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Durham University Library's first automation venture arose from the decision in 1967 to establish a Short Loan Collection of books and periodicals in heavy demand. The collection, divided between the Main Library and the at present separate Science Section, may eventually grow to something like 10, 000 items. While it was initially to be drawn in large part from existing stock, and administration was to be integrated with the library's other operations, the project presented an opportunity to try new methods.

This exploratory study seeks to understand whether an arts event designed with/by/for disabled people (the InterACT Disability Arts Festival in New Zealand) has the potential to create revolutionary futures, defined as those which help determine new paths, make the future less fearsome and allow more positive outcomes.

A qualitative approach was taken in this study. Interviews were carried out with ten disabled event attendees, two support workers, one family member, four event volunteers, two activity providers and the main event organiser of the 2019 festival. Active participant observation was also used to collect data. Deductive thematic analysis was used to determine themes and subthemes in the material.

The findings suggest the case study arts event does help to create revolutionary futures for disabled attendees through disrupting the narratives of disability, making sense of lives lived and changing lives yet to be lived.

Limited windows of opportunity were available to interact with attendees, and just 17 in-the-moment interviews were conducted. However, the findings still have value as data saturation was reached. A “revolutionary futures” conceptual framework is presented to understand the nexus between disability worlds and events and thus amplify the benefits for attendees.

Research carried out to date has provided much-needed understanding about the challenges facing disabled people at events, but this study turns this deficit approach around to focus on the opportunities provided by event participation.

The paper aims to provide a non‐technical introduction to the new procedures being used to extract knowledge from databases. The reasons for developing knowledge discovery methods are discussed ± primarily, the current production of very large databases that may include many data relations not explicit in the database structure. The background in machine learning is indicated. The methods used are described for such techniques as classification (sorting data into predefined classes), clustering (developing ab initio a data classification) and the detection of deviations from pre‐established norms. Examples of the applications of these methods are given. The paper concludes with some brief thoughts about the potential use of knowledge discovery in the information field.