Failover Mechanism for Real-Time Data Streams
A system and method using a real-time data stream to make recommendations to users also predicts the future state of that data. If the data is interrupted or becomes unreliable, the predictions are used to make the recommendations in question.
1 . A system for improving the effective stability of a real-time data stream, comprising:
a real-time data stream;
a first analysis system that uses said data stream to make real-time recommendations to a plurality of users;
a predictive system that uses said data stream itself to make and store probabilistic predictions regarding the data stream's future contents;
a second analysis system that uses the probabilistic predictions to make recommendations to the plurality of users; and
a switch that monitors the data stream to identify circumstances where the data has become unreliable, and makes recommendations based on the first analysis system when the data stream is reliable and based on the second analysis system when the data stream is not reliable.
2 . The system of claim 1 , wherein the monitoring identifies times when the data stream is unreliable because it has been interrupted.
3 . The system of claim 1 , where the predictive system only makes predictions when the future behavior of the data stream can be predicted with certainty.
4 . The system of claim 1 , where the data stream identifies the state of a traffic light.
5 . The system of claim 4 , where the predictions are based on the identification of specific timing patterns in the behavior of the traffic light.
6 . A method for improving the effective stability of a real-time data stream, comprising:
use of a real-time data stream to make real-time recommendations to a plurality of users;
using the data stream itself to make and store probabilistic predictions regarding the data stream's future contents;
monitoring the data stream to identify circumstances where the data has become unavailable or unreliable; and
making the recommendations based on the probabilistic predictions at such times.
7 . The method of claim 6 , wherein the monitoring identifies times when the data stream has been interrupted.
8 . The method of claim 6 , where the predictions are made only when the future behavior of the data stream can be predicted with certainty.
9 . The method of claim 6 , where the data stream identifies the state of a traffic light.
10 . The method of claim 9 , where the predictions are based on the identification of specific timing patterns in the behavior of the traffic light.
11 . A method for improving an effective stability of a real-time data stream comprising a sequential series of data elements, comprising:
receiving a sequence of real data elements from a data source;
computing a sequence of predictive data elements based on earlier-received real data elements, each predictive data element of the sequence thereof corresponding to an expected real data element and computed in advance of or contemporaneously with an expected arrival time of the expected real data element;
when the expected real data element arrives at the expected arrival time, selecting the expected real data element as an output data element;
when the expected real data element does not arrive at the expected arrival time, selecting the predictive data element as the output data element; and
computing a recommendation based on the output data element; and
transmitting the recommendation to at least one client user.
12 . The method of claim 11 , further comprising:
basing the recommendation on elements of the output data element and the predicted data element
13 . The method of claim 12 , further comprising:
when the expected real data element arrives at the expected arrival time, comparing the expected real data element with the predictive data element to produce a discrepancy; and
applying the discrepancy to the computing operation to improve an accuracy of the sequence of predictive data elements.
14 . The method of claim 12 , further comprising:
when the expected real data element arrives at the expected arrival time, comparing the expected real data element with the predictive data element to produce a discrepancy; and
when the discrepancy exceeds a certain threshold selecting the predictive data element instead of the expected real data element.