Network server arrangement for processing non-parametric, multi-dimensional, spatial and temporal human behavior or technical observations measured pervasively, and related method for the same are disclosed. An example computer system to process usage data received from a wireless device, the computer system including a memory including machine readable instructions; and a processor to execute the instructions to: process the usage data to identify applications which were sequentially accessed on the wireless device in a time period; build a behavior model based on the identified applications, the behavior model to describe user behavior associated with the wireless device; execute the behavior model to predict usage of an application on the wireless device; based on the prediction, monitor usage of the wireless device to determine an accuracy of the prediction; and update the behavior model based on the accuracy of the prediction.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer system to process usage data received from a wireless device, the computer system comprising: a memory including machine readable instructions; and a processor to execute the instructions to: process the usage data to identify applications which were sequentially accessed on the wireless device in a time period; build a behavior model based on the identified applications, the behavior model to describe user behavior associated with the wireless device; execute the behavior model to predict usage of an application on the wireless device; based on the prediction, monitor usage of the wireless device to determine an accuracy of the prediction; and update the behavior model based on the accuracy of the prediction. 2. The computer system of claim 1, wherein the processor is to provide an advertisement to the wireless device based on the prediction. 3. The computer system of claim 2, wherein content of the advertisement is associated with the predicted usage of the application. 4. The computer system of claim 1, wherein the behavior model includes time-stamped vectors. 5. The computer system of claim 4, wherein the processor is to executed the behavior model to predict the usage by correlating the time-stamped vectors to predict the usage of the application on the wireless device. 6. The computer system of claim 4, wherein the processor is to correlate two or more of the time-stamped vectors to identify a change in a usage pattern of one of the identified applications. 7. The computer system of claim 1, wherein the processor is to perform a cluster analysis on the usage data and to update the behavior model with results of the cluster analysis. 8. The computer system of claim 1, wherein the processor is to build the behavior model by aggregating the usage data. 9. The computer system of claim 1, wherein the usage data is first usage data, the wireless device is a first wireless device, and the processor is to correlate the first usage data and second usage data from a second wireless device to determine a similarity value between the first usage data and the second usage data. 10. The computer system of claim 1, wherein the processor is to process the usage data to identify application usage data and real-time location data, and the processor is to correlate the application usage data and the real-time location data to identify a place where at least one of the applications was used. 11. The computer system of claim 10, wherein the processor is to provide an advertisement to the wireless device based on the place. 12. The computer system of claim 1, wherein the behavior model is a Markov model. 13. A computer implemented method to process usage data received from a wireless device, the method comprising: processing, by executing an instruction with a processor, the usage data to identify applications accessed sequentially on the wireless device in a time period; building, with the processor, a behavior model based on the identified applications, the behavior model to describe user behavior associated with the wireless device; executing the behavior model with the processor to predict usage of an application on the wireless device; based on the prediction, monitoring usage of the wireless device with the processor to determine an accuracy of the prediction; and updating the behavior model with the processor based on the accuracy of the prediction by executing an instruction. 14. The method of claim 13, further including providing an advertisement to the wireless device based on the prediction. 15. The method of claim 14, wherein content of the advertisement is associated with the prediction. 16. The method of claim 13, wherein the behavior model includes time-stamped vectors. 17. The method claim 16, further including correlating the time-stamped vectors to predict the usage of the application on the wireless device. 18. The method of claim 16, further including correlating two or more of the time-stamped vectors to identify a change in a usage pattern of one of the identified applications. 19. The method of claim 13, further including performing a cluster analysis on the usage data and updating the behavior model with results of the cluster analysis. 20. The method of claim 13, wherein the building of the behavior model includes building the behavior model by aggregating the usage data. 21. The method of claim 13, wherein the usage data is first usage data, the wireless device is a first wireless device, further including correlating the first usage data and second usage data from a second wireless device to determine a similarity value between the first usage data and the second usage data. 22. The method of claim 13, further including processing the usage data to identify application usage data and location data including real-time location data and to correlate the application usage data and the real-time location data to identify a place where at least one of the applications was used. 23. The method of claim 22, further including providing an advertisement to the wireless device based on the place. 24. The method of claim 13, wherein the executing of the behavior model includes using a Markov model. 25. A tangible machine-readable medium comprising instructions which, when executed, cause a machine to at least: process usage data to identify applications which were accessed sequentially on a wireless device sequentially in a time period; build a behavior model based on the identified applications, the behavior model to describe user behavior associated with the wireless device; execute the behavior model to predict usage of an application on the wireless device; based on the prediction, monitor usage of the wireless device to determine an accuracy of the prediction; and update the behavior model based on the accuracy of the prediction. 26. The tangible machine-readable medium of claim 25, wherein the instructions, when executed, cause the machine to provide an advertisement to the wireless device based on the behavior model. 27. The tangible machine-readable medium of claim 25, wherein the behavior model includes time-stamped vectors. 28. The tangible machine-readable medium of claim 27, wherein the instructions, when executed, cause the machine to correlate the time-stamped vectors to predict the usage, the usage of the application on the wireless device. 29. The tangible machine-readable medium of claim 27, wherein the instructions, when executed, cause the machine to correlate two or more of the time-stamped vectors to identify a change in a usage pattern of one of the identified applications. 30. The tangible machine-readable medium of claim 25, wherein the instructions, when executed, cause the machine to perform a cluster analysis on the usage data and to update the behavior model with results of the cluster analysis. 31. The tangible machine-readable medium of claim 27, wherein the instructions, when executed, cause the machine to build the behavior model by aggregating the usage data. 32. The tangible machine-readable medium of claim 27, wherein the usage data is first usage data, the wireless device is a first wireless device, wherein the instructions, when executed, and cause the machine to correlate the first usage data and second usage data from a second wireless device to determine a similarity value between the first usage data and the second usage data. 33. The tangible machine-readable medium of claim 27, wherein the instructions, when executed, cause the machine to process the usage data to identify application usage data and real-time location data and correlate the application usage data and the real-time location data to identify a place where at least one of the applications was used.