## 2 Case Studies
### Migrating Annotations across Multiple Representations of Museum Artefacts
The interpretation of animal accelerometry data is an onerous task due to: the volume and complexity of the data streams; the variability of activity and behavioural patterns across animals (due to age, environment, season); the lack of visualization and analysis tools; the inability to share data and knowledge between experts; the lack of ground truth data (e.g., in many cases there is no observational video); and the inaccessibility of machine learning/automatic recognition tools. As a result of the challenges identified above, we have been collaborating with the ECO-Lab developing the SAAR (Semantic Annotation and Activity Recognition) system, with the aim being:

* To provide a set of Web services and Graphical User Interfaces (GUIs) that enable ecologists to quickly and easily analyse and tag 3D accelerometry datasets and associated video using terms from controlled vocabularies (pre-defined ontologies) that describe activities of interest (e.g., _walking, running, foraging, climbing, swimming, standing, lying, sleeping, feeding_);
* To build accurate, re-usable, automatic activity recognition systems (using machine learning techniques (Support Vector Machines))--by training classifiers using training sets that have been manually annotated by domain experts. Figure 7 illustrates the overall system architecture of the SAAR system. SAAR uses the OA model to represent tags created manually by experts via the tagging system as well as tags that have been automatically generated by the classification model. This case study is interesting from the Open Annotation data model perspective because:

* It provides a situation whereby the tags, that describe animal activities (_running, walking, swimming, climbing, standing, lying, sleeping, feeding_), are generated automatically by the SVM Classification model;
* It provides an example of the application of the OA _Fragment Selector_--tags are attached to a temporal segment of the 3D accelerometry streams as well as the associated video content where available. The client interface comprises a Web-based Plot-Video visualization interface (see top of Figure 7) developed using a combination of AJAX, Flot (a plotting jQuery library) [20], HTML 5 Video Player library (Video.js) [21] with JavaScript. This interface enables users to interactively visualize both tri-axial accelerometer data alongside simultaneously recorded videos. Users invoke the semantic annotation service by selecting a segment of accelerometer data from the timeline or a segment of video from the video timeline, and then attaching an activity tag chosen from a pull-down menu (whose values are extracted from a pre-defined ontology). The manually created annotation is stored in a backend RDF triple store (see Section 3) implemented using the Apache Tomcat java server and Sesame 2.0. Additional annotation functions such as edit, refresh, and retrieve annotations are also supported. The automatic activity recognition component is implemented using the Libsvm Java library. At the training stage, users can interactively search for and retrieve specific segments/annotations using the following annotation search terms: _species, creator_, _animal ID_ and _activity tag_. The query is converted to the SPARQL query language which queries the annotation server. The retrieved annotations and data streams are processed to generate a set of application-dependent features that correspond to each tag. After the specific hierarchical SVM classification model is built for all of the activity tags, new tri-axial accelerometer data are input to the trained SVM classifier which then automatically tags the fresh input data (based on similarity between features). The automatically assigned tags are displayed in the timeline visualization pane, where experts can check or correct them.