Along with our recent release of VDK 0.2.254, we’ve added a few new example apps to help developers get started with the VDK.
There is also an example app for various queries of a document database containing the Enron Email dataset. Some details on this dataset are here: https://www.cs.cmu.edu/~./enron/
The example applications have the same architecture.
The components are:
- VertX application server, making use of the Vital-Vertx module.
- VitalPrime server using DataScripts to implement server-side functionality, such as generating predictions using a Prediction Model.
- Prediction Models to make predictions or recommendations. A Prediction Model can be trained based on a training set, or it could interface to an external prediction service. If trained, we often use Apache Spark with the Aspen library to create the trained prediction model.
- A Database such as DynamoDB, Allegrograph, MongoDB, or other to store application data.
Here is a quick overview of some of the examples.
We’ll post detailed instructions on each app in followup blog entries.
MetaMind Image Classification App:
This example uses a MetaMind ( https://www.metamind.io/ ) prediction model to classify an image.
AlchemyAPI/IBM Bluemix Document Classification App
This example app uses an AlchemyAPI (IBM Bluemix) prediction model to classify a document.
Movie Recommendation App
Source Code (Web Application):
Source Code (Training Prediction Model):
This example uses a prediction model trained on the MovieLens data to recommend movies based on a user’s current movie ratings. The prediction model uses the Collaborative Filtering algorithm trained using an Apache Spark job. Each user has a user-id such as “1010” in the screenshot above.
Spark’s collaborative filtering implementation is described here:
The MovieLens data can be found here:
Enron Document Search App
This example demonstrates how to implement different queries against a database, such as a “select” query — find all documents with certain keywords, and a “graph” query — find documents that are linked to users.
Example Data Visualizations:
The Cytoscape graph visualization tool can be used to visualize the above sample data using the Vital AI Cytoscape plugin.
The Cytoscape plugin is available from:
An example of visualizing the MovieLens data:
An example of visualizing the Wordnet Dataset, viewing the graph centered on “Red Wine”:
For generating and importing the Wordnet data, see sample code here:
Information about Wordnet is available here:
Another example of the Wordnet data, with some additional visual styles added: