Hello, I am trying to detect signal patterns in a system with numerous signals overlaying each other in time. I am aware that there is a whole class of problem solving methods for this task, yet trying my own algorithm, that may be wrong of course :) Next I use a simple example to describe the problem and my algorithm. Maybe someone will find this problem interesting enough to comment. Thanks in advance! In the following example signals from varying sources are represented by chars - unique signal = unique char. Examples of signal patterns (Pk) to detect: P1 = abcd P2 = 12345 Examples of a mixture of signals (pattern signals are underscored): M1 = s s n a_b_c_d_ r t 6 1_ 2_ 3_ 4_ 5_ 8 7 k l t M2 = g h a_ r 1_ w e 2_ j k b_ c_ l m p 3_ d_ 4_ e r 5_ v x M3 = ... MN = ... In the above examples two patterns P1 and P2 are interleaved with each other and other patterns. All mixtures are recorded for 24 hours for 12 month. One mixture corresponds to one 24 period (day). To find signal patterns in all these mixtures (M1, M2) my algorithm goes through these steps: 1) For every day: For every signal 'Y' create a list of 'weight pairs' such as: (Y, X1, 1), (Y, X2, 1), .. (Y, XN, 1) where X1, X2, ... XN are signals 'accessible from' (or following) signal 'Y' during this day So, for example, given the sequence: "abcde", chars in a left column of a table can access a list of chars in the right column: a | b c d e b | c d e c | d e d | e 2) For every signal sum all its 'weight pairs' for all days of the year: W(Y) = Sum((Y, Xk, 1), j=1... 364) For the above example, M1+ M2 will double the value of weight pairs in patterns: P1 = abcd P2 = 12345 such as: (a,b,2) (a,c,2) (a,d,2) (b,c,2) (b,d,2) (b,e,2) (c,d,2) (c,e,2) (d,e,2) and also: (1,2,2) (1,3,2) (1,4,2), (1,5,2) (2,3,2) (2,4,2) (2,5,2) etc... On the other hand, after summation M1+M2, weight pairs for other char sequences, that do not form patterns of often repeated signals, will not increase much. To demonstrate: M_1 and M_2 are sequences with repeating patterns being removed: M_1 = s s n r t 6 8 7 k l t M_2 = g h r w e j k l m p e r v x Here most pair weights will be small, same as before addition: (s,s,2) (g,h,1), (w,e, 1) ... 3) Compute weight of all possible chains in this year. Each chain consists of chars that can be accessed from one to another. Chars are examined, proceeding from the beginning of the sequence to its end, and never in the opposite direction. Going always in one direction and being bound with sequence end, makes chains finite. Chains for 'abcde' example will be: abcde, acde, ade, ae, bcde, bde, be, cde, cd, ce, de Weight of the chain is computed as a sum of its pairs: W(Ci) = Sum(Pj, j=1,..R) where R is a number of pairs that this chain is built from. For example for chain a 'acde' the weight will be: W('acde') = W(a,c)+ W(a,d) + W(a,e) 4) Split chains in groups by its length 5) For each group select chains with maximum weight. With pattern P1 = 'abcd' spread over some signal mixture, my algorithm will find (I hope) the following 4 classes of chains with maximum weight: 1) abcde, acde, ade, ae, 2) bcde, bde, be, 3) cde, cd, ce, 4) de In the same way, chains for pattern P2 = 12345 should be found. Thus, grounding on the idea of 'signal precedence' found in repeating pattern of several signals, and using pair 'weights' to mark signal chains - patterns are detected. Summation of pair weights allows to stress out, or highlight the repeating patterns. Inserting other signals inside a 'pattern pair' does not obscure it because in a long run (after many summations) its weight will make such pair stand out of the crowd. I have not yet fully developed this algorithm and have not tested it on big enough training sets. I hope it will work on training sets exhibiting some regularities - which in this case means repeating patterns. Of course it will not work on randomly generated data, where all pairs have approximately the same weight. Working with pairs and chains will allow for natural parallelism in processing huge sequences of events, I hope. It would be nice to use some analog of Hadoop map-reduce for this task. Any experience with Haskell analog of Apache Hadoop cluster (http://hadoop.apache.org/mapreduce/)? Does such thing exist in Haskell world at all? I would be happy to get criticism of my algorithm and comments on 'route-cause analysis' algorithms in general, thanks! Dmitri