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Efficiently Evaluating Order Preserving Similarity Queries over Historical Market-Basket DataPowerPoint Presentation

Efficiently Evaluating Order Preserving Similarity Queries over Historical Market-Basket Data

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Efficiently Evaluating Order Preserving Similarity Queries over Historical Market-Basket Data

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Efficiently Evaluating Order Preserving Similarity Queries over Historical Market-Basket Data

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Efficiently Evaluating Order Preserving Similarity Queries over Historical Market-Basket Data

Reza Sherkat and Davood Rafiei

Department of Computing Science

University of Alberta

Canada

Travel assistance provided by the Mary Louise Imrie Graduate Student Award

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- Introduction
- Histories and Time-series
- Similarity model for histories

- Problem Definition
- Proposed Approach
- Results Highlight
- Conclusions

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- Querying multiple snapshots of data
- Temporal selection, projection, and join queries

- Finding similar time-series
- Finding companies having similar stocks

- Is it possible to define a notion of similarity for objects based on the similarity of their histories?

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- the history of a web-page

: bag of word

History: A sequence of time-stamped observations

- Time-series: observations are real-values
- Observations can be more general

the history of a patient

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History for 3 patients

- Similarity of two histories depends on:
- Pair-wise similarity of their observations

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History for 3 patients

- Similarity of two histories depends on:
- Pair-wise similarity of their observations

- The order that similar observations are recorded
- Constraints on time-stamps of observations

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Given a history as a query:

- Evaluate k-NN and Range queries efficiently.
- For each history in the result, find its common signature with the query - where the similarity comes from?

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Alignment of histories:

- An approach to line-up subsequences of two histories
- Denoted by a sequence of matches:
- is an observation in A (B) or a gap ( ).
- is the score of a match.
- Alignment score measures the quality of an alignment.

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The best alignment of two histories:

Alignment score can be the sum of the score of matches in the alignment.

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Alignment score can be the sum of the score of matches in the alignment.

The best alignment of two histories:

What is the best alignment of length 3?

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Alignment score can be the sum of the score of matches in the alignment.

The best alignment of two histories:

What is the best alignment of length 3?

If the match could not be considered, what would be the best alignment of length 2?

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- The number of matches in the alignment.
- l-alignment: alignment with l matches

- The r-neighborhood constraint
- For each match

- r ,l : parameters of the similarity query.

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p(A)

p(B)

s(A)

s(B)

: optimal alignment of p(A) and p(B)

: optimal alignment of s(A) and s(B)

: optimal alignment of A and B

: concatenation operator

The principle of optimality holds if:

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b

,

,

b

,

b

,

,

b

K

K

+

1

1

j

j

n

- Optimal l-alignment of suffixes can formed by:
- Concatenating with optimal (l-1)-alignment of suffixes

- Matching with gap, and considering l-alignment of suffixes

- Matching with gap, and considering l-alignment of suffixes

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- can be used to find common signature of histories:
- A sequence of observations that appear in the same order in
- two histories.
- Generalizes the notion of longest common subsequence.

: the score of optimal l-alignment of two histories.

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- Straightforward (not practical) approach: naïve scan
- Indexing techniques are proposed for metric spaces,
but is not metric:

- when the distance between observations is not metric.
- when an r-neighberhood constraint is specified.

- We propose upper bounds to prune history search space.

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Intuition: The score of an optimal relaxed l-alignment is not less than the score of optimal l-alignment.

- For each observation, find an optimal match.
- Aggregate the scores for top l optimal matches to find an upper bound for .

This upper bound can prune some extra computations,

but still all histories will be accessed to evaluate a query.

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This upper bound can be evaluated efficiently by exploiting an inverted index if is Cosine or Extended Jaccard Coefficient.

- Intuitions:
- Observations are sparse in real life applications.
- The score of an optimal relaxed match is not less
- than the score of an optimal match.
- The score of an optimal relaxed alignment is not
- less than the score of optimal relaxed l-alignment.

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- Experiments performed on AMD/XP 2600 512 Mb RAM
- Datasets:
- DBLP
- Synth1: Our synthetic data
- Synth2: Modified IBM synthetic data generator

- Investigated:
- Effectiveness of similarity measure
- Efficiency of our approach
- Pruning power, Running time, Saleability

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…

…

…

…

: Poisson distribution

V(i+1): bit string following V(i)

in a pre-determined order

[Cho et al. VLDB 2000]

V(1)

V( i+1 )

V( n )

V( i )

- Synth2 dataset contains:
- 20,000 histories
- for each history is selected randomly from {1,…,10}
- Length of histories: {32,…,64}

observation: document modeled as bit string

First observation: randomly selected

…

…

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Mean deviation of from for k-NN queries:

* For 2,000 randomly generated queries

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Fraction of database examined

0 20 40 60 80 100

1 10 100 1024

No. of neighbours in k-NN query (LOG scale)

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Time (msec)

0 100 200 300 400 500 600

1 10 100 1024

Dataset: Synth2, 8,000 Histories, 1,000 items

No. of neighbours in k-NN query (LOG scale)

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Time (msec)

8,000 16,000 32,000 64,000

No. of histories in the collection

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Time (msec)

256 512 1,024 2,048 4,096 8,092

No. of items (LOG scale)

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- Introduced a domain-independent framework to formulate and evaluate similarity queries over historical data.
- Generalized few concepts, including edit distance and longest common subsequence to histories.
- Developed upper bounds to efficiently evaluate queries. One of our upper bounds can directly take advantage of an index even though it is not metric.
- Our experiments confirm the effectiveness and efficiency of our approach.

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Thank you for your attention!

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- Detecting, representing, querying histories
- [Chawathe 1998], [Chien 2001]

- Similarity-based sequence matching
- [Altschul 1990], [Pearson 1990], [Bieganski 1994]

- Finding similar sequence of events
- [Wang 2003]

- Finding similar time series
- [Agrawal 1995], [Rafiei 1997], [Keogh 2002], [Vlachos 2002, 2003], ...

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