Keywords: Data integration, Peer-to-peer system, mappings

Transcription

1 ifuice Iformatio Fusio utilizig Istace Correspodeces ad Peer Mappigs Erhard Rahm, Adreas Thor, David Aumueller, Hog-Hai Do, Nick Golovi, Toralf Kirste Uiversity of Leipzig, Germay {rahm, thor, aumueller, hog, golovi, ABSTRACT We preset a ew approach to iformatio fusio of web data sources. It is based o peer-to-peer mappigs betwee sources ad utilizes correspodeces betwee their istaces. Such correspodeces are already available betwee may sources, e.g. i the form of web liks, ad help combie the iformatio about specific objects ad support a high quality data fusio. Sources ad mappigs relate to a domai model to support a sematically focused iformatio fusio. The ifuice architecture icorporates a mappig mediator offerig both a iteractive ad a script-drive, workflow-like access to the sources ad their mappigs. The script programmer ca use powerful geeric operators to execute ad maipulate mappigs ad their results. The paper motivates the ew approach ad outlies the architecture ad its mai compoets, i particular the domai model, source ad mappig model, ad the script operators ad their usage. Keywords: Data itegratio, Peer-to-peer system, mappigs Copyright is held by the authors/owers. Eighth Iteratioal Workshop o the Web ad Databases (WebDB 2005), Jue 16-17, 2005, Baltimore, Marylad. 1. INTRODUCTION Most proposed data itegratio approaches rely o the otio of a global schema to provide a uified ad cosistet view of the uderlyig data sources [10]. This approach has bee especially successful for data warehouses, but is also used for virtual itegratio of web data sources. Ufortuately, the maual effort to create such a schema ad to keep it up-to-date is substatial, despite recet advaces, e.g. i the area of semi-automatic schema matchig [14]. Likewise, the effort to itegrate ew sources is usually high makig it difficult to scale to may sources or to use such systems for ad-hoc (explorative) itegratio. Notwithstadig the high effort associated with a global schema, it caot guaratee good data quality at the istace level. Itegratig the real data, e.g. durig query processig over differet web sources, may still require extesive data cleaig to achieve good results, e.g. to deal with duplicate data [15]. The ifuice approach (iformatio Fusio utilizig istace correspodeces ad peer mappigs) focuses o the istace data of differet sources ad mappigs betwee them. May web sources expose explicit, high quality istace-level correspodeces to other sources, e.g. i the form of web liks. Such correspodeces represet oe type of mappig ifuice uses to fuse iformatio from differet sources. Sources ad mappigs are related to a domai model to support sematically meaigful iformatio fusio. The ifuice architecture icorporates a mappig mediator offerig both iteractive ad script-drive, workflow-like access to the sources ad their mappigs. The script programmer ca use powerful geeric operators to execute ad maipulate mappigs ad their results. Bioiformatics is oe area where this approach holds great promise. There are hudreds of web-accessible data sources o molecular-biological objects such as gees, proteis, metabolic pathways, etc. which highly cross-referece each other [5]. Creatig a global schema for a sizable fractio of these sites is virtually impossible due to the high diversity, complexity ad fast evolutio of the data. The existig cross-refereces represet a low-level way to obtai additioal iformatio from other sources for a specific object, e.g. a gee. The additioal iformatio is typically of high quality sice liks are mostly established ad maitaied by domai experts. However, the maual avigatio is usuitable for evaluatig large sets of objects, e.g. for gee expressio aalysis, so that there is a strog eed for a more powerful itegratio approach. Moreover, the sematics of the liks is typically ot made explicit so that the user has to kow exactly what kid of relatioship they represet. The bioiformatics area also has a strog demad for experimetal workflows to repetitively perform a series of aalysis steps iterrelatig ad aggregatig iformatio from differet sources. The ifuice script facility aims at supportig such requiremets with little developmet effort o the user side. Istace-level cross-refereces are available i may other domais or ca be geerated with little effort, e.g. to iterrelate bibliographic iformatio, product descriptios ad prices, etc. For simplicity we use examples from bibliographic data sources throughout this paper to illustrate the approach. Figure 1a shows three sample data sources ad associated mappigs. A physical data source (PDS), e.g., may offer objects of differet types. We call the object types of oe PDS the logical data sources (LDS), e.g., Author, ad Coferece as provided by. Object types combied across sources are represeted i the abstract domai model (Figure 1b). Each mappig betwee source istaces has a mappig type which is also represeted i the domai model. Mappigs map istaces of a iput object type to istaces of a output object type, e.g. all mappigs of type AuthorPubs relate author istaces to their associated publicatios. A importat mappig type is sigified by the same-mappigs iterrelatig istaces of the same object type across PDS, ad provides a meas to fuse the iformatio for the respective istaces. Typically, same-mappigs are based o uique object ids, e.g. accessio umbers i molecular-biological data sources or stable web URIs. Figure 1a idicates three such same-mappigs, of which some already exist (e.g. liks its author pages to the ACM author etries). All mappigs of the source-mappig model are executable, e.g. implemeted by a query or web service. ifuice allows for explorative data fusio by browsig alog these mappigs, e.g. to derive from a author all publicatios from the directly or trasitively coected LDS. The executio of several mappigs ad maipulatio of their results ca be specified withi scripts to allow repeated executios for differet iput objects or to use the script as a implemetatio of a complex mappig. For example, we may wat to have a script determiig for a give coferece

2 PDS Leged Author LDS Coferece same Author ACM GoogleScholar Author PubAuth PubCof Coferece AuthPub CofPub X its most frequetly refereced papers, e.g. to determie cadidates for a 10-year best paper award. A ifuice represetatio of such a script is show later. Iformally, it locates coferece X i, executes the PubCof mappig to get all publicatios of that coferece, uses the same-mappig to Google Scholar to get the correspodig publicatios together with a attribute idicatig the umber of citatios, sortig the publicatios o the umber of citatios, ad returig the top-most publicatios. The example shows that mappigs eed to be executable o a set of iput objects ad retur a set of output objects. Mappig executio ca be restricted to specific sources or to all sources of a specific type for which a correspodig mappig implemetatio exists. The mai cotributio of the paper is a ew geeric way to dyamic iformatio fusio based o istace correspodeces ad executable mappigs betwee sources. Source ad mappig sematics are reflected i a domai model which is at a higher abstractio (otological) level tha a global schema ad easier to costruct. Mappigs are executable o sets of objects ad highly composable thereby supportig powerful aggregatio of iformatio over several sources. We propose differet types of mappigs o web data sources, icludig basic ad aggregated mappigs, same- ad associatio mappigs, ad id- ad query mappigs. Furthermore, we itroduce a set of declarative operators to execute the differet kids of mappigs, to perform data aggregatio (fusio), ad to maipulate mappig results. Lastly, we show the usage of the operators for script programmig. I the ext sectio, we itroduce the represetatio of sources, mappigs ad the domai model, ad how to add ew sources ad mappigs. Sectio 3 itroduces the ifuice operators o mappigs ad mappig results icludig operators for data fusio. We illustrate the use of operators by a script for the itroductory example. Sectio 4 briefly outlies the architecture of the mappig mediator. I Sectio 5, we discuss related work. Fially, we coclude with a summary ad outlook. 2. SOURCES AND MAPPIN I this sectio we describe the metadata used by the mappig mediator to provide uiform access to the sources ad their mappigs both for iteractive exploratio ad script executio. The mediator s metadata is held i a repository ad specified by a metadata model as show i Figure 2. It cosists of two mai parts, a source-mappig model ad a domai model. The source-mappig model describes both the accessible data sources ad their associated mappigs. The domai model specifies object types ad mappig types. The sample models of Figure 1 coform to metadata model of Figure 2. I the followig, we describe the modelig ad use of sources, mappigs ad the domai model. Fially we discuss the steps for CoAuthor same a) source mappig model b) domai model Figure 1. Fusio sceario for a bibliographic domai (same-mappigs are deoted by dashed lies) addig a ew source ad mappig to the system. The descriptio itroduces several kids of mappigs, for which specific operators will be defied i Sectio Sources We distiguish betwee a physical data source (PDS) ad logical data source (LDS). A PDS ca be a database, website, private user files or ay other iformatio base. A PDS ca hold istaces of differet object types. We separate a PDS ito LDS s each cotaiig istaces of exactly oe object type of the domai model. The structure of a LDS is described by a set of attributes (Fig. 2). We oly madate the specificatio of a idetifyig key attribute per LDS to access its istaces ad to esure that each istace is provided with a uique object id. Website istaces are typically idetified by URLs. Uiqueess for database istaces ca be established by cocateatig istace key values with the ids of the correspodig PDS ad LDS. Requirig oly a id attribute per LDS allows us to itegrate a variety of heterogeeous data sources icludig ustructured ad semi-structured data sources, e.g. websites. Furthermore, it makes it easy to add ew data sources, ad helps to isulate the mediator iformatio agaist structural chages i the sources ad thus to support a high degree of data source autoomy. Each LDS has to provide a source-specific mappig for id-based istace access, i.e. to retur for a give id the associated istace icludig all attribute values. We call these mappigs getistace mappigs. The implemetatio of such mappigs may be very simple (e.g. database lookup), but may also extract specific attributes from a webpage. The actual attributes retured are thus determied by the mappig implemetatio ad deped o the curret source cotet. The mappig mediator supports such variably structured result sets ad their dyamic fusio with data from other sources at rutime. I additio to the id attribute, further attributes ca be optioally specified i the mappig mediator for ehaced fuctioality, e.g. to offer query access o the attributes or to select sources based o the availability of specific result attributes (e.g. umber of citatios for publicatios). Query access ca optioally be provided by LDS-specific mappigs, which we call QueryIstaces mappigs. The LDS attributes o which queries are supported should explicitly be registered i the mediator metadata. Aother optioal specificatio is correspodeces betwee attributes of two sources. Such correspodeces are useful to ehace the fusio of istace values (see Figure 3). For istace, specifyig that.author.ame correspods to ACM.Author.author ca help avoid author ames appearig twice i author istaces fused from ad ACM. Attribute correspodeces ca also be used to map queries specified o oe source to equivalet queries o other sources (query trasformatio). 2.2 Mappigs Mappigs describe directed relatioships betwee istaces of two object types. They are used to uiformly iterrelate istaces withi ad betwee physical data sources irrespective of the uderlyig data maagemet systems. The sematics of the mappig relatioship is expressed by a mappig type of the domai model. We distiguish betwee several kids of mappigs, icludig basic (simple) vs. aggregated mappigs, same- vs. associatio mappigs ad id- vs. query mappigs. Basic mappigs iterrelate istaces of oe iput ad oe output LDS ad retur a result set from the output LDS. All mappigs show i Fig. 1a are such basic mappigs. Note that the iput ad output LDS may be the

3 Leged Primary Key Foreig Key Attribute Attribute Id LogicalSource Id IsKeyAttribute IsNullable 1 1 Correspodece Correspodece Id Attribute1 Id Attribute2 Id Domai Model 1 ObjectType ObjectType Id IsA ObjectType Id 1 Logical DataSource LogicalSource Id ObjectType Id PhysDataSource Id 1 1 MappigType MappigType Id Iput ObjectType Id Output ObjectType Id MapigType IsSameMappig Physical DataSource PhysDataSource Id 1 1 Mappig Mappig Id MappigType Id Iput LDS Id Output LDS Id RequiresIputID IDOutputOly Source Mappig Model Figure 2. Metadata model of mappig mediator same, e.g. for mappigs of type CoAuthor or the source-specific getistace ad queryistaces mappigs. Aggregated mappigs are the result of mediator operatios or scripts, ad iterrelate aggregated objects from several LDS of the same type. A example is a mappig to combie authors with their publicatios from several LDS (e.g.,, ACM ad Google Scholar). Same-mappigs represet sematic equality relatioships betwee physical data sources at the istace level, i.e. each correspodece should refer to the same real-world object. They thus iterrelate LDS of the same object type from differet PDS (e.g..author ad ACM.Author). These correspodeces at the istace level are much more specific tha attribute correspodeces ad ca guide a high quality data fusio. Note that the compositio of same-mappigs results i ew same-mappigs which ca thus be used to iterrelate data from may sources. Associatio mappigs are o-same-mappigs ad mostly represet domai-specific relatioships betwee LDS of the same PDS (e.g. AuthorPubs). By composig them with same-mappigs they ca relate to ad fuse with data of other PDS (see sectio 3). Mappigs ca be further categorized o whether they are id-based or query-based with respect to their iput istaces (id- vs. query mappig). The output of basic mappigs is always assumed to iclude the id of the retured istaces. A mappig may i fact oly retur ids, e.g. as iput for subsequet id-mappigs ad to limit the amout of data to trasfer to the mediator ad to process there. Id-mappigs iterrelate ids (ad thus istaces) of two LDS or PDS ad ca easily be composed. The result of id-mappigs ca be represeted by a set of istace correspodeces (id1, id2). Query mappigs are helpful to fid relevat istaces ad their ids i the first place. Oe example are source-specific QueryIstace mappigs. Their results iclude istace ids ad ca thus be combied with id-mappigs to obtai related query results from differet sources. The attribute values of a query result may also be used to query aother source. The mappig specificatio i Figure 2 oly cosiders basic mappigs for simplicity. It derives the distictio betwee same- ad associatio mappig from the used mappig type of the domai model. The attribute RequiresIputID idicates whether or ot the mappig is id-based. The implemetatio of mappigs ca use other mappigs, utilize database queries, etc. To hide implemetatio differeces, ifuice mappigs are uiformly ecapsulated as web service operatios ad use XML for data exchage. Ideally, id-mappigs, icludig same-mappigs, ca be based o existig istace correspodeces such as web liks. Alteratively, they may be implemeted by a query mappig, e.g. to use istace values from oe source (e.g. obtaied from a getistace mappig) to search for correspodig istaces of a secod source (iput for query mappig). For istace, the same-mappig betwee publicatios ad Google Scholar ca be implemeted by usig the ame ad author of a publicatio as a keyword query to Google Scholar. For improved performace, the results of id-mappigs, i.e. the set of istace correspodeces, may be stored or cached i biary (id/id) mappig tables [8]. Compositio betwee such mappigs the becomes a joi operatio. Materialized id-mappigs ca also be iverted, eve for :m cardialities (e.g., a AuthorPub mappig ca be derived from a Id-based PubAuthor mappig ad vice versa). 2.3 Domai model The domai model defies domai-specific object types ad mappig types to sematically (otologically) categorize data sources ad mappigs. A hierarchical (taxoomical) categorizatio of object types is possible to classify sources i more detail (e.g., cofereces based o disciplie). We do ot iclude attributes for object types to accommodate a large variety of data sources ad to make it much easier to costruct the domai model tha a global schema. I may cases, we expect a small set of object types to be sufficiet. New object types may be added as eeded to accommodate ew sources, i.e. the domai model ca be icremetally exteded i a bottom-up fashio. A mappig type iterrelates two object types. A special attribute idicates whether the mappig type represets same-mappigs (sematic equality relatioship). 2.4 Addig Sources ad Mappigs Oe goal of ifuice is to make it easy to add ew sources ad mappigs. A ew physical data source requires to register at least oe logical data source. Registerig a LDS requires to assig it to the correspodig object type, specificatio of a id attribute, ad provisio of a getistace mappig. Furthermore, a peer mappig P to at least oe other LDS should be provided to permit data fusio with other sources. Optioally, a QueryIstaces mappig ca be provided, ad additioal attributes (e.g. kow output attributes of P or for query iput) ad attribute correspodeces ca be specified. For a LDS of a ew object type, the domai model must be exteded with the correspodig object type (e.g. Joural) ad at least oe associated mappig type (e.g. JouralPubs). Provisio of a ew mappig requires its registratio at the mappig mediator. This ivolves the specificatio of the mappig characteristics ad possibly the registratio of a ew mappig type i the domai model. The mappig must be executable, i.e. a implemetatio must be provided. The mappig implemetatio ca hide may details of the uderlyig data sources ad typically exposes oly selected iput ad output attributes at the iterface. As discussed, we do ot require that the iput ad output attributes of a mappig be registered i the source-mappig-model. To illustrate the ease ad beefit of providig data sources ad mappigs cosider the followig simple example: A user keeps a list of her favorite authors (icludig hadpicked iformatio like address or atioality, which are accessible by a getistace operatio) i a local file ad wats to bid it to the mappig mediator so that she ca periodically check the iformatio about the authors publicatios. This ca be achieved by establishig a same-mappig betwee the local file ad the LDS author, e.g. by providig a list of URLs. Thereafter the ex-

4 istig mappigs betwee, ACM ad Google Scholar ca be used to gather the iformatio of iterest. 3. OPERATORS AND SCRIPTS Iteractive users ad script programmers should ot be limited to the executio of oe mappig at a time but are provided with more powerful operators icludig data fusio capabilities. Therefore, the ifuice mappig mediator supports a variety of operators which ca be used withi script programs or to implemet derived mappigs. This idea is ispired by the script approach for model maagemet, e.g. as implemeted i Rodo [11]. While Rodo focuses o metadata maipulatio, ifuice provides operators for mappig executio ad maipulatio of istaces. We desiged operators of differet complexity to allow users to focus access o specific data sources ad mappig paths. Compared to trasparet access o may sources, this ot oly helps improve performace but is also importat for user acceptace ad data quality. This is because users ofte have specific prefereces for some data sources, ad the clealiess of merged data teds to decrease with more sources. Therefore we desiged two sets of operators, oe for processig basic mappigs returig objects from oe source (getistaces, traverse, map, queryistaces, query- Traverse querymatch) ad oe for aggregated mappigs ad aggregated objects (aggregatesame, aggregatequerytraverse aggregatemap, fuseattributes). I both cases we have a set of operators to process the respective results, similar to query laguages (uio, itersect, project, sort, joi, ). All operators are set-orieted, i.e. they work o sets of simple or aggregated iput objects ad determie a set of result objects or a mappig. The ext two subsectios itroduce these two sets of operators. I 3.4 we preset a script program usig the operators. 3.1 Operators for basic mappigs Basic mappigs relate istaces of oe iput LDS with istaces of oe output LDS, i.e. we obtai a homogeeous set of result objects. A object (istace) o i cosists of a uique id plus a (possibly empty) list of attribute values. The id is assumed to also idetify the logical data source to which the object belogs. We first itroduce operators for id-mappigs ad the for query mappigs Operators for id-mappigs Let L 1, L 2, deote logical data sources, O 1, O 2, sets of L 1 objects, L 2 objects ad m 1, m 2, id-mappigs (same- or associatio mappigs). traverse (O 1, m 2,, m k ) O k traverse (O 1, m 2,, m k ) = m k (m k-1 ( m 2 (O 1 ))) cosecutively executes m 2, m 3 thereby traversig via L 2 L 3 to L k. Of course, the iput source of m i must correspod to the output source of m i-1. Note that both same- ad associatio mappigs ca be used withi a traversal path. For same-mappigs the LDS ames ca be used istead of the mappig ames. The output is required to be a set, i.e. o duplicates are allowed. Example: Let O 1 be a list of author URLs, traverse (O 1, ACM, ACMAuthorPub) returs their correspodig ACM publicatios. traverse (O 1, AuthPub, PubCof) returs the cofereces i which the authors i O 1 have published (without returig the authors ad publicatios). For same-mappigs we provide a variatio of traverse traversesame (O 1, LDS k ) O k It is ot restricted to a sigle traversal path but cosiders all paths of same-mappigs from the iput LDS 1 to LDS k ad takes the uio of their LDS k results. The traverse ad traversesame operators oly retur the istaces of the last LDS o the mappig path which ca thus be the iput for other operators o objects. Frequetly oe wats to see the correlatios betwee objects of the first ad last source. This is achieved by map(o 1, m 2,, m k ) O 1 O k map(o 1, m 2,, m k ) = {(o 1,o k ) o 1 O 1, o k traverse({o 1 },m 2,,m k )} I the special case k=2, map returs the istace correspodeces of a sigle mappig (there may be just id-id combiatios, e.g. for same-mappigs). For more tha oe mappig, the sematics correspods to that of a classical compose operatio. Example: map (O 1, AuthPub, PubCof) returs authors together with the cofereces i which they published, i.e. these differet istaces are 'fused' together by the mappig result. The support operator getistaces (O 1 ) O 1 determies for the iput istaces i O 1 the available attribute values. This operator usually is applied to objects that oly hold a id value or a subset of attributes. Example: Give a list of author URLs, getistaces adds attribute values, e.g. ame, o. of co-authors etc. I the previous operators, the implemetatio of the mappigs, especially m k, determies which attributes are preset i the output istaces. Applyig the getistaces operator o these istaces helps to obtai additioal attribute values if eeded Operators for (basic) query mappigs I ifuice, queries are posed for oe source ad ca the be propagated to other sources by applyig id-mappigs or query matchig. For queryig a source we use queryistaces : (L 1, {cod} ) O 1 which returs all object istaces (i.e. at least their ids) from L 1 which fulfill the give set of attribute coditios {cod}. To propagate a query, we use derived operators combiig queryistaces with traverse or traversesame. querytraverse (L 1, {cod}, m 2,, m k ) = traverse (queryistaces (L 1, {cod}), m 2,, m k ) O k querytraversesame (L 1,{cod}, L k ) = traversesame (queryistaces (L 1,{cod}), L k )) Example: querytraversesame (, {ame= Berstei }, ACM) returs the ACM author objects of all authors with that ame. Operator querymatch trasforms a iput query for oe source to a equivalet oe o a secod source: querymatch (L 1, {cod}, L 2 ) = queryistaces (L 2, attrtrasf ({cod})) The fuctio attrtrasf utilizes specified attribute correspodeces to map the L 1 query coditio ito a correspodig L 2 query coditio. Hece, querymatch is oly applicable to L 2 sources for which a queryistaces implemetatio ad attribute correspodeces have bee provided. This operator typically is used to build the uio of the source-specific results which leads to aggregated objects. 3.2 Operators for aggregated mappigs Same-mappigs idetify sematically equivalet objects (syoyms, duplicates) which should be combied to reduce redudacy ad merge (complemet) the available iformatio from differet sources. We separate this ito two steps, called aggregatio ad fusio. Figure 3 exemplifies this for two sematically equivalet publicatio objects. I step 1 we combie them ito oe aggregated object which is a combiatio of all attributes from the origial objects. I step 2 we fuse the attributes to reduce

5 aggregatio : Geeric schema matchig with Cupid URL: Coferece: VLDB 2001 Authors: Jayat Madhava, Philip A. Berstei, Erhard Rahm : Geeric schema matchig with Cupid URL: data.cs.washigto.edu... NoOfCit: 243 Authors: J Madhava, PA Berstei, E Rahm redudacy without losig iformatio. The first step is usually the most difficult oe, but is well supported i ifuice by the samemappigs thereby facilitatig good data quality. The secod step ca use attribute correspodeces or actually aalyse the existig values for merge possibilities. Most ifuice operators for aggregated mappigs deal with aggregated objects where all attribute values are still available for further processig. The fusio of attribute values is performed by a separate operator, fuseattributes, which is best applied before returig aggregated objects to the user. Let {o 1, o 2, o 3, }) be a set of sematically equivalet objects of object type T from oe or more logical data sources. The aggregatio of these objects agg ({o 1,o 2,o 3, }) = (o 1 -o 2 -o 3 - ) is called a aggregated object ad also refers to object type T. disagg (o 1 -o 2 -o 3 - ) = {o 1,o 2,o 3, } returs the compoets of a aggregated object. Aggregatig objects of the same type Let AO 1, AO 2, be sets of aggregated objects of type T, ad L 2 a LDS of object type T. The operator aggregatesame (AO 1, L 2 ) AO 2 ={agg(ao 1,{traverseSame ({o 1 },L 2 ) o 1 disagg(ao 1 )}) ao 1 AO 1 } aggregates all AO 1 objects with sematically equivalet objects i L 2 by evaluatig the same-mappigs from the iput objects to the correspodig L 2 objects. Note that the operator ca also be applied to simple iput objects from oe iput LDS. Note further that the same-mappigs implemet the duplicate detectio ad ca thus support efficiet ad high quality data aggregatio. The defiitio of aggregatesame ca easily be geeralized to more tha oe target LDS sice the operator works o sets of aggregated objects, e.g. the output of a previous aggregatesame executio. Combiig the results for a propagated query at differet sources leads to aggregated objects ad is supported by aggregatequerytraverse (L 1,{cod}, L k ) = aggregatesame (queryistaces (L 1,{cod}), L k ). The stadard set-orieted (relatioal) operators itersect, diff, uio, restrict, project, sort etc. ca be exteded to deal with aggregated objects ad duplicates. Due to space costraits we oly defie itersectio. itersect (AO 1, AO 2 ) = {(ao 1 -ao 2 ) ao 1 AO 1, ao 2 AO 2, ao 1 ao 2 } Thereby, deotes that two aggregated objects are sematically equivalet. This is the case if they share at least oe compoet object or if they are related by a same-mappig. Aggregatig objects of differet types Associatio mappigs typically iterrelate objects of differet types which should ot be aggregated together like equivalet objects. For these mappigs, we geeralize the traverse operatio to both mappig types ad aggregated objects. aggregatetraverse (AO 1, mt) AO 2 : Geeric schema matchig with Cupid URL: Coferece: VLDB 2001 Authors: Jayat Madhava, Philip A. Berstei, Erhard Rahm : Geeric schema matchig with Cupid URL: data.cs.washigto.edu... NoOfCit: 243 Authors: J Madhava, PA Berstei, E Rahm attribute fusio : URL: Authors: Coferece: NoOfCit: Geeric schema matchig with Cupid data.cs.washigto.edu... Jayat Madhava, Philip A. Berstei, Erhard Rahm J Madhava, PA Berstei, E Rahm VLDB Figure 3. Example for aggregatio ad attribute fusio ={agg({ traverse({o k },m) o k disagg(ao k ), m of mt }) ao k AO 1 } applies all associatio mappigs of type mt for all objects i AO 1 ad aggregates the resultig objects. Similarly, we geeralize the map operator for associatio mappigs ad aggregated objects to obtai biary aggregated mappigs: aggregatemap (AO 1, mt) AO 1 AO 2 ={(ao 1, ao 2 ) ao 1 AO 1, ao 2 aggregatetraverse ({ao 1 }, mt)} Example: Give a set AO 1 of aggregated objects of ad ACM authors, aggregatemap (AO 1, AuthPubs) returs authorpublicatio pairs of aggregated objects that cotai object istaces from or ACM or both. For aggregated mappig results MR 1 AO 1 AO 2 ad MR 2 AO 3 AO 4 the operators joi ad compose are defied as follows joi (MR 1, MR 2 ) = {(ao 1,(ao 2 -ao 3 ),ao 4 ) (ao 1,ao 2 ) MR 1,(ao 3,ao 4 ) MR 2, ao 2 ao 3 } compose(mr 1,MR 2 ) = ({ao 1,ao 4 } (ao 1,ao 2,ao 4 ) joi (MR 1, MR 2 )) The give joi sematics refers to a ier joi, but left outer joi etc. ca be specified aalogously. Joi ad compose also eed a duplicate detectio to aggregate sematically equivalet objects. 3.3 Script Example A script is a sequece of operator calls. Each operator call stores the results ito a variable (deoted by a $ -prefix). The followig simple script example presets a approach to determie cadidates for the 10-Year Best Paper Award. $SIGMODPubs := querytraverse (LDS=.Cof, {= SIGMOD 1995 }, CofPubs) $CombiedCofPub:= aggregatesame ($SIGMODPubs, GoogleScholar) $CleaedPubs := fuseattributes ($CombiedCofPub) $Result := sort ($CleaedPubs, "NoOfCitigs ) I this example, step 1 uses a querytraverse operatio to query o the LDS.Cof to determie the id for the coferece of iterest ad traversig to the associated publicatios. The used mappig CofPubs is assumed to determie complete istaces. Step 2 utilizes the same-mappig o publicatios betwee ad Google Scholar to aggregate the values with the correspodig istaces i Google Scholar. I step 3 we clea the aggregated objects by applyig fuseattributes. Fially, we sort the resultig set of fused publicatios o the attribute deotig the umber of citatios. The top items/publicatios i the fial result set idicate likely cadidates for the 10-Year Best Paper Award. Sice operators ca process may iput objects at a time, the script does ot oly apply to a sigle coferece but may. To determie the most-cited publicatios of, say, a whole coferece series, oe could use a modified query i step 1 to select these cofereces, e.g. SIGMOD or VLDB. The rest of the script ca remai uchaged. 4. MEDIATOR ARCHITECTURE Figure 4 gives a overview of the ifuice mediator architecture. Its mai compoets are the repository (already described i Sectio

6 Repository Object- ad Mappig Types Executio Properties Mediator Iterface Script / Batch Iteractive (step by step) request Fusio Cotrol Uit Mappig Hadler Duplicate Detectio Mappig Results respose mappig call Mappig Executio Service mappig result Webservice SQL-Query Applicatio Figure 4. Architecture of the ifuice mediator 2), mediator iterface (MI), the fusio cotrol uit (FCU), ad the mappig executio service (MES). The MI provides two modes of operatio. The iteractive mode supports a explorative approach where users ca execute mappigs step by step. The script-based mode defies a batch of executio steps for the mediator, returig a fial result set. Operatios are executed withi the FCU, the cetral uit of the system. The mappig hadler coordiates sigle mappig calls accordig to the appropriate mappig defiitio of the metadata model. It maages (temporary) mappig results for further operatios. It also performs duplicate hadlig for aggregatig objects. The mappig executio service actually executes the called mappigs ad returs their results to the FCU. As a proof of cocept we have implemeted a first subset of the mediator fuctioality for iteractive mappig executio (explorative avigatio). The prototype is implemeted i Java ad utilizes a relatioal database system for the repository ad mappig results. It ca execute mappigs ad operatio sequeces as i the examples show, icludig the example o the 10-year best paper award. 5. RELATED WORK Most previous data itegratio approaches for web data sources utilize a global schema ad a query mediator. I cotrast to ifuice, these approaches do ot utilize peer mappigs ad istace correspodeces. Moreover, the global schema teds to be much more complex tha our domai model leadig to icreased effort to add sources or to deal with chagig sources. More related to our approach is recet work o P2P databases ad biological databases. P2P prototypes such as PeerDB ad Piazza [12][17] focus o query processig across peer mappigs without a global schema. PeerDB propagates IR searches, whereas Piazza reformulates queries based o metadata mappigs. Queries refer to the peer schema where the query was iitially posed. By cotrast, ifuice focuses o istace-level correspodeces ad ca apply a variety of executable peer mappigs icludig queries. Moreover, we support a set of powerful operators (icludig aggregatio operators) ad the executio of script programs. Several itegratio approaches i the bioiformatics area [16], [9], [7] utilize cross-refereces at the istace level to combie data from differet sources. Systems like SRS [4] ad our Ge- Mapper prototype [1] materialize istace correspodeces i mappig tables for improved performace. These efforts lack a sufficiet cosideratio of the sematics of the cross refereces but expect the users to kow what the cross refereces mea. The ifuice domai model differetiatig differet mappig types, icludig differet same-mappigs, allows a much more focused data fusio. To our kowledge, the proposed framework of mappig operators ad scripts is also ukow so far i the bioiformatics domai. SEMEX [3] is a iterestig persoal iformatio maagemet system which utilizes a domai model ad mappigs similar to our approach. However, it oly deals with cetrally stored data, while we itegrate data from differet web data. Most previous work o data cleaig was doe i the data warehouse area with a focus o duplicate idetificatio [1][15]. The use of sematic object-ids for data itegratio i the TSIMMIS mediator [13] has similarities to the utilizatio of ids i our same-mappigs. 6. CONCLUSION AND OUTLOOK ifuice combies a set of techiques to a ew approach for itegratig iformatio from diverse web data sources. It does ot deped o a global schema ad utilizes explicit istace correspodeces ad executable peer mappigs. We proposed the use of a domai model ad a mappig mediator to cotrol the executio of a variety of such mappigs. Furthermore, we itroduced a set of powerful operators for mappig executio ad data aggregatio. A iitial prototype for iteractive mappig executio showed the viability ad flexibility of the approach. I future work, we will fully implemet the outlied approach ad ivestigate techiques based o cachig mappig tables to improve performace. We pla to adopt the ifuice implemetatio to differet domais ad to support itegratio of both web data sources ad local / private data sources. Ackowledgemets. A. Thor ad N. Golovi are fuded by the Germa Research Foudatio (DFG) withi the Graduiertekolleg Kowledge Represetatio. H. Do ad T. Kirste are supported by DFG grat BIZ 6/1-1. Phil Berstei ad Sergey Melik gave helpful commets. 7. REFERENCES [1] Chaudhuri, S. et al.: Robust ad efficiet fuzzy match for olie data cleaig. Proc. SIGMOD 2003 [2] Do, H.-H., Rahm, E.: Flexible itegratio of molecular-biological aotatio data: The GeMapper Approach. Proc. of EDBT 2004 [3] Dog, X., Halevy, A. Y.: A platform for persoal iformatio maagemet ad itegratio. CIDR 2005 [4] Etzold, T. et al.: SRS: A itegratio platform for databaks ad aalysis tools i bioiformatics. I [9]: [5] Galperi, M.Y.: The molecular biology database collectio update. Nucleic Acids Research 32, Database issue, [6] Greco, S. et al: Itegratig ad maagig coflictig data. Proc. Of Cof. o Perspectives of System Iformatics [7] Heradez, T, Kambhampati, S: Itegratio of biological sources: curret systems ad challeges ahead. SIGMOD Record 33(3), 2004 [8] Kemetsietsidis, A. et al.: Mappig data i peer-to-peer systems:sematics ad algorithmic issues. Proc. SIGMOD 2003 [9] Lacroix, Z., Critchlow T. (Eds.): Bioiformatics: Maagig Scietific Data. Morga Kaufma, 2003 [10] Lezerii, M: Data itegratio: a theoretical perspective. Proc. PODS 2002 [11] Melik, S. et al.: Developig metadata-itesive applicatios with Rodo. Joural o Web Sematics, 2003 [12] Ng, W. S., et al.: PeerDB: A P2P-based System for Distributed Data Sharig. Proc. ICDE 2003 [13] Papakostatiou, Y. et al.: Object Fusio i Mediator Systems. Proc. VLDB 1996 [14] Rahm, E., Berstei, P. A.: A survey of approaches to automatic schema matchig. VLDB Joural, 10(4), 2001 [15] Rahm, E., Do, H.-H.: Data cleaig: problems ad curre approaches. IEEE Bull. Tech.Com. Data Egieerig, 23 (4), 2000 [16] Stei, L. D.: Itegratig biological databases. I Nature Review Geetics, 4, 2003 [17] Tatariov, I., et al.: The Piazza peer data maagemet project. SIGMOD Record, 32(3), 2003