Abstract: Systems and methods are disclosed for identifying resources responsible for events. In one embodiment, a method may include determining a number of unique actors in a plurality of actors that have accessed the resource. The method may further include identifying from the plurality of actors a set of affected actors that has been affected by an event and identifying from the set of affected actors a subset of resource-affected actors that accessed the resource prior to being affected by the event. The method may further include determining a number of resource-affected actors in the subset of resource-affected actors and, based on the number of unique actors and the number of resource-affected actors, determining an event score for the resource. The event score may be a lower bound of a confidence interval of a binomial proportion of the number of resource-affected actors to the number of unique actors.

Abstract: Systems and methods are disclosed for identifying resources responsible for events. In one embodiment, a method may include determining a number of unique actors in a plurality of actors that have accessed the resource. The method may further include identifying from the plurality of actors a set of affected actors that has been affected by an event and identifying from the set of affected actors a subset of resource-affected actors that accessed the resource prior to being affected by the event. The method may further include determining a number of resource-affected actors in the subset of resource-affected actors and, based on the number of unique actors and the number of resource-affected actors, determining an event score for the resource. The event score may be a lower bound of a confidence interval of a binomial proportion of the number of resource-affected actors to the number of unique actors.

Abstract: Exemplary embodiments of the present disclosure provide for cleansing data generated by one or more servers in response to database interactions resulting from an automated software robot interacting with the one or more servers via a telecommunications network. Log entries in usage logs corresponding to events during a session can be analyzed to determine relationships between events and the usage logs can be classified based on the relationships as either corresponding to human behavior or automated software robot behavior. Usage logs corresponding to automated software robot behavior can be removed from further analysis.

Abstract: The present invention provides a method, system and software that provide a predictive model responsive to the correlation of news articles to stock price movement. The invention analyzes the derivative or ratio of events to drive predictions in a responsive manner. The invention considers derivatives or ratios of news meta-data within a small window in the past relative to a larger window of news items in the past. The invention may use a sentiment engine and apply the derivative of sentiment to predict volatility and/or trend direction of price of a security. The invention may look to the content, context, and derivative of sentiment to weigh news stories according to a predetermined taxonomy factoring in recency, criticality, repeatedness, trustworthiness, etc. to predict stock price behavior. Also, the invention may be used to forecast events given stock price movement and news to predict an impending story or release of significance.

Abstract: Systems and methods are disclosed for identifying resources responsible for events. In one embodiment, a method may include determining a number of unique actors in a plurality of actors that have accessed the resource. The method may further include identifying from the plurality of actors a set of affected actors that has been affected by an event and identifying from the set of affected actors a subset of resource-affected actors that accessed the resource prior to being affected by the event. The method may further include determining a number of resource-affected actors in the subset of resource-affected actors and, based on the number of unique actors and the number of resource-affected actors, determining an event score for the resource. The event score may be a lower bound of a confidence interval of a binomial proportion of the number of resource-affected actors to the number of unique actors.

Abstract: Systems and methods are disclosed for identifying resources responsible for events. In one embodiment, a method may include determining a number of unique actors in a plurality of actors that have accessed the resource. The method may further include identifying from the plurality of actors a set of affected actors that has been affected by an event and identifying from the set of affected actors a subset of resource-affected actors that accessed the resource prior to being affected by the event. The method may further include determining a number of resource-affected actors in the subset of resource-affected actors and, based on the number of unique actors and the number of resource-affected actors, determining an event score for the resource. The event score may be a lower bound of a confidence interval of a binomial proportion of the number of resource-affected actors to the number of unique actors.

Abstract: Exemplary embodiments of the present disclosure provide for cleansing data generated by one or more servers in response to database interactions resulting from an automated software robot interacting with the one or more servers via a telecommunications network. Log entries in usage logs corresponding to events during a session can be analyzed to determine relationships between events and the usage logs can be classified based on the relationships as either corresponding to human behavior or automated software robot behavior. Usage logs corresponding to automated software robot behavior can be removed from further analysis.

Abstract: Exemplary embodiments of the present disclosure provide for cleansing data generated by one or more servers in response to database interactions resulting from an automated software robot interacting with the one or more servers via a telecommunications network. Log entries in usage logs corresponding to events during a session can be analyzed to determine relationships between events and the usage logs can be classified based on the relationships as either corresponding to human behavior or automated software robot behavior. Usage logs corresponding to automated software robot behavior can be removed from further analysis.

Abstract: Systems and methods are disclosed for identifying resources responsible for events. In one embodiment, a method may include determining a number of unique actors in a plurality of actors that have accessed the resource. The method may further include identifying from the plurality of actors a set of affected actors that has been affected by an event and identifying from the set of affected actors a subset of resource-affected actors that accessed the resource prior to being affected by the event. The method may further include determining a number of resource-affected actors in the subset of resource-affected actors and, based on the number of unique actors and the number of resource-affected actors, determining an event score for the resource. The event score may be a lower bound of a confidence interval of a binomial proportion of the number of resource-affected actors to the number of unique actors.

Abstract: Systems and methods are disclosed for identifying resources responsible for events. In one embodiment, a method may include determining a number of unique actors in a plurality of actors that have accessed the resource. The method may further include identifying from the plurality of actors a set of affected actors that has been affected by an event and identifying from the set of affected actors a subset of resource-affected actors that accessed the resource prior to being affected by the event. The method may further include determining a number of resource-affected actors in the subset of resource-affected actors and, based on the number of unique actors and the number of resource-affected actors, determining an event score for the resource. The event score may be a lower bound of a confidence interval of a binomial proportion of the number of resource-affected actors to the number of unique actors.

Abstract: Systems and methods are disclosed for identifying resources responsible for events. In one embodiment, a method may include determining a number of unique actors in a plurality of actors that have accessed the resource. The method may further include identifying from the plurality of actors a set of affected actors that has been affected by an event and identifying from the set of affected actors a subset of resource-affected actors that accessed the resource prior to being affected by the event. The method may further include determining a number of resource-affected actors in the subset of resource-affected actors and, based on the number of unique actors and the number of resource-affected actors, determining an event score for the resource. The event score may be a lower bound of a confidence interval of a binomial proportion of the number of resource-affected actors to the number of unique actors.

Abstract: Systems and methods are disclosed for identifying resources responsible for events. In one embodiment, a method may include determining a number of unique actors in a plurality of actors that have accessed the resource. The method may further include identifying from the plurality of actors a set of affected actors that has been affected by an event and identifying from the set of affected actors a subset of resource-affected actors that accessed the resource prior to being affected by the event. The method may further include determining a number of resource-affected actors in the subset of resource-affected actors and, based on the number of unique actors and the number of resource-affected actors, determining an event score for the resource. The event score may be a lower bound of a confidence interval of a binomial proportion of the number of resource-affected actors to the number of unique actors.

Abstract: Systems and methods are disclosed for identifying resources responsible for events. In one embodiment, a method may include determining a number of unique actors in a plurality of actors that have accessed the resource. The method may further include identifying from the plurality of actors a set of affected actors that has been affected by an event and identifying from the set of affected actors a subset of resource-affected actors that accessed the resource prior to being affected by the event. The method may further include determining a number of resource-affected actors in the subset of resource-affected actors and, based on the number of unique actors and the number of resource-affected actors, determining an event score for the resource. The event score may be a lower bound of a confidence interval of a binomial proportion of the number of resource-affected actors to the number of unique actors.

Abstract: A method includes analyzing a cluster of conceptually-related portions of text to develop a model and calculating a novelty measurement between a first identified conceptually-related portion of text and the model. The method further includes transmitting a second identified conceptually-related portion of text and a score associated with the novelty measurement from a server to an access device via a signal. Another method includes determining at least two corpora of conceptually-related portions of text. The method also includes calculating a common neighbors similarity measurement between the at least two corpora of conceptually-related portions of text and if the common neighbors similarity measurement exceeds a threshold, merging the at least two corpora of conceptually-related portions of text into a cluster or if the common neighbors similarity measurement does not exceed a threshold, maintaining a non-merge of the at least two corpora of conceptually-related portions of text.

Abstract: Exemplary embodiments of the present disclosure provide for cleansing data generated by one or more servers in response to database interactions resulting from an automated software robot interacting with the one or more servers via a telecommunications network. Log entries in usage logs corresponding to events during a session can be analyzed to determine relationships between events and the usage logs can be classified based on the relationships as either corresponding to human behavior or automated software robot behavior. Usage logs corresponding to automated software robot behavior can be removed from further analysis.

Abstract: A method includes analyzing a cluster of conceptually-related portions of text to develop a model and calculating a novelty measurement between a first identified conceptually-related portion of text and the model. The method further includes transmitting a second identified conceptually-related portion of text and a score associated with the novelty measurement from a server to an access device via a signal. Another method includes determining at least two corpora of conceptually-related portions of text. The method also includes calculating a common neighbors similarity measurement between the at least two corpora of conceptually-related portions of text and if the common neighbors similarity measurement exceeds a threshold, merging the at least two corpora of conceptually-related portions of text into a cluster or if the common neighbors similarity measurement does not exceed a threshold, maintaining a non-merge of the at least two corpora of conceptually-related portions of text.

Abstract: The present invention provides a method, system and software that provide a predictive model responsive to the correlation of news articles to stock price movement. The invention analyzes the derivative or ratio of events to drive predictions in a responsive manner. The invention considers derivatives or ratios of news meta-data within a small window in the past relative to a larger window of news items in the past. The invention may use a sentiment engine and apply the derivative of sentiment to predict volatility and/or trend direction of price of a security. The invention may look to the content, context, and derivative of sentiment to weigh news stories according to a predetermined taxonomy factoring in recency, criticality, repeatedness, trustworthiness, etc. to predict stock price behavior. Also, the invention may be used to forecast events given stock price movement and news to predict an impending story or release of significance.

Abstract: Various embodiments are directed to a system and method providing associative matching of terms. Candidate terms are selected for building one or more associative matching models from one or more selected candidate sources. Associativity is defined to give editors the ability to label sample associative term pairs from the one or more candidate sources. The editors label sample candidate term pairs as being related. Features are determined that can differentiate associative from non-associative pairs. The selected features are used to build a model. The model is applied to determine whether a received query-candidate pair are associative.

Abstract: Various embodiments are directed to a system and method providing associative matching of terms. Candidate terms are selected for building one or more associative matching models from one or more selected candidate sources. Associativity is defined to give editors the ability to label sample associative term pairs from the one or more candidate sources. The editors label sample candidate term pairs as being related. Features are determined that can differentiate associative from non-associative pairs. The selected features are used to build a model. The model is applied to determine whether a received query-candidate pair are associative.