high-performance pattern detection and discovery for databases and data streams barzan mozafari...

High-performance Pattern Detection and Discovery

for Databases and Data Streams

Barzan Mozafari

Adviser: Prof. Carlo ZanioloCommittee Members:Prof. Junghoo Cho,

Prof. D. Stott Parker, and Prof. Mark Hansen

Winter 2011

UCLA Computer Science Department

Big Picture1. Query Languages that

allow for the expression of complex patterns

2. Scalable Systems that support such languages and can handle massive, high-arrival data

3. Efficient, One-pass Algorithms that can mine large amounts of stored or streaming data and extract useful patterns

Query

Query

Patte

rns

Patte

rns M

atches

Matches

Data Mining

Data MiningDataData

Overview• Introduction• Query Languages for Pattern Detection

– Kleene-* Constructs in SQL– Nested Words [SIGMOD’10, VLDB’10]

– Optimization [Work in progress]

– XSeq [Work in progress]

• Conclusion

Complex Event Patterns• Sequences in DBs and CEP over data

streams

• Academic and industrial interest:– SQL-TS [PODS ‘01]– SASE [2006], SASE+ [2008]– SQL Change proposal, 2007 (by Oracle, IBM and

Streambase)

– Other industrial and academic languages:• Cayuga & CEL• CEDR• Microsoft CEP & LINQ

Our Contribution: K*SQL1. A powerful language for:

i. Expressing more complex patterns on relational streams and sequences

ii. Querying data with more complex structures, e.g, XML and genomic data

2. A unifying engine for sequence patterns and XML3. New optimization techniques

• pattern search over nested words

4. Efficient query execution backend for other languages

5. XSeq: An XPath-resembling language to bring Kleene-* to XML applications

Regular Expressions in SQLrfid_readings (Time, SensorType, ensorId, ItemId)rfid_readings (Time, SensorType, ensorId, ItemId)

Nested Kleene-*: K*SQLTimestamp BadgeID Room

1226633804799 26 Room12

1226633805799 2 Room7

1226633806799 26 Room14

1226633807799 5 Room37

1226633808799 5 Room37

… … …

SELECT badgeID

FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN

Employees who spend Employees who spend >1 hour>1 hour in the lab but leave in the lab but leave without going to decontamination roomwithout going to decontamination room

Lab

Room2

Room12

Room7

Lab

Room2

Room7

Exit

Lab

SELECT badgeID

FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN ( L )WHERE L.room = ‘Lab’


Lab

Room2

Room12

Room7

Lab

Room2

Room7

Exit

Lab

L

L

SELECT badgeID

FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN ( L+ )WHERE L.room = ‘Lab’


Lab

Room2

Room12

Room7

Lab

Room2

Room7

Exit

Lab

L

LL+

SELECT badgeID

FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN ( L+ O+ )WHERE L.room = ‘Lab’ AND O.room != ‘Decontamination’


Lab

Room2

Room12

Room7

Lab

Room2

Room7

Exit

Lab

L

L

O

O

O

L+

O+

SELECT badgeID

FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN ( (R: L+ O*) )WHERE L.room = ‘Lab’ AND O.room != ‘Decontamination’


Lab

Room2

Room12

Room7

Lab

Room2

Room7

Exit

Lab

L

L

L

R

R

R

R

R

L+

O+

L+

O+

R

R

SELECT badgeID

FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN ( (R: L+ O*)+ )WHERE L.room = ‘Lab’ AND O.room != ‘Decontamination’


Lab

Room2

Room12

Room7

Lab

Room2

Room7

Exit

Lab

L

L

L

R

R

R

R

R

L+

O+

L+

O+

R

RR+

SELECT badgeID

FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN ( (R: L+ O*)+ X)WHERE L.room = ‘Lab’ AND O.room != ‘Decontamination’ AND X.room = ‘Exit’


Lab

Room2

Room12

Room7

Lab

Room2

Room7

Exit

Lab

L

L

L

R

R

R

R

R

X

L+

O+

L+

O+

R

RR+

SELECT badgeID

FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN ( (R: L+ O*)+ X)WHERE L.room = ‘Lab’ AND O.room != ‘Decontamination’ AND X.room = ‘Exit’ AND sum(R.Last(L).timestamp – R.First(L).timestamp) > 3600


Lab

Room2

Room12

Room7

Lab

Room2

Room7

Exit

Lab

L

L

L

R

R

R

R

R

X

L+

O+

L+

O+

R

RR+

SELECT badgeID

FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN ( (R: L+ O*)+ X)WHERE L.room = ‘Lab’ AND O.room != ‘Decontamination’ AND X.room = ‘Exit’ AND sum(R.Last(L).timestamp – R.First(L).timestamp) > 3600

Strictly More Expressive, through:Strictly More Expressive, through:(i)Nested Kleene-*, (ii) Labels, i.e. Aliases(i)Nested Kleene-*, (ii) Labels, i.e. Aliases

SELECT badgeID,Last(R).Last(L).timestamp – First(R).First(L).timestamp)FROM rfidPARTITION BY badgeIDORDER BY timestampAS PATTERN ( (R: L+ O*)+ X)WHERE L.room = ‘Lab’ AND O.room != ‘Decontamination’ AND X.room = ‘Exit’ AND sum(R.Last(L).timestamp – R.First(L).timestamp) > 3600

Lab

Room2

Room12

Room7

Lab

Room2

Room7

Exit

Lab

L

L

L

R

R

R

R

R

X

L+

O+

L+

O+

R

RR+

Strictly More Expressive, through:Strictly More Expressive, through:(i)Nested Kleene-*, (ii) Labels, i.e. Aliases(i)Nested Kleene-*, (ii) Labels, i.e. Aliases

1. A powerful language with a very efficient implementation based on FSA

2. Subsumes SQL-MR, SASE+, Cayuga, SQL-TS

3. Many interesting applications– including queries on semistructured documents

Very natural question:

Can we handle full XML?

K*SQL Checkpoint

Automata and XMLWord Automata (FSA): only linear structure is explicit, cannot model parenthesis languages

Ordered Tree Automata (OTA): only hierarchical structure is explicit, exponentially less succinct for word queries

Pushdown Automata (PDA): Many problems are undecidable; expensive complexity

20

Advances in the Automata World

Linear sequence + well-nested edges

Positions labeled with symbols in

a1a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12

Positions classified as: Call positions: both linear and hierarchical successors

Return positions: both linear and hierarchical predecessors

Internal positions: otherwise

Nested Words [Alur’06]

Nested Word ApplicationsXML Document

<conference> <name> CAV 2006 </name> <location> <city> Seattle </city> <hotel> Sheraton </hotel> </location> <sponsor> MSR </sponsor> <sponsor> Cadence </sponsor></conference>

Programglobal int x;bool P() { … x = 3; if Q x = 1 ; …}

bool Q () { local int y; … x = y; return (x==0);}

Primary structure: Linear sequence of nucleotides (A, C, G, U)

Secondary structure: Hydrogen bonds between nucleotides

G

C

U

GA

A

U

AC

G C

G

C

U

C

G

RNA Sequence

Odious ComparisonProperty FSA NWA PDA

input is read from left to right Yes Yes Yes

Deterministic automata as expressive as non-deterministic ones

Yes Yes No

Closed under complementation Yes Yes Only for DPDA w/ final state

Closed under union, intersection, concatenation, and Kleene-*

Yes Yes No

Emptiness Decidable Decidable Decidable

membership, language inclusion, language equivalence

Decidable Decidable Undecidable

Can recognize paranthesis languages? No Yes Yes

NWA is exponentially more succinct than Tree Automata

No query language has been proposed for NW

XML Sigmod Record:SAX-3<!ELEMENT SigmodRecord (issue)* > <!ELEMENT issue

(volume,number,articles) > <!ELEMENT volume (#PCDATA)> <!ELEMENT number (#PCDATA)> <!ELEMENT articles (article)* > <!ELEMENT article

(title,initPage,endPage,authors) > <!ELEMENT title (#PCDATA)> <!ELEMENT initPage (#PCDATA)> <!ELEMENT endPage (#PCDATA)> <!ELEMENT authors (author)* > <!ELEMENT author (#PCDATA)> <!ATTLIST author position CDATA

#IMPLIED>

tagIndex

Type Token Value

1 open SigmodRecord

_

2 open issue _

3 open volume _

4 text _ 11

5 close volume _

6 open number _

… … … …

25 open author _

26 attribute position 01

27 text _ Karen Botnich

… … … …

XPathXPath

Find articles of Carlo Zaniolo Find articles of Carlo Zaniolo as the 2as the 2ndnd co-author co-author

//article[authors/author [@position = "01" and

text()="Carlo Zaniolo"]

]/title/text()

<SigmodRecord><issue>…<article> <title> Implementation of GEM </title> <initPage> 45 </initPage> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….

K*SQLK*SQL

Question: Question: Can we query nested words in Can we query nested words in K*SQL?K*SQL?

In particular:In particular:

can we express traditional XML queriescan we express traditional XML queries– i.e. those often expressed via XPath/XQuery:i.e. those often expressed via XPath/XQuery:

Find articles of Carlo Find articles of Carlo Zaniolo Zaniolo

as the 2as the 2ndnd co-author co-authorSELECTSELECT Title.token ASAS articleName

FROMFROM sigmod_record

AS PATTERN AS PATTERN

(

)

WHEREWHERE

<SigmodRecord><issue>…<article> <title> Implementation of GEM </title> <initPage> 45 </initPage> … <aut hors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….





(OpArt

)

WHEREWHERE OpArt.value = ‘<article>’






(OpArt

)

WHEREWHERE OpArt = open(‘article’)






(OpArt OpTitl

)


ANDAND OpTitl = open(‘title’)






(OpArt OpTitl Title

)


ANDAND OpTitl = open(‘title’)

<SigmodRecord><issue>…<article> <title> Implementation of

GEM </title> <initPage> 45 </initPage> … <authors> … <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….





(OpArt OpTitl Title ClTitl

)


ANDAND OpTitl = open(‘title’) ANDAND ClTitl = close(‘title’)






(OpArt OpTitl Title ClTitl E*

)



ANDAND isElement(E)







OpAuths)



ANDAND isElement(E)

ANDAND OpAuths = open(‘authors’)







OpAuths E*)



ANDAND isElement(E)


ANDAND ClArt = close(‘article’)

<SigmodRecord><issue>…<article> <title> Implementation of GEM </title> <initPage> 45 </initPage> … <authors>

… <author position="01"> Carlo Zaniolo </author> … </authors> </article> ….






OpAuths E* OpAu)



ANDAND isElement(E)


ANDAND OpAu = open(‘author’)







OpAuths E* OpAu Pos)



ANDAND isElement(E)



ANDAND pos.type = ‘attr’ AND pos.value = ’01’

AND AND pos.token = ‘position’







OpAuths E* OpAu Pos

)



ANDAND isElement(E)



ANDAND pos = attribute (‘position’, ’01’)







OpAuths E* OpAu Pos Author)



ANDAND isElement(E)



ANDAND pos = attribute(‘position’, ‘01’)

ANDAND author.token = `Carlo Zaniolo’







OpAuths E* OpAu Pos Author ClAu)



ANDAND isElement(E)


ANDAND OpAu = open(‘author’)ANDAND pos = attribute(‘position’, ‘01’) ANDAND author.value = `Carlo Zaniolo’

ANDAND ClAu = close(‘author’)







OpAuths E* OpAu Pos Author ClAu E*)



ANDAND isElement(E)




ANDAND author.value = `Carlo Zaniolo’


<SigmodRecord><issue>…<article> <title> Implementation of GEM </title> <initPage> 45 </initPage> … <authors> … <author position="01"> Carlo Zaniolo </author>

… </authors> </article> ….






OpAuths E* OpAu Pos Author ClAu E*

ClAuths ClArt)WHEREWHERE OpArt = open(‘article’)


ANDAND isElement(E)






ANDAND ClAuths = close(‘authors’)








OpAuths E* OpAu Pos Author ClAu E*

ClAuths ClArt)



ANDAND isElement(E)






ANDAND ClAuths = close(‘authors’)



Sequence Queries over XML: ‘W’-Patterns in Stocks

<!ELEMENT Stocks (Stock)* ><!ELEMENT Stock (symbol, date, price, volume)><!ELEMENT symbol (#PCDATA)><!ELEMENT date (#PCDATA)><!ELEMENT price (#PCDATA)><!ELEMENT volume (#PCDATA)>

W-patterns in NASDAQ transactions with volume>1000

SELECTSELECT FIRST(Z).FIRST(X).Sym.token

FROMFROM Nasdaq PARTITION BY Y.X.Sym.token

AS PATTERNAS PATTERN

(Z: (X: OpSt Sym Date OP Price1 CP

OpV Volume ClV ClSt)*

(Y: OpSt Sym Date OP Price2 CP


)^2

WHERE WHERE

OpSt = open(‘Stock’) ANDAND ClSt = open(‘Stock’)

ANDAND OP = open(‘price’) ANDAND CP = close(‘price’)

ANDAND OpV = open(‘volume’) ANDAND ClV = close(‘volume’)

ANDAND INT(volume.token) >= 100

ANDAND Z.X.price1.token < Z.PREV(X).price1.token

ANDAND Z.Y.price2.token > Z.PREV(Y).price2.token

<Stock symbol=“YHOO” date=“01-01-2010 23:10:00”>

<price> 18.50 </price><volume> 21 </volume></Stock><Stock symbol=“YHOO”

date=“01-01-2010 23:16:00”>

<price> 18.70 </price><volume> 11 </volume></Stock>…

W-patterns in NASDAQ transactions with volume>1000

SELECTSELECT FIRST(Z).FIRST(X).Sym.token

FROMFROM Nasdaq PARTITION BY Y.X.Sym.token

AS PATTERNAS PATTERN

(Z: (X: OpSt Sym Date OP Price1 CP


(Y: OpSt Sym Date OP Price2 CP


)^2WHERE WHERE

OpSt = open(‘Stock’) ANDAND ClSt = open(‘Stock’)

ANDAND OP = open(‘price’) ANDAND CP = close(‘price’)

ANDAND OpV = open(‘volume’) ANDAND ClV = close(‘volume’)

ANDAND INT(volume.token) >= 100

ANDAND Z.X.price1.token < Z.PREV(X).price1.token

ANDAND Z.Y.price2.token > Z.PREV(Y).price2.token

<Stock symbol=“YHOO” date=“01-01-2010 23:10:00”>

<price> 18.50 </price><volume> 21 </volume></Stock><Stock symbol=“YHOO”

date=“01-01-2010 23:16:00”>

<price> 18.70 </price><volume> 11 </volume></Stock>…

X* Y*X*

Y*

Optimization in K*SQL

• Compile-Time:– Inferring inter-predicate implications– Query re-writing, e.g. adding more constrainst– Greedy predicate assignment

• Run-Time: Avoiding unnecessary backtracks

– VPSearch: Extending KMP search algorithm to nested words and visibly pushdown words

– Optimizing non-determinisitc queries• i.e. all-match query modes

K*SQL vs. XML Engines

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