| Hi-index | 0.00 |
Diagrammatic visual data query (DiViDaQue) systems let users specify queries by directly manipulating graphical data representations. For example, selection can be performed by adjusting sliders on a data axis to specify an upper and lower bound on a range of values or by dragging a rubber band in a data plot to select points of interest. Some systems support more advanced visual query operations such as aggregation, join, and composition. There are several difficulties with working in this domain. First, it is time consuming to conduct background research and create a new system. Second, relevant papers have been published in different areas, (e.g., information visualization, human-computer interface design, database research, etc.) and investigators familiar with one area may be unaware of findings in others. Third, high-level design errors can be difficult to detect and costly to resolve. One goal in conducting this research was to develop a systematic understanding of the systems in the DiViDaQue domain. To this end, we have conducted the first domain analysis of DiViDaQue systems, including a detailed survey of a dozen systems within the domain. As part of our analysis, we bounded the domain by listing features a system must implement to be considered a member. We also created a dictionary and thesaurus of domain terms. Another goal was to help developers new to the domain to understand rationales for DiViDaQue system design. For this reason, we developed a design space model of the domain's most salient features and the tradeoffs associated with their implementation. We also discussed design patterns common to the domain. We integrated these results in a domain-specific software architecture (DSSA) that describes components useful in implementing common domain features. We employed two methods to validate our research. First, we used our model to implement our own DiViDaQue system. Second, we compared our model with the design of a toolkit for building DiViDaQue's that was published after we had completed our implementation. We discuss the lessons learned from our validation procedures, and their impact on our final domain model.