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The analysis of large graphs plays a prominent role in various fields of research and is relevant in many important

We present MotionExplorer, an exploratory search and analysis system for sequences of human motion in large motion capture data collections. This special type of multivariate time series data is relevant in many research fields including medicine, sports and animation. Key tasks in working with motion data include analysis of motion states and transitions, and synthesis of motion vectors by interpolation and combination. In the practice of research and application of human motion data, challenges exist in providing visual summaries and drill-down functionality for handling large motion data collections. We find that this domain can benefit from appropriate visual retrieval and analysis support to handle these tasks in presence of large motion data. To address this need, we developed MotionExplorer together with domain experts as an exploratory search system based on interactive aggregation and visualization of motion states as a basis for data navigation, exploration, and search. Based on an overview-first type visualization, users are able to search for interesting sub-sequences of motion based on a query-by-example metaphor, and explore search results by details on demand. We developed MotionExplorer in close collaboration with the targeted users who are researchers working on human motion synthesis and analysis, including a summative field study. Additionally, we conducted a laboratory design study to substantially improve MotionExplorer towards an intuitive, usable and robust design. MotionExplorer enables the search in human motion capture data with only a few mouse clicks. The researchers unanimously confirm that the system can efficiently support their work.

Visual-inte ra ctive cluste r analysis provid es valuable tools for effectively ana lyzin g la rge and compl ex data sets. Owing to desirabl e properties and an inhere nt predisposition for visu a li zation, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular a nd widely used visu al clu stering techniques. However, the unsupervised nature of the algorithm may be dis adva ntageous in certa in app lications. Depe nding on initia li zation a nd data cha racte ri sti cs, clu ste r m aps (cluster layouts) m ay e merge th at do not comply with user preferences, expectations or the a pplication co ntext. Considering SOM-based ana lysis of traj ectory data, we propose a compre hensive visual-interactive monitoring and control framework extending the bas ic SOM algorithm. Th e framework implem e nts the general Visual Analytics id ea to effectively combine a utomatic data analys is w ith huma n expert supe rvi sio n. It provid es simple, yet effective faci li ties for visua lly monitoring and in teractive ly co ntrolling th e traj ecto ry clu sterin g process at arbitrary levels of detail. Th e approach all ows th e us e r to leverage existing dom ain knowl edge and user preferences, arrivin g at improved cluste r maps. We apply the fram ewo rk on severa l traj ectory clustering prob lem s, demonstrating its potential in combining both unsupe rvised (machin e) and supervised (human expert) processing, in producing appropriate clu ste r results .

We propose a method for the semi-automated refinement of the results of feature subset selection algorithms. Feature subset selection is a preliminary step in data analysis which identifies the most useful subset of features (columns) in a data table. So-called filter techniques use statistical ranking measures for the correlation of features. Usually a measure is applied to all entities (rows) of a data table. However, the differing contributions of subsets of data entities are masked by statistical aggregation. Feature and entity subset selection are, thus, highly interdependent. Due to the difficulty in visualizing a high-dimensional data table, most feature subset selection algorithms are applied as a black box at the outset of an analysis. Our visualization technique, SmartStripes, allows users to step into the feature subset selection process. It enables the investigation of dependencies and interdependencies between different feature and entity subsets. A user may even choose to control the iterations manually, taking into account the ranking measures, the contributions of different entity subsets, as well as the semantics of the features

Visual Analytics seeks to combine automatic data analysis with visualization and human-computer interaction facilities to solve analysis problems in applications characterized by occurrence of large amounts of complex data. The financial data analysis domain is a promising field for research and application of Visual Analytics technology, as it prototypically involves the analysis of large data volumes in solving complex analysis tasks. We introduce a Visual Analytics system for supporting the analysis of large amounts of financial time-varying indicator data. A system, driven by the idea of extending standard technical chart analysis from one to two-dimensional indicator space, is developed. The system relies on an unsupervised clustering algorithm combined with an appropriately designed movement data visualization technique. Several analytical views on the full market and specific assets are offered for the user to navigate, to explore, and to analyze. The system includes automatic screening of the potentially large visualization space, preselecting possibly interesting candidate data views for presentation to the user. The system is applied to a large data set of time varying 2-D stock market data, demonstrating its effectiveness for visual analysis of financial data. We expect the proposed techniques to be beneficial in other application areas as well

Visual-interactive cluster analysis provides valuable tools for effectively analyzing large and complex data sets. Owing to desirable properties and an inherent predisposition for visualization, the Kohonen Feature Map (or Self-Organizing Map or SOM) algorithm is among the most popular and widely used visual clustering techniques. However, the unsupervised nature of the algorithm may be disadvantageous in certain applications. Depending on initialization and data characteristics, cluster maps (cluster layouts) may emerge that do not comply with user preferences, expectations or the application context. Considering SOM-based analysis of trajectory data, we propose a comprehensive visual-interactive monitoring and control framework extending the basic SOM algorithm. The framework implements the general Visual Analytics idea to effectively combine automatic data analysis with human expert supervision. It provides simple, yet effective facilities for visually monitoring and interactively controlling the trajectory clustering process at arbitrary levels of detail. The approach allows the user to leverage existing domain knowledge and user preferences, arriving at improved cluster maps. We apply the framework on several trajectory clustering problems, demonstrating its potential in combining both unsupervised (machine) and supervised (human expert) processing, in producing appropriate cluster results.

Abstract:Many companies have recognized the strategic importance of the knowledge hidden in their large databases and have built data warehouses. Typically, updates are collected and applied to the data warehouse periodically in a batch mode, e.g., over night. Then, all derived information such as index structures has to be updated as well. The standard approach of bulk incremental updates to data warehouses has some drawbacks.First, the average runtime for a single update is small but the total runtime for the whole batch of updates may become rather large. Second, the contents of the data warehouse is not always up to date. In this paper, we introduced the DC-tree, a fully dynamic index structure for data warehouses modeled as a data cube. This new index structure is designed for applications where the above drawbacks of the bulk update approach are critical. The DC-tree is a hierarchical index structure -similar to the X-tree -exploiting the concept hierarchies typically defined for the dimensions of a data cube. The DC-tree uses minimum describing sets and the partial ordering of the attribute values induced by the concept hierarchies instead of minimum bounding rectangles and an artificial total ordering. Furthermore, for each minimum describing set in the directory the values of the measure attributes are materialized. We conducted an extensive experimental performance evaluation using the TPC-D benchmark data. Our results demonstrate that the DC-tree yields a significant speed-up compared to the X-tree and the sequential search when processing general range queries on a data cube.