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.
'Know Your Customer!' - Advanced Data Science for Audience Segmentation
Have you ever wondered how Cisco does Customer Segmentation? What is Cisco’s technology stack to deal with Big Data? What tools and technologies are adopted to bring best-of-breed algorithms from data science to inform on the problem of identifying segments in the audience. How does supervised and semi-supervised machine-learning along with Bayesian predictive analytics combine to produce a very interesting cocktail of technology solutions?
What is the system design and lambda architecture that we use to successfully deploy the solution to scale?
What is the practical business use of audience segmentation? What applications can derive benefit from Audience Segmentation?
I will provide answers to the above questions and many more in this demo plus talk on Cisco’s Audience Segmentation solution.
Audience Segmentation is a very important practical necessity in pretty much every field. Whether it is internet subscribers or paytv subscribers there is an intense need from the advertisers and service providers to know who is living in a household and what their demography profiles are and what their interests are?
An ensemble of techniques which include advanced linear and non-linear dimensionality reduction, unsupervised learning algorithms, bayesian predictive analytics and expert systems come together to form a compelling pragmatic data science solution stack for the big data environment.
Some background knowledge of Big Data tools and data science algorithms like Bayes theorem, machine-learning algorithms, knowledge-based systems. Some background knowledge about the business requirements of Audience Segmentation. Some exposure to Mahout would be helpful.
I am Prabhakar Srinivasan. I work as a data scientist at Cisco. I have invented a unique technique to do customer segmentation which works for the PayTv domain of Cisco products running on Big data infrastructure. I wish to share this success story with the community with the hope that others who are trying to solve a similar problem can gain some practical insight on doing data science on big data stack that really gives business value.
I have successfully deployed my invention in Europe for some Telecom giants. I have presented this technique during talks in Monaco during Cisco’s internal conferences. It is time to share this success story with the wider Big data community.