Abstract: Described herein are systems and methods for prediction of job times within an analytics environment. As jobs are executed, for example, within an application analytics environment, and the complexity of the jobs grow, it is becoming increasingly important to provide an accurate estimation of job completion time. Artificial intelligence and/or machine learning can be utilized, together with one or more of a pre-trained model and a transferred-learning model, to develop a job prediction time model that can be utilized to provide an estimation for a job runtime prediction. In addition, an alert can be provided in the event that a job runs longer than predicted.

Abstract: A method of using a non-GPS-derived technique to estimate the location of an Assisted-GPS-enabled wireless terminal for the purposes of generating location-specific assistance data for the wireless terminal is disclosed. The wireless terminal then uses the assistance data to acquire and process one or more GPS signals and to derive information that is probative of the wireless terminal's location. The GPS-derived location information is then combined with non-GPS-derived location to form an estimate of the location of the wireless terminal that is better than can be derived from either alone. This combination of GPS-derived and non-GPS techniques is particularly useful when the wireless terminal can only acquire one or two GPS signals because it is not possible to determine the location of the wireless terminal with only two GPS signals alone.

Abstract: A technique for estimating the location of a wireless terminal at an unknown location in a geographic region is disclosed. The technique is based on the recognition that there are traits of electromagnetic signals that are dependent on topography, the receiver, the location of the transmitter, and other factors. For example, if a particular radio station is known to be received strongly at a first location and weakly at a second location, and a given wireless terminal at an unknown location is receiving the radio station weakly, it is more likely that the wireless terminal is at the second location than at the first location.

Abstract: A process and machine for estimating the location of a wireless terminal is disclosed. The illustrative embodiment of the present invention is based on the observation that the signal strength of a signal from a transmitter is different at some locations, and, therefore, the location of a wireless terminal can be estimated by comparing the signal strength it currently observes against a map or database that correlates locations to signal strengths. In accordance with a first example, if a particular radio station is known to be received well at a first location and poorly at a second location, and a given wireless terminal at an unknown location is receiving the radio station poorly, it is more likely that the wireless terminal is at the second location than it is at the first location.

Abstract: A method of estimating the location of a wireless terminal is disclosed. The illustrative embodiment of the present invention is based on the observation that the signal strength of a signal from a transmitter is different at some locations, and, therefore, the location of a wireless terminal can be estimated by comparing the signal strength it currently observes against a map or database that correlates locations to signal strengths. Furthermore, the illustrative embodiment deduces the signal strength of one or more base stations' control channels at the wireless terminal based on the principal of reciprocity, whether or not the wireless terminal can actually receive the base stations' control channels but so long as the base station can receive and measure the uplink signal from the wireless terminal. The deduced signal-strength measurements can then used—alone or in combination with the empirical signal-strength measurements—to estimate the location of the wireless terminal.