Scholarly chronographics: can a timeline be useful in historiography?

Our paper is concerned with the visualisation of historical events and artefacts in the context of time. It arises from a project bringing together expertise in visualisation, historiography and software engineering. The work is the result of an extended enquiry over several years which has included investigation of the prior history of such chronographics and their grounding in the temporal ontology of the Enlightenment. Timelines - visual, spatial presentations of chronology - are generally regarded as being too simple, perhaps too childish, to be worthy of academic attention, yet such chronographics should be capable of supporting sophisticated thinking about history and historiography, especially if they take full advantage of the capabilities of digital technologies. They should enable even professional academic historians to 'make sense' of history in new ways, allowing them insights they would not otherwise have achieved. In our paper we highlight key findings from the history of such representations, principally from the eighteenth and nineteenth centuries, and show how, in a project to develop new digital chronographics for collections of cultural objects and events, we have explored new implementations of the important ideas we have extracted about timewise presentation and interaction. This includes the representation of uncertainty, of relations between events, and the epistemology of time as a 'space' for history. We present developed examples, in particular a chronographic presentation of a large database of works by a single author, a composer, and discuss the extent to which our ambitions for chronographics have been realised in practice. Keywords: timeline, chronographic

Latent variable models for a probabilistic timeline browser

Probabilistic models have been extensively applied in Information Retrieval (IR) systems; they treat the process of document retrieval as probabilistic inference. Integrated with a relevance feedback mechanism, an IR system is able to infer both the search query and document relevance from the browsing pattern of a user. However, if there are no constraints imposed on the query, the model over fits easily and results in poor predictive performance. In this thesis, several latent variable models with feature selection are proposed for a probabilistic proactive timeline browser. The proactive timeline browser is suitable for finding events from timelines, in particular from life logs and other timelines containing a familiar narrative. The proposed models are based on several classical variable selection methods in linear regression, including Gibbs Variable Selection and Stochastic Search Variable Selection. Feature selection helps the model effectively avoid over-fitting and hence achieve better predictive performance. The new proposed models are more robust against noisy features, compared to models without feature selection. The models proposed in this thesis are general enough to apply to a wide variety of IR problems