In 2014, infrastructure components such as Hadoop, Berkeley Data Stack and other commercial tools have stabilized and are thriving. The challenges have moved higher up the stack from data collection and storage to data analysis and its presentation to users. The focus for this year’s conference on analytics – the infrastructure that powers analytics and how analytics is done.
Talks will cover various forms of analytics including real-time and opportunity analytics, and technologies and models used for analyzing data.
Proposals will be reviewed using 5 criteria:
Domain diversity – proposals will be selected from different domains – medical, insurance, banking, online transactions, retail. If there is more than one proposal from a domain, the one which meets the editorial criteria will be chosen.
Novelty – what has been done beyond the obvious. Insights – what insights does the proposal share with the audience that they did not know earlier. Practical versus theoretical – we are looking for applied knowledge. If the proposal covers material that can be looked up online, it will not be considered.
Conceptual versus tools-centric – tell us why, not how. Tell the audience what was the philosophy underlying your use of an application, not how an application was used. Presentation skills – proposer’s presentation skills will be reviewed carefully and assistance provided to ensure that the material is communicated in the most precise and effective manner to the audience.
For queries about proposals / submissions, write to firstname.lastname@example.org
Data Collection and Transport – for e.g, Opendatatoolkit, Scribe, Kafka, RabbitMQ, etc.
Data Storage, Caching and Management – Distributed storage (such as Gluster, HDFS) or hardware-specific (such as SSD or memory) or databases (Postgresql, MySQL, Infobright) or caching/storage (Memcache, Cassandra, Redis, etc).
Data Processing, Querying and Analysis – Oozie, Azkaban, scikit-learn, Mahout, Impala, Hive, Tez, etc.
Big data and security
Big data and internet of things
Data Usage and BI (Business Intelligence) in different sectors.
Please note: the technology stacks mentioned above indicate latest technologies that will be of interest to the community. Talks should not be on the technologies per se, but how these have been used and implemented in various sectors, enterprises and contexts.
Machine learning at scale with Spark
Take the audience throught my journey of learning machine learning from scarath using various freely available resources and building applications it on the big data using Apache Spark and MLLib.
With bigdata explosion, machine learning is becoming more and more important in various fields of data analysis.But with lot of mathematical notations and jargons it has become extremely difficult to understand for a beginner.Morever even though you understand the tequniques, you will start wondering how to implement or use it on larger real world data.
In this talk, I will be sharing my and my team’s experience of learning machine learning from scartch and using it on bigdata using Spark and MLLib. All the examples and code for the examples will be avaliable on github.
Passion for machine learning.
Madhukara phatatak is a Bigdata developer @ ZinniaSystems. He has been actively working in Hadoop,Spark and its ecosystem projects from last 4 years.
He was lead developer of Nectar, a ML library for hadoop.He also contributed to hadoop source code to improve cyclic checks in Jobcontrol api.With raise of apache Spark, he with his team has open sourced courseera machine learning course examples on spark here.
Currently working on machine learning applications for retail domain using Spark and its ecosystem.
- Machine learning applications using Spark http://mlapps.zinniasystems.com/
- Experience of using Spark and ML in Bigdata hackathon http://www.slideshare.net/madhukaraphatak/spark-athackthon8jan2014