Abstract: In one example embodiment, a computer-implemented method and system for issuing alerts are disclosed. The method includes receiving device information for one or more mobile devices; sorting the received device information based on pre-determined criteria; evaluating the sorted device information to determine if the device information satisfies a specified condition; and issuing alert based on the determination. The system for issuing alerts includes a storage database, wherein the storage database receives device information for one or more mobile devices; an analytics engine, wherein the analytics engine sorts the received device information based on pre-determined criteria; a rules engine, wherein the rules engine evaluates the sorted device information to determine if the device information satisfies a specified condition and issues alerts based on the determination.

Abstract: In one example embodiment, a computer-implemented method and system for learning places of interest are disclosed. The method includes learning and storing location information of at least one mobile device; detecting a location where no movement of the at least one mobile device has occurred over a pre-determined duration of time; determining whether the detected location is classified as a location of interest based on a predefined criteria; and clustering the learned location of interest into bigger groups based on location information of the learned location of interest using a pre-defined criteria.

Abstract: In one example embodiment, a computer-implemented method and system for learning places of interest are disclosed. The method includes learning and storing location information of at least one mobile device; detecting a location where no movement of the at least one mobile device has occurred over a pre-determined duration of time; determining whether the detected location is classified as a location of interest based on a predefined criteria; and clustering the learned location of interest into bigger groups based on location information of the learned location of interest using a pre-defined criteria.

Abstract: In one example embodiment, a computer-implemented method and system for issuing alerts are disclosed. The method includes receiving device information for one or more mobile devices; sorting the received device information based on pre-determined criteria; evaluating the sorted device information to determine if the device information satisfies a specified condition; and issuing alert based on the determination. The system for issuing alerts includes a storage database, wherein the storage database receives device information for one or more mobile devices; an analytics engine, wherein the analytics engine sorts the received device information based on pre-determined criteria; a rules engine, wherein the rules engine evaluates the sorted device information to determine if the device information satisfies a specified condition and issues alerts based on the determination.

Abstract: In one example embodiment, a computer-implemented method and system for providing visualization of information are disclosed. The method includes receiving current device status information for one or more IoT devices; receiving historical information for one or more IoT devices; correlating the received current device status information with the received historical information for each of the one or more IoT devices; determining overall state of each of the one or more mobile devices based on the correlation of the current device status information with historical information for each of the one or more IoT devices; and presenting the information in visual format based on the determination.

Abstract: In one example embodiment, a computer-implemented method and system for learning places of interest are disclosed. The method includes learning and storing location information of at least one mobile device; detecting a location where no movement of the at least one mobile device has occurred over a pre-determined duration of time; determining whether the detected location is classified as a location of interest based on a predefined criteria; and clustering the learned location of interest into bigger groups based on location information of the learned location of interest using a pre-defined criteria.