HTML Injection Attacks: Impact and Mitigation Strategies
Mapping Commodity Trading
1. Mapping Commodity Trading in the 19th Century
Benjamin Bach,
INRIA,
Paris
Asma Malik,
University of
Strathclyde,
Glasgow
Michael
Mauderer,
University of
St Andrews
Sadiq Sani,
Robert Gordon
University,
Aberdeen
Joe Wandy,
University of
Glasgow
7. The Data
● PostgreSQL Database in Edinburgh
○ Not accessible
● PostgreSQL Database in St Andrews
○ Low Performance
● PostgreSQL Database Backup
○ 2.5GB compressed binary data
○ Cannot be imported into Amazon RDS
8. Solution 1
● Create a more compatible SQL export to
import into Amazon RDS
○ 24GB raw text file containing SQL statements
○ still incompatible
○ hard to correct errors in a timely manner
14. Visualization
- Space and time
-> Finding related terms + documents
- find related documents
- what are documents talking about
- Implicit knowledge:
- Co-occurrences of terms in documents
For every commodity:
1) Get top 10 documents,
2) Limit related terms to 6
3) Sum up co-occurrences
16. Future work
- Query by Location
- Time diagrams for term frequency over time
- Encode information in matrix cells (#doc,collection..)
- Show and browse documents
- Handle big data: diseases, disasters, ..
- Co-occurrences ?