METHODS AND DEVICE FOR SELF-ORGANIZATION OF DATA COLLECTION
Methods and devices for self organization of data collection are disclosed. A device can include a data acquisition circuit to request and receive a plurality of detection values, each of the plurality of detection values corresponding to a plurality of input sensors forming a self-organizing data pool and communicatively coupled to the data acquisition circuit, a data storage component to store specifications and anticipated state information for the plurality of input sensors of the data pool, and a response circuit to provide the data acquisition circuit with direction regarding a manner in which the plurality of detection values is requested.
1 . A device comprising:
a data acquisition circuit adapted to request and receive a plurality of detection values, each of the plurality of detection values corresponding to a plurality of input sensors forming a data pool and communicatively coupled to the data acquisition circuit;
a data storage component adapted to store specifications and anticipated state information for the plurality of input sensors of the data pool; and
a response circuit adapted to provide the data acquisition circuit with direction regarding a manner in which the plurality of detection values is requested.
2 . The device of claim 1 , wherein the data pool is self-organizing
3 . The device of claim 2 , wherein the data pool is self-organized based, at least in part, on at least one of a yield metric and a utilization metric.
4 . The device of claim 3 , wherein the utilization metric accounts for a cost of at least one of acquiring and storing data.
5 . The device of claim 2 , wherein the data pool is managed by a self-organizing machine learning facility.
6 . The device of claim 5 , wherein the self-organizing machine learning facility is adapted to configure the data pool via at least one of managing one or more sources of the plurality of detection values, managing an availability of one or more data streams and managing at least on API for moving data into and out of the data pool.
7 . The device of claim 2 , wherein the data pool is organized, at least in part, by cognitive capabilities.
8 . The device of claim 2 , wherein the data pool is self-organized based, at least in part, in response to learning feedback.
9 . The device of claim 8 , wherein the learning feedback is calculated in an analytic system.
10 . The device of claim 2 , wherein a content of a data pool is varied based, at least in part, on a state of a host system.
11 . A method comprising:
requesting and receiving a plurality of detection values, each of the plurality of detection values corresponding to a plurality of input sensors forming a data pool;
storing component specifications and anticipated state information for the plurality of input sensors of the data pool; and
providing a direction regarding a manner in which the plurality of detection values is requested.
12 . The method of claim 11 , wherein the data pool is self-organizing.
13 . The method of claim 12 , wherein the data pool is self-organized based, at least in part, on at least one of a yield metric and a utilization metric.
14 . The method of claim 13 , wherein the utilization metric accounts for a cost of at least one of acquiring and storing data.
15 . The method of claim 12 , wherein the data pool is managed by a self-organizing machine learning facility.
16 . The method of claim 14 , further comprising configuring the data pool via at least one of managing one or more sources of the plurality of detection values, managing an availability of one or more data streams and managing moving data into and out of the data pool.
17 . The method of claim 12 , wherein the data pool is organized, at least in part, by cognitive capabilities.
18 . The method of claim 12 , wherein the data pool is self-organized based, at least in part, in response to learning feedback.
19 . The method of claim 18 , wherein the learning feedback is calculated in an analytic system.
20 . The method of claim 12 , wherein a content of the data pool is varied based, at least in part, on a state of a host system.