A method of maintaining interoperability amongst Internet of Things (IoT) devices connected via an IoT integration platform is disclosed. The method includes: receiving a selection of a semantic label associated with an IoT device; determining a recommendation of an interoperable rule based on the semantic label, the interoperable rule having a condition trigger and an action policy for execution at satisfaction of the condition trigger; presenting the recommendation on a rule management interface; and receiving a confirmation from a user through the rule management interface to activate the interoperable rule.
Please help me write a proper abstract based on the patent claims. CLAIM: 1-20. (canceled) 21. A method of deriving semantic meaning of Internet of Things (loT) devices connected via an loT integration platform, comprising: collecting device data from a plurality of loT devices connected via the loT integration platform; determining a set of features by analyzing corresponding time series of events among any of the plurality of loT devices and generating one or more clusters correlating the device data using natural language processing; performing behavioral analysis to identify one or more user behaviors, wherein the behavioral analysis comprises incrementally populating a profile with device data associated with either of a particular loT device or a particular user, and applying a model to the profile to identify behavioral patterns and behavior changes; and determining, by utilizing a probabilistic measure, a semantic meaning associated with any device among the plurality of loT devices based on any of the set of features and the identified one or more behaviors. 22. The method of claim 21, wherein the device data includes geolocation data, broadcast data, device protocol, data received by an loT device, data transmitted by an loT device, data services information, or any combination thereof. 23. The method of claim 21, further comprising: collecting public data from a public library and/or a public device; mapping the public data to the collected device data; and determining another set of features based on identified correlation based on the mapping. 24. The method of claim 23, wherein determining the semantic meaning is further based on the determined another set of features. 25. The method of claim 21, wherein determining the set of features is performed using a machine learning model, a statistical model, a rules model, a scoring model, incremental calculations, or any combination thereof. 26. The method of claim 21, wherein determining the set of features is performed by applying an adaptive correlation mechanism to one or more previously generated data models to generate an aggregate data model. 27. The method of claim 21, wherein determining the set of features further comprises: identifying an event trigger resulting from analyzing the corresponding time series of events. 28. The method of claim 21, wherein determining the set of features further comprises: labeling any of the one or more clusters with a label corresponding to a meaning identified in the device data associated with a particular cluster. 29. The method of claim 28, wherein determining the semantic meaning is further based on the label corresponding to the meaning identified in the device data associated with a particular cluster. 30. The method of claim 21, further comprising: generating, based on the determined semantic meaning, an interoperable rule that includes a condition trigger to be detected by at least a first loT device and an action policy to be executed by at least a second loT device at satisfaction of the condition trigger, wherein the loT device associated with the determined semantic meaning is the first loT device for detecting the condition trigger, the second loT device for executing the action policy, or both. 31. The method of claim 30, further comprising: upon detecting the condition trigger, executing the action policy on the second loT device, wherein the action policy includes inducing a label. 32. A method of deriving semantic meaning of Internet of Things (loT) devices connected via an loT integration platform, comprising: monitoring device data from a plurality of loT devices connected via the loT integration platform; determining a set of features by analyzing corresponding time series of events among any of the plurality of loT devices by applying an adaptive correlation mechanism to one or more previously generated data models to generate an aggregate data model, and generating one or more clusters correlating the device data using natural language processing; performing behavioral analysis to identify one or more user behaviors; and determining, by utilizing a probabilistic measure, a semantic meaning associated with any device among the plurality of loT devices based on any of the set of features and the identified one or more behaviors. 33. The method of claim 32, wherein the behavioral analysis comprises incrementally populating a profile with device data associated with either of a particular loT device or a particular user, and applying a model to the profile to identify behavioral patterns and behavior changes. 34. The method of claim 32, further comprising: collecting public data from a public library and/or a public device; mapping the public data to the collected device data; and determining another set of features based on identified correlation based on the mapping. 35. The method of claim 32, wherein determining the set of features further comprises: labeling any of the one or more clusters with a label corresponding to a meaning identified in the device data associated with a particular cluster, wherein determining the semantic meaning is further based on the label corresponding to the meaning identified in the device data associated with a particular cluster. 36. A machine-implemented integration platform system for deriving semantic meaning of Internet of Things (loT) devices connected thereto, the integration platform system comprising: a communication interface having protocols to communicate with the plurality of IOT devices; one or more processors connected to the communication interface, wherein the one or more processors are configured to: collect device data, via the communication interface, from a plurality of loT devices; determine a set of features by analyzing corresponding time series of events among any of the plurality of loT devices and generating one or more clusters correlating the device data using natural language processing; perform behavioral analysis to identify one or more user behaviors, wherein the behavioral analysis comprises incrementally populating a profile with device data associated with either of a particular loT device or a particular user, and applying a model to the profile to identify behavioral patterns and behavior changes; and determine, by utilizing a probabilistic measure, a semantic meaning associated with any device among the plurality of loT devices based on any of the set of features and the identified one or more behaviors. 37. The system of claim 36, wherein the one or more processors are further configured to: labeling any of the one or more clusters with a label corresponding to a meaning identified in the device data associated with a particular cluster, wherein determining the semantic meaning is further based on the label corresponding to the meaning identified in the device data associated with a particular cluster. 38. The system of claim 36, wherein the one or more processors are further configured to: identify an event trigger resulting from analyzing the corresponding time series of events. 39. The system of claim 36, further comprising: an event track module configured to detect an event trigger among the plurality of loT devices connected to the loT integration platform by monitoring data collected from one or more sensors of the plurality of loT devices. 40. The system of claim 39, further comprising: a rule execution module, coupled to the event track module to receive an indication that the event trigger is detected, configured to execute an action policy by controlling one or more of the plurality of loT devices when the event trigger is detected by the event track module.