1. COMAD 2008 Gong Show 1

2. Application monitoring data mining
Objective: Mining application monitoring data to understand load profiles patterns and provide real time alerting about possible problems with applications
Challenges:
The application being monitored is multi-tier application, with each tear having thousands of components.
The application monitoring data is captured using pre-defined key performance indicators (KPIs) at the frequency of 30 seconds for each tier resulting in a huge amount of data.
The application should able to identify load profiles at each application tier, load behavior modeling of adjacent tiers and rules for providing real-time alerts.
Our approach:
Clustering for identification of load behavior modeling
Association rule mining for load behavior modeling and alerting 2

3. Analyzing sentiments , buzz and opinion about product reviews
Problem Description
Input is mobile phone review
“Sony Ericsson V640i phone is light in weight, great color, great features(MOBILE TV) etc easy to use generally a great phone to use and have. The screen is quite small for watching TV, or Music Videos, the battery life could be better, you can't assign Ringtones(MP3) as message alert Tones or for individual people apart from that its a good phone. ”
Sample Output
Positive: [weight, color, TV, Sony Ericsson V640i]
Negative: [screen, battery]
Neutral: [Ringtones, MP3]
Challenges
Identification of relevant articles
NER of products and product features
Understanding the sentiment and relating it to the products and features 3