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KNIME - Open for Innovation

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KNIME

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END TO END DATA SCIENCE

At KNIME, we build software to create and productionize data science
using one easy and intuitive environment, enabling every stakeholder
in the data science process to focus on what they do best.

KNIME Software
KNIME Open Source Philosophy

How can KNIME Software help you?

Create

Gather & Wrangle

Access, merge, and transform all of your data
Learn more

Model & Visualize

Make sense of your data with the tools you choose
Learn more

Productionize

Deploy & Manage

Support enterprise-wide data science practices
Learn more

Consume & Optimize

Leverage insights gained from your data
Learn more



KNIME INTEGRATED DEPLOYMENT

Closing the gap between data science creation and putting results into
production.
Read more

EXTENDED SUMMIT: WHAT'S COMING UP?

* April 14: Webinar - Use of KNIME to Automate Data Transfer from
Sharepoint to PerkinElmer Inventory, by Fabio Rancati (Chiesi
Farmaceutici)
* April 14 - 20: Course - KNIME Analytics Platform for Data
Wranglers: Basics
* April 16: Workshop - Sharing and Deploying Data Science with KNIME
Server
* April 20 - 24: Course - KNIME Analytics Platform for Data
Wranglers: Advanced
* April 20 - 24: Course - Introduction to Text Processing
* April 21:Workshop - Working with the RDKit in KNIME Analytics
Platform
 

View program and register



Anomaly Detection

Predict when critical equipment parts will go bad to prevent failures
and downtime.

Read more

Inventory Level Optimization

Achieve the perfect trade-off between inventory costs and service
level.

Read more

Disease Tagging

Reduce time spent sifting through medical literature with automatic
disease tagging.

Read more

Recommendation Engine for Retailers

Increase store level sales through better brand portfolio decision
making.

Read more

Customer Sentiment Measurement

Evaluate customer pain points to better allocate and manage resources.

Read more

Risk Information Extraction

Remove the need for manual work by automatically gathering and
harmonizing text-based information.

Read more

ANOMALY DETECTION

INVENTORY LEVEL OPTIMIZATION

DISEASE TAGGING

RECOMMENDATION ENGINE FOR RETAILERS

CUSTOMER SENTIMENT MEASUREMENT

RISK INFORMATION EXTRACTION


Events
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Summit

EXTENDED KNIME SPRING SUMMIT 2020

April 6 - May 29, 2020 - Online


Blog
Show all

GUIDED LABELING BLOG SERIES - EPISODE 2: LABEL DENSITY




14 Apr 2020
‐ by Paolo Tamagnini


KNIME TV
Visit channel

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KNIME Software: Creating and Productionizing Data Science

BE PART OF THE KNIME COMMUNITY

Join us, along with our global community of users, developers,
partners and customers in sharing not only data science, but also
domain knowledge, insights and ideas. 

Visit KNIME Forum

KNIME Hub

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KNIME USERS PRESENTING THEIR USE CASES

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* Customer Intelligence
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* Finance
* Credit Scoring

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* Manufacturing
* Energy Usage Prediction


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* Pharma / Health Care
* Chemical Library Enumeration

* Virtual High Throughput Screening

* Outlier Detection in Medical Claims


* Back
* Retail
* Social Media Music Recommendation

* Market Basket Analysis



* Back
* Cross Industry
* Combining Text and Network Mining

* Address Deduplication

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* Government
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* Applications
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* Data Access
* Data Generation
* WebLog Files
* Audio Files
* Git
* ZIP and Remote Files

* Common Type Files
* Structured Data
* REST Web Services
* Databases

* Back
* Data Generation
* Splitting data and rejoining for manipulating only subpart

* Generating data sets containing association rules
* Generation of data set with more complex cluster structure

* Random combination of two sets of data
* Parallel Generation of a Data Set containing Clusters

* Advantages of Quasi Random Sequence Generation
* Data generation model example
* Generating clusters with Gaussian distribution
* Generating random missing values in an existing data set

* Generating a Shopping Market Data Set

* Back
* WebLog Files
* Example for Apache Logfile Analysis



* Back
* Audio Files
* Example Speech-to-Text

* Feature Extraction from Audio Files



* Back
* Git
* Visualizing Git Statistics for Guided Analytics


* Tagging from Multiple Git Repositories

* Calculating Git Statistics


* Back
* ZIP and Remote Files

* AmazonS3-MSBlobStorage Census Data


* SAS SPSS MATLAB meet S3

* Amazon S3 Remote File Example


* ZIP Local vs remoteHTTP

* Azure Blob Store Remote File Example



* Back
* Common Type Files
* Data Loading Example

* Table Reader

* Multiple Sheet in one Excel File


* Google Sheet-meets-Excel

* Reading Excel Files

* Read a CSV file

* Read an XLS file

* Read all sheets from an XLS file in a loop


* Use the File Reader


* Back
* Structured Data
* Basic XML processing with XPath


* XML processing (Bush books) XPath


* XML meets JSON


* Back
* REST Web Services
* Translate Using Google API

* Access YouTube REST API

* Private Google Sheet-meets-Excel


* SugarCRM meets Salesforce

* Public Google Sheet-meets-Excel


* IBM Watson News-Google News


* Data API Using REST Nodes


* Back
* Databases
* GoogleBigQuery meets SQLite

* Snowflake meets Tableau

* Database Jam Session

* Teradata Aster meets KNIME Table

* MSAccess meets H2

* Binning in Databases

* Database Advanced Example

* Pivoting in Databases

* Read and Write PNG Images in Databases


* Sampling in Databases

* Database Simple IO Example


* Back
* Big Data
* Big Data Connectors
* Spark Executor

* Back
* Big Data Connectors
* Big Data Preprocessing Example


* HDFS and File Handling Example



* Back
* Spark Executor
* SparkSQL meets HiveQL

* Parameter Optimization in Spark

* Recommendation Engine w Spark Collaborative Filtering


* Spark MLlib Decision Tree

* Hive to Spark to Hive

* PMML to Spark Comprehensive Mode Learning Mass Prediction


* Big Data Irish Meter on Spark only


* Mass Learning Event Prediction MLlib to PMML


* Learning Asociation Rule for Next Restaurant Prediction


* Modularized Spark Scripting


* Back
* Community
* HCS Tools
* RDKit
* Image Processing
* Vernalis

* Back
* HCS Tools
* Visualization of screening data

* Dose Response
* Quality Control
* Normalization of screening data

* Visualization of screening data 2

* Metadata utilities for screening data



* Back
* RDKit
* Find Scaffolds And Sidechains

* RDKit with Java Snippet Example

* Working In 3D
* Clustering
* Template Enumeration

* Chemical Topic Modeling

* Chemical Transformations

* Reaction Enumeration


* Back
* Image Processing
* High Content Screening


* Bouncing Ball

* Yeast Colonies

* Count Chromosomes


* Larva Tracking

* Car Counting

* Trackmate

* Intensity Measurement


* Gradient Magnitude


* Find Seeds


* Watershed Segmentation


* Simple Hilite


* Median vs Average Filter


* Nearest Neighbor


* Measure Distances


* Count Cells


* Measure Lengths


* Column Selections


* DoG Node


* Joiner Calculator


* Overlay RGB


* Masking using Thresholder


* Basic Setup


* Advanced Setup Part 1


* Advanced Setup Part 2


* Spot Detection


* 3D Segmentation


* Dilate


* Erode


* Example CT Image


* Close


* Open


* RGB Splitting


* Image Montage


* Numeric Labels


* Apply Colors


* Interactive Annotator


* Basic Setup


* Feature Coloring


* Minimum


* Absolute


* Difference


* Image Reader subsets and series


* Image Reader


* Image Reader Table


* Feature Calculation


* Interactive Labeling Editor


* Group By


* Labeling Filter


* Dimension Selection


* Streaming


* Basic Views


* Interactive Annotator


* Slice Loop


* Image Cropper


* Projector


* Basic Segmentation


* Cell Clump Splitter 2D


* Dont Save


* Table Creation Mode


* Pixel Types


* Convolver


* Python Scripting


* ImageJ

* Tess4J

* OCR meets SemanticWeb


* CellProfiler CometAssay


* CellProfiler Human


* CellProfiler Tumor


* CellProfiler Colocalization


* CellProfiler TrackObjects


* CellProfiler Speckles



* Back
* Vernalis
* SMARTSViewer
* PDB Query Download and Save Locally

* Databased MMP Example

* Simple MMP Example

* Speedy SMILES ChEMBL Preprocessing Benchmarking



* Back
* Scripting
* R
* Java
* Python

* Back
* R
* Example of R Snippet


* Back
* Java
* Example of Java Snippet


* Back
* Python
* embedding documents with jupyter

* Using Python Script in Databases

* Fetch ChEMBL Target Data

* Using Jupyter from KNIME to embed documents



* Back
* Other Analytics Types
* Network Mining
* Semantic Web
* Text Processing

* Social Media
* Chemistry and Life Sciences


* Back
* Network Mining
* DrugBank Network analysis


* Schools Wiki Partition Analysis


* Filtering in Networks


* Schools Wiki Network Analysis


* Networks and Prediction


* Looping in Networks

* Create Network from Edge Table


* Clustering Networks based on Distance Matrix


* Pubmed Author Network Analysis



* Back
* Semantic Web
* SPARQL INSERT Query from table


* SPARQL INSERT from file


* Using Semantic Web to generate Simpsons TagCloud


* SPARQL Executor

* Doing a large Query on a public endpoint


* SPARQL SELECT Query from different endpoints


* SPARQL UPDATE Query

* Read and write triples from and to file


* SPARQL Queries and named graphs


* Semantic Web Analysis Accessing DBpedia


* SPARQL DELETE Query


* Back
* Text Processing

* Blend Languages in Tag Cloud


* Document clustering


* DocumentVector Hashing


* Document Classification


* Sentiment Classification with NGrams


* Analyzing Twitter Posts with Custom Tagging


* epub JPEG Romeo Juliet


* Topic Detection LDA


* Sentiment Analysis Lexicon Based Approach


* NER Tagger Model Training


* RSS Feed Reader

* Topicriver Red-Riding-Hood


* DocumentVector FeatureSpaceAdaption


* Lemmatizer Preprocessing


* Word Embedding Distance


* Fuzzy String Matching


* TopicExtraction with the ElbowMethod


* Dictionary based Tagging


* NY Times RSS Feed Tag Cloud


* Named Entity Tag Cloud


* Discover Secret Ingredient


* Hierarchical Clustering Visualization


* Streaming Sentiment Classification


* Sentiment Classification


* Tika Parsing

* Analyse and Visualize Job Postings



* Back
* Social Media
* NetworkAnalytics meets TextProcessing


* Twitter meets PostgreSQL



* Back
* Chemistry and Life Sciences

* Fetch And Transform PubChem Data


* Example for using MoSS


* Fun with Tags



* Back
* Partners
* Microsoft
* Amazon

* Back
* Microsoft
* Predict DepartureDelays with MicrosoftR


* HDI Hive KNIME
* SQL Server InDB Processing(Azure)

* HDI Hive Spark
* SQLServer BlobStorage andKNIMEModels


* Back
* Amazon
* Model Deployment as REST API


* Back
* Visualization
* JavaScript
* Geolocation

* Back
* JavaScript
* Interactive Webportal Visualisation of Neighbor Network


* Using the Sunburst Chart for Titanic


* Example for JS Line Plot Advanced


* Bivariate Visual Exploration with Scatter Plot


* Disease Genes
* Example for JS Box Plot

* Example for JS Parallel Coordinates


* Example for JS Scatter Plot

* Example for JS Line Plot Basic

* Example for JS Bar Chart

* Univariate Visual Exploration with Data Explorer


* DataVisualization AirlineDataset



* Back
* Geolocation
* Static geolocation of IP addresses


* GeoIP Visualization using Open Street Map (OSM)


* Visualizing Routes and Tracks with OSM


* FIFA World Cup

* Dynamic geolocation of IP addresses


* Choropleth World Map

* Google Geocode API-OSM-text file


* Visualization of the World Cities using Open Street Map (OSM)



* Back
* Analytics
* Classification and Predictive Modelling

* Regressions
* Clustering
* Scoring
* Optimization
* XGBoost
* Deep Learning
* Active Learning
* Time Series
* Statistics
* H2O Machine Learning
* Preprocessing
* PMML
* Meta Learning

* Back
* Classification and Predictive Modelling

* Decision Tree


* Example for Learning a Naive Bayes Model


* Exporting a Decision Tree as Image


* Gradient Boosted Trees


* Example for Learning a Neural Network


* Logistic Regression


* Example for Learning a Decision Tree



* Back
* Regressions
* Learning a Simple Regression Tree


* Back
* Clustering
* Performing a k-Medoids Clustering

* Performing a k-Means Clustering


* Back
* Scoring
* Evaluating Classification Model Performance



* Back
* Optimization
* Optimizing Subset Selection

* Parameter Optimization two examples


* Cross Validation with SVM

* Cross Validation with SVM and Parameter Optimization


* Model Optimization and Selection

* Parameter Optimization

* Score Erosion for Multi Objective Optimization


* Meassuring Variable Importance


* Back
* XGBoost
* Housing Value Regression with XGBoost

* Classify Forest Covertypes with XGBoost



* Back
* Deep Learning
* Read And Execute a SavedModel on MNIST


* Train MNIST classifier


* Training Tensorflow MLP


* Edit MNIST SavedModel

* Translating From Keras to TensorFlow


* Training


* Deployment


* Preprocess image data


* Fine-tune VGG16 Python


* Train simple CNN


* Fine-tune VGG16


* Deployment


* Training


* Deployment


* Training


* Classify images using InceptionV3


* Neural Machine Translation

* Classify images using ResNet50


* Semantic Segmentation

* Wide and Deep Learning on Census Dataset


* Sentiment Analysis with Deep Learning


* Train MNIST classifier

* Sentiment Analysis with Deep Learning KNIME nodes


* Using DeepLearning4J to classify MNIST Digits


* Sentiment Classification Using Word Vectors


* Network Example Of A MLP For Images


* Housing Value Prediction Using Regression


* Celebrity Detection Using Alex Net


* Simple Regression Of Simple Functions


* Calculate Document Distance Using Word Vectors


* Network Example Of A Simple Convolutional Net


* Font Detection Using A Convolutional Net


* Basic Learner View Tutorial

* Basic Concepts Of Deeplearning4J Integration


* Simple Anomaly Detection Using A Convolutional Net


* Simple Document Classification Using Word Vectors


* Network Example Of A Simple MLP



* Back
* Active Learning
* Active Learning PBCA modular Score


* Active Learning Uncertainty Sampling


* Active Learning PBCA default


* Back
* Time Series
* Example of Time Series Functionality


* Example for Predicting Time Series


* Back
* Statistics
* Calculating the Cronbach Alpha

* Independent groups t-test

* Performing a Linear Discriminant Analysis


* Kolmogorov-Smirnov Matrix

* Example for Statistical Tests

* Simple Example with Statistics


* Back
* H2O Machine Learning
* H2O Data import and export


* H2O Crossvalidation

* H2O GBM Classification Model


* H2O Scoring

* H2O GBM parameter optimization


* H2O GLM Regression Model

* Customer prediction with H2O



* Back
* Preprocessing
* Perform Feature Selection

* Feature Elimination with Naive Bayes


* Techniques for Dimensionality Reduction



* Back
* PMML
* Example for Using PMML for Transformation and Prediction



* Back
* Meta Learning
* Cross-Platform Ensemble Model

* Learning a Random Forest

* Combining Classifiers using Prediction Fusion


* Learning a Tree Ensemble Model


* Back
* Strange but Educational
* Strange Loops

* Back
* Strange Loops
* Feature Elimination Done Manually



* Back
* Applications
* Swiss Actuarial Example

* Reproducibility
* Churn Prediction
* TwitterAnalysis
* Forest Fire Prediction

* Customer Segmentation Use Case

* BlackJack
* Credit Scoring
* Inventory Optimization

* Price Benchmarking
* Credit Risk Assessment

* Hitlist Processing
* Forum Analysis of the KNIME Forum

* Deployment Options
* Churn Analysis
* RESTful ChEMBL
* Predicting Departure Delays

* Recomendation Engine for Retailers

* NYC Taxi Visualization

* AnomalyDetection
* Social Media clustering

* BLAST from the PAST
* Energy Usage
* Fraud Detection
* Histopathology Blog Post

* Anomaly Detection
* LastFM Recommendations

* Guided Analytics for ML Automation

* GDPR examples
* DataCleaning WebPortal

* Customer Experience and Sentiment Analysis

* RESTDemo
* Address Deduplication

* Patent Network Analysis

* Network Traffic Reporting

* Model Selection and Management

* MarketBasketAnalysis

* Medical Claims
* Emil the TeacherBot
* Customer Intelligence

* Image Recognition for Retail

* Sentiment Prediction via REST

* Model Process Management


* Back
* Swiss Actuarial Example

* Swiss Actuarial Example



* Back
* Reproducibility
* Validating KNIME Workflows


* Back
* Churn Prediction
* Training a Churn Predictor

* Deploying the Churn Predictor



* Back
* TwitterAnalysis
* Visualizing Twitter Network with a Chord Diagram


* Analyzing Twitter Data

* Collecting data from Twitter



* Back
* Forest Fire Prediction

* Forest Fire Prediction



* Back
* Customer Segmentation Use Case

* Customer Segmentation Use Case


* Basic Customer Segmentation Use Case



* Back
* BlackJack
* BlackJack

* Back
* Credit Scoring
* CreditScoring


* Back
* Inventory Optimization

* Data Preparation

* Guided Analytics


* Back
* Price Benchmarking
* Guided Analytics


* Back
* Credit Risk Assessment

* Scoring Deployment

* Scoring Training


* Back
* Hitlist Processing
* Select Followup Compounds


* Hitlist Filter


* Back
* Forum Analysis of the KNIME Forum

* Simple Statistics on KNIME Forum


* Text Classification from Forum Posts


* Parsing the KNIME Forum


* Applying Text and Network Analysis Techniques to Forums



* Back
* Deployment Options
* Model Deployment with report and email


* Model Deployment file to database scheduling


* Model Deployment dashboard WebPortal


* Model Deployment as REST API


* Model Deployment SQL Java recoding



* Back
* Churn Analysis
* ChurnAnalysis


* Back
* RESTful ChEMBL
* ChEMBL Structure Search

* ChEMBL Bioactivity Search

* ChEMBL REST Services


* Back
* Predicting Departure Delays

* Analytics

* OpenSourceVizAndModeling


* Scaling Analytics w BigData



* Back
* Recomendation Engine for Retailers

* Data Preparation


* Guided Analytics



* Back
* NYC Taxi Visualization

* Visualization

* Preparation


* Back
* AnomalyDetection
* Creating a ControlChart of a Time Series


* Email to start checkup

* Preprocessing Time Alignment and Visualization


* Time Series AR Deployment

* Time Series AR Training

* Time Series AR Testing

* Time Series AR Training dataviz



* Back
* Social Media clustering

* Clustering the Social Community



* Back
* BLAST from the PAST
* from the PAST


* Back
* Energy Usage
* Energy Usage Time Series Prediction



* Back
* Fraud Detection
* Detection Model Training

* Detection Deployment


* Back
* Histopathology Blog Post

* Download Dataset

* Preprocessing

* WebPortal Predictions


* Read Images and Train VGG



* Back
* Anomaly Detection
* Preprocessing Time Alignment and Visualization


* Time Series AR Deployment

* Time Series AR Training


* Back
* LastFM Recommendations

* LastFM Recommendations



* Back
* Guided Analytics for ML Automation

* Guided Analytics for ML Automation



* Back
* GDPR examples
* Anonymize personal data

* GDPR Examples Overview

* Consolidate workflow documentation


* Basic Data Preparation

* Identify PII and Special Category Data


* Explain Model


* Back
* DataCleaning WebPortal

* WebPortal


* Back
* Customer Experience and Sentiment Analysis

* Data Preparation


* Rest API Sentiment Prediction


* Guided Analytics


* Model Building



* Back
* RESTDemo
* Call a Workflow on a Server

* Call Local Workflow


* Back
* Address Deduplication

* Deduplication of Address Data


* 01 Deduplication of Address data



* Back
* Patent Network Analysis

* Tarceva neighbor network from SureChEMBL


* neighbor network - From SureChEMBL



* Back
* Network Traffic Reporting

* Network Traffic Reporting



* Back
* Model Selection and Management

* Model Selection Sampled



* Back
* MarketBasketAnalysis

* Apply Association Rules for MarketBasketAnalysis


* Build Association Rules for MarketBasketAnalysis



* Back
* Medical Claims
* Interactive Outlier Detection



* Back
* Emil the TeacherBot
* AL First Try Assign Classes via Distance


* Emil the TeacherBot

* AL Extend Expert Classes with kNN


* Initial Model Training

* AL Labelling

* AL Re-label Uncertain Classes


* AL Training Subset Uncertain Classes



* Back
* Customer Intelligence

* B2B Customer Analytics



* Back
* Image Recognition for Retail

* Guided Analytics


* Data Preparation and CNN Training



* Back
* Sentiment Prediction via REST

* Rest API Sentiment Prediction


* Link to web application



* Back
* Model Process Management

* Evaluate Price RMSD


* Tables Locally


* Init ModelName


* Log Tables


* Transform andTimeDiff


* Load productData


* Evaluate price Trend


* Learn Trend


* Score Trend


* Load for Cities


* Init for Cities


* Model Factory

* PricePred 05 Training


* PricePred 03 ET


* PricePred 04 Evaluation


* PricePred 01 Init


* PricePred 02 Load


* rentAppPred 03 ET


* rentAppPred 04 Evaluation


* rentAppPred 05 Training


* rentAppPred 01 Init


* rentAppPred 02 Load


* CheckState


* SeeAll


* SetThresholdsToMedian


* Update Type Change


* Update


* Factory


* Back
* ETL Data Manipulation
* Date and Time Manipulation

* Filtering
* Aggregations
* Transformation
* Joining and Concatenating

* Indexing Searching

* Basic Examples

* Back
* Date and Time Manipulation

* Generate Fixed Time Intervalls


* Customers Trx Money vs Loyalty


* Filter TimeSeries Data Using FlowVariables


* ETL Energy autocorr stats



* Back
* Filtering
* Techniques Outlier Detection


* Column Filter

* Advanced Row Filters

* More Row Filter Examples

* More Column Filter Examples


* Validating Datatables

* Row Filtering


* Back
* Aggregations
* Working with Collection Supported Nodes


* Working with Collection Supported Types


* Working with Collection Creation and Conversion


* Calculating Rank Correlations


* Examples for Using the Pivoting Node


* More GroupBy Examples

* Advanced Usage of the GroupBy node


* Generating a Ranking value


* Basic Examples for Using the GroupBy Node



* Back
* Transformation
* StringManipulation MathFormula RuleEngine


* Handling Missing Values



* Back
* Joining and Concatenating

* Joiner

* Concatenate


* JoinConcatenate Examples



* Back
* Indexing Searching

* Advanced Queries


* Example for Fuzzy Address Matching


* Document Queries



* Back
* Basic Examples
* Visual Analysis of Sales Data


* Example for Standard Preprocessing


* ETL Basics


* Back
* Enterprise
* Server
* WebPortal

* Back
* Server
* Create basic service

* Create basic service2

* Write files into the Server Repository

* Generate a Report and Save the File


* Back
* WebPortal
* Quickforms to table

* Example for a Data Mining WebPortal

* Select most recent date and time cells


* Read file send as EMail

* Visualize Scatterplot on file

* Guided Analytics

* Showing an autogenerated time series line plot


* Extract System and Environment Variables (Linux only)


* DataVisualization AirlineDataset


* Back
* Innovation Notes
* Customer Intelligence

* Sentiment Prediction via REST

* Inventory Optimization

* Price Benchmarking

* Anomaly Detection
* Image Recognition for Retail

* Customer Experience and Sentiment Analysis


* Recomendation Engine for Retailers


* Back
* Customer Intelligence

* B2B Customer Analytics



* Back
* Sentiment Prediction via REST

* Rest API Sentiment Prediction


* Link to web application



* Back
* Inventory Optimization

* Data Preparation

* Guided Analytics


* Back
* Price Benchmarking

* Guided Analytics


* Back
* Anomaly Detection
* Preprocessing Time Alignment and Visualization


* Time Series AR Deployment


* Time Series AR Training



* Back
* Image Recognition for Retail

* Guided Analytics


* Data Preparation and CNN Training



* Back
* Customer Experience and Sentiment Analysis


* Data Preparation


* Rest API Sentiment Prediction


* Guided Analytics


* Model Building



* Back
* Recomendation Engine for Retailers

* Data Preparation


* Guided Analytics



* Back
* Control Structures
* Workflow Orchestration

* Meta Nodes and Wrapped Nodes

* Loops
* Quickforms
* Flow Variables
* External Applications

* Switches

* Back
* Workflow Orchestration

* call server workflow (JSON)


* call local workflow (JSON)


* call server workflow (REST)


* call server workflow (Table)


* call workflow (Table)


* call workflow parallel (Table)


* Fault Tolerant Workflow Orchestration



* Back
* Meta Nodes and Wrapped Nodes

* Simple Streaming and Wrapped Nodes



* Back
* Loops
* Example for Recursive Replacement of Strings


* Looping a fixed number

* Looping over all columns and manipulation of each


* Usage of Generic Loop Start

* Loop over a set of parameter for k means


* Example for Reading a List of Files


* Looping over defined Intervals

* Writing a data table column wise to multiple csv files


* Collecting Variables in Loop

* Write each row in one file

* Recover from Breakpoint in Loop

* Looping for Multiple Target Prediction


* Using TableRows as FlowVariables in Loop


* Usage of Breakpoint in Loops

* Looping over Chunks of the Data

* Example for Recursive Looping

* Looping over Groups of the Data

* Collecting Columns in Loop


* Back
* Quickforms
* Quickforms and Metanodes


* Back
* Flow Variables
* Extract Data for Highest Sale Country


* Create and Consume Flow Vars


* Using Flow Variables to control Execution Order


* Using Flow Variables for the Time Range



* Back
* External Applications

* Example for the external tool (Linux or Mac only)



* Back
* Switches
* Using a If Switch

* Using JAVA for If Switch

* Switch on Empty Tables

* Case Switch

* Back
* Reporting
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