摘要:
An advanced metering infrastructure comprises intermediate nodes. The intermediate nodes receive data from child nodes and aggregate the data according to groups of child nodes. The aggregation provides for a reduced version of the data. The reduction is performed based on groups determined by clustering. The reduced version of the data comprises data describing a group of measurements over time, such as a centroid in an n-dimension space, number of customers in the group, radius of the group and the like. The centroid may shift over time based on a consumption profile, such as low consumption at noon, and high consumption at evening. The consumption profiles may be determined in a learning phase, as well as shifting of centroids of each group over time.
摘要:
An advanced metering infrastructure comprises intermediate nodes. The intermediate nodes receive data from child nodes and relay a subset of the data that is not according to an expected value. The expected value may be determined based on a forecasting function computed based on past data. The expected value may be a spatial shape in an n-dimension space. A data not within the spatial shape may be considered not in accordance with the expected value. In some case, the spatial shape is defined by a centroid and a radius. The spatial shape may shift over time based on a consumption profile, such as low consumption at noon, and high consumption at evening. The consumption profiles may be determined in a learning phase, as well as shifting of spatial shapes of each group over time.
摘要:
An advanced metering infrastructure comprises intermediate nodes. The intermediate nodes receive data from child nodes and aggregate the data according to groups of child nodes. The aggregation provides for a reduced version of the data. The reduction is performed based on groups determined by clustering. The reduced version of the data comprises data describing a group of measurements over time, such as a centroid in an n-dimension space, number of customers in the group, radius of the group and the like. The centroid may shift over time based on a consumption profile, such as low consumption at noon, and high consumption at evening. The consumption profiles may be determined in a learning phase, as well as shifting of centroids of each group over time.
摘要:
An advanced metering infrastructure comprises intermediate nodes. The intermediate nodes receive data from child nodes and relay a subset of the data that is not according to an expected value. The expected value may be determined based on a forecasting function computed based on past data. The expected value may be a spatial shape in an n-dimension space. A data not within the spatial shape may be considered not in accordance with the expected value. In some case, the spatial shape is defined by a centroid and a radius. The spatial shape may shift over time based on a consumption profile, such as low consumption at noon, and high consumption at evening. The consumption profiles may be determined in a learning phase, as well as shifting of spatial shapes of each group over time.
摘要:
Determining a network transmitter that is more likely to cause handoff failures in a telecommunication service based on historical data records. The historical data records may be standard Call Data Records. A probability that a service provided by a first network transmitter will be handoffed to a second network transmitter is determined. An indication that a target network transmitter is overly busy is determined based on the number of failed services for each network transmitter that may handoff a service the target network transmitter and the probability that a service will be handoffed to the target network transmitter. Based on the indication, measures may be taken to increase quality level of the target network transmitter.
摘要:
Determining a network transmitter that is more likely to cause handoff failures in a telecommunication service based on historical data records. The historical data records may be standard Call Data Records. A probability that a service provided by a first network transmitter will be handoffed to a second network transmitter is determined. An indication that a target network transmitter is overly busy is determined based on the number of failed services for each network transmitter that may handoff a service the target network transmitter and the probability that a service will be handoffed to the target network transmitter. Based on the indication, measures may be taken to increase quality level of the target network transmitter.
摘要:
Determining a network transmitter that is more likely to cause handoff failures in a telecommunication service based on historical data records. The historical data records may be standard Call Data Records. A probability that a service provided by a first network transmitter will be handoffed to a second network transmitter is determined. An indication that a target network transmitter is overly busy is determined based on the number of failed services for each network transmitter that may handoff a service the target network transmitter and the probability that a service will be handoffed to the target network transmitter. Based on the indication, measures may be taken to increase quality level of the target network transmitter.
摘要:
Determining a network transmitter that is more likely to cause handoff failures in a telecommunication service based on historical data records. The historical data records may be standard Call Data Records. A probability that a service provided by a first network transmitter will be handoffed to a second network transmitter is determined. An indication that a target network transmitter is overly busy is determined based on the number of failed services for each network transmitter that may handoff a service the target network transmitter and the probability that a service will be handoffed to the target network transmitter. Based on the indication, measures may be taken to increase quality level of the target network transmitter.
摘要:
Data records of a service provider may be utilized to estimate data regarding to users who are customers of an alternative service provider, such as a competitor. The data records may indicate interaction between users. An estimated value of a selected user may be determined based on a statistical model. The statistical model may be built using training data. The statistical model may take into account social activity of the selected user, such as which users are socially proximate to him. The statistical model may take into account interactions of the selected user with users who are customers of the service provider. The statistical model may take into account demographic data. The statistical model may take into account data regarding users who are socially proximate to the selected user.
摘要:
Churn prediction is performed by monitoring quality of service levels provided to customers. A time in which the customer is due to either churn or renew his agreement with the service provider may be monitored or computed. Machine learning methods may be utilized to determine a probability of churn based on historic data. Based upon the determination an output to retention personnel may be provided and an improved offer may be made to customers that are deemed in risk of churning.