Useful white papers from KNIME.
Here we show how we prepared and visualized FFT-transformed sensor data from a rotor equipment: frequency binning, time alignment, and visualization.
This second whitepaper of the anomaly detection series approaches the prediction of the “unknown” and possibly catastrophic event from a time series perspective. Chart Control and Auto-Regressive models are used to trigger alarms when the underlying system starts wandering off the known working condition.
In this whitepaper we show step-by-step how to integrate a big data platform into a KNIME workflow, using dedicated and/or generic connector nodes to connect to big data platforms and SQL helper nodes. Example workflow can be found on the EXAMPLES server under 004_Database/004005_Energy_Prepare_Data (Big Data).
This whitepaper focuses on smart energy data from the Irish Smart Energy Trials. The first goal is to identify a few groups with common electricity behavior to create customized contract offers. The second goal is a reliable prediction of the overall energy consumption using time series prediction techniques.
This paper describes a number of techniques for data enrichment through responses from external RESTful services-analytics, model optimization, and visualization - from R graphic libraries to geo-localization with Open Street Maps and network visualization.
This whitepaper covers all steps to extract knowledge from a web forum:crawls the forum and downloads the data, calculates some simple statistics, detects the discussed topics, and shows the experts for each topic.
Text mining and network analytics are combined here to better position negative and positive users in context with their weight as influencers or followers inside the discussion forum.
This whitepaper shows an example of how advanced analytics combined with real-time execution can provide an end to end solution from model development to operational deployment and real time execution within any business process.
This whitepaper extracts IP addresses from a web log file and transforms them to points on a world map, producing a report with images and movie of the daily IP addresses. Example workflows are available on the EXAMPLES Server under 008_WebAnalytics_and_OpenStreetMap.
Exploring and comparing seven different dimensionality reduction techniques: Missing Values, Low Variance Filter, High Correlation Filter, PCA, Random Forests, Backward feature Elimination, Forward feature Construction.
This technical report explains the fundamentals of text processing feature in KNIME along with detailed descriptions and examples of all key node categories.
Here we provide a few experience based guidelines about the DWH infrastructure needed for a data science lab: production, development, testing, and fall-back, environment segregation, customized data sets, role management, user permissions, resource sharing, user authentication, rollover, versioning, dashboards, and many more features needed to build the infrastructure of a modern data science lab.