Systems and methods for automatic resolutions of wireless signals
Disclosed herein are embodiments of systems, methods, and products that generate semantic resolutions of wireless signals. An analytic server may train a plurality of inductive classifiers generate a respective set of deduction rules. For example, the analytic server may train a first inductive classifier to mine a first set of deduction rules matching service set identifiers (SSIDs) with business entities. As another example, the analytic server may train a second inductive classifier to mine a second set of deduction rules matching proximal groupings of wireless signals (also referred to as hyperclusters) with business entities. Upon receiving an unresolved wireless signal, the analytic server may apply at least one of the first and second set of deduction rules to assign a semantic meaning to the wireless signal.
1. A computer-implemented method comprising:
in a batch processing mode:
receiving, by a computer, observation data of a plurality of wireless signals observed by a plurality of observer devices;
training, by the computer, a first inductive classifier to generate a first set of deductive rules for syntactic matches between service set identifiers of the plurality of wireless signals and entities associated with the service set identifiers;
applying a multi-dimensional clustering process to generate one or more hyperclusters based on both temporal persistence and spatial proximity of the plurality of wireless signals;
training, by the computer, a second inductive classifier to generate a second set of deductive rules based on the one or more generated hyperclusters, wherein the second inductive classifier is trained to assign a syntactic and/or semantic meaning to a given wireless signal based on a hypercluster of the given wireless signal;
aggregating the first set of deductive rules and the second set of deductive rules to generate an aggregated set of deductive rules for assigning a syntactic and/or semantic meaning to a given wireless signal;
in a real-time deduction mode:
receiving, by the computer, information of an unresolved wireless signal;
generating, by the computer, a semantic resolution of the wireless signal based upon applying one or more rules from the aggregated set of deductive rules; and
displaying, by the computer, the semantic resolution of the wireless signal on a graphical user interface.
2. The computer-implemented method of claim 1 , further comprising:
in the batch processing mode:
training, by the computer, a third inductive classifier to generate a third set of deductive rules for syntactic matches between machine access codes of devices generating the plurality of wireless signals and manufacturers of the devices; and
in the real-time deduction mode:
generating, by the computer, the semantic resolution of the wireless signal based upon applying at least one of the first, second, and third set of deductive rules.
3. The computer-implemented method of claim 1 , wherein the semantic resolution includes at least one of a manufacturer of a device generating the wireless signal or an entity associated with the wireless signal.
4. The computer-implemented method of claim 3 , wherein the semantic resolution includes a probability score indicating a likelihood that the wireless signal is associated with the manufacturer or the entity.
5. The computer-implemented method of claim 3 , further comprising:
associating, by the computer, the semantic resolution of the wireless signal with a location.
6. The computer-implemented method of claim 5 , further comprising:
displaying, by the computer, the semantic resolution of the wireless signal and the associated location within a digital map on the graphical user interface.
7. The computer-implemented method of claim 1 , wherein the training of the first inductive classifier comprises:
removing, by the computer, stop words from the service set identifiers of the plurality of wireless signals;
tokenizing, by the computer, the remaining words from the service set identifiers of the plurality of wireless signals to generate tokenized service set identifiers; and
training, by the computer, the first inductive classifier utilizing the tokenized service set identifiers.
8. The computer-implemented method of claim 7 , wherein the training of the first inductive classifier comprises:
identifying, by the computer, a plurality of word-like tokens in the tokenized service set identifiers; and
probabilistically associating, by the computer, one or more words corresponding to one or more word-like tokens to each service set identifier.
9. The computer-implemented method of claim 8 , wherein the generating of the semantic resolution of the wireless signal comprises:
identifying, by the computer, at least one of a manufacturer of a device generating the wireless signal or an entity associated with the wireless signal utilizing the probabilistic association of the one or more words to each service set identifier.
10. The computer-implemented method of claim 1 , wherein the information of the unresolved wireless signal comprises at least one of service set identifier of the wireless signal or a machine access code of a device generating the wireless signal.
11. A system comprising:
a non-transitory storage medium storing a plurality of computer program instructions; and
a processor electrically coupled to the non-transitory storage medium and configured to execute the plurality of computer program instructions to:
in a batch processing mode:
receive observation data of a plurality of wireless signals observed by a plurality of observer devices;
train a first inductive classifier to generate a first set of deductive rules for syntactic matches between service set identifiers of the plurality of wireless signals and entities associated with the service set identifiers;
apply a multi-dimensional clustering process to generate one or more hyperclusters based on both temporal persistence and spatial proximity of the plurality of wireless signals;
train a second inductive classifier to generate a second set of deductive rules the one or more generated hyperclusters, wherein the second inductive classifier is trained to assign a syntactic and/or semantic meaning to a given wireless signal based on a hypercluster of the given wireless signal;
aggregating the first set of deductive rules and the second set of deductive rules to generate an aggregated set of deductive rules for assigning a syntactic and/or semantic meaning to a given wireless signal;
in a real-time deduction mode:
receive information of an unresolved wireless signal;
generate a semantic resolution of the wireless signal based upon applying one or more rules from the aggregated set of deductive rules; and
display the semantic resolution of the wireless signal on a graphical user interface.
12. The system of claim 11 , wherein the processor is configured to further execute the plurality of the computer program instructions to:
in the batch processing mode:
train a third inductive classifier to generate a third set of deductive rules for syntactic matches between machine access codes of devices generating the plurality of wireless signals and manufacturers of the devices; and
in the real-time deduction mode:
generate the semantic resolution of the wireless signal based upon applying at least one of the first, second, and third set of deductive rules.
13. The system of claim 11 , wherein the semantic resolution includes at least one of a manufacturer of a device generating the wireless signal or an entity associated with the wireless signal.
14. The system of claim 13 , wherein the semantic resolution includes a probability score indicating a likelihood that the wireless signal is associated with the manufacturer or the entity.
15. The system of claim 13 , wherein the processor is configured to further execute the plurality of computer program instructions to:
associate the semantic resolution of the wireless signal with a location.
16. The system of claim 15 , wherein the processor is configured to further execute the plurality of computer program instructions to:
display the semantic resolution of the wireless signal and the associated location within a digital map on the graphical user interface.
17. The system of claim 11 , wherein, to the train of the first inductive classifier, the processor is configured to further execute the computer program instructions to:
remove stop words from the service set identifiers of the plurality of wireless signals;
tokenize the remaining words from the service set identifiers of the plurality of wireless signals to generate tokenized service set identifiers; and
train the first inductive classifier utilizing the tokenized service set identifiers.
18. The system of claim 17 , wherein, to train the first inductive classifier, the processor is configured to further execute the plurality of computer program instructions to:
identify a plurality of word-like tokens in the tokenized service set identifiers; and
probabilistically associate one or more words corresponding to one or more word-like tokens to each service set identifier.
19. The system of claim 18 , wherein, to generate the semantic resolution of the wireless signal, the processor is configured to further execute the computer program instructions to:
identify at least one of a manufacturer of a device generating the wireless signal or an entity associated with the wireless signal utilizing the probabilistic association of the one or more words to each service set identifier.
20. The system of claim 11 , wherein the information of the unresolved wireless signal comprises at least one of service set identifier of the wireless signal or a machine access code of a device generating the wireless signal.