Abstract: A system provides data profile information describing attributes of a dataset. The system determines relative frequency of occurrences of attribute values with respect to a set of bins from a histogram of another attribute. The system presents a user interface that presents statistical information describing attributes of a dataset based on the relative frequency of occurrences of attribute values. The system generates a transformation script based on the user interactions for transforming records of the dataset. The transformation script is configured to preprocess data of the dataset for further analysis.

Abstract: A system and method parses one or more fields from a file by receiving example locations of the field in the file, fashioning rules that describe the field from the locations, and then scoring the rules against some or all of the file.

Abstract: A method and computer program product for generating a data visualization based, at least in part, upon a data set. A first user is allowed to add an annotation to at least a portion of the data visualization. A determination is made concerning whether the annotation is associatable with any portion of the data set.

Abstract: Techniques for determining user types based on multi-modal clustering are provided. The topology, content and usage of a document collection or web site is determined. The user paths are identified using longest repeating subsequence techniques and a multi-modal information need vector is determined for each significant user path. Multi-modal vectors for each document in the significant path, content, uniform resource locators, inlink and outlink multi-modal vectors are determined and combined based on path position and access frequency. Multi-modal clustering is performed based on a multi-modal similarity function and a specified measure of similarity using a type of multi-modal clustering such as K-means or wavefront clustering. The identified clusters may be further analyzed based on changes to the weighting of the corresponding content, url, inlinks and outlinks multi-modal feature vectors.

Abstract: Embodiments of the present invention address deficiencies of the art in respect to data visualization and provide a novel and non-obvious method, system and computer program product for doubly linked visual discussions for data visualization. In one embodiment of the invention, a method for doubly-linked data visualization can be provided. The method can include rendering a data visualization in a data visualization service user interface, identifying comments corresponding to the rendered data visualization, concurrently displaying the identified comments in the user interface, selecting a comment in the user interface, and replacing the rendered data visualization in the user interface with a different data visualization corresponding to the selected comment.

Abstract: Techniques for determining user types based on multi-modal clustering are provided. The topology, content and usage of a document collection or web site is determined. The user paths are identified using longest repeating subsequence techniques and a multi-modal information need vector is determined for each significant user path. Multi-modal vectors for each document in the significant path, content, uniform resource locators, inlink and outlink multi-modal vectors are determined and combined based on path position and access frequency. Multi-modal clustering is performed based on a multi-modal similarity function and a specified measure of similarity using a type of multi-modal clustering such as K-means or wavefront clustering. The identified clusters may be further analyzed based on changes to the weighting of the corresponding content, url, inlinks and outlinks multi-modal feature vectors.

Abstract: Apparatus, methods, and computer program products are disclosed that perform computationally efficient layout of hierarchical data structures.

Abstract: Techniques for estimating user interest in graph structures are provided. A graph structure containing at least two nodes, a threshold disinterest value and at least one interesting node within the graph structure are determined. Each determined interesting node is added to a set of active nodes. Adjacent nodes connected to the set of active nodes and associated with Degree-Of-Interest values more interesting than the threshold disinterest value are in turn added to the set of active nodes until no additional adjacent connected nodes have a Degree-Of-Interest value more interesting than the threshold value. A new visualization of the graph structure is determined based on the nodes in the set of active nodes. The interesting nodes may be determined based on specific indications of interest in a node, such as a mouse selections, or may be based on the user's focus of attention within the graph based information structure.

Abstract: Techniques for determining user types based on multi-modal clustering are provided. The topology, content and usage of a document collection or web site is determined. The user paths are identified using longest repeating subsequence techniques and a multi-modal information need vector is determined for each significant user path. Multi-modal vectors for each document in the significant path, content, uniform resource locators, inlink and outlink multi-modal vectors are determined and combined based on path position and access frequency. Multi-modal clustering is performed based on a multi-modal similarity function and a specified measure of similarity using a type of multi-modal clustering such as K-means or wavefront clustering. The identified clusters may be further analyzed based on changes to the weighting of the corresponding content, url, inlinks and outlinks multi-modal feature vectors.