November 6 - 9, 2018
KNIME
- Solutions
- Innovation Notes
- Customer Intelligence
- Social Media
- Finance
- Manufacturing
- Pharma / Health Care
- Retail
- Cross Industry
- Government
- Pharma / Health Care
- Chemical Library Enumeration
- Virtual High Throughput Screening
- Outlier Detection in Medical Claims
- Learning
- Getting Started
- FAQ
- Learning Hub
- E-Learning Course
- NODE GUIDE
- Documentation
- Events and Courses
- Developers
- White Papers
- KNIME Press
- KNIME TV
- NODE GUIDE
- Data Access
- Big Data
- Community
- Scripting
- Other Analytics Types
- Partners
- Visualization
- Analytics
- Strange but Educational
- Applications
- ETL Data Manipulation
- Innovation Notes
- Enterprise
- Control Structures
- Reporting
- Data Access
- Data Generation
- WebLog Files
- Audio Files
- Git
- ZIP and Remote Files
- Common Type Files
- Structured Data
- REST Web Services
- Databases
- 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
- Git
- Visualizing Git Statistics for Guided Analytics
- Tagging from Multiple Git Repositories
- Calculating Git Statistics
- 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
- 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
- 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
- Databases
- GoogleBigQuery meets SQLite
- 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
- Spark Executor
- SparkSQL meets HiveQL
- Parameter Optimization in Spark
- 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
- HCS Tools
- Visualization of screening data
- Dose Response
- Quality Control
- Normalization of screening data
- Visualization of screening data 2
- Metadata utilities for screening data
- RDKit
- Find Scaffolds And Sidechains
- RDKit with Java Snippet Example
- Working In 3D
- Clustering
- Template Enumeration
- Chemical Topic Modeling
- Chemical Transformations
- Reaction Enumeration
- 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
- Vernalis
- SMARTSViewer
- PDB Query Download and Save Locally
- Databased MMP Example
- Simple MMP Example
- Speedy SMILES ChEMBL Preprocessing Benchmarking
- Other Analytics Types
- Network Mining
- Semantic Web
- Text Processing
- Social Media
- Chemistry and Life Sciences
- 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
- 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
- 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
- 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
- Microsoft
- Predict DepartureDelays with MicrosoftR
- HDI Hive KNIME
- SQL Server InDB Processing(Azure)
- HDI Hive Spark
- SQLServer BlobStorage andKNIMEModels
- JavaScript
- Interactive Webportal Visualisation of Neighbor Network
- Using the Sunburst Chart for Titanic
- Example for JS Line Plot Advanced
- 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
- 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)
- Analytics
- Classification and Predictive Modelling
- Regressions
- Clustering
- Optimization
- Deep Learning
- Active Learning
- Time Series
- Statistics
- H2O Machine Learning
- Preprocessing
- PMML
- Meta Learning
- 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
- 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
- Deep Learning
- Read And Execute a SavedModel on MNIST
- Train MNIST classifier
- Training Tensorflow MLP
- Edit MNIST SavedModel
- Translating From Keras to TensorFlow
- Preprocess image data
- Fine-tune VGG16 Python
- Train simple CNN
- Fine-tune VGG16
- 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
- Active Learning
- Active Learning PBCA modular Score
- Active Learning Uncertainty Sampling
- Active Learning PBCA default
- Statistics
- Calculating the Cronbach Alpha
- Performing a Linear Discriminant Analysis
- Example for Statistical Tests
- Simple Example with Statistics
- 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
- Preprocessing
- Perform Feature Selection
- Feature Elimination with Naive Bayes
- Techniques for Dimensionality Reduction
- Meta Learning
- Cross-Platform Ensemble Model
- Learning a Random Forest
- Combining Classifiers using Prediction Fusion
- Learning a Tree Ensemble Model
- Applications
- Swiss Actuarial Example
- Reproducibility
- Churn Prediction
- TwitterAnalysis
- Forest Fire Prediction
- Customer Segmentation Use Case
- Credit Scoring
- Inventory Optimization
- Price Benchmarking
- Hitlist Processing
- Forum Analysis of the KNIME Forum
- Deployment Options
- Churn Analysis
- RESTful ChEMBL
- Predicting Departure Delays
- Recomendation Engine for Retailers
- AnomalyDetection
- Social Media clustering
- Energy Usage
- 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
- Model Selection and Management
- MarketBasketAnalysis
- Medical Claims
- Emil the TeacherBot
- Customer Intelligence
- Image Recognition for Retail
- Sentiment Prediction via REST
- Model Process Management
- 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
- 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
- AnomalyDetection
- Creating a ControlChart of a Time Series
- Preprocessing Time Alignment and Visualization
- Time Series AR Deployment
- Time Series AR Training
- Histopathology Blog Post
- Download Dataset
- Preprocessing
- WebPortal Predictions
- Read Images and Train VGG
- Anomaly Detection
- Preprocessing Time Alignment and Visualization
- Time Series AR Deployment
- Time Series AR Training
- GDPR examples
- Anonymize personal data
- GDPR Examples Overview
- Consolidate workflow documentation
- Basic Data Preparation
- Identify PII and Special Category Data
- Explain Model
- Customer Experience and Sentiment Analysis
- Data Preparation
- Rest API Sentiment Prediction
- Guided Analytics
- Model Building
- MarketBasketAnalysis
- Apply Association Rules for MarketBasketAnalysis
- Build Association Rules for MarketBasketAnalysis
- Emil the TeacherBot
- AL First Try Assign Classes via Distance
- Emil the TeacherBot
- AL Extend Expert Classes with kNN
- AL Labelling
- AL Re-label Uncertain Classes
- AL Training Subset Uncertain Classes
- Model Process Management
- Tables Locally
- Log Tables
- Evaluate Price RMSD
- Init ModelName
- Transform andTimeDiff
- Load productData
- Evaluate price Trend
- Learn Trend
- Score Trend
- 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
- Load for Cities
- Init for Cities
- CheckState
- SeeAll
- SetThresholdsToMedian
- Update Type Change
- Update
- Factory
- Model Factory
- ETL Data Manipulation
- Date and Time Manipulation
- Filtering
- Aggregations
- Transformation
- Joining and Concatenating
- Indexing Searching
- Basic Examples
- Date and Time Manipulation
- Generate Fixed Time Intervalls
- Customers Trx Money vs Loyalty
- Filter TimeSeries Data Using FlowVariables
- ETL Energy autocorr stats
- Filtering
- Column Filter
- Advanced Row Filters
- More Row Filter Examples
- More Column Filter Examples
- Validating Datatables
- Row Filtering
- Aggregations
- Working with Collection Supported Nodes
- Working with Collection Supported Types
- Working with Collection Creation and Conversion
- Calculating Rank Correlations
- More GroupBy Examples
- Advanced Usage of the GroupBy node
- Generating a Ranking value
- Basic Examples for Using the GroupBy Node
- 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
- Anomaly Detection
- Preprocessing Time Alignment and Visualization
- Time Series AR Deployment
- Time Series AR Training
- Customer Experience and Sentiment Analysis
- Data Preparation
- Rest API Sentiment Prediction
- Guided Analytics
- Model Building
- Server
- Create basic service
- Create basic service2
- Write files into the Server Repository
- Generate a Report and Save the File
- 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
- Control Structures
- Workflow Orchestration
- Meta Nodes and Wrapped Nodes
- Loops
- Quickforms
- Flow Variables
- External Applications
- Switches
- 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
- 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
- BIRT
- Report for Soccer Data with R
- Example for a Chemical Report
- Report with Radarplot and R
- BIRT Example Basic
- Example for Reporting
- Simple Example for Reporting
- Using KNIME Colors in BIRT Report
- BIRT Example Advanced
- Documentation
- Reporting
- CSS styling
- Guide to Intelligent Data Analysis
- Big Data Extensions
- Meta Nodes
- Database Documentation
- Changelogs
- Install KNIME Extensions
- Reporting
- Getting Started
- Screencasts
- Getting Started with BIRT in KNIME
- Advanced Reports in BIRT
- Connecting to Tableau with KNIME
- Developer Guide
- Overview Plugin Concept
- New Node Wizard
- NodeFactory
- NodeDialog
- Default Dialog Components
- NodeModel
- NodeView
- Test Your Node
- Export And Deploy Your Node as a Plugin
- Default Dialog Components
- Available Default Dialog Components
- Disabling Components
- Example NodeModel
- Example Code Dialog
- SettingsModelListener
- DialogComponents and NodeDialogPane
- DefaultDialogComponents Troubleshooting
- NodeModel
- configure
- execute
- loadInternals
- loadValidatedSettingsFrom
- notifyViews
- reset
- saveInternals
- saveSettingsTo
- setWarningMessage
- validateSettings
- Example Implementation
- First Steps
- NodeModel
- NodeDialog
- NodeView
- Save and Load Your Internal Representation
- HiLiting
- Save And Load an External Model
- Adapt Your Node Description
- Sample Code
- KNIME Press
- KNIME Beginner's Luck
- KNIME Advanced Luck
- From Words to Wisdom
- Will They Blend? The Blog Post Collection
- From SAS to KNIME
- From Excel to KNIME
- Partners
- Trusted Partners
- Technology Partners
- Sponsors
- Benefits and Requirements
- Partner Contact Form
- Community
- Forum
- Workflow Hub
- KNIME Labs
- Community Contributions
- Community Developers
- Free Partner Extensions
- Special Interest Groups
- KNIME Labs
- Installation
- Indexing and Searching
- Neighborgrams
- Network Mining
- Pipeline Pilot Connector
- Perl Scripting Node
- Python Scripting
- SAS7BDAT Node
- Text Processing
- TIBCO Spotfire
- Web Analytics
- Web Service Client
- File Handling
- Optimization
- Active Learning
- Deeplearning4J
- Deep Learning
- Documentation
- IO
- Enrichment
- Preprocessing
- Frequencies
- Transformation
- Custom Tag Sets (for developers)
- Custom Tagger (for developers)
- Examples
- Dictionary based Tagging
- Document Classification
- Document Clustering
- Tag Cloud
- Term Coocurrence Heatmap
- Named Entity Recognizer and Tag Cloud
