Abstract
The EUROPANGE project, involving both medievalists and computer scientists, aims to study the emergence of a corps of administrators in the Angevin controlled territories in the XIII–XV centuries. Our project attempts to analyze the officers' careers, shared relation networks and strategies based on the study of individual biographies. In this paper, we describe methods and tools designed to analyze these prosopographical data. These include OLAP analyzes and network analyzes associated with cartographic and chronological visualization tools.
Highlights
The data to be gathered and analyzed include all the biographical details of the officers and their acquaintances, extracted from the inventory of archival and iconographical sources (Angevin chancery records, deeds, epigraphic sources)
A prosopographical database used by the above applications. All these elements make up the software suite Prosopange which is presently deployed on the French CNRS TGIR Huma-Num site
Concerning the OLAP analysis, the user dynamically chooses among these 3 categories, the dimensions he is willing to include in his multidimensional table, with the option for combining categories: for example, (“place of origin” x “religion”) or (“place of origin” x “religion” x “types of offices”)
Summary
The data to be gathered and analyzed include all the biographical details of the officers and their acquaintances, extracted from the inventory of archival and iconographical sources (Angevin chancery records, deeds, epigraphic sources). Concerning the OLAP analysis, the user dynamically chooses among these 3 categories, the dimensions he is willing to include in his multidimensional table, with the option for combining categories: for example, (“place of origin” x “religion”) or (“place of origin” x “religion” x “types of offices”). For the spatial dimension used in places of origin, residence, work, and duty, the predefined hierarchical schema is : location < subdivision < political space < territory < all; concerning the public office types, an unbalanced parent-child tree hierarchy is used. The hypercube is fully materialized; the aggregate set is calculated and sent to the client This choice of both full materialization of the cube and officer table scan is made possible due to the small amount of data ( ̃7000 officers) and it allows insuring good performances during the OLAP navigation. A click on a table cell highlights the cell in green and allows getting the list of the concerned officers and for each of them his prosopographical page can be displayed
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
