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VADA: Value Added Data Systems Principles and Architecture The VADA Architecture for Cost-Effec>ve Data Wrangling 1. Automa*c Bootstrapping: The user iden>fies a collec>on of sources and defines a target schema. The system automa>cally orchestrates a collec>on of transducers that together generate an ini>al result data set. 2. Data context: The user associates the target schema with related extents (e.g. master data). Such data allows various of the steps from bootstrapping to be revisited, including matching and mapping valida>on. Furthermore, it is now also possible to learn quality rules, and thereby to carry out repairs to the mapping results. The result data should now be of beLer quality. Demonstra>on The VADA architecture: (i) combines wrangling components that between them cover the complete data wrangling lifecycle; (ii) builds on automa>on wherever possible, using whatever informa>on is available; (iii) refines the results of automated processes in the light of user feedback; and (iv) takes into account the user’s priori>es. User Interface: The data scien>st provides informa>on about the data they need, their priori>es and feedback. Transducers: Components with input and output dependencies defined as Datalog ± rules over the knowledge base. User Context: Informa>on about the requirements of the user. Data Context: Informa>on about the applica>on domain. Vadalog Reasoner: Supports reasoning over the knowledge base using Vadalog, a member of the Datalog ± family of languages. End-To-End Data Wrangling 3. Feedback: The user provides feedback to indicate that some of the results are correct or incorrect. Depending on the feedback provided, this will enable some of the previous steps in the wrangling process to be revisited, giving rise to a revised result. 4. User context: The result is now hopefully of reasonable quality, but the data included in the result may not be especially well-suited to the task at hand. The user specifies the user context by indica>ng the rela>ve (pairwise) importance of different features in the result. The pairwise comparisons are used to derive weights that inform the selec>on of mappings based on mul>- dimensional op>miza>on. A Real Estate Scenario The demonstra>on acts on real estate data, bringing together: Data about proper>es for sale from web data extrac>on over deep web sources. Open government data that provides informa>on about the areas in which the proper>es are located. 1 4 3 2 www.vada.org.uk

The VADA Architecture for Cost-Effec>ve Data Wrangling · 2017-05-23 · VADA: Value Added Data Systems – Principles and Architecture The VADA Architecture for Cost-Effec>ve Data

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Page 1: The VADA Architecture for Cost-Effec>ve Data Wrangling · 2017-05-23 · VADA: Value Added Data Systems – Principles and Architecture The VADA Architecture for Cost-Effec>ve Data

VADA:ValueAddedDataSystems–PrinciplesandArchitecture

TheVADAArchitectureforCost-Effec>veDataWrangling

1. Automa*c Bootstrapping: Theuseriden>fiesacollec>onofsourcesand defines a target schema. Thesystem automa>cally orchestrates acollec>on of transducers thattogether generate an ini>al resultdataset.2.Datacontext:Theuserassociatesthe target schema with relatedextents(e.g.masterdata).Suchdataallows various of the steps frombootstrapping to be revisited,including matching and mappingvalida>on. Furthermore, it is nowalso possible to learn quality rules,and thereby to carry out repairs tothemappingresults.TheresultdatashouldnowbeofbeLerquality.

Demonstra>on

The VADA architecture: (i) combines wrangling components thatbetween themcover the completedatawrangling lifecycle; (ii)buildson automa>on wherever possible, using whatever informa>on isavailable;(iii)refinestheresultsofautomatedprocessesinthelightofuserfeedback;and(iv)takesintoaccounttheuser’spriori>es.UserInterface:Thedatascien>stprovidesinforma>onaboutthedatatheyneed,theirpriori>esandfeedback.Transducers:ComponentswithinputandoutputdependenciesdefinedasDatalog±rulesovertheknowledgebase.UserContext:Informa>onabouttherequirementsoftheuser.DataContext:Informa>onabouttheapplica>ondomain.VadalogReasoner:SupportsreasoningovertheknowledgebaseusingVadalog,amemberoftheDatalog±familyoflanguages.

End-To-EndDataWrangling

3.Feedback:Theuserprovidesfeedbacktoindicatethatsomeoftheresultsarecorrectorincorrect.Dependingonthefeedbackprovided,thiswillenablesomeofthepreviousstepsinthewranglingprocesstoberevisited,givingrisetoarevisedresult.4. User context: The result is now hopefully of reasonable quality, but thedata included in the resultmay not be especiallywell-suited to the task athand.Theuserspecifiestheusercontextbyindica>ngtherela>ve(pairwise)importanceofdifferentfeatures intheresult.Thepairwisecomparisonsareusedtoderiveweightsthatinformtheselec>onofmappingsbasedonmul>-dimensionalop>miza>on.

ARealEstateScenarioThe demonstra>on acts on realestatedata,bringingtogether:•  Dataaboutproper>esforsale

fromwebdataextrac>onoverdeepwebsources.

•  Open government data thatprovides informa>on aboutthe areas in which theproper>esarelocated.

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www.vada.org.uk