This document discusses big data analytics tools for non-technical users. It introduces Tuktu, a platform that makes big data science accessible through a visual drag-and-drop interface. It also describes using deep learning models trained on linguistic resources to perform natural language tasks across languages with less effort. Finally, it presents CEMistry, a customer experience monitoring product that analyzes text, web, mobile, and backend data to build customer profiles.

1. (Technical) Big Data
Analytics
for non-technical end-users
ErikTromp – CEO UnderstandLing

2. Contents
• (Short) into
• Rationale
• Tuktu platform
• Deep learning for computational linguistics
• CEMistry – Customer Experience Monitoring on steroids

3. (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

4. (Short) Intro
Quantify every touchpoint of a customer with your company
4 major areas
• Text Analytics
• Web Analytics
• Mobile Analytics
• CRM/Backend Analytics

5. (Short) Intro
• ErikTromp
• Age: 28
• CEO UnderstandLing
• Graduated on Sentiment Analysis in 2011
• Multilingual Sentiment Analysis on Social Media
• Software engineer – Scala
• Machine learning
• Author of platformTuktu

6. Contents
• (Short) into
• Rationale
• Tuktu platform
• Deep learning for computational linguistics
• CEMistry – Customer Experience Monitoring on steroids

7. Rationale
Big data science allows to utilize opportunities

8. Rationale
Big data science allows to utilize opportunities
Big data science drives business

9. Rationale
Big data science allows to utilize opportunities
Big data science drives business
But is very much a technical revolution, with business implications

10. Rationale
Many companies want to utilize the opportunities big data science brings

11. Rationale
Many companies want to utilize the opportunities big data science brings
These companies do not have sufficient capabilities to do so

12. 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

13. Rationale
But these companies often do have their own (business) analysts

14. Rationale
IDEA
Make big data science accessible to non-technical users

15. Contents
• (Short) into
• Rationale
• Tuktu platform
• Deep learning for computational linguistics
• CEMistry – Customer Experience Monitoring on steroids

16. Tuktu
http://www.tuktu.io
https://github.com/UnderstandLingBV/Tuktu

17. 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

18. 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

19. 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!

20. Tuktu – Now
Your one-stop shop for everything big data science

21. 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!

22. Tuktu
DEMO

23. Contents
• (Short) into
• Rationale
• Tuktu platform
• Deep learning for computational linguistics
• CEMistry – Customer Experience Monitoring on steroids

24. Deep Learning for Computational Linguistics
IDEA
Learn language models generically

25. Deep Learning for Computational Linguistics
IDEA
Learn language models generically
Model every CL-problem on top on the generic model

26. Deep Learning for Computational Linguistics
This way, we can do almost any task on almost any language
Without too much/with less effort

27. Deep Learning for Computational Linguistics
How?

28. Deep Learning for Computational Linguistics
There are many linguistics resources available
Sadly; most is for English
In particular: AnnotatedTreebanks for deep parsing

29. Deep Learning for Computational Linguistics
We can use this however

30. Deep Learning for Computational Linguistics
1. Co-train word vectors for target language and English

31. Deep Learning for Computational Linguistics
1. Co-train word vectors for target language and English
2. Train parsing models on English language

32. Deep Learning for Computational Linguistics
1. Co-train word vectors for target language and English
2. Train parsing models on English language
3. Co-finetune models on co-trained word vectors

33. Deep Learning for Computational Linguistics
1. Co-train word vectors for target language and English
2. Train parsing models on English language
3. Co-finetune models on co-trained word vectors
4. Pre-train (recursive) auto-encoder using parsing model for target
language

34. Deep Learning for Computational Linguistics
1. Co-train word vectors for target language and English
2. Train parsing models on English language
3. Co-finetune models on co-trained word vectors
4. Pre-train (recursive) auto-encoder using parsing model for target
language
5. Use recursive auto-encoder for specific task in target language
• Topic detection, sentiment analysis, named entity recoginition, authorship profiling

35. Deep Learning for Computational Linguistics
DEMO
Unsupervised parsing in Dutch

36. Contents
• (Short) into
• Rationale
• Tuktu platform
• Deep learning for computational linguistics
• CEMistry – Customer Experience Monitoring on steroids

37. CEMistry
TEXT ANALYTICS
MOBILEANALYTICS
WEB ANALYTICS
BACKEND/CRM
Customer Profile
Tuktu.js
Visitor Customer
Events
Page views
“SDK”
Visitor Customer
Events
AppTriggers
User Customer
Collectors
Events
Communication
(NLP)
(Database)
Connectors
Customer
Events
Transactions

38. Questions?

39. ErikTromp
CEO UnderstandLing
erik@understandling.com
http://www.understandling.com
http://www.tuktu.io
http://www.linkedin.com/in/eriktromp
https://github.com/UnderstandLingBV/Tuktu
Talk to us on Gitter!
https://gitter.im/UnderstandLingBV/Tuktu