Abstract: Embodiments include overflow sensor assemblies for water heaters, HVAC systems, and other devices for which temperature control systems may be used. An example overflow sensor assembly for detecting fluid leaks at a device may include a sensor probe configured to be in electrical communication with a power supply, and a sensor mounting bracket that forms a ground, where the sensor mounting bracket is configured to be adjustably coupled to a mounting surface on the device, such that the sensor mounting bracket can deflect about the mounting surface. The sensor probe is suspended from the sensor mounting bracket.

Abstract: A computing device creates a state of an analytic model. An analytic engine of an analytic model of an analytic model type is initialized. The analytic model is trained using a dataset and the analytic engine. A request to save a state of the analytic model is received. In response to receipt of the third indicator, the state of the trained analytic model is serialized. The state includes a reentry point name of a function of the analytic model type called to instantiate the trained analytic model. The serialized state is written to an output file. The written state is read from the output file. The state of the trained analytic model is restored using the read state. An analytic result is computed based on data in a second dataset different from the named dataset using the restored state of the trained analytic model. The computed analytic result is output.

Abstract: A computing device creates a state of an analytic model. An analytic engine of an analytic model of an analytic model type is initialized. The analytic model is trained using a dataset and the analytic engine. A request to save a state of the analytic model is received. In response to receipt of the third indicator, the state of the trained analytic model is serialized. The state includes a reentry point name of a function of the analytic model type called to instantiate the trained analytic model. The serialized state is written to an output file. The written state is read from the output file. The state of the trained analytic model is restored using the read state. An analytic result is computed based on data in a second dataset different from the named dataset using the restored state of the trained analytic model. The computed analytic result is output.

Abstract: A computing device restores a state of an analytic model. A first indicator of a dataset is received. A second indicator is received of an analytic model state file in which a state of an analytic model is stored. A reentry point name of a function to restore the analytic model is extracted from the state stored in the analytic model state file indicated by the received second indicator. The function is called using the extracted reentry point name to restore the state of the analytic model. The dataset indicated by the received first indicator is scored using the restored state of the analytic model. Scoring results from the scored dataset are written to an output file.

Abstract: A computing device creates and stores a state of an analytic model. A first indicator of a dataset is received. A second indicator of an analytic model type of a plurality of analytic model types to train using the dataset is received. An analytic engine of an analytic model of the analytic model type is instantiated. The analytic model of the analytic model type is trained using the dataset and the instantiated analytic engine. A third indicator to save a state of the analytic model is received. The state of the trained analytic model is generated. The generated state of the trained analytic model is written to an output file. The generated state includes a reentry point name of a function of the analytic model type called to instantiate the trained analytic mode.

Abstract: A computing device restores a state of an analytic model. A first indicator of a dataset is received. A second indicator is received of an analytic model state file in which a state of an analytic model is stored. A reentry point name of a function to restore the analytic model is extracted from the state stored in the analytic model state file indicated by the received second indicator. The function is called using the extracted reentry point name to restore the state of the analytic model. The dataset indicated by the received first indicator is scored using the restored state of the analytic model. Scoring results from the scored dataset are written to an output file.

Abstract: In accordance with the teachings described herein, systems and methods are provided for data sorting. A method for use with one or more processing devices in order to merge sorted runs of data may include the steps of: defining a plurality of floating buffers; calculating a number of data blocks for each floating buffer; configuring the floating buffers to store the number of data blocks; and using the floating buffers to perform an external data sorting operation. A data sorting system may include one or more programs, and may be used with a plurality of floating buffers and a data storage device for storing a plurality of sorted runs of data blocks, each data block including a plurality of data records. The one or more programs in a data sorting system may be operable to calculate a number of data blocks for each floating buffer and configure the plurality of floating buffers to store the number of data blocks.

Abstract: In accordance with the teachings described herein, systems and methods are provided for data sorting. A method for use with one or more processing devices in order to merge sorted runs of data may include the steps of: defining a plurality of floating buffers; calculating a number of data blocks for each floating buffer; configuring the floating buffers to store the number of data blocks; and using the floating buffers to perform an external data sorting operation. A data sorting system may include one or more programs, and may be used with a plurality of floating buffers and a data storage device for storing a plurality of sorted runs of data blocks, each data block including a plurality of data records. The one or more programs in a data sorting system may be operable to calculate a number of data blocks for each floating buffer and configure the plurality of floating buffers to store the number of data blocks.