Compare current and previous
Excel rows.
➔ Sum attribute values with the
previous row.
3. Time-travelling
attributes.
FPInnovations
Matt Kurowski
23. Summary
1. DateTime transformers and Text Editor
functions help with:
○ Date/time formatting
○ Calculations
○ Time zones
2. Regex and string functions help with
patterns.
3. Work with current and previous attribute
values in the AttributeManager.
28. Achieving Automation
• Define schema to use for import to database (52+ attributes, one
feature type)
• AttributeFilter: Separate data streams for each company
• FeatureReader: Reads the actual data
• AttributeManager: Convert to common schema
• Can be run at scheduled intervals when a file arrives
29. Achieving Quality
Custom transformers to improve and split data, reused on multiple files:
• SplittFornavnEtternavn: Separate firstname and lastname into 2 different
attributes.
• SplitTelefonOgMobiltlf: Decide if number is a cellular or landline and
create 2 different attributes.
• SplitStreetNameNumberLetter: You have one attribute in which contains
streetname, housenumber, houseletter. Output is 3 different attributes.
30. Achieving Quality
Use existing services and databases to look up and verify values:
• CheckAIDToOwner: Checks if this is the official owner of that property.
• NorkartGeocoder: API to check the validity of an address, handles
misspellings, validates postal number, municipality number, etc. Fresh data
every day!
31. Achieving a Common Schema
Translate each customer’s
schema to the common
schema:
AttributeManager
One separate
AttributeManager for each
company.