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 email@example.com
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.
What would you recommend?
This workshop will provide the audience with a quick overview of recommendation systems & how to build one from scratch. We shall build user-user collaborative filter (CF) based recommendation engines as well as item-item CF recsys. The audience will get a flavour of the range of statistical & mathematical computations that go into a recsys.
Both beginners & mildly experienced folks would have a good set of skills to take away from this session. We will together de-mystify all that one imagines about recommendation systems.
We will cover what user-user CFs are as well as item-item CFs. We will then design and build an implementation (in R) of each & apply them to standard data sets. We will go into Mahout’s pre-defined algorithms & how to use them (on a single node Hadoop setup).
Anand has been dabbling in systems software & application software development for the past 12 years. For the past few years his interest in data analytics has taken a lion’s share of his energy. While not in front of the computer, he writes, travels, shoots (pictures), cooks (a wandering chef) and does a lot more he doesn’t confess to.