Various implementations provide an aquatic conditions optimization management system accesses aquatic sensor data generated by one or more aquatic sensors, identifies a collection of aquatic data that includes data generated by and collected from the aquatic sensor(s), generates a set of cross-correlation matrices based on the collection of aquatic data, executes a set of unsupervised machine learning algorithms using the set of cross-correlation matrices, and determines one or more optimum conditions for one or more aquatic resources based on the executed set of unsupervised machine learning algorithms. The optimum condition(s) may be communicated to one or more individuals and may include one or more corrective actions to improve one or more of the aquatic resources.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. At least one machine accessible storage medium having instructions stored thereon, the instructions when executed on a machine, cause the machine to: identify a collection of aquatic data, wherein the collection of data comprises data generated by a plurality of different aquatic sensors and describes a plurality of conditions within a particular aquatic environment; generate a set of cross-correlation matrices from the collection of aquatic data; provide the set of cross-correlation matrices as an input to one or more unsupervised machine learning algorithms; and determine one or more optimum conditions for the particular aquatic environment based on a result of the one or more unsupervised machine learning algorithms. 2. The storage medium of claim 1, wherein the instructions, when executed, further cause a machine to determine one or more recommendations for corrective action to address the one or more optimum conditions. 3. The storage medium of claim 2, wherein the instructions, when executed, further cause a machine to communicate to a user at least one of: information that identifies the determined optimum conditions for the particular aquatic environment; and information that identifies the one or more recommendations for corrective action. 4. The storage medium of claim 1, wherein the collection of aquatic data is collected from aquatic sensors in a plurality of different aquatic environments including the particular aquatic environment. 5. The storage medium of claim 1, wherein the aquatic data comprises asynchronous samples from the plurality of aquatic environments. 6. The storage medium of claim 1, wherein the particular aquatic environment comprises one of a water tank, a fish tank, a swimming pool, or portion of an open water environment. 7. The storage medium of claim 1, wherein the one or more unsupervised machine learning algorithms comprises a Bayesian unsupervised machine learning algorithm. 8. The storage medium of claim 7, wherein the Bayesian unsupervised machine learning algorithm comprises a Bayesian context mining algorithm. 9. The storage medium of claim 1, wherein generating a set of cross-correlation matrices from the collection of aquatic data comprises calculating pairwise cross-correlations between combinations of the plurality of conditions at each of a series of time epochs. 10. The storage medium of claim 9, wherein the set of cross-correlation matrices comprises a plurality of cross-correlation matrices, and the plurality of cross-correlation matrices comprises a respective cross-correlation matrix for each of the series of time epochs. 11. The storage medium of claim 10, wherein each pairwise cross-correlation value is based on data samples of the combinations of conditions representing a respective pair of different conditions in the plurality of conditions. 12. The storage medium of claim 11, wherein each pair of different conditions (x, y) includes a cross-correlation value rxy that is calculated in accordance with a first calculation: r xy = c xy  ( k ) s x  s y   k = 0 , ± 1 , ± 2 , … , wherein, a covariance cxy(k) of each pair of cross-correlated pair of different parameters is calculated in accordance with a second calculation: c xy  ( k ) = { 1 n  ∑ t = 1 n - k   ( x t - x _ )  ( y t + k - y _ ) ; k = 0 , 1 , 2 , … 1 n  ∑ t = 1 n + k   ( y t - y _ )  ( x t - k - x _ ) ; k = 0 , - 1 , - 2 , … ; and s, and sy are defined according to: sx=√{square root over (cxx(0))}; and sy=√{square root over (cyy(0))}. 13. A method, comprising: identifying a collection of aquatic data, wherein the collection of data comprises data generated by a plurality of different aquatic sensors and describes a plurality of conditions within a particular aquatic environment; generating a set of cross-correlation matrices from the collection of aquatic data; providing the set of cross-correlation matrices as an input to one or more unsupervised machine learning algorithms; and determining one or more optimum conditions for the particular aquatic environment based on a result of the one or more unsupervised machine learning algorithms. 14. The method of claim 13, further comprising calculating pairwise cross-correlations between combinations of the plurality of conditions at each of a series of time epochs, wherein the set of cross-correlation matrices are generated based on the pairwise cross-correlations. 15. The method of claim 14, wherein the set of cross-correlation matrices comprises a plurality of cross-correlation matrices, and the plurality of cross-correlation matrices comprises a respective cross-correlation matrix for each of the series of time epochs. 16. The method of claim 15, wherein generating the set of cross-correlation matrices comprises populating each cross-correlation matrix with a respective subset of the calculated pairwise cross-correlations. 17. A system comprising: a data processor device; computer memory; and an aquatic conditions optimization management system, executable by the data processor device to: identify a collection of aquatic data, wherein the collection of data comprises data generated by a plurality of different aquatic sensors and describes a plurality of conditions within a particular aquatic environment; generate a set of cross-correlation matrices from the collection of aquatic data; provide the set of cross-correlation matrices as an input to one or more unsupervised machine learning algorithms; and determine one or more optimum conditions for the particular aquatic environment based on a result of the one or more unsupervised machine learning algorithms. 18. The system of claim 17, further comprising one or more of the plurality of aquatic sensors. 19. The system of claim 18, further comprising a gateway to communicate with the plurality of aquatic sensors and receive data in the collection of data. 20. The system of claim 17, wherein the one or more unsupervised machine learning algorithms comprises one of a neural network to perform a Bayesian unsupervised machine learning algorithm or a recursive state estimation.