In process mining, computational processing of sequential data allows the discovery and analysis of processes followed by organisations. These can be either explicitly understood processes, captured in documents or rules, or implicit process paths known in more informal or emergent ways. This paper examines a long-lived institution of historical interest, the Qing (1644-1911) Chinese civil service, using data assembled by historians on civil officials during the 19th century. Mapping the promotion process by following paths of officials through civil service postings helps illuminate the everyday operation of the institution and the society around it. Two distinctive features of this data set are that it records states, not events, and careers often include holding multiple concurrent roles. The combination is a poor match for existing process discovery techniques. We describe this structure as a state snapshot log, and present a new discovery technique, the State Snapshot miner, for constructing stochastic Petri net models from such logs. A case study shows its use in analysing promotion paths for elite graduates in the Qing civil service.

The increased availability of quantitative historical datasets has provided new research opportunities for multiple disciplines in social science. In this article, we work closely with the constructors of a new dataset, CGED-Q (China Government Employee Database-Qing), that records the career trajectories of over 340,000 government officials in the Qing bureaucracy in China from 1760 to 1912. We use these data to study career mobility from a historical perspective and understand social mobility and inequality. However, existing statistical approaches are inadequate for analyzing career mobility in this historical dataset with its fine-grained attributes and long time span, since they are mostly hypothesis-driven and require substantial effort. We propose CareerLens, an interactive visual analytics system for assisting experts in exploring, understanding, and reasoning from historical career data. With CareerLens, experts examine mobility patterns in three levels-of-detail, namely, the macro-level providing a summary of overall mobility, the meso-level extracting latent group mobility patterns, and the micro-level revealing social relationships of individuals. We demonstrate the effectiveness and usability of CareerLens through two case studies and receive encouraging feedback from follow-up interviews with domain experts.