We describe a Visual Analytics (VA) infrastructure, rooted on techniques in machine learning and logic-based deductive reasoning that will assist analysts to make sense of large, complex data sets by facilitating the generation and validation of models representing relationships in the data. We use Logic Programming (LP) as the underlying computing machinery to encode the relations as rules and facts and compute with them. A unique aspect of our approach is that the LP rules are automatically learned, using Inductive Logic Programming, from examples of data that the analyst deems interesting when viewing the data in the highdimensional visualization interface. Using this system, analysts will be able to construct models of arbitrary relationships in the data, explore the data for scenarios that fit the model, refine the model if necessary, and query the model to automatically analyze incoming (future) data exhibiting the encoded relationships. In other words it will support both model-driven data exploration, as well as data-driven model evolution. More importantly, by basing the construction of models on techniques from machine learning and logic-based deduction, the VA process will be both flexible in terms of modeling arbitrary, user-driven relationships in the data as well as readily scale across different data domains.  Modern day enterprises, be they commerce, government, science, engineering or medical, have to cope with voluminous amounts of data. Effective decision making based on large, dynamic datasets with many parameters requires a conceptual high-level understanding of the data. Acquiring such an understanding is a difficult problem, especially in the presence of incomplete, inconsistent, and noisy data acquired from disparate real-world sources. To make progress on this problem one must draw on the complementary strengths of computing machinery and human insight. Recognizing this promising human-computer synergy, Visual Analytics (VA), defined as the science of analytical reasoning facilitated by interactive visual interfaces [22] , has become a major development thrust. It seeks to engage the fast visual circuitry of the human brain to quickly find relations in complex data, trigger creative thoughts, and use these elements to steer the underlying computational analysis processes towards the extraction of new information for further insight. VA has widespread applications, such as homeland security, the financial industry and internet security among others. Research papers and tools related to VA are beginning to emerge (see e.g. However, thus far the main emphasis in VA has been mostly on visualization, data management, and user interfaces (see e.g. [5] and other work mentioned in the following). As far as analytical computing goes, VA research has mainly focused on relatively low-level tasks, such as image [24] and video [12] analysis and database operations [21] . In today's VA systems, it is the human analyst who performs the actual reasoning and abstraction. Obviously this type of workflow is of limited scalability in terms of reasoning chain complexity. While there has been recent promising work on explicit knowledge management [25] , this system has been mainly designed to help users shape and keep track of emerging insight, but not to derive higher-level models from the data. In this paper, we present a framework that allows human analysts to off-load some of the reasoning to a computational analyst. Collaboration with this machine agent occurs by pointing out interesting data patterns discovered in the visual interface. The VA infrastructure we describe is rooted on techniques in machine learning and logic-based deductive reasoning. They assist analysts in making sense of large data sets by facilitating the generation and validation of models representing relationships in the data. By basing the construction of models on techniques from machine learning and logic-based deduction, the VA process will both be flexible in terms of modeling arbitrary, user-driven relationships in data and it will readily scale across different data domains. Locating patterns in high-dimensional space via visual inspection can be challenging. The method of parallel coordinates (PC) [8] , which has seen various refinements in recent years (see e.g., [17] ), maps a high-dimensional point into a piece-wise linear line that spans the vertical dimension axes. With PC, clusters can be isolated by brushing the appropriate data axes. There are, however, certain limits on the spatial complexity of the clusters. Scatter plots, on the other hand, provide an intuitive way to observe possibly arbitrarily complex relationships. However, in most applications, scatter plots are confined to projections onto two data axes at a time, and in order to view higher-order relationships spanning more than two dimensions, the associated projections must be serialized, viewed as a matrix, or be stacked [10] . We seek a visual interface that allows users to point out arbitrary high-dimensional patterns in a more direct manner. An early high-dimensional exploration paradigm providing a combined scatter plot of an arbitrary number of variables is the Grand Tour (GT) [2] , which is part of the GGobi package [19] . The user is guided through hyper-space along a trajectory that is decided along the way by selecting the maximum of a given projection pursuit metric. The user stops when the current view point is the local maximum of this metric. While traveling, the user views projections (in form of scatter plots) of the data onto a 2D hyperplane. In GT the motion parallax resulting from the quick but
al. REF described a visual analytics infrastructure that adopts logic reasoning to help reduce the complexity of visual analysis by automating the selection of interesting patterns.
Model-driven Visual Analytics
2008 IEEE Symposium on Visual Analytics Science and Technology
2008 IEEE Symposium on Visual Analytics Science and Technology
Computer Science