Development roadmap: 2020/21
Published: 21 February 2020
Published: 21 February 2020
Keeping Culture is currently working to integrate the management of externally stored digitised archival assets and high-resolution media within the Keeping Culture KMS® framework.
Currently, Keeping Culture KMS® stores low-resolution copies suitable for web delivery. Administrators are responsible for creating their own local repository of their high resolution digitised archival assets. These assets correlate with the records contained within their Keeping Culture archive.
By utilising a low-cost external cloud-based file storage, such as Amazon’s S3 service, Keeping Culture KMS® will be able to provide basic file management operations to administer the storage and retrieval of high resolution digitised archival assets. This creates a single, highly accessible, de-centralised repository of digitised archival assets, where access control, including content restriction privileges, are governed within the Keeping Culture KMS® functionality.
Furthermore, many Networked Attached Storage devices can be configured to automatically synchronise files stored in the Cloud with those stored on the device. Enabling customers to keep multiple versions of their data backed up locally and in the Cloud.
Having a flexible platform to create new functionality was one of the biggest motivations for developing the new system in late 2017. Equally inspiring are our customer’s ideas for the software and the exciting opportunities that are emerging in the Cloud computing space. So the question has to be asked, “Where to from here?”.
Below are some big concepts, grand visions and wild ideas for Keeping Culture’s future:
Dictionary: a multi-lingual dictionary tool for the recording and preservation of language. The dictionary functionality would be integrated with, and supplement, the existing software functionality.
Artificial Intelligence (AI) Cloud services: integrate image analysis to provide object and scene detection, facial recognition and face comparison using Amazon Rekognition. Further opportunities exist to leverage speech-to-text and other language based AI services.
Contributor framework: A framework that analyses an archive’s content in relation to a specific user, in order to determine the user’s authority on, or interest in, a specific subject matter. The analysis would take into account a user’s family, professional and friend networks, country, place, time and metadata contributions to determine which archive content is relevant to them; thus providing a ‘me and my world’ centric experience.
A suite of pages, tools and functionality would be developed to prompt users to contribute information, manage their own contributions (including ingested media), and explore their world via a social media inspired interaction. The more a user contributes to the archive, the larger their ‘me and my world’ experience grows.
Viewing derived relationships: A process whereby record relationships are traversed in order to locate derived relationships that exist in indirectly linked records. For example, when a person’s record is viewed, the system would display derived information such as:
the timeline of places visited and event participated in,
relationships to extended family members,
organisations they have been involved in,
annotations they have made,
archive items they may have created,
archive items they may appear in,
and so on.
Archive Nodes framework: Through a taxonomy approach to structure large and diverse collections of media and knowledge, while allowing community groups to access, manage and contribute to only a subsection of the larger structure. This high-level multi-archive management framework allows any number of archives to hang off a tree-like node structure; where content is shared and accessed based on the position in which an archive sits within the node structure.