Handoff support in asynchronous analysis tasks using knowledge transfer graphs

#Embodiments of the present invention relate generally to computer science and, more specifically, to handoff support in asynchronous analysis tasks using knowledge transfer graphs.

#When performing “sense-making” operations, users attempt to analyze, interpret, and extract meaning from various data sets. During sense-making operations, users oftentimes annotate their data sets in order to document and communicate observations and hypotheses about the data sets. For example, as part of analyzing a relatively large data set, various users could annotate the data set with a wide range of light-weight annotations and heavy-weight annotations, where the light-weight annotations may include short tags, and the heavy-weight annotations may include long comments.

#As the size and complexity of a data set grow, the number and diversity of annotations associated with the data set oftentimes increase as well. In addition, as the size and complexity of a data set grow, the complexity of the relationships between the different annotations and the data set typically increases too. For example, a user could structure a hierarchical relationship between tags and comments to provide insights into the data set at different levels of detail. Subsequently, the user could create relationships between specific tags, comments, and data items included in the data set to organize those insights.

#FIG. 1 is a conceptual illustration of a system configured to implement one or more aspects of the present invention;

#FIG. 2 is a more detailed illustration of the client computer of FIG. 1, according to various embodiments of the present invention;

#FIG. 3 is a more detailed illustration of the annotation subsystem of FIG. 2, according to various embodiments of the present invention;

#In the following description, numerous specific details are set forth to provide a more thorough understanding of the present invention. However, it will be apparent to one of skilled in the art that the present invention may be practiced without one or more of these specific details.

#FIG. 1 is a conceptual illustration of a system 100 configured to implement one or more aspects of the present invention. As shown, system 100 includes, without limitation, server 101, client computers 103, 105, and communications network 107. In alternative embodiments, system 100 may include any number of client computers 103, 105 and servers 101, in any combination. In some embodiments, system 100 includes a collaborative environment including client computers 103, 105 and server 101. In operation, client computer 103 creates session data 121 when performing sense-making operations while working on a data set. In operation, session data 121 is sent to server 101 via communications network 107. Client computer 105 may subsequently receive session data 121 from server 101 via communications network 107.

#Server 101 comprises a computer system configured to receive session data 121 from client computers 103, 105 and store session data 121, along with other data associated with a data set. As will be discussed in further detail below, other data associated with a data set can include visualization data, annotation data, and/or contextual data. When client computer 103, 105 retrieves the data set, client computer 103, 105 may also retrieve from server 101 session data 121, as well as other data associated with the data set. In some embodiments, client computer 103, 105 may use information in session data 121 to enable playback of the session of sense-making operations performed on client computer 103. Session data 121 may be formatted and stored on a mass storage system at server 101 using any technically-feasible technique. A data transfer protocol, such as hypertext transfer protocol (HTTP), may be used to download session data 121 from server 101 to client computer 103, 105.