Potential hazard warning system
Systems, apparatuses and methods may provide for technology that conducts a rule based inference analysis of crowdsource data to detect a hazard condition that is relevant to a user, personalize a warning of the hazard condition to the user based on one or more user preferences, and send the personalized warning to a client device associated with the user. In one example, the technology conducts a rule based classification of the crowdsource data, wherein the rule based inference analysis is conducted based on the rule based classification.
1. A computing system comprising:
network interface circuitry to receive crowdsource data from a plurality of source devices;
a processor coupled to the network interface circuitry; and
one or more memory devices coupled to the processor, the one or more memory devices including instructions, which when executed by the processor, cause the system to:
conduct a rule based inference analysis of the crowdsource data to detect a hazard condition that is relevant to a user;
personalize a warning of the hazard condition to the user based on one or more user preferences;
generate a reputation score based on one or more source identifiers corresponding to the crowdsource data;
incorporate the reputation score into the personalized warning; and
send, via the network interface circuitry, the personalized warning to a client device associated with the user.
2. The computing system of claim 1 , wherein the instructions, when executed, cause the computing system to conduct a rule based classification of the crowdsource data, and wherein the rule based inference analysis is to be conducted based on the rule based classification.
3. The computing system of claim 1 , wherein the instructions, when executed, cause the computing system to:
obtain contextual feedback with respect to the personalized warning; and
adapt one or more inference rules associated with the rule based inference analysis to the contextual feedback in real-time.
4. The computing system of claim 1 , wherein the personalized warning is to be sent to one or more of a wearable device, a handheld device or a vehicular device.
5. An apparatus comprising:
a substrate; and
logic coupled to the substrate, wherein the logic includes one or more of configurable logic or fixed-functionality hardware logic, the logic coupled to the substrate to:
conduct a rule based inference analysis of crowdsource data to detect a hazard condition that is relevant to a user;
personalize a warning of the hazard condition to the user based on one or more user preferences;
generate a reputation score based on one or more source identifiers corresponding to the crowdsource data;
incorporate the reputation score into the personalized warning; and
send the personalized warning to a client device associated with the user.
6. The apparatus of claim 5 , wherein the logic coupled to the substrate is to conduct a rule based classification of the crowdsource data, and wherein the rule based inference analysis is to be conducted based on the rule based classification.
7. The apparatus of claim 5 , wherein the logic coupled to the substrate is to:
obtain contextual feedback with respect to the personalized warning; and
adapt one or more inference rules associated with the rule based inference analysis to the contextual feedback in real-time.
8. The apparatus of claim 5 , wherein the personalized warning is to be sent to one or more of a wearable device, a handheld device or a vehicular device.
9. A method comprising:
conducting a rule based inference analysis of crowdsource data to detect a hazard condition that is relevant to a user;
personalizing a warning of the hazard condition to the user based on one or more user preferences;
generating a reputation score based on one or more source identifiers corresponding to the crowdsource data;
incorporating the reputation score into the personalized warning; and
sending the personalized warning to a client device associated with the user.
10. The method of claim 9 , further including conducting a rule based classification of the crowdsource data, wherein the rule based inference analysis is conducted based on the rule based classification.
11. The method of claim 9 , further including:
obtaining contextual feedback with respect to the personalized warning; and
adapting one or more inference rules associated with the rule based inference analysis to the contextual feedback in real-time.
12. The method of claim 9 , wherein the personalized warning is sent to one or more of a wearable device, a handheld device or a vehicular device.
13. At least one non-transitory computer readable storage medium comprising a set of instructions, which when executed by a computing system, cause the computing system to:
conduct a rule based inference analysis of crowdsource data to detect a hazard condition that is relevant to a user;
personalize a warning of the hazard condition to the user based on one or more user preferences;
generate a reputation score based on one or more source identifiers corresponding to the crowdsource data;
incorporate the reputation score into the personalized warning; and
send the personalized warning to a client device associated with the user.
14. The at least one non-transitory computer readable storage medium of claim 13 , wherein the instructions, when executed, cause the computing system to conduct a rule based classification of the crowdsource data, and wherein the rule based inference analysis is to be conducted based on the rule based classification.
15. The at least one non-transitory computer readable storage medium of claim 13 , wherein the instructions, when executed, cause the computing system to:
obtain contextual feedback with respect to the personalized warning; and
adapt one or more inference rules associated with the rule based inference analysis to the contextual feedback in real-time.
16. The at least one non-transitory computer readable storage medium of claim 13 , wherein the personalized warning is to be sent to one or more of a wearable device, a handheld device or a vehicular device.
17. A computing system comprising:
network interface circuitry to receive crowdsource data from a plurality of source devices;
a processor coupled to the network interface circuitry; and
one or more memory devices coupled to the processor, the one or more memory devices including instructions, which when executed by the processor, cause the system to:
conduct a rule based inference analysis of the crowdsource data to detect a hazard condition that is relevant to a user;
personalize a warning of the hazard condition to the user based on one or more user preferences, wherein the hazard condition is to include one or more of a language mismatch, an environmental danger, a crime risk or an illegal material, and wherein the personalized warning is to include one or more of an annotation to an image of a physical sign, a simulated sign or a boundary notification; and
send, via the network interface circuitry, the personalized warning to a client device associated with the user.
18. An apparatus comprising:
a substrate; and
logic coupled to the substrate, wherein the logic includes one or more of configurable logic or fixed-functionality hardware logic, the logic coupled to the substrate to:
conduct a rule based inference analysis of crowdsource data to detect a hazard condition that is relevant to a user;
personalize a warning of the hazard condition to the user based on one or more user preferences, wherein the hazard condition is to include one or more of a language mismatch, an environmental danger, a crime risk or an illegal material, and wherein the personalized warning is to include one or more of an annotation to an image of a physical sign, a simulated sign or a boundary notification; and
send the personalized warning to a client device associated with the user.
19. A method comprising:
conducting a rule based inference analysis of crowdsource data to detect a hazard condition that is relevant to a user;
personalizing a warning of the hazard condition to the user based on one or more user preferences, wherein the hazard condition includes one or more of a language mismatch, an environmental danger, a crime risk or an illegal material, and wherein the personalized warning includes one or more of an annotation to an image of a physical sign, a simulated sign or a boundary notification; and
sending the personalized warning to a client device associated with the user.
20. At least one non-transitory computer readable storage medium comprising a set of instructions, which when executed by a computing system, cause the computing system to:
conduct a rule based inference analysis of crowdsource data to detect a hazard condition that is relevant to a user;
personalize a warning of the hazard condition to the user based on one or more user preferences, wherein the hazard condition is to include one or more of a language mismatch, an environmental danger, a crime risk or an illegal material, and wherein the personalized warning is to include one or more of an annotation to an image of a physical sign, a simulated sign or a boundary notification; and
send the personalized warning to a client device associated with the user.