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.
Circuitscape - A Case Study on Scientific Computing
In this talk I would talk about some of the challenges faced in typical scientific computing applications and how to address them, taking Circuitscape as a case study. it would walk the audience briefly through what is possible through modern scientific computing platforms built on Python and Julia.
Circuitscape is an open-source program, which borrows algorithms from electronic circuit theory to predict patterns of movement, gene flow, and genetic differentiation among plant and animal populations in heterogeneous landscapes. It is used by academics, policy makers, and governments around the world in conservation planning.
Created by Brad McRae and Viral B. Shah, it equates life forms to electrons, the landscape as a grid of resistances and the movement of life forms across a landscape as current flowing through a circuit. Interestingly, this has been able to model reality much better than many other approaches. Circuitscape has been used to model raster landscapes containing as many as 20 million cells, covering vast geographies over thousands of square kilometres, resulting in jobs that run over days to compute wildlife corridors.
This talk is about Circuitscape and its application. I will cite the example of Circuitscape usage: "Connectivity of Tiger (Panthera tigris) Populations in the Human-Influenced Forest Mosaic of Central India” during the talk, and how this concept can be applied across other domains.
I am one of the co-creators of the Julia programming language and Circuitscape.
- Link to slides from this talk: https://drive.google.com/file/d/17yVavIsa7SsK6viMCFO96gpYHsJncoRV/view?usp=sharing