ad hoc exploration of large volumes of multidimensional data by providing a comprehensive framework of advanced visualization techniques for representing the retrieved data set along with a powerful navigation and interaction scheme for specifying, refining, and manipulating the subset of interest.” [160].
The key distinction between the traditional and the novel OLAP tools is the role of visualization: the former use visualization merely for expressivepresentationof the data, whereas the latter employ visualiza-tion as amethodof interactive ad hoc analysis. In addition to conventional OLAP operations, visual OLAP supports further interaction techniques, such as zooming and panning, filtering, brushing, collapsing, etc.
Continuous efforts are put into providing new approaches to visual exploration for OLAP. Russom [158]
summarizes the trends in business visualization as a progression from rudimentary data visualization to ad-vanced forms and recognizes three life-cycle stages of visualization techniques, such as maturing, evolving, and emerging, as depicted in Figure 2.16. Within this classification, visual OLAP clearly fits into the emerg-ing techniques for advanced interaction and visual queryemerg-ing.
2.3.2 Visual Exploration Framework
Figure 2.17 illustrates the data exploration process, also denotedknowledge crystallization, adopted from [20] and slightly modified for matching the context of OLAP. In this cycle, visualization clearly plays the key role in providing insight into the data and, thus, solving the task at hand. Also notice that searching for a solution may evolve in multiple iteration cycles.
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Search for Representaion
Instantiate Representaion Problem-Solve
Author, Decide, or Act
Aggregate Drill-down ZoomFilter Navigate Search query
Reorder Cluster Classify Average Promote Detect pattern Abstract Interpret
Compare Manipulate Create Eliminate Extract Compose
Task Forage for Data
Figure 2.17: Data exploration cycle
A general approach to enabling the above flexible data exploration scheme is to implement a comprehen-sive framework, in which the users can browse through the data and interactively generate satisfactory visual presentations using “point-and-click” and “drag-and-drop” interactions. The overall query specification cycle includesi)selecting a data source of interest,i)choosing a visualization technique (e.g., a scatterplot or a bar-chart), andi)mapping various data attributes to that technique’s structural elements (e.g., horizontal or vertical axis) as well as to other visual attributes, such as color, shape, and size. The main elements of the framework are a data navigation structure, or “data browser”, for visual querying of data sources, a taxonomy of available visual layouts and their attributes, and a toolkit of interaction techniques for dynamic query re-finement and visual presentation of the output. A unified framework is obtained by designing an abstraction layer for each element and providing mapping routines (e.g., navigation events to database queries, query results to a visual layout, etc.) that implement the interaction between different layers. Figure 2.18 shows an example of an advanced frontend tool for visual data analysis provided by Tableau Software [169]. Data navigation on the left-hand side enumerates available dimensions and measures. The central area is occupied by the visual display of the current query results. Additional windows and menu provide further options for refining the dataset itself (e.g., filter) and adjusting the visualization (e.g., color assignment, labeling, resizing).
DATA NAVIGATION SCHEME
Visual OLAP disburdens the end-user from composing queries in “raw” database syntax (e.g., SQL or MDX).
Instead, queries are specified visually (i.e., by means of using a computer mouse). Multidimensional data is represented as a browsable structure. Visual interface does not trade off advanced functionality for simplicity, it rather facilitates the process of specifying ad hoc queries of arbitrary complexity.
A common data browsing paradigm is that of a navigation tree, i.e., as a recursive nesting of element nodes. The nodes in OLAP navigation scheme may be of typesdatabase,schema,table(cube),dimension, classification level, andmeasure. In simplified configurations, the navigation may be limited to single data cubes and, thus, consist solely of dimension and measure attributes of a selected cube.
Data cubes are navigation object consisting of measures and dimensions. A dimension is represented either as a lattice of its aggregation levels or directly by the data hierarchy. Logical OLAP operations are incorporated into a visual framework in the form of navigation events. For example, a drill-down is performed by dragging the respective dimension category node into the visualization.
Mapping of a multidimensional structure to the navigation scheme, translation of navigation events into
2.3 : Visual Analysis and Exploration 33
Figure 2.18: Tableau Software as a powerful frontend for visual analysis
database queries and mapping the query output to a visual layout rely on the metadata of the underlying data warehouse system. Metadata describes the structure of the cubes and their dimensions, measures and their additivity. In an advanced user interface, the analysts are able to define new measures in addition to the ones configured via the metadata. New measures may be obtained by applying a different aggregate function (e.g., average, variance, count) or a more complex formula over a single or multiple data fields (e.g., computing a ratio between two aggregated values).
VISUALIZATION INTERFACE
The task of selecting a proper visualization technique for solving a particular problem is by far not trivial as various visual representations (metaphors) may be not only task-dependent, but also domain-dependent.
Successful visual OLAP frameworks need to be based on a comprehensive taxonomy of domains, tasks, and visualizations. The problem of assisting the analyst in identifying an appropriate visualization technique for a specific task is a still unsolved issue in state-of-the-art OLAP tools. Typically, a user has to find an appropriate solution manually by experimenting with different layout options.
A common approach to visualization in OLAP application relies on a set of templates, wizards, widgets, and a selection of visual formats. Hanrahan et. al [55] argue, however, that an open set of questions cannot be addressed by a limited set of techniques, and choose a fundamentally different approach for their visual
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Figure 2.19: Examples of sophisticated visualizations generated by VizQL statements (permission granted by Tableau Software, Inc.)
analysis tool commercialized as Tableau Software [169]: a declarative visual query language VizQLTMoffers high expressiveness and composability allowing users to create their own visual presentations by combining various visual components. Figure 2.19 illustrates the visualization approach of Tableau by showing a subset of sophisticated visual presentations created using simple VizQL statements and not relying on any pre-defined template layout. In a more recent work [106], the same authors propose a pioneering solution to automating the visual presentation based on the user experience in the Tableau system. Other prominent examples of advanced visual systems based on research findings are Advizor by Visual Insights [39] and MagnaView Explorer by MagnaView [185].