This slidedeck covers the topic of how to validate address data sets from various sources and convert the address information into coordinates (process of geocoding). Geocoded address data can be used to display them and on maps and to further do all kind of spatially-enabled analysis and mining.
Geodatenmanagement und -Visualisierung mit Oracle Spatial TechnologiesKarin Patenge
Der Foliensatz gibt einen Überblick darüber, wie und welche räumlichen Daten in der Oracle Datenbank gepflegt und ausgewertet werden können. Darüber hinaus zeigt er die Nutzung von Oracle Maps für die Visualisierung von räumlichen Daten in Form von Karten auf.
Bei Interesse gern auch weiterlesen auf dem deutschsprachigen Oracle Spatial Blog (http://oracle-spatial.blogspot.com).
The document discusses Oracle Spatial and GeoRaster capabilities for processing and analyzing raster imagery data within the Oracle database. It provides an overview of raster data concepts like cell size and resolution, multi-band images, blocking, and compression techniques for imagery and grid data. It also demonstrates how Oracle Spatial can be used to load, store, query, manipulate, analyze and process large volumes of raster data within the database for applications like remote sensing, mapping, and gridded data analysis.
The document discusses weather data stored in Oracle Spatial GeoRaster format. It provides an overview of weather forecast data from the German Weather Service (DWD) which is available in GRIB2 format from their open data server. It also describes how the weather data from DWD numerical models like ICON can be accessed and processed using Oracle Spatial GeoRaster.
Big Data Community Webinar vom 16. Mai 2019: Oracle NoSQL DB im ÜberblickKarin Patenge
Ein Key-Value Store mit nativer Unterstützung für JSON, der auch Graphen und SQL “kann”. Der Foliensatz enthält detaillierte Informationen zur Nutzung der Oracle NoSQL DB aus Sicht der Anwendungsentwicklung als auch aus Sicht der Administration / des Betriebs.
Geodatenmanagement und -Visualisierung mit Oracle Spatial TechnologiesKarin Patenge
Der Foliensatz gibt einen Überblick darüber, wie und welche räumlichen Daten in der Oracle Datenbank gepflegt und ausgewertet werden können. Darüber hinaus zeigt er die Nutzung von Oracle Maps für die Visualisierung von räumlichen Daten in Form von Karten auf.
Bei Interesse gern auch weiterlesen auf dem deutschsprachigen Oracle Spatial Blog (http://oracle-spatial.blogspot.com).
The document discusses Oracle Spatial and GeoRaster capabilities for processing and analyzing raster imagery data within the Oracle database. It provides an overview of raster data concepts like cell size and resolution, multi-band images, blocking, and compression techniques for imagery and grid data. It also demonstrates how Oracle Spatial can be used to load, store, query, manipulate, analyze and process large volumes of raster data within the database for applications like remote sensing, mapping, and gridded data analysis.
The document discusses weather data stored in Oracle Spatial GeoRaster format. It provides an overview of weather forecast data from the German Weather Service (DWD) which is available in GRIB2 format from their open data server. It also describes how the weather data from DWD numerical models like ICON can be accessed and processed using Oracle Spatial GeoRaster.
Big Data Community Webinar vom 16. Mai 2019: Oracle NoSQL DB im ÜberblickKarin Patenge
Ein Key-Value Store mit nativer Unterstützung für JSON, der auch Graphen und SQL “kann”. Der Foliensatz enthält detaillierte Informationen zur Nutzung der Oracle NoSQL DB aus Sicht der Anwendungsentwicklung als auch aus Sicht der Administration / des Betriebs.
This document discusses using Pandas and Python to access and analyze data in an Oracle database. It begins with an introduction to Python and Pandas for data analysis. It then discusses how to connect Python to an Oracle database using the cx_Oracle library. It provides examples of querying and manipulating spatial vector data stored in Oracle using GeoPandas. The document aims to help developers get started with leveraging Python and Pandas for data work with an Oracle backend.
This document discusses analyzing social media data from Meetup.com using graph technologies. It describes retrieving data via the Meetup API, modeling the data as a graph, analyzing the graph using algorithms and tools like PGX and PGQL, and visualizing results in Cytoscape. Potential questions that could be answered include identifying influential people and groups, relationships between groups, and hot topics. The demo environment uses Oracle Big Data Lite with Oracle NoSQL Database to store the graph and analyze it.
This document discusses analyzing Bitcoin transaction data as a graph using Oracle technologies. It provides an overview of modeling Bitcoin transactions as a graph with transactions and addresses as vertices and relationships between them as edges. It then describes the workflow of preparing the data, loading it into a graph database, and analyzing the graph using PGX and PGQL. Examples are given of graph queries and algorithms like PageRank and betweenness centrality that can be run on the Bitcoin transaction graph to identify important transactions and addresses.
Graph Analytics on Data from Meetup.comKarin Patenge
This document contains an agenda and slides from a presentation on analyzing data using graph analytics. The presentation discusses retrieving meetup data via API, transforming it into nodes and edges files, loading the data into a graph database, and analyzing the graph data using PGX and PGQL. Key topics analyzed include influential meetup groups, connections between groups in different locations, and popular topics.
The document provides an overview of various emerging technologies and trends that are influencing customers, including chatbots, blockchain, internet of things, and artificial intelligence. It discusses these technologies and how Oracle is addressing them through products and services like its blockchain cloud service, IoT cloud service, and intelligent bots platform.
Raster Algebra mit Oracle Spatial und uDigKarin Patenge
Im Foliensatz ist die Integration von Oracle Spatial mit Open Source Technolgien beschrieben. Am Beispiel von uDig wird Schritt-für-Schritt aufgezeigt, wie es zusammen mit Oracle Spatial für die Rasterdatenanalyse eingesetzt werden hier. Beispielhaft wird ein Vegetationsindex (NVDI) berechnet.
Bei Interesse gern auch weiterlesen auf dem Oracle Spatial Blog (http://oracle-spatial.blogspot.com).
This document discusses using Pandas and Python to access and analyze data in an Oracle database. It begins with an introduction to Python and Pandas for data analysis. It then discusses how to connect Python to an Oracle database using the cx_Oracle library. It provides examples of querying and manipulating spatial vector data stored in Oracle using GeoPandas. The document aims to help developers get started with leveraging Python and Pandas for data work with an Oracle backend.
This document discusses analyzing social media data from Meetup.com using graph technologies. It describes retrieving data via the Meetup API, modeling the data as a graph, analyzing the graph using algorithms and tools like PGX and PGQL, and visualizing results in Cytoscape. Potential questions that could be answered include identifying influential people and groups, relationships between groups, and hot topics. The demo environment uses Oracle Big Data Lite with Oracle NoSQL Database to store the graph and analyze it.
This document discusses analyzing Bitcoin transaction data as a graph using Oracle technologies. It provides an overview of modeling Bitcoin transactions as a graph with transactions and addresses as vertices and relationships between them as edges. It then describes the workflow of preparing the data, loading it into a graph database, and analyzing the graph using PGX and PGQL. Examples are given of graph queries and algorithms like PageRank and betweenness centrality that can be run on the Bitcoin transaction graph to identify important transactions and addresses.
Graph Analytics on Data from Meetup.comKarin Patenge
This document contains an agenda and slides from a presentation on analyzing data using graph analytics. The presentation discusses retrieving meetup data via API, transforming it into nodes and edges files, loading the data into a graph database, and analyzing the graph data using PGX and PGQL. Key topics analyzed include influential meetup groups, connections between groups in different locations, and popular topics.
The document provides an overview of various emerging technologies and trends that are influencing customers, including chatbots, blockchain, internet of things, and artificial intelligence. It discusses these technologies and how Oracle is addressing them through products and services like its blockchain cloud service, IoT cloud service, and intelligent bots platform.
Raster Algebra mit Oracle Spatial und uDigKarin Patenge
Im Foliensatz ist die Integration von Oracle Spatial mit Open Source Technolgien beschrieben. Am Beispiel von uDig wird Schritt-für-Schritt aufgezeigt, wie es zusammen mit Oracle Spatial für die Rasterdatenanalyse eingesetzt werden hier. Beispielhaft wird ein Vegetationsindex (NVDI) berechnet.
Bei Interesse gern auch weiterlesen auf dem Oracle Spatial Blog (http://oracle-spatial.blogspot.com).