Abstract: A processing system including at least one processor may obtain a first set of performance records of a database system, train a machine learning model in accordance with the first set of performance records, where the machine learning model that is trained in accordance with the first set of performance records is configured to predict a latency of a query transaction for a designated time period, present a user interface with a plurality of settings of the database system that are user-adjustable, where the plurality of settings is associated with at least a portion of the first set of performance records, calculate a first predicted latency of a query transaction at the designated time period via the machine learning model in accordance with a set of values of the plurality of settings, and present the first predicted latency via the user interface.

Abstract: A processing system including at least one processor may obtain a first set of performance records of a database system, train a machine learning model in accordance with the first set of performance records, where the machine learning model that is trained in accordance with the first set of performance records is configured to predict a latency of a query transaction for a designated time period, present a user interface with a plurality of settings of the database system that are user-adjustable, where the plurality of settings is associated with at least a portion of the first set of performance records, calculate a first predicted latency of a query transaction at the designated time period via the machine learning model in accordance with a set of values of the plurality of settings, and present the first predicted latency via the user interface.

Abstract: A processing system including at least one processor may obtain a first set of performance records of a database system, train a machine learning model in accordance with the first set of performance records, where the machine learning model that is trained in accordance with the first set of performance records is configured to predict a latency of a query transaction for a designated time period, present a user interface with a plurality of settings of the database system that are user-adjustable, where the plurality of settings is associated with at least a portion of the first set of performance records, calculate a first predicted latency of a query transaction at the designated time period via the machine learning model in accordance with a set of values of the plurality of settings, and present the first predicted latency via the user interface.

Abstract: Optimizing neighbor cell relationships for improving handover performance by interpreting handover failures is presented herein. A method can include receiving data representing a time series of failures of outgoing handovers corresponding to a wireless access point device, and determining, based on a determined condition corresponding to a metric, that a source cell of the wireless access point device is associated with an anomalous cell relation corresponding to the time series of the failures of the outgoing handovers in response to quantifying the time series based on the metric. In various examples, the quantifying can include quantifying the time series with respect to: a volume of the failures during a period of time, an entropy calculated on a probability distribution of the failures by determined relations between the source cell and target cells, and/or an entropy calculated on a probability distribution of determined outgoing handover outcomes.

Abstract: Optimizing neighbor cell relationships for improving handover performance by interpreting handover failures is presented herein. A method can include receiving data representing a time series of failures of outgoing handovers corresponding to a wireless access point device, and determining, based on a determined condition corresponding to a metric, that a source cell of the wireless access point device is associated with an anomalous cell relation corresponding to the time series of the failures of the outgoing handovers in response to quantifying the time series based on the metric. In various examples, the quantifying can include quantifying the time series with respect to: a volume of the failures during a period of time, an entropy calculated on a probability distribution of the failures by determined relations between the source cell and target cells, and/or an entropy calculated on a probability distribution of determined outgoing handover outcomes.

Abstract: Optimizing neighbor cell relationships for improving handover performance by interpreting handover failures is presented herein. A method can include receiving data representing a time series of failures of outgoing handovers corresponding to a wireless access point device, and determining, based on a determined condition corresponding to a metric, that a source cell of the wireless access point device is associated with an anomalous cell relation corresponding to the time series of the failures of the outgoing handovers in response to quantifying the time series based on the metric. In various examples, the quantifying can include quantifying the time series with respect to: a volume of the failures during a period of time, an entropy calculated on a probability distribution of the failures by determined relations between the source cell and target cells, and/or an entropy calculated on a probability distribution of determined outgoing handover outcomes.

Abstract: A method and system for determining whether an IP address is part of a bot-network are provided. The IP-address-aggregate associated with the IP address of an e-mail sender is determined. The IP-address-aggregate is associated with an IP-address-aggregate-category based on the current SMTP traffic characteristics of the IP-address-aggregate and the known SMTP traffic characteristics of an IP-address-aggregate-category. A bot-likelihood score of the IP-address-aggregate-category is then associated with IP-address-aggregate. IP-address-aggregate-categories can be established based on historical SMTP traffic characteristics of the IP-address-aggregates. The IP-address-aggregates are grouped based on SMTP characteristics, and the IP-address-aggregate-categories are defined based on a selection of IP-address-aggregates with similar SMTP traffic characteristics that are diagnostic of spam bots vs. non-botnet-controllers spammers.

Abstract: Optimizing neighbor cell relationships for improving handover performance by interpreting handover failures is presented herein. A method can include receiving data representing a time series of failures of outgoing handovers corresponding to a wireless access point device, and determining, based on a determined condition corresponding to a metric, that a source cell of the wireless access point device is associated with an anomalous cell relation corresponding to the time series of the failures of the outgoing handovers in response to quantifying the time series based on the metric. In various examples, the quantifying can include quantifying the time series with respect to: a volume of the failures during a period of time, an entropy calculated on a probability distribution of the failures by determined relations between the source cell and target cells, and/or an entropy calculated on a probability distribution of determined outgoing handover outcomes.