Abstract: Method and system for analyzing temporal data includes obtaining a first data set of temporal data of a first size and transforming the temporal data into a computer generated temporal graph containing a set of predefined data properties. The method includes generating a time decay function of a selected temporal attribute and fitting the time decay function into a temporal distribution of the selected temporal attribute in the temporal graph, selecting a data sampling time window in the temporal graph defining a second data set of temporal data of a second size less than the first size based on the time decay function, such that the set of predefined data properties is substantially preserved within the data sampling window, sampling data from the data source within the selected data sampling time window and generating a computer generated temporal subsample graph of the predefined data properties of the sampled network data.

Abstract: Identifying life events within social network feeds. The method may include receiving social media data. The method may include identifying life event data within the social media data. The method may include determining a life event probability score associated with a life event class for the life event data using metadata. The method may include assigning a first life event class to each item of life event data based on the life event probability score. The method may include creating conversations by grouping the life event data. The method may include extracting metadata. The method may include determining a conversation probability score for each conversation based on the metadata and the life event probability score associated with each item of life event data. The method may include assigning a second life event class based on the conversation probability score. The method may include displaying the conversations based on user preferences.

Abstract: Identifying life events within social network feeds. The method may include receiving social media data. The method may include identifying life event data within the social media data. The method may include determining a life event probability score associated with a life event class for the life event data using metadata. The method may include assigning a first life event class to each item of life event data based on the life event probability score. The method may include creating conversations by grouping the life event data. The method may include extracting metadata. The method may include determining a conversation probability score for each conversation based on the metadata and the life event probability score associated with each item of life event data. The method may include assigning a second life event class based on the conversation probability score. The method may include displaying the conversations based on user preferences.

Abstract: Method and system for analyzing temporal data includes obtaining a first data set of temporal data of a first size and transforming the temporal data into a computer generated temporal graph containing a set of predefined data properties. The method includes generating a time decay function of a selected temporal attribute and fitting the time decay function into a temporal distribution of the selected temporal attribute in the temporal graph, selecting a data sampling time window in the temporal graph defining a second data set of temporal data of a second size less than the first size based on the time decay function, such that the set of predefined data properties is substantially preserved within the data sampling window, sampling data from the data source within the selected data sampling time window and generating a computer generated temporal subsample graph of the predefined data properties of the sampled network data.

Abstract: A method and system for analyzing temporal data includes selecting a data source in which data from the source contains temporal attributes, obtaining temporal data of a first size from the selected data source and mapping the temporal data from the selected source in a temporal graph. The method and system selects a temporal attribute, generates a time decay function of the selected temporal attribute and selects a time window in the temporal graph defining a data set of a second size less than the first size based on the time decay function. The data set contains a set of predefined properties, each having a metric calculated from data within the time window. The method and system samples data from the data source based on the selected time window and generates a temporal graph of the metrics of the predefined properties of the sampled network data.

Abstract: A photographing method including: displaying, at an image acquisition device, a scene that includes a first person; displaying, at the image acquisition device, a first score in the scene, wherein the first score is indicative of the first person's contentment with the way he or she looks in the scene; and acquiring, at the image acquisition device, a photograph of the scene when the first score is equal to or greater than a threshold value.

Abstract: Aspects of the present invention include a method, system and computer program product for utilizing transactional type health care data within various types of relationships or social networks (e.g., doctor-patient) in the health care industry and for determining various patterns of data therefrom to support improved decision making based on the determined data patterns.

Abstract: A method and system for analyzing temporal data includes selecting a data source in which data from the source contains temporal attributes, obtaining temporal data of a first size from the selected data source and mapping the temporal data from the selected source in a temporal graph. The method and system selects a temporal attribute, generates a time decay function of the selected temporal attribute and selects a time window in the temporal graph defining a data set of a second size less than the first size based on the time decay function. The data set contains a set of predefined properties, each having a metric calculated from data within the time window. The method and system samples data from the data source based on the selected time window and generates a temporal graph of the metrics of the predefined properties of the sampled network data.

Abstract: A system for managing items. The system includes a sensor for identifying a first physical object and a second physical object in a building. A computer interface in communication with the sensor is configured to receive a first identification of the first physical object and a second identification of the second physical object. A computer with at least one central processing unit is configured to automatically search for trend information about the first physical object, determine a first interaction between the first physical object and the second physical object based on the first identification of the first physical object and the second identification of the second physical object, and recommend disposition of the first physical object based on the first interaction between the first physical object and the second physical object, and the trend information about the first physical object.

Abstract: A service monitoring and evaluation system, method and program product for monitoring and evaluating services, e.g., healthcare. A client or patient timeline storage stores social media information for any Event of Interest (EoI) for patients, including information indicating the effectiveness of patient treatment being provided by healthcare providers. Healthcare providers have access to the patient timeline storage for respective patients to track treatment. A monitoring bot continually searches social media for patient EoIs, and extracts treatment effectiveness information from the EoIs and adds any new information to the patient timeline storage. An evaluation bot receives requests for provider evaluation, filters patient timeline information for treatment effectiveness, and rates and ranks the treatment effectiveness for the requestor.

Abstract: Identifying life events within social network feeds. The method may include receiving social media data. The method may include identifying life event data within the social media data. The method may include determining a life event probability score associated with a life event class for the life event data using metadata. The method may include assigning a first life event class to each item of life event data based on the life event probability score. The method may include creating conversations by grouping the life event data. The method may include extracting metadata. The method may include determining a conversation probability score for each conversation based on the metadata and the life event probability score associated with each item of life event data. The method may include assigning a second life event class based on the conversation probability score. The method may include displaying the conversations based on user preferences.