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Unformatted text preview: 9 Sequence Matching
Sequences are lists of values S = x1; x2; : : :; xk, although we shall often think of the same sequence as a continuous function de ned on the interval 0to1. That is, the sequence S can be thought of as sample values from a continuous function S t, with xi = S i=k. 9.1 SequenceMatching Problems In the simplest case, we are given a collection of sequences fS1 ; S2 ; : : :; Sn g, and a query sequence Q, each of the same length. Our problem is to nd that sequence Si whose distance from Q is the ,minimum, where R distance" is de ned by the energy" of the di erence of the sequences; i.e., DS; T = 01 S t , T t 2dt. For instance, the Si 's might be records of the prices of various stocks, and Q is the price of IBM stock, delayed by one day. If we found some Si that was very similar to Q, we could use the price of the stock Si to predict the price of IBM stock the next day, Notes: Do not try this at home. Anything easy to mine about stock prices is already being done, and the market has adjusted to whatever knowledge can be gleaned. Sequence matching is a great opportunity to violate the Bonferroni principal, since there has to be a closest sequence." For instance, a famous mistake was looking in the UN book of world statistics to nd the statistic that best predicted the DowJones average. It was cotton production in Bangladesh." 9.2 Fourier Transforms as Indexes for Sequences We could treat sequences of length k as points in a kdim vector space, but doing so is not likely to be useful. Usually, k will be so high, that spacial index techniques like kdtrees or R trees will be useless. The trick adopted by Faloutsos and his colleagues is to map sequences to the rst fewRterms of their Fourier transforms. Formally, the j th term of the Fourier Expansion of the function S t is 01 S te2jit dt. Recall that the imaginary exponential ei x is de ned to be sin x + i cos x. Thus, the real and imaginary parts of Xj tell how well S t matches sine and cosine functions that have j periods within the interval 0to1, i.e., sin 2jt and cos 2jt. Example 9.1 : Figure 25 suggests a simple function S t solid and compares it to the singleperiod sine function dotted and singleperiod cosine function dashed. The integral of the product S t sin 2t + iS t cos 2t is the complex number X1 . 2 Figure 25: Function S t matches the sine only slightly; the cosine better but not perfectly Key point: Parsival's Theorem states that the energy in a signal S t is the same as the energy in its P Fourier transform: i0 jXi j2. Note jX j is the magnitude of a complex number X . 38 Key application: IfRS t is actually the di erence of two sequences, then the distance between those sequences, which is 01 S 2 tdt is the same as the sum of the squares of the magnitudes of the di erences of the Fourier coe cients of the two sequences. Since the square of a magnitude is always positive, we can nd all the sequences whose distance from a given query sequence Q is no more than 2 by nding all those sequences S the sum of whose magnitudes of the di erences of the rst m Fourier coe cients is no more than 2 . We shall retrieve some false drops"  sequences that are close to Q in their rst m coe cients, but di er greatly in their higher coe cients. There is some justi cation for the rule that all the energy is in the loworder Fourier coe cients," so retrieval based on the rst few terms is quite accurate in practice. 9.3 Matching Queries to Sequences of the Same Length As long as stored sequences and queries are of the same length, we can arrange the sequences in a simple, lowdimension data structure, as follows: 1. Compute the rst m Fourier coe cients of each sequence Si , for some small, xed m. It is often su cient to use m = 2 or m = 3. Since Fourier coe cients are complex, that is the equivalent of a 4or 6dim space. 2. Place each sequence Si in this space according to the values of its rst m Fourier coe cients. The points themselves are stored in some suitable multidimensional data structure. 3. To search for all sequences whose distance from query Q is no more than 2 , compute the rst m Fourier coe cients of Q, and look for points in the space no more distant than from the point corresponding to Q. 4. Since there are false drops, compare each retrieved sequence with Q to be sure that the distance is truly no greater than 2. In some applications, it may make sense to normalize" sequences and queries to make the 0th Fourier coe cient which is the average value 0 and perhaps to make the variance i.e., the energy" of each sequence the same. Example 9.2 : In Fig. 26a are two sequences that have a large di erence, but are in some sense the same
sequence, shifted and scaled vertically. We could shift them vertically to make their averages be 0, as in Fig. 26b. However, that change still leaves one sequence always having twice the value of the other. If we also scaled them by multiplying the more slowly varying by 2, they would become the same sequence. 2 9.4 Queries That are Shorter than the Sequences Assume that queries are at least of length w. The sequential scan method for nding close within distance 2 matches to a query Q is as follows: 1. Store the rst m coe cients of the Fourier transform of each subsequence of length w for each sequence. As a result, if sequences are of length k, we store k , w + 1 points for each sequence. 2. Given a query Q, take the rst w values of Q and compute the Fourier transform of that sequence of length w. Retrieve each sequence S and position p that matches within . 3. For each such S and p, compare the entire query Q with sequence S starting at position p. If the distance is no more than 2, report the match. 39 (a) (b) Figure 26: Shifting and scaling can make two sequence that look rather di erent become the same 9.5 Trails The problem with sequentialscan is that the number of points that must be stored is almost the product of the number of sequences and the length of the sequences. The FRM paper takes advantage of the fact that as we slide a window of length w across a sequence S , the loworder Fourier coe cients will not vary too rapidly. We thus get a trail if we plot the points corresponding to consecutive windows of length w, as suggested in Fig. 27. Figure 27: Trials and the rectangles that bound them Instead of storing individual points, we store rectangles in the appropriate number of dimensions that bound the points in one segment of the trail, which is also suggested by Fig. 27. A rectangle can be stored using two opposite vertices, for example. Thus, if rectangles tend to represent many points, then the space ued to store the rectangles is much less than the space needed to store individual points. To retrieve the points whose distance from a query Q of length w is no more than , nd the rectangles that are within of Q. Match Q only against the sequences that correspond to at least one retrieved rectangle, and only at begining positions represented by those rectangles. Partitioning trails into rectangles is an opimization problem. FRM uses as the cost of storing a rectangle whose side in dimension i is Li as a fraction of the length of the total distance along the Q ith axis: i L + i + 0:5. For example, a rectangle of fractional sides 1=4 and 1=3 would have cost 40 3=45=6 = 5=8. Start from the beginning of the trail, and form rectangles by adding points as we treavel along the trail. When deciding whether or not to add another point to the current rectangle, make sure that the cost of the new rectangle per point covered decreases. If it increases, then start a new rectangle instead. 9.6 Matching Queries of Arbitrary Length If the query Q has length w, just nd the rectangles within distance from Q, as discussed in Section 9.5. Note that Q may actually be within a rectangle, and even so, Q may be distant from actual points on the trail, as suggested in Fig. 28.
Q 1 Q 2 Figure 28: Two examples of queries near a rectangle; note that the query inside the rectangle is actually further from the points on the trail than the query outside the rectangle, but both need to be considered However, if the query Q has length greater than w, say pw for some constant p things we can do: 1, there are several 1. Search for just the rst w values of Q, retrieve the matching sequences and their positions, and then compare the entire Q with the sequence starting at that position. This method is like sequential scan, but it takes advantage of the storage e ciency of trails. 2. Search for the rectangles that are su ciently close within to the rst w values of Q, the next w values of Q, and so on. Only a sequence that has at least one rectangle that is within of each of these subsequences of Q is a candidate match. Check the candidate matches. 3. Like 2, but check a sequence S only if it has a rectangle within distance =pp from at least one of the p subsequences of QpNote that the distance of sequences must be at most 2, and if all p subsequences . of Q are at least = p from any point on the trail of S , then p =pp2 = 2 is a lower bound on the distance between Q and any subsequence of S . 41 ...
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This note was uploaded on 01/31/2011 for the course CS 345 taught by Professor Dunbar,a during the Fall '07 term at UC Davis.
 Fall '07
 Dunbar,A

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