Transcript Identifying Representative Trends In Massive Time Series

Applications of Sketch Based
Techniques to Data Mining Problems
Nikos Koudas
AT&T Labs Research
joint work with:
G. Cormonde, P. Indyk, S.
Muthukrishnan
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Taming Massive Data Sets
• Requirements of data mining algorithms
– operate on very large data sets
– scalability
– incremental
• Most data mining algorithms have super-linear
complexity
• Deploying mining algorithms on very large data
sets, most likely will result in terrible
performance
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Sketching
• Reduce the dimensionality of the data in a
systematic way, constructing short “data
summaries”
• Effectively reduce data volume.
• Deploy mining algorithms on the reduced data
volume.
• Main issue: Preserve data properties, the mining
algorithm is concerned with, (e.g., distances) in
the reduced data volume.
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Applications
• Clustering time series data
• Clustering tabular data sets
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Introduction
• Time series data abound in many applications.
– Financial, performance data, geographical and
meteorological information, solar and space data etc.
• Various works deal with management and
analysis aspects of time series data:
– Indexing, storage and retrieval
– Analysis and mining (forecasting, outlier and
deviation detection, etc)
• Active research area in many research
communities.
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Representative Trends
Relaxed period
Average trend
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Usage Examples
• Mining/Prediction
– Identifying ‘periodic trends’
– Uncovering unexpected ‘periodic trends’
• Performance management
– Networking (routing, traffic engineering, bandwidth
allocation)
– System Tuning
• Financial databases
– Cyclic behavior
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Definitions
• Given a time series V and an integer T define
V(T) = {(V[iT+1], V[iT+2], …, V[iT+T])},
0 <= i<= n/T-1
• Define:
– Ci(V(T)) = S1<j<n/T-1 D(vi,uj)
• Thus:
– Relaxed Period: min C0(V(T)), for T in [l,u]
– Average Trend: min Ci(V(T)), for T in [l,u]
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Definitions
n points in each time series
n/T vectors for each T, T in [l,u]
Relaxed period
T
n/T vectors for each T, each of them a candidate avg. trend, T in [l,u]
Average Trend
T
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Algorithms
• There exists a quadratic algorithm for identifying
relaxed periods.
• There exists a cubic algorithm for identifying
average trends.
• Simply evaluate the clustering for each T in [l,u],
it takes linear time to evaluate relaxed periods for
each T and quadratic time to evaluate for average
trends.
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Algorithms
• But can we really run these?
– Consider length of sessions in an AT&T service for
each second for a year, it is more than 31M values
and approximately 256MB.
– Consider running the previous algorithms for say 10
years or on a finer time scale.
• Both brute force algorithms are impractical.
• Can we run faster on large datasets, too large to
be in memory?
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Our Approach
• Identify representative trends faster but provide
approximate answers:
– General approach (expresses various notions of representative
trends)
– Provides guaranteed approximation performance, with high
probability
• We present our approach in the following steps:
– Define the sketch of a vector
– Algorithms for finding the sketch of all sub-vectors of width T
– Determine the sketch of all sub-vectors of width in a given
range.
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Sketch of a vector
• Given a vector t of length l, we generate its
sketch S(t) as follows:
• Pick a random vector u of length l, by picking
each component u[i] from a normal distribution
N(0,1) (normalized to 1).
• Define:
– S(t)[i] = t.u = Sjt[j].u[j]
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Sketch Properties
• Theorem
– For any given set L of vectors of length l,
for a fixed e < 1/2, if k = 9 log|L|/e2, then
for any pair of vectors u,w we have
– (1-e)||u-w||2 < ||S(u)-S(w)||2<(1+e)||u-w||2 with
probability 1/2.
• By increasing k we can increase the probability
of success
• This is the Johnson-Lindenstrauss (JL) lemma.
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Fixed window sketches
• Compute all sketches of sub-vectors of length l
in a sequence of length n.
• There are n-l+1 such sub-vectors.
• Straightforward application of JL would require
O(nlk) time since there are O(n) sub-vectors,
each of length l and the sketch is of size k, not
practical.
l
k
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Key Observation
• We can compute ALL such sketches fast by
using the fast fourier transform.
• The problem of computing sketches of all subvectors of length l simultaneously is exactly the
problem of computing the convolution of two
vectors t and u
• Given two vectors A[1…a] and B[1…b] their
convolution is C[1…a+b] where
– C[k] = S1<i<bA[k-i]B[i] for 2 <= k <= a+b
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Example
S1[0] S2[0] S3[0]
2
1
3
1
(-0.97,-0.2) = (-0.4,-2.14,-1.57,-3.11,-0.97)
Convolution with
(0.11,0.99) = (1.98,1.21,3.08,1.32,0.11)
S1[1] S2[1] S3[1]
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Computing all sketches of width in a given
range
• Compute all sketches of all sub-vectors of length
between l and u.
• Brute force is cubic and prohibitive
• Applying our observation would be quadratic and
still prohibitive
• Can we compute all sketches of width in a given
range faster?
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Approach
• We will construct a pool of sketches that we
will pre-compute and store. Following this
preprocessing we will be able to determine
the sketch of any sub-vector in O(1) fairly
accurately.
• Pick an l <= L <= u and construct all
sketches of length L as before using
convolutions, this is O(nlog2n) in the worst
case. Assume for now that L a power of 2
actually construct two such pools S1 and S2
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Approach
• Consider any vector t[i,….i+j-1] we have
two cases:
– j = some power of 2 (L), in this case we have it
in the pool and we can look it up in O(1)
– 2r < j < 2r+1 in this case we can compute the
sketch as follows:
• S(t[i,….,i+j-1])[j] = S1(t[i,…,i+2r-1])[j] + S2[t[i+j2r,….,i+j-1])[j] both terms belong to the pool
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Example
S(U) = S10+ S21
U
2 1 3 1 2 3 2 1
S1
S2
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Why is this enough?
• Theorem
– For any given set L of vector of length l, for fixed e
< 1/2 if k = 9 log L/e2, then for any pair of vectors
u,w in L
• (1-e)||u-w||2 <= ||S(u)-S(w)||2 <= 2 (1+e) ||u-w||2 with
probability 1/2
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Putting it all together
• Given V and [l,u] range
– relaxed period:
• Compute sketches in time O(nlog(u-l)klogu)
• Consider every T in [l,u] and compute C0(V(T)) for every
T.
• Choosing k as described will guarantee that we are at most
2+e away from the true relaxed period
– Average Trends
• Proceed similarly by evaluating Ci(V(T)) for every i
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Implementation Issues
• Computing Sketches
– The pool of sketches can be computed with a single
pass over the data set. We only need to keep a
window worth of data across successive sketch
computations.
• Retrieving Sketches
– Required sketches are retrieved by performing
random IO. However across successive evaluations
for various values if T, required sketches are related.
Random IO can be limited due to prefetching.
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Experimental Evaluation
• Real data from a service AT&T provides
(utilization information).
• Size varying from 16MB (approx. 1 month) to
256MB (approx 1 year) worth of data.
• Evaluated:
– Time to construct sketches
– Scalability of sketch construction
– Efficiency of the proposed convolution based technique
– Time to compute relaxed period and average trends
• computing sketches from scratch and with pre-computed sketches
• Using brute force approaches
– Accuracy of sketching
– Comparison with other time series reduction techniques
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Time to construct sketches
450
400
Time (seconds)
350
300
Processor
250
Write
200
Read
150
100
50
0
4K
8K
16K
log(n)
32K 64K 128K
4K
8K
Sketch Window Size
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16K 32K 64K 128K
2*log(n)
Time to construct sketches
140
100
Processor
80
Write
60
Read
40
20
log(n)
Data Size
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12
8M
25
6M
64
M
32
M
16
M
12
8M
25
6M
64
M
32
M
0
16
M
Time (minutes)
120
2*log(n)
Time to construct without convolution
30000
20000
Processor
15000
Write
Read
10000
5000
log(n)
8K
16
K
32
K
64
K
12
8K
4K
8K
16
K
32
K
64
K
12
8K
0
4K
Time (minutes)
25000
Window Size
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2*log(n)
Time to construct sketches without
convolution
18000
Time (minutes)
16000
14000
12000
Processor
10000
Write
8000
Read
6000
4000
2000
0
16M
32M
64M
128M
Data Size
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256M
Computing relaxed periods
120
80
60
Construct/
Check
40
20
Relaxed Period Range
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64K
32K
16K
8K
4K
2K
1K
0.5K
64K
32K
16K
8K
4K
2K
1K
0
0.5K
Time (Minutes)
100
Computing Relaxed Periods
300
200
150
Construct/Check
100
50
log(n)
16
MB
32
MB
64
MB
12
8M
B
25
6M
B
0
16
MB
32
MB
64
MB
12
8M
B
25
6M
B
Time (minutes)
250
Data Size
2*log(n)
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Computing relaxed period with precomputed
sketches
Time (Seconds)
Check
8
6
4
Check
2
0
0.5K
1K
2K
4K
8K
16K 32K 64K
Relaxed Period Size
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Computing Relaxed Periods Without
Precomputed Sketches
Time (Seconds)
Check
140
120
100
80
60
40
20
0
Check
16MB
32MB
64MB 128MB 256MB
Data Size
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Time (Minutes)
Brute Force Algorithms
450
400
350
300
250
200
150
100
50
0
Total Time
0.5K 1K
2K
4K
8K 16K 32K 64K
Relaxed Period Size
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Brute Force Algorithms
Total Time
Time (Minutes)
1000
800
600
Total Time
400
200
0
16MB
32MB
64MB 128MB 256MB
Data Size
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Computing Average Trend
3500
Time (seconds)
3000
2500
2000
1500
1000
500
0
0.5K
1K
2K
4K
8K
Relaxed Period Size
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16K
32K
64K
Computing Average Trend
700
Time (minutes)
600
500
400
300
200
100
0
16MB
32MB
64MB
Data Size
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128MB
256MB
Accuracy of Sketches
200
Absolute Relative Error
180
160
140
120
100
80
60
40
20
0
5
10
20
40
Data Set 1
80 160 320
Sketch Size
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5
10
20
40
Data Set 2
80 160 320
Clustering Tabular Data
• Many applications produce data in two
dimensional array form.
• Consider traditional telecommunication
applications:
– Data are collected from a variety of collection
stations across the country, recording call volume at
some temporal granularity.
– 2d call volume data set (spatial ordering of collection
stations versus time) recording temporal call activity,
approx. 18MB/day.
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Clustering tabular data
• Data elements to be clustered are rectangular
data regions.
• Clustering might reveal interesting similarities
(in call volume and time) between geographical
regions.
• One month ~ 600MB of data.
• Sketch rectangular regions:
– extend sketches in 2d
– sketching with respect to any Lp norm p in (0.2]
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Summary of results
• Sketch construction scales nicely with respect to data volume and
sketch size.
• Convolution based sketch computation is very effective.
• Sketch based approach is orders of magnitude better than brute
force for computing relaxed periods and average trends.
• Performance benefits increase for larger data sets.
• If sketches are pre-computed, clustering can be performed in
seconds even for very large data sets.
• In practice sketches of low dimensionality provide great accuracy.
• Compared with other dimensionality reduction techniques, the
sketch based approach is more accurate and effective.
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Conclusions
• Scalability to large data volume requirement of
the data mining process.
• Effectively reduce data volume using sketches.
• Preserve data properties required by mining
algorithms (e.g., various distances).
• Core techniques, various algorithms could
benefit from them.
• Very large performance benefits, small loss in
accuracy.
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Contact
• [email protected]
• www.research.att.com/~koudas
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