Two Six Labs, LLC

Invincea, Inc.

Robert Paul Gove, Jr

Chae A Clark

Violet Lingenfelter

Casey Aaron Haber

Anthony Wong

A network incident summarization and visualization tool presents a summarized or filtered rendering of an analysis or narrative of a collection of events gathered from a stream of incident reports. A graph structure stores entities and relationships between network entities in a graph form based on temporal correspondence between different incidents. An evaluation of the entities and the relationships implements a scoring methodology that identifies the most significant incidents based on the entities and component groups of entities associated by a sequence of relationships. The scoring methodology provides a normalized score that is filtered based on a threshold that removes entities having little to no correspondence or effect. The resulting filtered rendering defines a graph of entities and relationships ordered by type and significance such that the most important aspects of the rendered narrative are apparent.

Incident report summary and visualization

A network disruption detection engine gathers and stores network routing path update messages that routinely modify routing tables used by internet routers for transporting message traffic between a source and a destination. Routing path messages continually maintain a network transport infrastructure both for intranets used for particular entities such as corporations, and internet traffic between arbitrary source and destination nodes. Major disruptions or outages typically result in an increase in routing path messages, typically focused on a particular set, region or network entity where the disruption occurred, as other routing entities seek to avoid the troubled region. Analysis of this sudden activity of routing messages and extracting message content about the network region they seek to avoid allows identification and queries of a widespread network outage.

Internet disruption detection

One example method of operation may include creating a force approximation of a number of nodes in a defined space at an initial time (t0), the force approximation being based on a data realization simulation model of an n-body simulation, where n is an integer greater than one. The method may also include determining initial displacement changes of one or more of the nodes within the defined space has occurred in the force approximation, summing the initial displacement changes of the one or more of the nodes to create a summed total displacement, creating an initial displacement threshold (Td) based on the summed total displacement. At a later time (t1), determining additional displacement changes of one or more of the nodes have occurred, summing the additional displacement changes of the one or more of the nodes to create a new summed total displacement, comparing the new summed total displacement to the summed total displacement, and determining whether to create a new force approximation based on the comparison of the new summed total displacement to the summed total displacement.

Dynamic updating of a force approximation data model

A software library recommendation engine provides an analysis and aggregate dashboard comparison of metrics denoting maintainability trends in a plurality of libraries from a publicly available software repository. Maintainability trends include regularity and magnitude of changes (commits), resolution of user inquiries for issues, problems and bugs, and an estimation of core contributors for estimating inertia and longevity of a plurality of candidate libraries under consideration for a particular usage. Usage metrics coalesce and summarize usage data of libraries under consideration for comparison, and a dashboard of computed metrics provides an indication of trends that indicate reliability or longevity to mitigate vulnerability of the library user from dependence on the collaborative intent of the library author and contributors.

Software repository recommendation engine

A graph processing system, method and apparatus classifies graphs based on a linearly computable set of features defined as a feature vector adapted for comparison with the feature vectors of other graphs. The features result from graph statistics ('gragnostics') computable from the edges and vertices of a set of graphs. Graphs are classified based on a multidimensional distance of the resulting feature vectors, and similar graphs are classified according to a distance, or nearest neighbor, of the feature vector corresponding to each graph. Projection of the feature vector onto two dimensions allows visualization of the classification, as similar graphs appear as clusters or groups separated by a relatively shorter distance. Different types or classifications of graphs also appear as other, more distant, clusters. An initial training set defines the classification types, and sampled graphs are evaluated and classified based on the feature vector and nearest neighbors in the training set.

Gragnostics rendering

In some implementations, a processor can compute a relationship graph to be visually rendered on a display. The processor can define a set of graphical nodes, and can randomly select a first subset of graphical nodes from the set of graphical nodes. The first subset of graphical nodes is less than the set of graphical nodes of the relationship graph. The processor can update a position for each graphical node from the first subset of graphical nodes based on a repulsion value between that graphical node from the first subset of graphical nodes and each graphical node from a second subset of graphical nodes from the set of graphical nodes. The processor can then render each graphical node from the set of graphical nodes based on the updated position for each graphical node from the first subset of graphical nodes.

Growing community of inventors

Reston, VA, United States of America

Robert Paul Gove, Jr