Abstract: An approach is provided for providing spatio-temporal key performance indicators (ST-KPIs). The approach tracks metrics, such as crowd density, pertaining to a number of locations. The tracking is performed over time to develop a history of past values that correspond to the metrics. The history is used to predict a trend of future metrics at the locations with the trend resulting in predicted future values that correspond to the metrics at the locations. In this manner, the ST-KPIs reflect the predicted future values at the various locations that are being monitored. A city view is displayed to a user showing the current and predicted values at defined ST-KPI locations throughout the city.

Abstract: An approach is provided for providing spatio-temporal key performance indicators (ST-KPIs). The approach tracks metrics, such as crowd density, pertaining to a number of locations. The tracking is performed over time to develop a history of past values that correspond to the metrics. The history is used to predict a trend of future metrics at the locations with the trend resulting in predicted future values that correspond to the metrics at the locations. In this manner, the ST-KPIs reflect the predicted future values at the various locations that are being monitored. A city view is displayed to a user showing the current and predicted values at defined ST-KPI locations throughout the city.

Abstract: For distributed analysis of time-series data in a smart entity environment, the data is received from a data source in the environment. An overall analysis of the data is distributed to a first node in the environment. In a network operating the environment the first node is at a smaller distance from the data source as compared to a second node. A first portion of the overall analysis is performed on the data at the first node to produce a first conclusion. The first conclusion is routed to the second node. The second node performs a second portion of the overall analysis. Using the first conclusion, from the first node, a first action is caused to occur on a component of the environment. The data source is associated with the component, the data is indicative of a condition in the environment, and the component participates in the condition.

Abstract: For distributed analysis of time-series data in a smart entity environment, the data is received from a data source in the environment. An overall analysis of the data is distributed to a first node in the environment. In a network operating the environment the first node is at a smaller distance from the data source as compared to a second node. A first portion of the overall analysis is performed on the data at the first node to produce a first conclusion. The first conclusion is routed to the second node. The second node performs a second portion of the overall analysis. Using the first conclusion, from the first node, a first action is caused to occur on a component of the environment. The data source is associated with the component, the data is indicative of a condition in the environment, and the component participates in the condition.

Abstract: A system for facilitating sale of venue event tickets involves: a venue interface; a venue offering database containing records of: currently available tickets, past event ticket acceptances, and acceptance history data; a subscriber database containing, for individual subscribers, at least an identification, interests, and contact information; a pricing module which will identify at least one subscriber to whom ticket(s) should be offered, on an urgent basis, at a reduced price determined using an algorithm taking into account at least: subscriber location, time until start for the event, estimated travel time between subscriber location and venue, and content of the acceptance history data; a communications module which will proactively communicate urgent offers to specific subscribers identified by the pricing module; and an evaluation and learning module which employs unsupervised machine learning to analyze acceptance history data and modify the algorithm used by the pricing module.

Abstract: For distributed analysis of time-series data in a smart entity environment, the data is received from a data source in the environment. An overall analysis of the data is distributed to a first node in the environment. In a network operating the environment the first node is at a smaller distance from the data source as compared to a second node. A first portion of the overall analysis is performed on the data at the first node to produce a first conclusion. The first conclusion is routed to the second node. The second node performs a second portion of the overall analysis. Using the first conclusion, from the first node, a first action is caused to occur on a component of the environment. The data source is associated with the component, the data is indicative of a condition in the environment, and the component participates in the condition.

Abstract: For distributed analysis of time-series data in a smart entity environment, the data is received from a data source in the environment. An overall analysis of the data is distributed to a first node in the environment. In a network operating the environment the first node is at a smaller distance from the data source as compared to a second node. A first portion of the overall analysis is performed on the data at the first node to produce a first conclusion. The first conclusion is routed to the second node. The second node performs a second portion of the overall analysis. Using the first conclusion, from the first node, a first action is caused to occur on a component of the environment. The data source is associated with the component, the data is indicative of a condition in the environment, and the component participates in the condition.

Abstract: An approach is provided for providing spatio-temporal key performance indicators (ST-KPIs). The approach tracks metrics, such as crowd density, pertaining to a number of locations. The tracking is performed over time to develop a history of past values that correspond to the metrics. The history is used to predict a trend of future metrics at the locations with the trend resulting in predicted future values that correspond to the metrics at the locations. In this manner, the ST-KPIs reflect the predicted future values at the various locations that are being monitored. A city view is displayed to a user showing the current and predicted values at defined ST-KPI locations throughout the city.

Abstract: An approach is provided for providing spatio-temporal key performance indicators (ST-KPIs). The approach tracks metrics, such as crowd density, pertaining to a number of locations. The tracking is performed over time to develop a history of past values that correspond to the metrics. The history is used to predict a trend of future metrics at the locations with the trend resulting in predicted future values that correspond to the metrics at the locations. In this manner, the ST-KPIs reflect the predicted future values at the various locations that are being monitored. A city view is displayed to a user showing the current and predicted values at defined ST-KPI locations throughout the city.

Abstract: Methods 300 and systems 400 are provided for creating customizable user interface wrappers for a web application using a software development platform with a plug-in architecture 100. The development platform is used to create one or more declarative descriptions in a predefined plug-in format which define the customized user interface. The declarative description is accessed to generate the user interface wrapper, and the web application is enabled to operate using the user interface wrapper. The declarative description may be in the form of an XML file implemented as an extension listed in an extension directory of a plug-in. The attributes specified in the declarative description may be tailored to run the web application on a given platform, such as an operating system implemented on a predefined type of computer.

Abstract: Methods 300 and systems 400 are provided for creating customizable user interface wrappers for a web application using a software development platform with a plug-in architecture 100. The development platform is used to create one or more declarative descriptions in a predefined plug-in format which define the customized user interface. The declarative description is accessed to generate the user interface wrapper, and the web application is enabled to operate using the user interface wrapper. The declarative description may be in the form of an XML file implemented as an extension listed in an extension directory of a plug-in. The attributes specified in the declarative description may be tailored to run the web application on a given platform, such as an operating system implemented on a predefined type of computer.