SAD Tagus 1 AJAX: Model, Declarative Language, and Algorithms Helena Galhardas

1SAD Tagus

AJAX:Model, Declarative Language,

and Algorithms

Helena Galhardas

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Plan

Context

• Problem statement

• Contributions

• Our data cleaning solution

• Validation

• Related solutions

• Conclusions

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Application context

– Eliminate errors and duplicates within a single

source

– Integrate data from different sources

– Migrate poorly structured data into structured

data

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Typical architecture HumanKnowledge

HumanKnowledge

DataExtraction

DataLoading

DataTransformation

Metadata Dictionaries DataAnalysis

SchemaIntegration

... ...

SOURCE DATA TARGET DATA

DataTransformation

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Data cleaning

Activity of transforming source data into target data without errors, duplicates, and inconsistencies

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Motivating example (1)

DirtyData(paper:String)

Data Cleaning

Events(eventKey, name)

Publications(pubKey, title, eventKey, url, volume, number, pages, city, month, year)

Authors(authorKey, name)

PubsAuthors(pubKey, authorKey)

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Motivating example (2)

[1] Dallan Quass, Ashish Gupta, Inderpal Singh Mumick, and Jennifer Widom. Making Views Self-Maintainable for Data Warehousing. In Proceedings of the Conference on Parallel and Distributed Information Systems. Miami Beach, Florida, USA, 1996[2] D. Quass, A. Gupta, I. Mumick, J. Widom, Making views self-maintianable for data warehousing, PDIS’95

DirtyData

Data Cleaning

PDIS | Conference on Parallel and Distributed Information Systems

Events

QGMW96| Making Views Self-Maintainablefor Data Warehousing |PDIS| null | null | null | null | Miami Beach | Florida, USA | 1996

PublicationsAuthors

DQua | Dallan Quass

AGup | Ashish Gupta

JWid | Jennifer Widom…..

QGMW96 | DQua

QGMW96 | AGup….

PubsAuthors

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Plan

• Context Problem statement

• Contributions

• Our data cleaning solution

• Validation

• Related solutions

• Conclusions

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Modeling a data cleaning process

A data cleaning process is modeled by a directed acyclic graph of data transformations

DirtyData

DirtyAuthors

Authors

Duplicate Elimination

Extraction

Standardization

Formatting

DirtyTitles... DirtyEvents

CitiesTags

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Existing technology

• Ad-hoc code– difficult to maintain

• Extraction Transformation Loading (ETI, Informatica, Sagent)

– limited cleaning functionality

• Data Reengineering (Integrity) – fixed implementation for certain operators

• Specific-domain cleaning (idCentric, PureIntegrate)

– names and addresses

• Duplicate elimination (DataCleanser, matchIt)

– finds/eliminates duplicates

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Problems of existing solutions (1)

The semantics of some data transformations is defined in terms of their implementation algorithms

App. Domain 1

App. Domain 2

App. Domain 3

Data cleaning transformations

...

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There is a lack of interactive facilities to tune a data cleaning application program

Problems of existing solutions (2)

Dirty Data

Cleaning process

Clean data Rejected data

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AJAX

• An extensible data cleaning framework

• A declarative language for logical operators

• Efficient implementation of the match operator

• A debugger facility for tuning a data cleaning program application

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Data cleaning framework

• Logical level: set of logical operators to express cleaning criteria enclosed in each data transformation

• Physical level: set of algorithms that implement the logical operations

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Logical level: parametric operators

• View: arbitrary SQL query• Map: iterator-based one-to-many mapping with

arbitrary user-defined functions• Match: iterator-based approximate join • Cluster: uses an arbitrary clustering function• Merge: extends SQL group-by with user-defined

aggregate functions• Apply: executes an arbitrary user-defined

algorithm

Map Match

Merge

ClusterView

Apply

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Logical level

DirtyData

DirtyAuthors

Authors


Extraction

Standardization

Formatting

DirtyTitles...

CitiesTags

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Logical level

DirtyData

DirtyAuthors

Map

Cluster

Match

Merge

Authors

Map

Map


Extraction

Standardization

Formatting

DirtyTitles...

CitiesTags

DirtyData

DirtyAuthors

TC

NL

Authors

SQL Scan

Java Scan

Physical level

DirtyTitles...

Java Scan

Java Scan

CitiesTags

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Contributions


A declarative language for logical operators


• A debugger facility for tuning a data cleaning program application

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Match• Input: 2 relations• Finds data records that correspond to the same

real object• Calls distance functions for comparing field values

and computing the distance between input tuples• Output: 1 relation containing matching tuples and

possibly 1 or 2 relations containing non-matching tuples

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Example

Cluster

Match

Merge


Authors

DirtyAuthors

MatchAuthors

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ExampleCREATE MATCH MatchDirtyAuthors

FROM DirtyAuthors da1, DirtyAuthors da2

LET distance = editDistance(da1.name, da2.name)

WHERE distance < maxDist

INTO MatchAuthorsCluster

Match

Merge


Authors

DirtyAuthors

MatchAuthors

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ExampleCREATE MATCH MatchDirtyAuthors


LET distance = editDistance(da1.name, da2.name)

WHERE distance < maxDist

INTO MatchAuthors

Input:

DirtyAuthors(authorKey, name)861|johann christoph freytag

822|jc freytag

819|j freytag

814|j-c freytag

Output:

MatchAuthors(authorKey1, authorKey2, name1, name2)861|822|johann christoph freytag| jc freytag

822|814|jc freytag|j-c freytag ...

Cluster

Match

Merge


Authors

DirtyAuthors

MatchAuthors

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Implementation of the match operator

s1 S1, s2 S2

(s1, s2) is a match if

editDistance (s1, s2) < maxDist

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Nested loopS1 S2

...

• Very expensive evaluation when handling large amounts of data

Need alternative execution algorithms for the same logical specification

editDistance

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A database solution

CREATE TABLE MatchAuthors ASSELECT authorKey1, authorKey2, distance

FROM (SELECT a1.authorKey authorKey1, a2.authorKey authorKey2,

editDistance (a1.name, a2.name) distance

FROM DirtyAuthors a1, DirtyAuthors a2)

WHERE distance < maxDist;

No optimization supported for a Cartesian product with external function calls

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Window scanning

S

n

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Window scanning

S

n

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Window scanning

S

n

May loose some matches

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String distance filtering

S1 S2

maxDist = 1

John Smith

John Smit

Jogn Smith

John Smithe

length

length- 1

length

length + 1

editDistance

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Annotation-based optimization

• The user specifies types of optimization • The system suggests which algorithm to

use

Ex:

CREATE MATCHING MatchDirtyAuthors


LET dist = editDistance(da1.name, da2.name)

WHERE dist < maxDist

% distance-filtering: map= length; dist = abs %

INTO MatchAuthors

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Contributions


• A declarative language for logical operators


A debugger facility for tuning a data cleaning program application

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Management of exceptions

• Problem: to mark tuples not handled by the cleaning criteria of an operator

• Solution: to specify the generation of exceptional tuples within a logical operator– exceptions are thrown by external functions– output constraints are violated

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Example (1)

CREATE MAP ExtractionCities

FROM StandardizedDirtyData dd

LET city = extractCities(dd.paper, Cities),

{ SELECT dd.paperKey AS pubKey, city AS city

INTO ExtractedCities

CONSTRAINT NOT NULL city } Map

ExtractedCities(pubKey, city)

Extraction

CitiesStandardizedDirtyData (pubKey, paper)

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Example(2)

ExtractionCities

Cities

ExtractedCitiesStandardizedDirtyDataexc

4| ManyDifferentCities

StandardizedDirtyData

4|y ioannidis r ng k shim and t sellis parametric query optimization technical report univ of wisconsin madison and univ of maryland college park

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Debugger facility

• Supports the (backward and forward) data derivation of tuples wrt an operator to debug exceptions

• Supports the interactive data modification and the incremental execution of some logical operators

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4| Y. Ioannidis, R. Ng, K. Shim, and T. Sellis. Parametric query optimization. Technical Report, Univ. Of Wisconsin, Madison and Univ. Of Maryland, College Park, 1992


4|Technical Report, Univ. Of Wisconsin, and Univ. Of Maryland

StandardizedDirtyDataForExtraction

StandardizeDataForExtraction

ExtractionAuthorsTitleEvent

DirtyEvents

KeyDirtyData

StandardizeData


ExtractionCities


BackwardDerivationForwardDerivation

Backward/forward data derivation

Cities

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4|Technical Report, Univ. Of Wisconsinand Univ. Of Maryland




DirtyEvents

KeyDirtyData

StandardizeData


ExtractionCities


4| Y. Ioannidis, R. Ng, K. Shim, and T. Sellis. Parametric query optimization. Technical Report, Univ. Of Wisconsin, Madison, 1992101| Y. Ioannidis, R. Ng, K. Shim, and T. Sellis. Parametric query optimization. Technical Report, Univ. Of Maryland, College Park, 1992

Interactive data correction (1)

Cities

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4| Y. Ioannidis, R. Ng, K. Shim, and T. Sellis. Parametric query optimization. Technical Report, Univ. Of Wisconsin, Madison, 1992101| Y. Ioannidis, R. Ng, K. Shim, and T. Sellis. Parametric query optimization. Technical Report, Univ. Of Maryland, College Park, 1992

KeyDirtyData

Interactive data correction(2) 4| Technical Report, Univ. Of Wisconsin101| Technical Report, Univ. Of Maryland 4| Madison

101| College Park




DirtyEvents

StandardizeData


ExtractionCities

ExtractedCities

incrementalincremental

incrementalincremental

Cities

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AJAX Architecture

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AJAX Demo

Documents

SAD Tagus 1 AJAX: Model, Declarative Language, and Algorithms Helena Galhardas