I also wanted to load up that example in some cool Camel GUI tooling that James created at FuseSource.

The new article is Open Source Integration with Apache Camel and How Fuse IDE Can Help. Check it out and let me know what you think!
Apache Camel was designed to be deployable nearly anywhere; you have your choice of standalone in a JVM, Tomcat, J2EE, ActiveMQ, Spring, OSGi, and more. One particularly suitable deployment option is an OSGi container like Apache ServiceMix. In this session, Jon will show you how to take advantage of the many features that ServiceMix brings to the table and also how to best design your Camel applications to get the most out of OSGi.I urge anyone who uses Camel, ServiceMix, ActiveMQ or CXF and can afford the trip to attend CamelOne. It's going to be a blast and it would really be great to chat with fellow community members about these projects.
An eclectic gathering of infosec people to hear awesome talks and have outrageously fun discussions! Our mission is to provide an inclusive, open environment for the sharing and collaborative discourse on topics that most interest you.
Apache Camel is an open source Java framework that focuses on making integration easier and more accessible to developers. It does this by providing: concrete implementations of all the widely used Enterprise Integration Patterns (EIPs), connectivity to a great variety of transports and APIs, and an easy to use Domain Specific Language (DSL) to wire EIPs and transports together to form routes.
Interacting with secure services and also hosting secure services is essential in most integration projects. In this session, Jon will go over the four categories of security features in Camel, which include securing: routes, message payload, endpoints, and configuration.
GRADUATE STUDENT SEMINAR
JONATHAN S. ANSTEY
WILL GIVE A TALK ON
“TIME SERIES NOVELTY DETECTION WITH APPLICATION TO PRODUCTION SENSOR SYSTEMS”
WEDNESDAY, JANUARY 19, 2010
9:00 A.M.
EN-4002
MR. ANSTEY IS A GRADUATE STUDENT
IN THE M.ENG. PROGRAM
UNDER THE SUPERVISION OF DR. D. PETERS
ALL INTERESTED ARE WELCOME
Modern fiber manufacturing plants rely heavily on the use of automation. Automated facilities use sensors to measure fiber state and react to data patterns, which correspond to physical events. Many patterns can be predefined either by careful analysis or by domain experts. Instances of these patterns can then be discovered through techniques such as pattern recognition. However, pattern recognition will fail to detect events that have not been predefined, potentially causing expensive production errors. A solution to this dilemma, novelty detection, allows for the identification of interesting data patterns embedded in otherwise normal data. In this thesis we investigate some of the aspects of implementing novelty detection in a fiber manufacturing system. Specifically, we empirically evaluate the effectiveness of currently available feature extraction and novelty detection techniques on data from a real fiber manufacturing system.
Our results show that piecewise linear approximation (PLA) methods produce the highest quality features for fiber property datasets. Motivated by this fact, we introduced a new PLA algorithm called improved bottom up segmentation (IBUS). This new algorithm produced the highest quality features and considerably more data reduction than all currently available feature extraction techniques for our application.
Further empirical results from several leading time series novelty detection techniques revealed two conclusions. A simple Euclidean distance based technique is the best overall when no feature extraction is used. However, when feature extraction is used the Tarzan technique performs best.