The document outlines a project focused on mapping commodity trading in the 19th century, detailing the dataset's scope, including 18,580 documents covering commodities and locations from 1600 to 1952. It discusses the challenges of data accessibility, database performance, and data importation into Amazon RDS, offering potential solutions such as creating EC2 instances for PostgreSQL. Future work includes enhancing query capabilities, visualizing temporal trends, and exploring co-occurrences of terms.

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

2. Outline
● Project Overview
● Data
● Technology
● Demo
● Future Work

3. Overview
19th Century
Commodities Diseases
Locations Disasters

4. Process

5. Tasks
● Retrieve documents mentioning
○ Commodities
○ Locations
○ Time range
● Relations between retrieved terms
○ Spatial relations
○ Temporal relations
○ Co-occurrence relations
Users:
Historians

6. Data
● Commodities: 1067
● Time: 1600 - 1952 (452 years)
● Documents: 18 580
● Location occurrences: 91 650 469
● Commodity occurrences: 29 020 013

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

9. Solution 2
● Create EC2 instance running a PostgreSQL
database
○ Powerful enough
○ Enough storage
○ Accessible

10. Big Data Problems
● Simple things take a long time
● Incremental finding of errors/new problems

11. The Pipeline
● D3 for client-side presentation
● Java+SQL for server-side processing
data
Database
Web Service
Client
Commodities, date range

12. Initial Sketches

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

15. Demo

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 ?

17. Thank you for listening!