Machine Learning, Distributed and Parallel Computing, and High-performance Computing are the themes for this year’s edition of Fifth Elephant.
The deadline for submitting a proposal is 15th June 2015
We are looking for talks and workshops from academics and practitioners who are in the business of making sense of data, big and small.
This track is about general, novel, fundamental, and advanced techniques for making sense of data and driving decisions from data. This could encompass applications of the following ML paradigms:
- Statistical Visualizations
- Unsupervised Learning
- Supervised Learning
- Semi-Supervised Learning
- Active Learning
- Reinforcement Learning
- Monte-carlo techniques and probabilistic programming
- Deep Learning
Across various data modalities including multi-variate, text, speech, time series, images, video, transactions, etc.
This track is about tools and processes for collecting, indexing, and processing vast amounts of data. The theme includes:
- Distributed and Parallel Computing
- Real Time Analytics and Stream Processing
- MapReduce and Graph Computing frameworks
- Kafka, Spark, Hadoop, MPI
- Stories of parallelizing sequential programs
- Cost/Security/Disaster Management of Data
HasGeek believes in open source as the binding force of our community. If you are describing a codebase for developers to work with, we’d like it to be available under a permissive open source license. If your software is commercially licensed or available under a combination of commercial and restrictive open source licenses (such as the various forms of the GPL), please consider picking up a sponsorship. We recognize that there are valid reasons for commercial licensing, but ask that you support us in return for giving you an audience. Your session will be marked on the schedule as a sponsored session.
If you are interested in conducting a hands-on session on any of the topics falling under the themes of the two tracks described above, please submit a proposal under the workshops section. We also need you to tell us about your past experience in teaching and/or conducting workshops.
HawkEye: A Real-Time Anomaly Detection System
In this talk, I will present the details of the HawkEye system with insights on selection of algorithms and parameter tuning. I intend to share our mistakes and learnings while deveoloping HawkEye.
HawkEye is a real-time anomaly detection framework for detecting anomalies in IT infrastructure data e.g. CPU, memory, response time of a data center machine. The framework uses a combination of anomaly detection techniques to detect local and global anomalies. The system detects several types of local anomalies using different anomaly detection techniques over a sliding window. We used rigorous data experiments to perform data preprocessing and select hyperparameters. We have employed statistics-based techniques to detect local point anomalies and statistical detection theory (Page’s Test) to detect local contextual anomalies. Time-series models are employed to detect seasonality in the data and detect global anomalies.
In this talk, I will showcase results of the HawkEye system on real-data.
Basic knowledge of statistics, machine learning, data mining will be helpful in understanding the talk.