Barna Saha, AT&T Research Laboratory Joint work with: Lukasz Golab, Howard Karloff, Flip Korn,...

Discovering Conservation Rules

Barna Saha, AT&T Research Laboratory

Joint work with:Lukasz Golab, Howard Karloff, Flip Korn, Divesh

Srivastava

Discovering Conservation Rules

Data QualityData Cleaning

IRS Vs Federal Mathematician“You owe us $10,000 plus accrued interest in taxes for last year. You earned $36,000 last year but only had $1 withheld from your paycheck for Federal taxes.”

“ How could I work the entire year and only have $1 withheld ? I do not have time to waste on this foolishness. Goodbye !”

The Federal Government Agency had only allocated enough storage on the computer to handle withholding amounts of $9999.99 or less . Amount withheld was $10001.00. The last $1 made the crucial difference.

The Risk of Massive ID Fraud

In May 2004, Ryan Pirozzi of Edina, Minnesota opened his mail box and found more than a dozen bank statements inside.

None of the accounts were his !

Because of a data entry error made by a clerk at the processing center of Wachovia Corp, a large bank headquartered in the Southeastern USA, over the course of 9 months, Pirozzi received the financial statements of 73 strangers. Their names, SSN, bank account numbers constitute an identity thief's dream !

The Risk of Massive ID Fraud

Pirozzi began receiving completed 1099 tax forms belonging to many of these people .

Finally one day in January 2005, a strange thing happened. Mr. Pirozzi went to his mail box and discovered an envelope from Wachovia that contained his completed 1099 tax form. That was the first piece of correspondence that he received from the bank that actually belonged to them.

Source of these storiesMARYLAND RESIDENTS

BEWARE!800 houses in Montgomery County,

Maryland, were put on auction block in 2005 due to mistakes in the tax

payment data of Washington Mutual Mortgage

FOR SALE!

Data Quality ToolsReal world data is often dirty:

InconsistentInaccurateIncompleteStale

Enterprises typically find data error rates of approximately 1%-5%, for some companies, it is above 30%.Dirty data costs US businesses 600 billion dollars annually.Data cleaning accounts for 30%-80% of development time and budget in most data warehouse projects.

Data Quality Tools:• Detect and repair errors• Differentiate between dirty and clean

data

A Systematic Approach to Improve Data Quality

Impose integrity constraints Semantic rules for dataErrors and inconsistencies in data emerge as

violation of the constraints

Integrity Constraints: Functional Dependency [Codd, 1972]

Functional Dependency:(Name, Type, State) (Price, Vat)

Name Type State Price VatCH1 Clothing Maryland $50 $3BK45 Book New

Jersey$120 $15

FN30 Furniture Washington

$100 $0

CH1 Clothing Maryland $100 $6BK66 Book Washingto

n$80 $10

New Integrity Constraints for Data Quality

Functional Dependency:(Name, Type, State) (Price, Vat)

Name Type State Price VatCH1 Clothing Maryland $50 $3BK45 Book New

Jersey$120 $15

FN30 Furniture Washington

$100 $0

CH1 Clothing Maryland $100 $6BK66 Book Washingto

n$80 $10

1. If (Type=Book) then the above FD holds. 2.If (State=Washington) then (Vat=0)

Conditional Functional Dependency

New Integrity Constraints for Data Quality

Conditional Functional Dependency

Sequential Dependency

Aggregation Dependency: Discovering Conservation Rules

MotivationsInfrastructure networks are continuously

monitored over time.Example:

Highway monitoring systems use road sensors to identify traffic buildup and suggest alternate routes.

Routers in an IP telecommunications network maintain counters to keep track of the traffic flowing through them.

Power meters measure electricity flowing through different systems

Monitored to troubleshoot customer problems, check network performance and understand provisioning requirements.

Data Quality Problems in Infrastructure NetworksMissing or delayed data, especially over large

interval of times, can be detrimental to any attempt to ensure reliable and well-functioning network.

IP network monitoring typically uses the UDP protocol, so measurements can be delayed (or even lost) when there is high network congestion.

Sometimes a new router interface is activated and traffic is flowing through it, but this interface is not known to the monitoring system; in this case, there is missing data that is hard to detect.

Data Quality Problems in Infrastructure NetworksMissing or delayed data, especially over large

interval of times, can be detrimental to any attempt to ensure reliable and well-functioning network.

Monitoring road networks in the presence of sensor failures or unmonitored road segments

Monitoring electricity networks in the presence of hacked power meters or if someone is diverting (stealing) electricity, etc.

Detecting data quality issues is difficult when monitoring large and complex

networks

ApproachImpose integrity constraints to capture the

semantics of dataProvide concise summary of data where the

rules hold/fail efficiently.

Integrity Constraint: Conservation RulesIn many infrastructure networks, there exists

a conservation law between related quantities in monitored data. Kirchoff’s Node Law of

Conservation of Electricity : The current flowing into a node in an electric circuit equals the current flowing out of the node.

Road Network Monitoring: Every car that enters an intersection must exit.

Telecommunication Networks: Every packet entering a router must exit.

And many more…

Conservation Rules

One to One Matching

Match each incoming event to each outgoing event, and report average delay/ loss as measure for violation of conservation laws. Infeasible with respect to storage and processing costs to collect individual packets/ monitor individual events

Monitoring systems provide aggregate counts at regular intervals.

Conservation Rules

Incoming traffic at a router

Outgoing traffic at a router time

We expect the two time series to be identical

Matching incoming and outgoing aggregated traffic at every time point may not reveal true data quality issues.

Clock synchronization errorQueuing delay

Compare aggregated total over time windows.

Conservation Rules: Confidence of an Interval

Confidence of an interval = Ignores duration of violation

Incoming traffic at a router

Outgoing traffic at a router

10 8 6 4 6

10 8 6 4 6

IN

OUT

a1 a2 a3 a4 a5

a1 a2 a3 a4 a5

b1 b2 b3 b4 b5 b1 b2 b3 b4 b5

34

∑ ai

∑ bi

Conservation Rules: Confidence of an interval

Rightward Matching between IN and OUT

Confidence=1

Incoming traffic at a router

Outgoing traffic at a router

Confidence=0

10 8 6 4 6

5

10 8 6 4 6

IN

OUT

a1 a2 a3 a4 a5 a1 a2 a3 a4 a5

b1 b2 b3 b4 b5b1 b2 b3 b4 b5

34

Earth Mover DistanceA measure of distance  between

two distributions over some region D. Interpret the distributions as two different

ways of piling up a certain amount of dirt over the region D.EMD is the minimum cost of turning one pile

into the other. Cost is assumed to be amount of dirt moved times

the distance by which its is moved.

Also known as  Wasserstein distance.

Rightward Matching (RM): A special case of Earth Mover Distance (EMD)

Only right shiftingSimple greedy algorithm works

Confidence=1

Incoming traffic at a router

Outgoing traffic at a router

Confidence=0

10 8 6 4 6

5

10 8 6 4 6

IN

OUT

EMD=114Maximum EMD Possible=114

EMD=0Maximum EMD Possible=114

RM: Interpretation by area over cumulative counts

Confidence of an interval I= area(CUM-OUT(I))/ area (CUM-IN(I))

time

CUM-IN

CUM-OUT

Cu

mu

lative

co

un

t

CUM-IN

CUM-OUT

RM: Interpretation by area over cumulative counts

Find all intervals with confidence >= 0.9 (say)

Cu

mu

lative

co

un

t

time

CUM-IN

CUM-OUT

CUM-IN

CUM-OUT

RM: Interpretation by area over cumulative counts

Return a minimum collection of intervals with confidence >= 0.9 (say) covering at least 95%

(say) of data

Cu

mu

lative

co

un

t

time

CUM-IN

CUM-OUT

CUM-IN

CUM-OUT

Finding intervals with high confidence

• Trivial using O(n3) time• Try all possible n2 intervals• For each interval using O(n) time find the confidence

Cu

mu

lative

co

un

t

time

CUM-IN

CUM-OUT

CUM-IN

CUM-OUT

Finding intervals with high confidence

• Easy to do in O(n2) time• Compute in linear time confidence of all the

intervals that start from a specific point

time

Cu

mu

lative

co

un

t

time

CUM-IN

CUM-OUT

CUM-IN

CUM-OUT

• How do you solve it in sub-quadratic time ?• Only maximal intervals

time

Cu

mu

lative

co

un

t

Finding intervals with high confidence

CUM-IN

CUM-OUT

CUM-IN

CUM-OUT

Relax confidence:If outputs I, then conf(I) ≥ c/(1+ε)

(no false positives)If conf(I*) ≥ c, output I I* with conf(I) ≥ c/(1+ε)

(no false negatives)

Finding intervals with high confidence

Algorithm 1

Finding intervals with high confidence

Generating Sparse Set of Intervals = =

Compute the confidence of intervals with growing geometrically by a factor of

Finding intervals with high confidence

Generating Sparse Set of Intervals = =

Compute the confidence of intervals with growing geometrically by a factor of

Finding intervals with high confidence

Finding intervals with high confidence

Running time depends on areaB

Finding intervals with high confidence:Avoiding dependency on area

• Main Idea:• Consider each possible ending point of

intervals instead of starting points• Compute confidence of intervals with interval

lengths growing exponentially in 1+ε

Finding intervals with high confidence:Avoiding dependency on area

Finding intervals with high confidence:Avoiding dependency on area

Discount Models

Finding minimum collection of maximal intervals with support threshold

Partial set cover on lineCan be solved exactly in quadratic time using dynamic

programmingCan be solved in linear time if we allow constant factor

approximation using greedy algorithm Greedy gives 7-approximation

Finding minimum collection of maximal intervals with support threshold

Partial set cover using greedy algorithmIf OPT chooses t intervals then

We can choose at most t intervals that do not intersect any of the OPT intervals.

We can choose at most 6 intervals that intersect a particular OPT intervals.

Credit Card DataDec Jan

Entrance-Exit Data

Network Monitoring Data

Running Time on Job-log Data

Area Based Non-area Based

SummaryWe study data quality problems that arise

frequently in many infrastructure networks.We propose rules that express conservation laws

between related quantities, such as those between the inbound and outbound counts reported by network monitoring systems.

We present several confidence metrics for conservation rules.

We give efficient approximation algorithms for finding a concise set of intervals that satisfy (or fail) a supplied conservation rule given a confidence threshold