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.
Tickets: http://fifthel.doattend.com
Website: https://fifthelephant.in/2014
For queries about proposals / submissions, write to info@hasgeek.com
Theme
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Data Collection and Transport – for e.g, Opendatatoolkit, Scribe, Kafka, RabbitMQ, etc.
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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).
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Data Processing, Querying and Analysis – Oozie, Azkaban, scikit-learn, Mahout, Impala, Hive, Tez, etc.
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Real-time analytics
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Opportunity analytics
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Big data and security
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Big data and internet of things
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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.
Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
GJ
GaganDeep Juneja
@gagandeepjuneja
Run Predictive Machine Learning algorithms on Hadoop without even knowing Mapreduce.
Submitted Jun 3, 2014
Section:
Full talk
Technical level:
Intermediate
In this talk I will try to bring some new concepts that will help data scientists to run their predictive algorithms on hadoop with the help of PMML and cascading.
Outline
Data Scientists are very much familiar with working on tools like R, SAS etc. for them writing or converting algorithms into mapreduce is bit difficult. There are libraries such as Mahout is available which provides mapreduce implementation of many algorithms but you can not run your algorithm directly on a hadoop cluster. Before that you need to create a Data Model based on data and decide the values for some tweaking parameters and changing these parameters multiple time in hadoop job and running again and again is bit pain for a Data scientist, for a java developer it could be a fun. Data scientists can do Data modeling or model training in SAS/R very easily and efficiently.
I am working on develpoment of GUI Interface whihc helps users in creating and triggering cascading jobs. I will try to showcase my talk demonstration using this GUI tool. Its like using any other web based/ desktop client application. If user is not aware of Hadoop componants such as MapReduce, HDFS can easily work and run their algorithms on massive amount of data.
Speaker bio
Gagan Deep Juneja is a Big Data Hacker at GoDataDriven. He has close to 6 years of experience in the Software Industry. He has worked on several projects using Java/J2ee and Hadoop as the primary technology. He has an inclination to open source technologies and likes to explore/delve into new frameworks . He is a committer and ppmc member to Apache Blur (incubating).He has been speaker at various meetup groups. He is a active blogger and in his free time loves exploring new technogolies and keeping himself updated with latest trends.
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Hosted by
The Fifth Elephant - known as one of the best data science and Machine Learning conference in Asia - has transitioned into a year-round forum for conversations about data and ML engineering; data science in production; data security and privacy practices. more
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