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.
Track 1: Discovering Insights and Driving Decisions
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.
Track 2: Speed at Scale
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
Commitment to Open Source
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.
IT Operations Analytics: Using Text Analytics and Statistical Modeling in IT Operations Data
Attendees will be exposed to the emerging area of IT Operations Analytics. Attendees will learn how text mining and statistical modeling techniques can be used to extract insights out of IT Operations Data.
In this talk we will introduce attendees to the up-and-coming area known as IT Operations Analytics (ITOA). A typical large organization with servers, middleware, network switches, and other applications generates hundreds of gigabytes of data each day. This data (known as IT operations data) in most cases is semi-structured in nature and comprises log data, server data, application data, and other machine-generated data. In this presentation we will showcase how some statistical modeling techniques and an IBM product known as Log Analysis were used to derive insights from gigabytes of data. IT operations data also contains ticket data (incident data) with text comments and feedback entered by systems support teams. We employ detailed text pre-processing, clustering, and text analytics algorithms such as LDA and bi-term topic models to indetify topics across support tickets handled by various teams to identify problem hot spots.
I am the Practice Head for Data Science and Engineering in IT Operations Analytics at IBM. I lead a talented team of Data Scientists and Data Engineers. I hold a PhD in Industrial Engineering from the US and have been an active researcher and academician in the US for over a decade. I have given over 26 talks across the world in unsupervised machine learning techniques and analytics with applications in the energy domain.