Visualization is the most potent means for LD consumers to analyze and explore a dataset. However, exploring and visualizing LD is not the same as exploring proprietary datasets. LD visualization is a particular task that differs from the classical data visualization since, usually, users do not have a priori knowledge of the dataset and do not know if the dataset might be relevant for their goals. Visualizing LD means to handle several issues: the large size, and the dynamic nature of data, the requests for exploratory searches, the variety of tasks, and users.
We argue that LD users try to address specific needs. For this reason, a long list of operations that might be of help to an LD user when he/she start exploring a dataset has been collected from participants at the International Semantic Web Conference in 2018 during the tutorial on "Challenges and Opportunities with Big Linked Data Visualization" and can be resumed in the following needs: (1) to provide a glimpse of the dataset; (2) to implement the exploratory search; (3) to offer customization capabilities to different user-defined scenarios; (4) to deal with large datasets (5) to highlight the evolution over time of the dataset; (6) to provide multiple visual perspectives; (7) to allow a panoramic and specific view on-demand over the data; and (8) to provide real-time response and progressive results.
These operations can be rendered into features implemented by LD visualization tools. In order to formalize the possible interactions between a tool and a user, in this chapter, we define a series of standard use cases that LD visualization tools should implement in order to provide clear and convincing visualization of the data contained in a LD source and encourage user comprehension. A use case describes how a user makes use of a system to accomplish a particular goal. Use cases help ensure that the system is developed by capturing the requirements from the user’s point of view.
In the following, we reported a study about how users’ requirements for LD consumption has been collected in the literature (Section 4.1). In Section 4.2, we identified 15 use cases. We classified them into three categories: (1) use cases related to the visualization of the structure of the dataset like classes, properties, relations etc., (2) use cases related to the visualization of the content of the dataset like instances , their properties and the relations to other resources inside or outside the same data source, (3) generic use cases that are not specific to the structure nor content. Finally, we formalize UML activity diagrams to model the 15 use cases (Section 4.3).
Use cases try to categorize the LD user needs. In this chapter, we focus on defining the use cases, while in Chapter 5, we evaluate several LD visualization tools w.r.t. these use cases to show which tools implement specific functionalities and which differences hold among the tools. From the best of our knowledge, this is the first time a formal definition of use cases that implement the LD user needs has been depicted.
|− T-Box related Use Cases|
|− A-Box related Use Cases|
|− T-Box and A-Box related Use Cases|