ADAMS – a different take on workflows. A fascinating new workflow for MOA is available: the Advanced Data mining And Machine learning System (ADAMS) is a novel, flexible workflow engine aimed at quickly building and maintaining real-world, complex knowledge workflows. It is written in Java and uses Maven as its build system. The core of ADAMS is the workflow engine, which follows the philosophy of less is more. Instead of letting the user place operators (or actors in ADAMS terms) on a canvas and then manually connect inputs and outputs, ADAMS uses a tree-like structure. This structure and the control actors define how the data is flowing in the workflow, no explicit connections necessary. The tree-like structure stems from the internal object representation and the nesting of sub-actors within actor-handlers. The MOA team recommends ADAMS as the best workflow tool for MOA. Website

Framework for Sentiment Analysis of a Stream of Texts (2012 Harvest Project) This project aims at building an online, real-time system able to analyze an incoming stream of text and visualize its main characteristics using a minimal desktop application. The software is designed to work in a “stream setting” with minimal memory/space requirements. Website

Pocket Data Mining PDM is a new term describing collaborative mining of streaming data in mobile and distributed computing environments by researchers Frederic Stahl, Mohamed Medhat Gaber, Max Bramer, and Philip S. Yu. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to the increasing power of these handheld devices. Wireless communication among these devices using Bluetooth and WiFi technologies has opened the door wide for collaborative mining among the mobile devices within the same range that are running data mining techniques targeting the same application. Video

Dariusz Brzeziński MOA extensions – Package that contains a distribution of MOA with extensions made during the writing of his master’s thesis. The extensions include the RemoveAttribute filter, Data Chunk evaluation method, a implementation of the Accuracy Weighted Ensemble algorithm, and an implementation of the Accuracy Diversified Ensembled proposed in his thesis. The package also contains the MOA Manual and a copy of his master’s thesis. Website

Thomas Lotze MOA extensions – Package that contains a generator of predictions for a test set of two classes. It generates a comma-separated file, which contains the item number as the first column and the probability of class 1 as the second column. Website

ADMIRE project MOA is used in the Advanced Data Mining and Integration Research for Europe (ADMIRE) project. The aim of the project is to create advanced, distributed data analysis platform, where one of the major goals is to provide ability of data stream processing. MOA has been very helpful during development of one of the use cases (churn prediction in analytical CRM), where the Hoeffding tree algorithm is used to classify customers stored in large datasets. The algorithm implementation has been wrapped as Process Element (step in workflow) named “BuildIterationalClassifier”. Website


Massive On-line Analysis is part of Weka Machine Learning Project at University of Waikato.