International Business Machines Corporation

Anat Hashavit

Roy Levin

Haggai Roitman

Ido Guy

Omer Y Boehm

Oren Sar-Shalom

Yousef Shajrawi

Esther Goldbraich

Shiri Kremer-Davidson

Maya Barnea

Gilad Kutiel

Tal Steier

The present invention provides a method, computer program product, and system of generating prioritized list. In an embodiment, the method, computer program product, and system include receiving, by a computer system, target user identification data identifying a target user, target action data, social network content for the one or more users, and social network activity data for the one or more users, analyzing, by a computer system, social network links between the source user and the target user and the social network activity data for the one or more users, determining, by a computer system, a prioritized list of probabilistic action paths that could result in the target user performing the target action on the content based on the analyzing, and outputting the prioritized list to the source user.

Method and system for generating a prioritized list

A computer implemented method, a computerized system and a computer program product for detecting scalable trends in a personalized search context. The computer implemented method comprising obtaining search results matching a search query, wherein the search query is a query defined by a user, wherein the search results comprise occurrences of events, wherein each occurrence is associated with a timestamp. The method further comprises calculating, by a processor, in response to obtaining the search results, trend scores for the events based on the occurrences comprised by the search results. The method further comprises providing an output to the user based on the trend scores.

Scalable trend detection in a personalized search context

Methods, computing systems and computer program products implement embodiments of the present invention that include selecting a training dataset including training instances having respective training features, and applying a classifier to the training dataset, thereby generating a training classification that assigns, to each of the training instances, one of a plurality of categories, the classifier having an expected classification. A classification bias is detected in the training classification relative to the expected classification, and in response to the classification bias, a calibration matrix is defined based on the training features, and the classification bias. A production dataset including production instances is selected, and the classifier and the calibration matrix are applied to the production dataset, thereby generating a production classification quantification that assigns, to each of the production instances, one of the plurality of categories.

Dataset classification quantification

A method for identifying trends in system faults. During a generating stage, monitoring via a software based performance monitoring unit, a state of a server on a network and generating hardware or software performance information which indicate system faults of the server. During an analysis stage including, creating a dataset from the hardware or software performance information and isolating events from the dataset and categorizing each of the isolated events into a type, each type representing one application program call return. For each event in the dataset, assigning a trend score which decays with time such that recent events receive greater weight in the assigning than less recent events. Finally, performing one or more of: outputting a notification of the trend score, utilizing an optimization unit or triggering operation of a fault system handler for the event, when the trend score is above a threshold.

Method for performance monitoring and optimization via trend detection and forecasting

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Haifa, Israel

Anat Hashavit