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(Technical) Big Data Analytics for non-technical end-users Erik Tromp – CEO UnderstandLing
| Date post: | 14-Jan-2017 |
| Category: |
Technology |
| Upload: | erik-tromp |
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(Technical) Big Data Analytics
for non-technical end-users
Erik Tromp – CEO UnderstandLing
Contents• (Short) into• Rationale• Tuktu platform• Deep learning for computational linguistics• CEMistry – Customer Experience Monitoring on steroids
(Short) Intro
• Big data science experts• Specialisms• Computational Linguistics• Customer Experience Management
• Service: strategic advices all the way to operational implementation• Own platform: Tuktu• Soon: own product: CEMistry• Trainings/education on big data science
(Short) IntroQuantify every touchpoint of a customer with your company
4 major areas
• Text Analytics• Web Analytics• Mobile Analytics
• CRM/Backend Analytics
(Short) Intro• Erik Tromp• Age: 28• CEO UnderstandLing• Graduated on Sentiment Analysis in 2011• Multilingual Sentiment Analysis on Social Media
• Software engineer – Scala• Machine learning• Author of platform Tuktu
Contents• (Short) into• Rationale• Tuktu platform• Deep learning for computational linguistics• CEMistry – Customer Experience Monitoring on steroids
Rationale
Big data science allows to utilize opportunities
Rationale
Big data science allows to utilize opportunities
Big data science drives business
Rationale
Big data science allows to utilize opportunities
Big data science drives business
But is very much a technical revolution, with business implications
Rationale
Many companies want to utilize the opportunities big data science brings
Rationale
Many companies want to utilize the opportunities big data science brings
These companies do not have sufficient capabilities to do so
Rationale
Many companies want to utilize the opportunities big data science brings
These companies do not have sufficient capabilities to do so
Nor are there many suppliers that can do tech, analytics and know their business
Rationale
But these companies often do have their own (business) analysts
Rationale
IDEA
Make big data science accessible to non-technical users
Contents• (Short) into• Rationale• Tuktu platform• Deep learning for computational linguistics• CEMistry – Customer Experience Monitoring on steroids
Tuktu
http://www.tuktu.io
https://github.com/UnderstandLingBV/Tuktu
Tuktu – Early Days• Started off as a personal project to make life easier• Out of a collaboration with the Maastricht University• Idea: save time on coding/engineering, focus on logic and
functionalities
Tuktu – Early Days• Started off as a personal project to make life easier• Out of a collaboration with the Maastricht University• Idea: save time on coding/engineering, focus on logic and
functionalities
Instead of writing code over and over again, have it present and configure its building blocks
Tuktu – Early Days• Started off as a personal project to make life easier• Out of a collaboration with the Maastricht University• Idea: save time on coding/engineering, focus on logic and
functionalities
Instead of writing code over and over again, have it present and configure its building blocks
In a visual and straightforward way!
Tuktu – Now
Your one-stop shop for everything big data science
Tuktu – Now• Realtime and batch
processing• Synchronous and
asynchronous processing• REST API• Drag-and-drop modelling of
jobs• Distributed file system: TDFS• Key/value-sture: TuktuDB• Real-time visualization
• Web analytics support• Scheduling• No master/slave architecture• Local or distributed
computing• Machine learning• Deep learning• Cross-platform due to JVM• Easy installation: just unzip!
Tuktu
DEMO
Contents• (Short) into• Rationale• Tuktu platform• Deep learning for computational linguistics• CEMistry – Customer Experience Monitoring on steroids
Deep Learning for Computational Linguistics
IDEA
Learn language models generically
Deep Learning for Computational Linguistics
IDEA
Learn language models generically
Model every CL-problem on top on the generic model
Deep Learning for Computational Linguistics
This way, we can do almost any task on almost any language
Without too much/with less effort
Deep Learning for Computational Linguistics
How?
Deep Learning for Computational Linguistics
There are many linguistics resources available
Sadly; most is for EnglishIn particular: Annotated Treebanks for deep parsing
Deep Learning for Computational Linguistics
We can use this however
Deep Learning for Computational Linguistics1.Co-train word vectors for target language and English
Deep Learning for Computational Linguistics1.Co-train word vectors for target language and English2.Train parsing models on English language
Deep Learning for Computational Linguistics1.Co-train word vectors for target language and English2.Train parsing models on English language3.Co-finetune models on co-trained word vectors
Deep Learning for Computational Linguistics1.Co-train word vectors for target language and English2.Train parsing models on English language3.Co-finetune models on co-trained word vectors4.Pre-train (recursive) auto-encoder using parsing model for
target language
Deep Learning for Computational Linguistics1.Co-train word vectors for target language and English2.Train parsing models on English language3.Co-finetune models on co-trained word vectors4.Pre-train (recursive) auto-encoder using parsing model for
target language5.Use recursive auto-encoder for specific task in target
language• Topic detection, sentiment analysis, named entity recoginition,
authorship profiling
Deep Learning for Computational Linguistics
DEMO
Unsupervised parsing in Dutch
Contents• (Short) into• Rationale• Tuktu platform• Deep learning for computational linguistics• CEMistry – Customer Experience Monitoring on steroids
CEMistryTEXT ANALYTICS
MOBILE ANALYTICS
WEB ANALYTICS
BACKEND/CRM
Customer Profile
Tuktu.jsVisitor Customer
EventsPage views
Link
“SDK”Visitor Customer
EventsApp Triggers
Link
User CustomerCollectors
EventsCommunicati
on(NLP)
Link
(Database)ConnectorsCustomer
EventsTransactions
Link
Questions?
Erik TrompCEO UnderstandLing
http://www.understandling.comhttp://www.tuktu.io
http://www.linkedin.com/in/eriktromphttps://github.com/UnderstandLingBV/Tuktu
Talk to us on Gitter! https://gitter.im/UnderstandLingBV/Tuktu
