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.
Twitter data collection framework for dummies.
This talk is about how I got 200 odd GB of tweets over a 45 day period to build a Trend Summarizer. I chose to build this as part of the dissertation for my MS Programme. The main objective here was to fetch tweets belonging to a trend in different locations. Additionally, I wanted this to be scalable out of the box i.e. if I increased the number of locations to look for, It shouldn’t run into problems with going over the twitter rate limit. This talk is about how I took the decisions to design for scalability and how I went around making the choices to build this data collection framework.
There have been many a time when I have wanted to do some analysis on twitter but then ended up pushing it because I didn’t have one important thing - The Data itself. When I set out to build a Trend Summarizer, I knew that I would have deal with this problem and wanted to solve it for myself. As I started building the framework, I faced a big hurdle of having to deal with rate limits.
The trend summarizer is a temporal and spatial system that required tweets belonging to trends in different locations. This meant that I had to have the ability to add locations and trends on a runtime basis without running into Twitter rate limit problems. Building a temporal and spatial system came with its own challenges of having to fetch data continuously and reduce redundant data as much as possible. To accomodate this, I had to spread out the calls to get data evenly over time and stay within limits. Also, given all these constraints, I had to collect large amounts of data for the summarizer to make sense.
This talk is about how I went around understanding the Twitter API along with its constraints and built a data collection framework. I built this out using Java with PostgreSQL as the backend.
Basic Java, SQL and Twitter API familiarity
Nischal HP is a Software Engineer working at SAP Labs Pvt. Ltd. building a Stock Market analysis product based on Algorithmic trading practices. This gets interesting because they bring in the concept of Text Analytics along with Algorithmic Trading to allow the user to make better business decisions keeping in mind the buzz around the stock. This product is built on the SAP In-Memory database - SAP HANA. He works majorly on the backend building out the core algorithms that run the system but has also contributed to the Text Analysis part as well.
He has a bachelors degree in Computer Applications from National College Jayanagar and a Masters in Software Engineering from BITS, Pilani. He enjoys having conversations about data mining, visualizations, machine learning and text analytics over a mug of beer(doh!). He is an ardent football fan and supports BFC and Manchester United.
This is Nischal’s first public talk.