Abstract: Web navigation pattern histories are used to navigate to a plurality of visited web sites. A navigation map is generated comprised of a node for each visited web site. Each node in the navigation map is connected by an edge to at least one other node in the navigation map, and the edge is associated with the link from the given visited web site to the next web site. For each visited web site, the corresponding node stores an address for the given web site, a relevance rating for the given visited web site, a link from the given visited web site to a next web site and optionally a title of the web site. A presentation of a node in the navigation map is based on the relevance rating. A plurality of navigation maps for a plurality of users can be merged by identifying navigation maps having similar search goals.

Abstract: Web navigation pattern histories are used to navigate to a plurality of visited web sites. A navigation map is generated comprised of a node for each visited web site. Each node in the navigation map is connected by an edge to at least one other node in the navigation map, and the edge is associated with the link from the given visited web site to the next web site. For each visited web site, the corresponding node stores an address for the given web site, a relevance rating for the given visited web site, a link from the given visited web site to a next web site and optionally a title of the web site. A presentation of a node in the navigation map is based on the relevance rating. A plurality of navigation maps for a plurality of users can be merged by identifying navigation maps having similar search goals.

Abstract: A selection parameter is applied to a set of risk assessment data and corresponding performance measure data for a completed, or active, project that is similar to a proposed project. Certain combinations of the risk assessment data and corresponding performance measure data are selected for training an optimal predictive model. The predictive model is applied to available data of a proposed project for predicting associated risks, or outcomes, of the proposed project.

Abstract: A method for predicting risks for information technology service contracts includes calculating a probability of occurrence of each target risk in a target contract; constructing clusters of root causes observed in historical contracts similar to the target contract, for each of the clusters, identifying root causes that co-occur with target contract risks by searching each cluster for root causes of similar historical contract risks such that the identified root causes represent additional new contract risks, and calculating the probability of occurrence of each new target risk identified for the target contract based on root causes identified in the similar historical contract risks. Two root causes are in the same cluster if both root causes occur in one or more contracts in the set of historical contracts, where two root causes co-occur if both root causes are in the same cluster.

Abstract: A method for predicting contract renewal ahead of contract expiration includes receiving comments and interview transcripts by a sentiment analysis program to generate sentiments, where the comments and interview transcripts are received from a plurality of clients who are contractees to one or more service contracts, combining the sentiments with contract assessment survey scores and historical renewal and growth data for the service contracts to generate a contract renewal and growth prediction model, providing a contract that is up for expiration to the predictive model, and providing the comments, interview transcripts, and risk assessment survey scores to the predictive model, where the predictive model outputs a prediction of renewal and growth for the contract up for expiration, and an analysis of root causes for the predictions.

Abstract: A method for predicting and quantifying risk in information technology (IT) service contracts includes comparing features of a new IT service contract with similar features from one or more previous IT service contracts selected from a plurality of previous IT service contracts to calculate a similarity value between each pair of the new IT service contract and one of the one or more previous IT service contracts, aggregating the similarity values, and using the aggregated similarity values with a prediction model to predict risk factors affecting the new IT service contract and to quantify an impact of each predicted risk factor on an expected gross profit margin.

Abstract: A method for predicting and quantifying risk in information technology (IT) service contracts includes comparing features of a new IT service contract with similar features from one or more previous IT service contracts selected from a plurality of previous IT service contracts to calculate a similarity value between each pair of the new IT service contract and one of the one or more previous IT service contracts, aggregating the similarity values, and using the aggregated similarity values with a prediction model to predict risk factors affecting the new IT service contract and to quantify an impact of each predicted risk factor on an expected gross profit margin.

Abstract: Web navigation pattern histories are used to navigate to a plurality of visited web sites. A navigation map is generated comprised of a node for each visited web site. Each node in the navigation map is connected by an edge to at least one other node in the navigation map, and the edge is associated with the link from the given visited web site to the next web site. For each visited web site, the corresponding node stores an address for the given web site, a relevance rating for the given visited web site, a link from the given visited web site to a next web site and optionally a title of the web site. A presentation of a node in the navigation map is based on the relevance rating. A plurality of navigation maps for a plurality of users can be merged by identifying navigation maps having similar search goals.

Abstract: A navigation to a plurality of visited web sites is traced and a navigation map is generated comprised of a node for each visited web site. Each node in the navigation map is connected by an edge to at least one other node in the navigation map, and the edge is associated with the link from the given visited web site to the next web site. For each visited web site, the corresponding node stores an address for the given web site, a time stamp of the visit to the given visited web site, a link from the given visited web site to a next web site and optionally a title of the web site. A keyword search of the nodes can be processed to identify a given web site in the navigation map. A signature portion of the given web site can be used to identify a new location of the given web site. A navigation map can be designated as a searchable public navigation map.

Abstract: Web navigation pattern histories are used to navigate to a plurality of visited web sites. A navigation map is generated comprised of a node for each visited web site. Each node in the navigation map is connected by an edge to at least one other node in the navigation map, and the edge is associated with the link from the given visited web site to the next web site. For each visited web site, the corresponding node stores an address for the given web site, a relevance rating for the given visited web site, a link from the given visited web site to a next web site and optionally a title of the web site. A presentation of a node in the navigation map is based on the relevance rating. A plurality of navigation maps for a plurality of users can be merged by identifying navigation maps having similar search goals.