1. Tomáš Jurníček, Jakub Jůza, Lenka Kmeťová
Big text data mining
Tomáš Jurníček, Jakub Jůza, Lenka Kmeťová

2. Introduction Text data analysis Sophisticaded analytic methods
Information extraction from data

3. Big data and data mining
datasets of large size and complexity
Companies have large amounts​ of data
Data needs to be analyzed
Problem: natural language
Data mining
Data cleaning
Data integration
Data selection
Mining methods
Evaluating results

4. Methods Information extraction Categorization Clustering Visualization
Key phrases and relations
Unstructured text
Categorization
Assign categories to documents
Clustering
Using clusters
Visualization
Present data in a form understable for humans
Summarization
Long documents
Expressing only core information

5. Tools
Large companies like Facebook or LinkedIn work on open-source projects. For example:
Apache Hadoop - for data-heavy distributed applications
Apache S4- for continuous processing of data streams
Storm (Twitter) - for streaming distributed data
Open source tools for Big Data Mining:
Apache Mahout, R, MOA,…

6. Nursing records A specific area of use for Big data mining
Electronic Medical Record (EMR) = information about patients
This data is not used to its full potential.
information is written in an unstructured style
expressions are highly subjective
-> Data mining is more complicated

7. Nursing records Result analyzed by KeyGraph
associations and frequent terms that represent basic concepts in the data

8. Future There are a lot of challanges:
Statistical significance – quality of statistical resultst for large sets of data
Distributed mining – more parallelize methods
Time evolving data - data is changing in conjuction with time
Hidden big data – a lot of data is unlabeled and unstructured. Currently, only 3% of data is usable for data mining!

9. Conclusion
We are at the beginning of a new era, when Big text data mining will allow to discover new, currently unknown, knowledge.