presentation(ppt)

Containment of Partially Specified

Tree-Pattern QueriesDimitri Theodoratos (NJIT, USA)

Theodore Dalamagas (NTUA, GREECE)

Pawel Placek (NJIT, USA)

Stefanos Souldatos (NTUA, GREECE)

Timos Sellis (NTUA, GREECE)

IntroductionData Model

Additional ConceptsQuery Containment

ExperimentsConclusion

Stefanos Souldatos - HDMS 2006 3

Motivating Example () Tree structure (e.g. XML) with motorbike spare parts. We search for spare parts. BUT…

rr

ATHENSATHENS

HONDAHONDA

GREECEGREECE USAUSA

YAMAHAYAMAHA BMWBMW

TRAVELTRAVEL

VARADEROVARADERO

125cc125cc 1000cc1000cc

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

650cc650cc

F650F650F650GSF650GS

YAMAHAYAMAHA BMWBMW

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

F650GSF650GS

650cc650cc

NJNJ


Motivating Example () Dimitri Theodoratos lives in NJ. He has a Yamaha Serrow motorbike in Greece. He searches for spare parts in Greece or USA.

structural differencerr

ATHENSATHENS

HONDAHONDA

GREECEGREECE USAUSA

YAMAHAYAMAHA BMWBMW

TRAVELTRAVEL

VARADEROVARADERO

125cc125cc 1000cc1000cc

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

650cc650cc


YAMAHAYAMAHA BMWBMW

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

F650GSF650GS

650cc650cc

NJNJ

?


Motivating Example () Theodore Dalamagas has a BMW motorbike. He looks for spare parts worldwide.

structural inconsistency

../F650GS/650ccrr

ATHENSATHENS

HONDAHONDA

GREECEGREECE USAUSA

YAMAHAYAMAHA BMWBMW

TRAVELTRAVEL

VARADEROVARADERO

125cc125cc 1000cc1000cc

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

650cc650cc


YAMAHAYAMAHA BMWBMW

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

F650GSF650GS

650cc650cc

NJNJ

../650cc/F650GS


Motivating Example () Stefanos Souldatos has a Honda Varadero. But, he is not fully aware of the tree structure.

unknown structure

rr

ATHENSATHENS

HONDAHONDA

GREECEGREECE USAUSA

YAMAHAYAMAHA BMWBMW

TRAVELTRAVEL

VARADEROVARADERO

125cc125cc 1000cc1000cc

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

650cc650cc


YAMAHAYAMAHA BMWBMW

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

F650GSF650GS

650cc650cc

NJNJ


rr

ATHENSATHENS

HONDAHONDA

GREECEGREECE USAUSA

YAMAHAYAMAHA BMWBMW

TRAVELTRAVEL

VARADEROVARADERO

125cc125cc 1000cc1000cc

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

650cc650cc


YAMAHAYAMAHA BMWBMW

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

F650GSF650GS

650cc650cc

NJNJ

Motivating Example () Pawel Placek wants to buy a motorbike that he can

easily find spare parts for. He searches in many different tree structures. source integration

rr

ATHENSATHENS

HONDAHONDA

GREECEGREECE USAUSA

YAMAHAYAMAHA BMWBMW

TRAVELTRAVEL

VARADEROVARADERO

125cc125cc 1000cc1000cc

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

650cc650cc


YAMAHAYAMAHA BMWBMW

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

F650GSF650GS

650cc650cc

NJNJ

rr

ATHENSATHENS

HONDAHONDA

GREECEGREECE USAUSA

YAMAHAYAMAHA BMWBMW

TRAVELTRAVEL

VARADEROVARADERO

125cc125cc 1000cc1000cc

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

650cc650cc


YAMAHAYAMAHA BMWBMW

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

F650GSF650GS

650cc650cc

NJNJ


Motivation

Querying tree-structured data

BUT

structure is not always strictly defined

user does not always deal with structure: Find Honda spare parts in Greece.


Dimension Graph

rr

ATHENSATHENS

HONDAHONDA

GREECEGREECE USAUSA

YAMAHAYAMAHA BMWBMW

TRAVELTRAVEL

VARADEROVARADERO

125cc125cc 1000cc1000cc

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

650cc650cc


YAMAHAYAMAHA BMWBMW

ON-OFFON-OFF

200cc200cc

SERROWSERROW

TRAVELTRAVEL

F650GSF650GS

650cc650cc

NJNJ

R (oot)

C (ountry)

B (rand)

T (ype)

L (ocation)

M (odel)

E (ngine)

R

C

B

T M

L

E

DIMENSIONS

dimension graph = summary of the tree structure


Partially Specified Tree-pattern QueryR

C

B

T M

L

E

C = {Greece}

B = {BMW}

M = ?

B = {BMW}

E = ?

R (oot)

C (ountry)

B (rand)

T (ype)

L (ocation)

M (odel)

E (ngine)

DIMENSIONS

Query: Find shops with spare parts for all models and all engines of BMW motorbikes in Greece. (+ structural info)


Partially Specified Tree-pattern Query


R

C

B

T M

L

E

partially specified paths (PSP)

C = {Greece}

B = {BMW}

M = ?

B = {BMW}

E = ?

PSP *p2PSP p1R (oot)

C (ountry)

B (rand)

T (ype)

L (ocation)

M (odel)

E (ngine)

DIMENSIONS


Partially Specified Tree-pattern QueryR

C

B

T M

L

E

output path (*)


C = {Greece}

B = {BMW}

M = ?

B = {BMW}

E = ?


C (ountry)

B (rand)

T (ype)

L (ocation)

M (odel)

E (ngine)

DIMENSIONS




parentchild

R

C

B

T M

L

E

output path (*)


ancestordescendant

C = {Greece}

B = {BMW}

M = ?

B = {BMW}

E = ?


C (ountry)

B (rand)

T (ype)

L (ocation)

M (odel)

E (ngine)

DIMENSIONS




parentchild

R

C

B

T M

L

E

node sharing expression

(NSE)

output path (*)


ancestordescendant

C = {Greece}

B = {BMW}

M = ?

B = {BMW}

E = ?


C (ountry)

B (rand)

T (ype)

L (ocation)

M (odel)

E (ngine)

DIMENSIONS



Additional Concepts

C = {Greece}

B = {BMW}

M = ?

B = {BMW}

E = ?

PSP *p2PSP p1

C = {Greece}

Full Form Query


Additional ConceptsR

C

B

T M

L

E

RC = {Greece}

B = {BMW}

T

ME

C = {Greece}

B = {BMW}

M = ?

B = {BMW}

E = ?

PSP *p2PSP p1

C = {Greece}

Full Form Query

Dimension Trees

DIMENSION TREES = QUERY + GRAPH


Absolute Containment

Q1 Q2 Each result of Q1 is a result of Q2.




homomorphism from Q2 to Q1




Q1 Q2

homomorphism from Q2 to Q1

PSP p2PSP *p1

C

B

M

B

E

C

PSP p4PSP *p3

C

ME

C


Relative Containment (w.r.t. G)

Q1 G Q2 Each result of Q1 in G is a result of Q2 in G.




homomorphism from the Dimension Trees of Q2 to the Dimension Trees of Q1




R

C

B

T

EM

A dimension tree of Q1

A dimension tree of Q2

R

C

B

T

E

homomorphism from the Dimension Trees of Q2 to the Dimension Trees of Q1


Relative Containment Heuristic

1msec


(AC)

100msec

Relative Containment

(RC)



1msec


(AC)

100msec


(RC)


Heuristic (RCH)

sound but not complete

extract structural information from the Dimension Graph insert it in the query Q1 check Q1 Q2 instead of Q1 G Q2



B = ?

T = ?

PSP *p1

B = ?

PSP *p2

C = ?

Q1 Q2

R

C

B

T M

L

E

Q1 Q2

Example



B = ?

T = ?

PSP *p1

B = ?

PSP *p2

C = ?

Q1 Q2

R

C

B

T M

L

E

Q1 Q2

Example

B=>T : R->C, C=>B



B = ?

T = ?

PSP *p1

B = ?

PSP *p2

C = ?

Q1 Q2

R

C

B

T M

L

E

Q1 Q2

C = ?

R = ?

Q1 G Q2

Example

B=>T : R->C, C=>B


Experiments We measured…

execution time for Absolute Containment (AC) Relative Containment (RC) Relative Containment Heuristic (RCH)

accuracy for RCH

…for various graph sizes …for various query sizes


TimeT

ime

(mse

c)

Graph paths: 10 - 80

Graph dimensions: 20 Graph dimensions: 30 Graph dimensions: 40

Graph paths: 15 - 120 Graph paths: 20 - 160

Query PSPs: 1 Query PSPs: 2

Tim

e (m

sec)

Nodes per PSP: 3 - 6 Nodes per PSP: 3 - 6

RC

RCH

AC

RC

RCH

AC

RCH

AC

RC

RCH

AC

RCH

AC

RC

RC


Accuracy of RCH 80% for graphs of common sizes

based on XML benchmarks (XMach, XMark, etc.)

50% for graphs of higher density


Conclusion Query Containment for Partially Specified Tree-

Pattern Queries (PSTPQs).

Sound technique for checking Relative Query Containment Time: one order of magnitude Accuracy: over 80%


Future Work Heuristics for checking Relative Containment

precomputed and on-the-fly trade-off between time and accuracy

Special forms of queries, e.g. swings:

B

PSP *p3PSP p1

B

A A

C C

PSP p2

Questions?


Links

Introduction (2-9)

Data Model (10-17)

Additional Concepts (18-20)

Query Containment (21-32)

Experiments (33-36)

Conclusion (37-41)

Appendix (42-46)

Appendix


Who defines the dimensions? Automatic

XML tags (dimension graph = “path summary”, “path index”, “structural summary”)

Semi-automatic Graph administrator + XML tags

(dimension = group of XML tags) Graph administrator + ontology

Manual Graph administrator


Inference RulesR

C

B

T M

L

E

C = {Greece}

B = {BMW}

M = ?

B = {BMW}

E = ?

PSP *p2PSP p1

C = {Greece}

1. Full Form Query

INFERENCE RULES(IR1) |- R[p1] R[p2](IR2) A[p1] A[p2], A[p2] A[p3] |- A[p1] A[p3](IR3) a structural expression that involves A[p] |- R[p] => A[p](IR4) A[p] B[p] |- A[p] => B[p](IR5) A[p] => B[p], B[p] => C[p] |- A[p] => C[p](IR6) A[p] B[p], A[p => C[p] |- B[p] => C[p](IR7) A[p] B[p], C[p] => B[p] |- C[p] => A[p](IR8) A[p1] B[p1], B[p1] B[p2] |- A[p2] B[p2](IR9) A[p1] => B[p1], B[p1] B[p2] |- A[p2] => B[p2](IR10) A[p1] => B[p1], A[p1] A[p2], R[p2] => B[p2] |- A[p2] => B[p2](IR11) A[p1] => B[p1], B[p1] B[p2] |- A[p1] A[p2](IR12) A[p1] B[p1], C[p2] B[p2], D[p1] D[p2] |- D[p1] => A[p1](IR13) A[p1] B[p1], A[p2] C[p2], D[p1] D[p2] |- D[p1] => A[p1](IR14) A[p1] => B[p1], B[p2] => A[p2], C[p1] C[p2] |- C[p1] => A[p1]


Dimension TreesR

C

B

T M

L

E

RC = {Greece}

B = {BMW}

T

M

E

r/Greece/BMW/

*T[*E]/*M

RC = {Greece}

B = {BMW}

T

ME

r/Greece/BMW/*T/*M [*E]

RC = {Greece}

B = {BMW}

T

E

M

RC = {Greece}

B = {BMW}

T

M

E

E

M

r/Greece/BMW/*T[*M/*E]/*E*M

r/Greece/BMW/*T/*E/*M

C = {Greece}

B = {BMW}

M = ?

B = {BMW}

E = ?

PSP *p2PSP p1

C = {Greece}


Previous Approaches Keyword-based search approach

Absence of structure Naive approach

All possible query patterns are generated

(Honda=>Greece, Greece=>Honda) Approximation techniques

Relax the query more answers Traditional integration approach

Global structure and mapping rules

presentation(ppt)

Documents

cc serrow travel f650gs

cc f650 f650gs yamaha

cc nj r c b t

bmw motorbike

yamaha serrow motorbike

honda varadero

motorbike spare parts

query formulation