A method estimates similarities in data streams. A data source receives input vectors from users. A sketch feature generator converts the input vectors into sketch feature vectors corresponding to the users, wherein each sketch feature vector represents data and meta-information from each user received in a most recent sample period. A similarity comparator compares each sketch feature vector against other sketch feature vectors to calculate similarity probabilities between the users. A processor running a decision loop determines a prediction result for at least one user based on the similarity probabilities.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for estimating similarities in data streams, comprising: receiving, from a data source, a plurality of input vectors from a plurality of users; converting, by a sketch feature generator, the plurality of input vectors into a plurality of sketch feature vectors each corresponding to one of the plurality of users, wherein each of the plurality of sketch feature vectors represent data and meta-information from each corresponding one of the plurality of users received in a most recent sample period of the data source; comparing, by a similarity comparator, each of the plurality of sketch feature vectors against one or more of other ones of the plurality of sketch feature vectors so as to calculate a plurality of similarity probabilities; and generating, by a processor running a decision loop, a prediction result for at least one of the plurality of users based on the plurality of similarity probabilities. 2. The method according to claim 1, further comprising generating a similarity graph of the plurality of users based on the plurality of similarity probabilities. 3. The method according to claim 1, wherein each of the plurality of sketch feature vectors represents a plurality of user actions at a plurality of timestamps. 4. The method according to claim 3, wherein the user actions include user ratings for generating the prediction result as a recommendation. 5. The method according to claim 1, wherein the plurality of input vectors is received by the data source as a stream of the input vectors in real time. 6. The method according to claim 5, wherein the sketch feature generator converts the stream of the input vectors to update the plurality of sketch feature vectors based on an order of the stream of the input vectors and to remove data received outside of the most recent sample period of the data source. 7. The method according to claim 6, further comprising storing, by a memory, a similarity graph of the plurality of users based on the plurality of similarity probabilities. 8. A system for data mining, comprising: a data source configured to receive a plurality of input vectors from a plurality of users; a sketch feature generator configured to convert the plurality of input vectors into a plurality of sketch feature vectors each corresponding to one of the plurality of users, wherein each of the plurality of sketch feature vectors represent data and meta-information from each corresponding one of the plurality of users received in a most recent sample period of the data source; a similarity comparator configured to compare each of the plurality of sketch feature vectors against one or more of other ones of the plurality of sketch feature vectors so as to calculate a plurality of similarity probabilities; and a processor running a decision loop configured to generate a prediction result for at least one of the plurality of users based on the plurality of similarity probabilities. 9. The method according to claim 8, wherein the similarity comparator is further configured to generate a similarity graph of the plurality of users based on the plurality of similarity probabilities. 10. The method according to claim 8, wherein each of the plurality of sketch feature vectors represents a plurality of user actions at a plurality of timestamps. 11. The method according to claim 10, wherein the user actions include user ratings for generating the prediction result as a recommendation. 12. The method according to claim 8, wherein the plurality of input vectors is received by the data source as a stream of the input vectors in real time. 13. The method according to claim 12, wherein the sketch feature generator converts the stream of the input vectors to update the plurality of sketch feature vectors based on an order of the stream of the input vectors and to remove data received outside of the most recent sample period of the data source. 14. The method according to claim 13, further comprising a memory configured to store the plurality of sketch feature vectors that are converted by the sketch feature generator from the plurality of input vectors. 15. The method according to claim 14, wherein the memory further stores a similarity graph of the plurality of users based on the plurality of similarity probabilities.