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.
big data analytics with machine learning
We crunch the numbers and turn your data into accessible and intuitive visuals that you can use for presentations, information sharing, and overall company transparency and most importantly for analysis.
Predictive analytics has been closely linked to big data. Based on the patterns of how your variables have behaved over time, our machine learning algorithms can predict how the same variables are likely to trend in the future.
About us :-
We are one of the very few companies which specifically work on predictive analytic to reveal the insight inside hidden data. We are expertized team for big data analytics mainly focusing on machine learning with in-memory computing Spark and Hadoop, We predict your next business values and business decision. We are expertized Data science team to reveal your hidden data and solve your business problem, hacking skills math, statistics and substanstive expertise in traditional research, data mugging and visualization. We learn the problem from your business domain expert about real problem and think creatively about how data can be used as part of solution. We focus on problem that actually improves the business. We start with real problem instead of starting with some interesting data set and begin specific question or begin with open exploration. We start exploration after we have an idea to solve problem.
Name Of project :- Decicion
Decicion AIM :-
Decicion crunch the numbers and turn your data into accessible and intuitive visuals that you can use for presentations, information sharing, and overall company transparency and most importantly for analysis.Predictive analytics has been closely linked to big data. Based on the patterns of how your variables have behaved over time, our machine learning algorithms can predict how the same variables are likely to trend in the future.
Name - Swapnil Birla
Skills - hadoop,R,D3