The Fifth Elephant 2015
A conference on data, machine learning, and distributed and parallel computing
Thursday 16 July 2015
Audi 1
Audi 2
Audi 3
BoF space
08:30–09:25
Check-in
09:25–09:45
Conference Introduction
09:45–10:30
Critical pipe fittings: What every data pipeline requires
Yagnik
09:45–10:30
Graph Algorithms and Computer Vision
Sumod Mohan
10:30–10:50
Dead Simple Scalability Patterns
Vedang Manerikar
10:30–10:50
Running natural language queries against NoSQL schema
Deepak Krishnan
10:50–11:20
Morning tea break
11:20–12:20
Keynote: Data Comes in Shapes
Tim Poston
12:20–13:00
Keynote: Future patterns in data processing (sponsored)
Amod Malviya
13:00–14:00
Lunch Break
14:00–14:45
Are these the same pair of shoes? - Matching retail products at scale
Nikhil Ketkar
14:00–14:45
A review of important results in distributed systems
Vaidhy Gopalan
14:00–15:05
Computing languages BOF – Julia and R
Viral Shah, Ravishankar R., Shashi Gowda, Dhanesh Padmanabhan
14:45–15:05
Search at Petabyte scale
Anup Nair
14:45–15:05
When Apache ZooKeeper is good fit
Rakesh R
15:05–15:25
HawkEye: A Real-Time Anomaly Detection System
Satnam Singh, PhD
15:05–15:25
Exploratory data analysis using Apache Lens and Apache Zeppelin
Bala Nathan
15:25–16:10
Approximate algorithms for summarizing streaming data
Himadri Sarkar
15:25–16:10
Apache Tez - Present and Future
Rajesh Balamohan
16:10–16:45
Evening tea Break
16:45–17:30
CAP Theorem: You don’t need CP, you don’t want AP, and you can’t have CA
Siddhartha Reddy
16:45–17:30
Instrumenting your kafka & storm pipeline
Bhasker Kode
17:30–17:50
Joining data streams at scale for fun and profit
Aniruddha Gangopadhyay
17:30–17:50
Making a contextual recommendation engine using Python and Deep Learning at ParallelDots
Muktabh Mayank
17:50–18:35
Revolutionizing travel with ML & Analytics – An insight into business optimization using Machine Learning and Advanced Analytics
Raghu Kashyap
Friday 17 July 2015
Audi 1
Audi 2
Audi 3
BoF space
08:30–09:45
Check-in
09:45–10:30
Deep Learning for Natural Language Processing
Devashish Shankar
09:45–10:30
The many ways of parallel computing with Julia
Viral B. Shah
10:30–10:50
Using Modes for Time Series Classification
Rohit Chatterjee
10:30–10:50
Harnessing the power of the Erlang VM at Housing
Abhijit Pratap Singh
10:50–11:20
Morning tea break
11:20–12:20
Keynote: "Thinking Machines"
Shailesh Kumar
12:20–13:00
Building a E-commerce search engine: Challenges, insights and approaches (sponsored)
Vinodh Kumar R
12:20–13:00
Deploying Batch and Streaming Architectures on AWS (sponsored)
Russell Nash
13:00–14:00
Lunch
14:00–14:45
Keeping Moore's law alive: Neuromorphic computing
Anand Chandrasekaran
14:00–14:45
Call me maybe: Jepsen and flaky networks
Shalin Mangar
14:00–15:05
Apache Spark BOF
Madhukara Phatak, Yagnik Khanna
14:45–15:05
Hardware Accelerated Big Data Processing
Reetinder Sidhu
14:45–15:05
POC: How to slice, dice & search billions of users events in seconds (from scratch)
Bhasker Kode
15:05–15:50
Visualising Multi Dimensional Data
Amit Kapoor
15:05–15:50
Building tiered data stores using Aesop to bridge SQL and NoSQL systems
Regunath Balasubramanian
15:05–16:10
Deep learning BOF
Shailesh Kumar, Bargava S., Raghotham, Anand C
15:50–16:10
Escher - democratizing beautiful visualizations
Shashi Gowda
15:50–16:10
Building Recommender system
Swaroop Krothapalli
16:10–16:45
Evening tea break
16:45–17:45
Two Years Wiser: The Nilenso Experiment
Steven Deobald
17:45–18:30
Recommendation System beyond traditional Collaborative filtering
Gagandeep Juneja
17:45–18:30
Processing large data with Apache Spark
Venkata Naga Ravi
Saturday 18 July 2015
Audi 1
Audi 2
Audi 3
BoF space
09:00–09:30
Registrations and check-ins
09:30–11:00
Understanding supervised machine learning hands on!
Harshad Saykhedkar
09:30–11:00
Introduction to Deep Learning
Bargava Subramanian
11:00–11:30
Morning tea break
11:30–13:30
Understanding supervised machine learning hands-on - continued
11:30–13:30
Introduction to deep learning – continued
13:30–14:15
Lunch
14:15–16:00
Igniting your data with Apache Spark
Yagnik
14:15–16:00
Introduction to deep learning continued
16:00–16:30
Evening tea break
16:30–18:30
Igniting your data with Apache Spark - continued
16:30–18:00
Introduction to deep learning – continued
15:05–16:10
Social circus – learn juggling, hooping, stretches
Kristen McQuillin