Method, apparatus, and article of manufacture for estimating parameters of a probability model on shared device usage probabilistic semantic analysis
Methods are disclosed for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs. In operation, usage job data of observed users and devices carrying out the jobs is recorded. A probability model is defined with an observed user variable, an observed device variable, a latent job cluster variable, and a latent job service class variable. A range of job service classes associated with the shared devices is determined, and an initial number of job clusters is selected. Parameters of the probability model are learned using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters. The learned parameters of the probability model are applied to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.
1. A computer-implemented method for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs to generate clusters of users with similar behavior for each class of service in a network of shared devices offering different classes of service, comprising:
recording usage job data of observed users and devices carrying out the jobs;
defining a probability model with an observed user variable, an observed device variable, a latent job cluster variable, a latent job service class variable, and the dependencies among the observed user variable, the observed device variable, the latent job cluster variable, and the latent job service class variable including a dependency of the latent job service class variable to the observed device variable constrained by knowledge of the service classes supported by devices;
wherein the distribution of the devices is constrained by the knowledge of the service classes they support, and
wherein defining a probability model according to generating the job cluster index, which has no dependencies, generating the user id, which is dependent on job clusters, generating the job service class, which is dependent on the user, generating the device choice, which is dependent on the job clusters and the job service classes;
determining a range of service classes associated with the shared devices;
selecting an initial number of job clusters;
learning parameters of the probability model using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters, wherein the latent job cluster variable and the latent job service class variable are learned together; and
applying the learned parameters of the probability model to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.
2. The method according to claim 1 , wherein said applying further comprises using the probability model with the learned parameters to smooth the recorded usage data.
3. The method according to claim 1 , wherein said learning further comprises:
estimating the parameters of the probability model for a plurality of different numbers of job clusters ranging from the initial number of job clusters to a number greater than one;
computing a score for the parameters of the probability model estimated for each of the plurality of different numbers of job clusters;
selecting the parameters of the probability model with the highest computed score.
4. The method according to claim 1 , wherein the probability model is given by the following equation:
p ( U, D, C, K )= p ( C ) p ( U|C ) p ( K|U ) p ( D|C, K ),
where:
U is the observed user variable;
D is the observed device variable;
C is the latent job cluster variable, which corresponds to an index of a job cluster;
K is the latent job service class variable, which corresponds to a job's service class.
5. The method according to claim 4 , wherein parameters of the probability model p(C), p(U|C), p(K|U), and p(D|C, K) are estimated by computing a maximum of a posteriori probability where a priori knowledge is defined using uniform priors.
6. The method according to claim 1 , wherein a priori knowledge is defined using informative priors that are a percentage of jobs in each class of service in the range of service classes associated with the shared devices.
7. The method according to claim 1 , wherein the devices are multifunctional devices.
8. The method according to claim 7 , wherein the jobs performed by the multifunctional devices comprise one of scanning, printing, faxing, archiving, and emailing.
9. The method according to claim 8 , wherein the range of service classes associated with the shared devices is a set of multi-class services for the multifunctional devices.
10. The method according to claim 9 , wherein the set of multi-class services for printing is one or more of color, speed, and quality.
11. An apparatus for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs, comprising:
a memory for storing processing instructions of the apparatus; and
a processor coupled to the memory for executing the processing instructions of the apparatus; the processor in executing the processing instructions:
recording usage job data of observed users and devices carrying out the jobs;
defining a probability model with an observed user variable, an observed device variable, a latent job cluster variable, a latent job service class variable, and the dependencies among the observed user variable, the observed device variable, the latent job cluster variable, and the latent job service class variable including a dependency of the latent job service class variable to the observed device variable constrained by knowledge of the service classes supported by devices;
wherein the distribution of the devices is constrained by the knowledge of the service classes they support, and
wherein defining a probability model according to generating the job cluster index, which has no dependencies, generating the user id, which is dependent on job clusters, generating the job service class, which is dependent on the user, generating the device choice, which is dependent on the job clusters and the job service classes;
determining a range of service classes associated with the shared devices;
selecting an initial number of job clusters;
learning parameters of the probability model using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters, wherein the latent job cluster variable and the latent job service class variable are learned together; and
applying the learned parameters of the probability model to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.
12. The apparatus according to claim 11 , wherein the processor in executing said applying processing instruction uses the probability model with the learned parameters to smooth the recorded usage data.
13. The apparatus according to claim 11 , wherein the processor in executing said learning processing instruction:
estimates the parameters of the probability model for a plurality of different numbers of job clusters ranging from the initial number of job clusters to a number greater than one;
computes a score for the parameters of the probability model estimated for each of the plurality of different numbers of job clusters;
selects the parameters of the probability model with the highest computed score.
14. The apparatus according to claim 11 , wherein the probability model is given by the following equation in processing instruction:
p ( U, D, C, K )= p ( C ) p ( U|C ) p ( K|U ) p ( D|C, K ),
where:
U is the observed user variable;
D is the observed device variable;
C is the latent job cluster variable, which corresponds to an index of a job cluster;
K is the latent job service class variable, which corresponds to a job's service class.
15. The apparatus according to claim 14 , wherein parameters of the probability model p(C), p(U|C), p(K|U), and p(D|C, K) are estimated in the processing instruction by computing a maximum of a posteriori probability where a priori knowledge is defined using uniform priors.
16. The apparatus according to claim 11 , wherein a priori knowledge is defined in the processing instruction using informative priors that are a percentage of jobs in each class of service in the range of service classes associated with the shared devices.
17. The apparatus according to claim 11 , wherein the devices are multifunctional devices.
18. An article of manufacture for estimating parameters of a probability model that models user behavior of shared devices offering different classes of service for carrying out jobs, the article of manufacture comprising computer usable media including computer readable instructions embedded therein that causes a computer to perform a method, wherein the method comprises:
recording usage job data of observed users and devices carrying out the jobs;
defining a probability model with an observed user variable, an observed device variable, a latent job cluster variable, a latent job service class variable, and the dependencies among the observed user variable, the observed device variable, the latent job cluster variable, and the latent job service class variable including a dependency of the latent job service class variable to the observed device variable constrained by knowledge of the service classes supported by devices;
wherein the distribution of the devices is constrained by the knowledge of the service classes they support, and
wherein defining a probability model according to generating the job cluster index, which has no dependencies, generating the user id, which is dependent on job clusters, generating the job service class, which is dependent on the user, generating the device choice, which is dependent on the job clusters and the job service classes;
determining a range of service classes associated with the shared devices;
selecting an initial number of job clusters;
learning parameters of the probability model using the recorded job usage data, the determined range of service classes, and the selected initial number of job clusters, wherein the latent job cluster variable and the latent job service class variable are learned together; and
applying the learned parameters of the probability model to evaluate one or more of: configuration of the shared devices, use of the shared devices, and job redirection between the shared devices.