Once the appropriate client driver is installed, select the option to connect to a MySQL server, enter your Stardog hostname (if running locally, use your IP address instead of localhost) and configured BI server port and provide credentials for a Stardog user.Īlthough not officially supported, other visualization and reporting tools should work with the BI Server. Supports connecting natively using the MySQL protocol.Ĭan connect using MySQL’s JDBC driver and the “Read Database” operator. Requires mysqlclient to be installed in the Dockerfile. Where you can simply select the Stardog connector Requires the MySQL Connector/ODBC driver. This means that a wide range of clients are supported including MySQL ODBC, JDBC and ADO.NET drivers.Ĭurrently the following BI tools are officially supported: Supported ClientsĬlients connect to the BI Server using the MySQL client/server protocol. After creating the schema, the full power of SQL becomes available to relational clients. This mapping will be used to generate a relational schema. Using this feature requires creating a schema mapping from Stardog’s data model to a relational data model. This enables seamless use of business intelligence and visualization tools such as Tableau and Power BI. Stardog provides a facility to bridge the flexible schema of the graph data model to the traditional relational model required by business intelligence tools using SQL. Mapping SPARQL Queries in a Schema Mapping.For information regarding administering the BI Server please see the section on Configuring the BI Server. Logistic Regression and Generalized Linear Model learners now use 0.This page contains contains information on the BI Server’s supported tools and their usage.Logistic Regression, Generalized Linear Model, Gradient Boosted Trees and Deep Learning operators no longer crash the software if certain temporary folder permissions are missing.Fixed Gradient Boosted Trees renderer, it no longer shows wrong edge labels and incorrect value sets.Generate Weights (LPR) and Local Polynomial Regression now provide additional kernel parameters for the numerical measure KernelEuclideanDistance instead of failing.Removed unnecessary warning for clustering operators with nominal input data.Fixed behavior of Loop Parameter inside parallel loops.Sending notification emails now uses the default connection timeout.Changing the default connection timeout setting in the preferences now takes effect immediately.Fixed reproducibility of results when concurrent operators (e.g.Updated Java for Windows and Mac OS X to version 8u141.Reduced some internal logging by moving it behind the debug flag which can be activated in the preferences.Improved shown capabilities for cluster operators and added quick fixes for inconsistent parameter selection.Improved checks for empty example sets in cluster operators.Made behavior of add as label parameter consistent for all cluster operators.Improved handling of missing or broken compatibility numbers in the process xml.Improved handling of missing and infinite values for Normalize.Logistic Regression and Generalized Linear Model learners now have a threshold output where they deliver a threshold value optimized for maximal F-measure.Write Message now has a parameter option to append to existing files instead of overwriting them.Changed default sampling type parameter for Validation operators to automatic.Improved handling of infeasible parameter values for Self-Organizing Map.Licenses are now valid for the full last day until midnight.The sender of notification emails can now be configured in the preferences.Sending notification emails can now be configured in the preferences to make use of all modern connection security and authentication mechanisms like TLS 1.2 + PFS.The following describes the enhancements and bug fixes in RapidMiner Studio 7.6.0: New features You are viewing the RapidMiner Studio documentation for version 7.6 - Check here for latest version What's New in RapidMiner Studio 7.6.0?
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