1. Shailendra Mishra
Director (CEP)

2. Case Study – Collusion detection in multi-player Games
Consider the following problem:
(A) Commits an identity theft.
(A) Acquires (n) credit cards as a result of the identity theft.
(A) Goes to an online gaming site uses the credit cards to play online poker with his (s) friends.
(A) loses all his money to his (s) friends.
The online gaming company has to now pay his friends.
Analysis of the problem
Assume, for a moment that (A) didn’t commit identity theft.
(A) is playing a fair game with his friends or otherwise.
The results of this game, generate a stream of outcomes of wins and losses by (A) to any no. of his friends where 1<= i <= s.
The problem is to detect whether the pattern of wins and losses are genuine or not.
More formally, we are asking:
“When is a certain number of a particular subsequence unlikely to be fortuitous”.

3. Modeling the Collusion Detection Problem
Let T be an ordered sequence of events.
Let W be the window observation of size w within which the analysis is confined.
Formally consider an alphabet  of cardinality |  |.
Consider an event sequence T = t1, t2,…tn of length n over .
We then define an episode over  as follows:
Single pattern S = s1s2s3…sm of length m
Set of patterns S1,S2,…,Sd .
Set of all distinct permutations of S where ordering within window of observation doesn’t matter.

4. Formal Statement of the Problem
Assume event sequence is generated by a memory less Bernoulli or Markov source.
Let’s restate our problem formally – we are interested in finding Ω∃(n, w, m) that represents the number of windows containing atleast one occurrence of S, when sliding the window n events over T.
To address this:
Compute the Expected value Ω∃ (n, w, m).
Compute Var(Ω∃ (n, w, m).
Show that Ω∃ (n, w, m) converges to a normal distribution.
Allows us to set a threshold (n, m, w) s.t for a given confidence level  that P{(Ω (n, w, m) > (n, w, m))} < .
Implies, For (n, w, m) occurrences of such windows, probability that such a number is generated by randomness is highly unlikely.

5. Formulation of equivalent Pattern Matching Problem
Given an alphabet Д = {a1, a2, …, a | Д |} and a pattern S=s1s2…sm of length m.
Search occurrences of S as subsequence within a window W of size w in another sequence known as the event sequence T = t1t2..tn of length n.
A valid occurrence of S in T corresponds to a set of integers i1, i2,..,im such that the following hold:
1 <=i1 < i2 < … < im <= n
ti1 = s1, ti2 = s2, … tim = sm
im – i1 < w
We now estimate Ω∃ (n, w, m, S, Д) which represents #(windows) that contains atleast one occurrence of S, when sliding window over n consecutive events in event sequence T over alphabet Д.

6. Theorams & Results – Gwadera, Attalah & Szpankowski (Purdue)
Consider a memoryless source with pi being the probability of generating symbol ai ε Д.
Also, assume P(S) = ∏m i=1 pi
Result -1: Probability that a window of size w contains atleast one occurrence of episode S.
For all m and w >= m we have:
P∃(w, m) = P(S) ∑ w-m i=0 ∑ ∑ k=0mnk ∏qknk
where qk = 1-pk
Result -2:
Let now m be fixed and i ≠ j => pi ≠ pj, then for any ε > 0:
P∃(w, m) = 1 - P(S) ∑ m i=1 ∑ (1-pi)w /pi ∏j≠I m 1/(pj-pi) + O(εw)
where w -> ∞

7. Computation of Bounds First we find an α0 for a given β s.t
Assume a memoryless source, then for x = O(1), we have
limn->∞P{Ω∃ (n, w, m)-E(Ω∃ (n, w, m))/√(Var(P(Ω∃ (n, w, m)) < x} =
1/2π∫-∞x exp(-t2/2)dt for a fixed m and w.
Now let’s establish the threshold for (n, m, w).
First we find an α0 for a given β s.t
β = ∫ α0 ∞ exp(-t2/2)dt = P {N(0, 1) > α0}
Where N(0, 1) is the standard normal distribution.
We set the threshold:
(n, w, m) = E(Ω∃ (n, w, m) + √Var(Ω∃ (n, w, m)
As long as we are in the region where central limit theoram applies
P{Ω∃ (n, w, m) > (n, w, m) <= β

8. Q
& A

9. Shailendra Mishra
Director (CEP)

10. SQL Standards update
Pattern Matching Proposal – Version 12 of the review draft has been circulated.
Participants – Coral8 <some parts>, IBM, Oracle, Streambase.
BEA systems also reviewed the draft.
Status – 12th version of the draft is ready and has been circulated.
Objective - Submit a working draft to ANSI SQL
Discussing a streams language proposal with IBM
Participants IBM & ORACLE
Status – Exchanged Docs. Regarding language specifications
Discussing convergence language proposal with Streambase
Participants IBM & Streambase
Status – Discussing convergence proposal for the last 6 months
Objective - Submit a paper to Transactions on Databases (TODS)

11. Pattern Query With ONE ROW PER MATCH
SELECT a_symbol, a_tstamp, /* start time */, a_price, /* start price */,
max_c_tstamp, /* inflection time */, last_c_price, /* low price */,
max_f_tstamp, /* end time */, last_c_price, /* end price */, Matchno
FROM Ticker MATCH_RECOGNIZE (PARTITION BY Symbol
MEASURES A.Symbol AS a_symbol, A.Tstamp AS a_tstamp,
A.Price AS a_price, MAX (C.Tstamp) AS max_c_tstamp,
LAST (C.Price) AS last_c_price, MAX (F.Tstamp) AS max_f_tstamp
MATCH_NUMBER AS matchno
ONE ROW PER MATCH
AFTER MATCH SKIP PAST LAST ROW
MAXIMAL MATCH
PATTERN (A B C* D E* F+)
DEFINE B AS (B.price < PREV(B.price)),
C AS (C.price <= PREV(C.price)),
D AS (D.Price > PREV(D.price)),
E AS (E.Price >= PREV(E.Price)),
F AS (F.Price >= PREV(F.price)
AND F.price > A.price))

12. Pattern Query With All ROWs PER MATCH
SELECT a_symbol, a_tstamp, /* start time */, a_price, /* start price */,
max_c_tstamp, /* inflection time */, last_c_price, /* low price */,
max_f_tstamp, /* end time */, last_c_price, /* end price */, Matchno
FROM Ticker MATCH_RECOGNIZE (PARTITION BY Symbol
MEASURES A.Symbol AS a_symbol, A.Tstamp AS a_tstamp,
A.Price AS a_price, MAX (C.Tstamp) OVER () AS max_c_tstamp,
LAST (C.Price) OVER () AS last_c_price,
MAX (F.Tstamp) OVER () AS max_f_tstamp
MATCH_NUMBER AS matchno
CLASSIFIER AS classy
AFTER ROW PER MATCH
AFTER MATCH SKIP PAST LAST ROW
MAXIMAL MATCH
PATTERN (A B C* D E* F+)
DEFINE B AS (B.price < PREV(B.price)),
C AS (C.price <= PREV(C.price)),
D AS (D.Price > PREV(D.price)),
E AS (E.Price >= PREV(E.Price)),
F AS (F.Price >= PREV(F.price)
AND F.price > A.price))

13. MATCH_RECOGNIZE syntax
The full syntax of the MATCH_RECOGNIZE clause is as under:
PARTITION BY — optional
MEASURES - optional, but we expect this will always be used
{ ONE ROW | ALL ROWS } PER MATCH — default to ONE ROW
AFTER MATCH SKIP { TO NEXT ROW | PAST LAST ROW |
TO <variable> | TO LAST<variable> | TO FIRST <variable> }
- default AFTER MATCH SKIP PAST LAST ROW
{ MAXIMAL | INCREMENTAL } MATCH - defaults to MAXIMAL MATCH
PERMUTE – optional
PERMUTE EXPAND - optional
PATTERN — mandatory
SUBSET — optional
DEFINE — mandatory
CLASSIFIER - optional (ALL ROWS PER MATCH only)
MATCH_NUMBER - optional

14. Q
& A