thu bernstein key_warp_speed

Processing Linked Data at Warp SpeedAbraham Bernstein

CCBY NASA http://www.flickr.com/photos/nasa_jsc_photo/sets/72157629726792248/with/7197236116/0

"Earth's Location in the Universe (JPEG)" by Andrew Z. Colvin - Own work. Licensed under Creative Commons Attribution-Share Alike 3.0 via Wikimedia Commons http://commons.wikimedia.org/wiki/File:Earth%27s_Location_in_the_Universe_(JPEG).jpg#mediaviewer/File:Earth%27s_Location_in_the_Universe_(JPEG).jpg

"IBM Electronic Data Processing Machine - GPN-2000-001881" NASA, Public Domain @ Wikimedia Commons -

http://commons.wikimedia.org/wiki/File:IBM_Electronic_Data_Processing_Machine_-_GPN-2000-001881.jpg

Processing Graphs



Semantic Web ReasoningKB: Asserted Triples

Entailed KB: Asserted & Infered Triples

DL

Reasoning

Inductive R.

Analogical R

.

Your R.

Signal/Collect

P. Stutz, A. Bernstein, and W. Cohen. Signal/Collect: Graph algorithms for the (semantic) web. International Semantic Web Conference–ISWC 2010. Springer Berlin Heidelberg, 2010. 764-780.

• Vertices as stateful processing units

• Vertices interact through signals along edges

• Which are collected by a processing function that updates the vertex state

Processing Graphs Naturally

• Define the graph structure

• Vertices represent RDFS classes

• Edges from superclasses to subclasses

• Vertex state initialized with the class that the vertex represents

Signal/Collect: An Intuition for RDFS subclass inference

id: animalstate: {animal}

id: birdstate: {bird}

id: owlstate: {owl}

id: penguinstate: {penguin}

id: animal

id: bird

id: owlid: penguin

Stutz et al, 2010

Signal/Collect: An Intuition for RDFS subclass inference

Stutz et al, 2010



id: owlstate: {owl}

id: penguinstate: {penguin}

{bird, animal}

{animal}

{bird, animal}



id: owlstate: {owl}

id: penguinstate: {penguin}id: penguinstate: {penguin, bird, animal}

id: birdstate: {bird, animal}

id: owlstate: {owl, bird, animal}

def collect =

state [[

s2signals

s

def signal = state

Scoring/Asychronicity: Single-Source Shortest Path

state: ∞

state: ∞

state: ∞

state: ∞

state: ∞

state: ∞

state: 0

∞

1

∞

∞∞1

1∞

state: 1 state: ∞

state: ∞

state: ∞state: 1

state: 1 2

1

2

∞21

1∞

state: 1 state: 2

state: 2

state: 2state: 1

state: 1 2

1

2

321

13

state: 1 state: 2

state: 2

state: 2state: 1

state: 1

def signal = state + weightdef collect = min (state, min (signals) )

state: 1

Scoring/Asynchronicity: Single-Source Shortest Path

state: ∞

state: ∞

state: ∞

state: 0

111

state: 1 state: ∞

state: ∞

state: ∞

state: 1 22

2

state: 2

state: 2

state: 2

state: 2

state: 2

var oldState = infinity

def scoreSignal =if (state ! = oldState)

1

else

0

33

PageRank in Code

class Document(id: Any) extends Vertex(id, 0.15) { def collect = 0.15 + 0.85 * signals[Double].foldLeft(0.0)(_ + _)}

Algorithm

class Citation(citer: Any, cited: Any) extends Edge(citer, cited) { def signal = source.state.asInstanceOf[Double] * weight / source.sumOfOutWeights}

ExecutionInitialization object Algorithm {

def executeCitationRank(db: SparqlAccessor) { val computeGraph = new AsynchronousComputeGraph() val citations = new SparqlTuples(db, "select ?source ?target where {" + "?source <http://lsdis.cs.uga.edu/projects/semdis/opus#cites> ?target}") citations foreach { case (citer, cited) => computeGraph.addVertex[Document](citer) computeGraph.addVertex[Document](cited) computeGraph.addEdge[Citation](citer, cited) } computeGraph.execute() }}

Signal/Collect as a Platform


T R I P L E R U S H F R A U D D E T E C T I O N

D C O P S

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Tr i p l e S t o r e

E LV I S D Y L A N

J O B S

?X I N S P I R E D ?Y ?Y I N S P I R E D ?Z

I N S P I R E D

DATA QUERY

INSP I R

ED

DylanElvis inspired

*Elvis inspiredDylan* inspired DylanElvis *

*Elvis *** inspired Dylan* *

** *

DylanElvis inspired

*Elvis inspiredDylan* inspired DylanElvis *

*Elvis *** inspired Dylan* *

** *

*Dylan inspired Jobs* inspiredJobsDylan *

JobsDylan inspired

*Dylan *Jobs* *

DylanElvis inspired

Dylan* inspired

** inspired

*Dylan inspired Jobs* inspired

JobsDylan inspired

Query Vertex

?X inspired ?Y!?Y inspired ?Z


Elvis inspired Dylan!Dylan inspired ?Z

Dylan inspired Jobs!Jobs inspired ?Z

No vertex with ID![ Jobs inspired * ]

Query Vertex

{ ?X = Elvis, ?Y = Dylan, ?Z = Jobs }

✗

Elvis inspired Dylan!Dylan inspired Jobs

✓


P e r f o r m a n c e R e s u l t s

Distributed (8 nodes), LUBM 10240 (~1.36 billion triples)

Single-node, LUBM 160 (~21 million triples)

Fastest L1 L2 L3 L4 L5 L6 L7 Geo.

of 10 runs mean

TripleRush 3,111.2 1,457.9 0.7 3.5 9.5 29.1 1,165.8 62.1

Trinity.RDF 12,648.0 6,018.0 8,735.0 5.0 4.0 9.0 31,214.0 450.0

TriAD 7,631.0 1,663.0 4,290.0 2.1 0.5 69.0 14,895.0 249.0

TriAD-SG 2,146.0 2,025.0 1,647.0 1.3 0.7 1.4 16,863.0 106.0

Fastest L1 L2 L3 L4 L5 L6 L7 Geo

of 10 runs mean

TripleRush 22.6 27.8 0.4 1.0 0.4 0.9 21.2 2.94

Trinity.RDF 281.0 132.0 110.0 5.0 4.0 9.0 630.0 46.0

TriAD 427.0 117.0 210.0 2.0 0.5 19.0 693.0 39.0

TriAD-SG 97.0 140.0 31.0 1.0 0.2 1.8 711.0 14.0

O n g o i n g W o r k : G r a p h P a r t i t i o n i n g

• https://github.com/uzh/triplerush


D C O P S


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D e c o m p o s i n g F r a u d P a t t e r n s

• Participants can be labeled as:

• Splitters

• Aggregators

• Forwarders

$10k$11k

2 x $5k $10k

10k CHF

2k CHF4k CHF

6k CHF8k CHF

E l i c i t a t i o n P r o c e s s

FilterConnectMatch

B i t c o i n Tr a n s a c t i o n s : R u n t i m e v s . M a t c h i n g C o m p l e x i t y

0"

1000"

2000"

3000"

4000"

5000"

6000"

7000"

4" 6" 8" 10" 12"

Time%(sec)%

Matching%Complexity%

Time"in"GC"

Total"Processing"Time"

Dataset Size: 50M Transactions, Matching Duration: 1 week

Runtime: 27 min Throughput: 1.8M / min




D C O P S


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≠

≠≠

≠

y

Distributed Constraint Optimization

z

q

x

x ≠ y y ≠ z z ≠ x q ≠ z

x ∈ {0,1,2} y ∈ {0,1} z ∈ {1,2}

q ∈ {0,1,2}

Vertex Coloring in action

Optimized Version of DSA Running on a MacBook Pro with 8 workers(slow, due to lots of IO for logging, bookkeeping, etc.)

• Scaled to: 10 Million vertices / variablesVerman and Bernstein, 2014

Industry Usage!

“We are using Signal/Collect to analyze millions of claims every day to iden9fy opportuni9es for our clients to save money through be=er healthcare or avoiding fraud, waste, and abuse.”

US Healthcare Analy/cs Company

Processing Graph Streams



147.1b € 97.5b €

147.1b € 97.5b €

5’100

3m Viewers 300 Events/s

250 TV Channels 25 frames/s

EPG for 7 days LOD enhanced

Traditional TripleStore

http://www.mpi.de/

http://www.ifi.uzh.ch/

http://www.uni-sb.de/courses/

http://www.universities.de/Saarbruecken http://www.ifi.uzh.ch/

http://www.ifi.uzh.ch/i_am_a_URI






http://www.lubm.org/teaches

http://www.lubm.org/

Semantic Flow Processing

Semantic Flow Processing

t

ViSTA-TV Viewership Data

0

7500

15000

22500

30000

0h 8h 16h 24h 8h 16h 24h 8h 16h 24h

Num of Valid Data Entries

day 1 day 2 day 3

Cac

he

ViSTA-‐TV project: UserLog and EPG streams

Semantic Flow Processing is:

• Time-stamped tripes t = <s,p,o> [time]

• Semantic flow F = [t1, t2, ... tn]

• Perform query matching on cached subset of F

• Subject to Stress—incoming data-rate overwhelms the system’s processing capability

Context: Time Window

last 1 min

?within 1 sec

Load Shedding

last 1 min

Eviction

last 1 min

?

Eviction is:

• Remove cached data

• Note: Eviction may lower recall

• Evict potential to produce future results

• Types of eviction strategies (considered):

• Random

• Time-based (i.e, FIFO)

• Least Recently Used (LRU)

Why not LRU?

LRU

C

B

Input: UserLog

,<watch>,Channel User 1 B

Join

EPG CacheTwo-way Join:

UserLog EPG

Why not LRU?

LRU

C

B

User 1

Input: UserLog

,<watch>,Channel C

Join

EPG Cache

Why not LRU?

LRU

B

C

Input

Input: EPG

Channel , <Play>, Show 4DD

EPG Cache

D

User 3

Input: UserLog

,<watch>,Channel

?

Why not LRU?

LRU

C

Input: EPG

Channel , <Play>, Show 5E

B

Input: EPG

Channel , <Play>, Show 4D

InputInput: EPG

Channel , <Play>, Show 6FInput: EPG

Channel , <Play>, Show 7G

Input: EPG

Channel , <Play>, Show 8H

D

EPG Cache

CLOCK is:

• Consider both recency (LRU) and past results

• Giving each data entry a score

• The score could be incremented and depreciated

• Named by the buffer management algorithm CLOCK

CLOCK

CLOCK Score

6

1

3C

B

Input: UserLog

,<watch>,Channel User 2 C

Join

EPG Cache

CLOCK

CLOCK Score

6

1

4C

B

Input: UserLog

,<watch>,Channel User 2 C

Join

EPG Cache

CLOCK

CLOCK Score

6

1

4C

B

Input: EPG


Inputdep()

EPG Cache

CLOCK

CLOCK Score

51

4C

B

Input: EPG


Inputdep()

EPG Cache

CLOCK

CLOCK Score

5

04C

B

Input: EPG


Input dep()D

EPG Cache

CLOCK

CLOCK Score

5

init = 1

4C

Input: EPG


Input dep()D

EPG Cache

CLOCK

CLOCK Score

5

1

4C

Input

dep()D

Input: EPG

Channel , <Play>, Show 5E EPG Cache

CLOCK

CLOCK Score

5

1

3C

Input

dep()D

Input: EPG


CLOCK

CLOCK Score

4

1

3C

Inputdep()

D

Input: EPG


CLOCK

CLOCK Score

4

0

3C

Input dep()D

Input: EPG

Channel , <Play>, Show 5E EPG CacheE

CLOCK

CLOCK Score

4

1

3C

Input

dep()E

EPG Cache

Input: EPG

Channel , <Play>, Show 6F

Input: EPG

Channel , <Play>, Show 7G

Input: EPG

Channel , <Play>, Show 8H

CLOCK Eviction is:

!

• The weight is adjusted by dep():

• Linear: dep(w) = w - 1

• Exponential: dep(w) = w * ρ (0< ρ < 1)

Recency History

Experimental Results TV Viewership Data

Rec

all

0%

25%

50%

75%

100%

Cache Size

100% 50% 25% 15% 1%

Random FIFO LRU CLOCK

Two-way join query with 1,919,216 input triples

Experimental Results: Depreciation Function

Exponential factor ρ = ρ in {0.25, 0.5, 0.75, 0.95}

Limitations

• Static depreciation function: dep()

• Experiment on other datasets

• Real implementation to investigate other performance metrics

• Only local eviction strategies considered




Semantic Web ReasoningKB: Asserted Triples

Entailed KB: !Asserted & Infered Triples

DL

Reasoning

Inductive R.

Analogical R

.

Your R.

Signal/Collect


CLOCK Eviction is:

!

• The weight is adjusted by dep():"

• Linear: dep(w) = w - 1"

• Exponential: dep(w) = w * ρ (0< ρ < 1)

Recency History


Philip Stutz, Mihaela Verman, Shen Gao, Daniel Strebel, Bibek Paudel, Lorenz Fischer, Thomas Keller, Robin Hafen, Genc Mazlami

thu bernstein key_warp_speed

Documents