View source for Real-time Data Analytics

= Description =

This topic plays an important role in real-world machine learning applications like re commender systems, stock market analysis, anomaly detections and Internet of Things sensor data [0].
The goal of the project is to create basic models using Spark [1,2] and Streaming Random Forest (Mondrian Forest) [3,4] and to apply the created algorithms in order to analyze streaming data like meeting calls [5] or financial data [6].

The analysis will obey investigation of a time window width (length of analyzed data) on the accuracy of resulting predictions.

= References =
: [0] [https://cran.r-project.org/web/packages/stream/vignettes/stream.pdf Introduction to stream: An Extensible Framework for Data Stream Clustering Research with R] 
: [1] [https://spark.apache.org/docs/latest/mllib-linear-methods.html#streaming-linear-regression https://spark.apache.org/docs/latest/mllib-linear-methods.html#streaming-linear-regression]
: [2] [https://spark.apache.org/docs/latest/streaming-programming-guide.html https://spark.apache.org/docs/latest/streaming-programming-guide.html]
: [3] [http://www.ment.at/blog-old/streaming-random-forest http://www.ment.at/blog-old/streaming-random-forest]
: [4] [http://research.cs.queensu.ca/home/cords2/ideas07.pdf http://research.cs.queensu.ca/home/cords2/ideas07.pdf]
: [5] [http://meetup.github.io/stream/rsvpTicker/ http://meetup.github.io/stream/rsvpTicker/]
: [6] [http://finance.google.com/finance/info?client=ig&q=NASDAQ%3AGOOG http://finance.google.com/finance/info?client=ig&q=NASDAQ%3AGOOG]

= Contact =
[mailto:Bogdan.Lobodzinski@kit.edu bogdan.Lobodzinski@kit.edu]