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.
Approximate algorithms for summarizing streaming data
1) Introduce two approximate algorithms which are considered cornerstone of big data infrastructure.
2) These algorithms can be used to obtain a first hand summary of massive dataset in a streaming manner
Approximate algorithms can be used for processing huge streams of incoming data using only a single pass. These algorithms consume finite amount of memory and cpu cycles. They enable us to maintain summaries which are sufficient to answer expected queries about the data.
Two such novel algorithms, finding lots of applications in the industry today are
1) Count min sketch (CMS)
This talk aims to:
1) Provide a brief introduction to theoritical aspects behind these algorithms
2) How they can be leveraged to summarize unstructured data for practical purposes.
3) How to choose the tuning parameters pertinent to your needs.
4) Demonstrate how we have used them in Sumologic service.
Interest in approximate algorithms, streaming algorithms
Himadri Sarkar is a Software Engineer at Sumologic India where he is currently working in the are of search performance. Sumo Logic is a cloud-based log management and analytics service that leverages machine-generated big data to deliver real-time IT insights. Search performance team is responsible for delivering all the search related capabilities of the system.