Annual Report 2010 - Fachgruppe Informatik an der RWTH Aachen ...

characteristic measures. However, as the data stream proceeds, previous results may become
invalid with respect to recently arrived data items. Thus, maintaining correct result in a data
stream environment, e.g. to a top-k query, makes efficient continuous query processing and
incremental algorithms necessary.
Anytime algorithms are capable of dealing with varying time constraints and high data
volumes as described above. The advantages of anytime algorithms can be summarized as
flexibility (exploit all available time), interruptibility (provide a decision at any time of
interruption) and incremental improvement (continue improvement from current position
without restart).
Figure 6: Bayes Tree as hierarchical organization of (Gaussian) mixture models for density estimation
MOA – Massive Online Analysis
Philipp Kranen, Hardy Kremer
MOA (Massive On-line Analysis) is a framework for data stream mining. It includes tools for
evaluation and a collection of machine learning algorithms. It is related to the WEKA project,
is also written in Java, but scales to more demanding problems. The goal of MOA is a
benchmark framework for running experiments in the data stream mining context by
providing
• storable settings for data streams (real and synthetic) for repeatable experiments,
• a set of existing algorithms and measures form the literature for comparison and
• an easily extendable framework for new streams, algorithms and evaluation methods.
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