Fan, Wenfei

Li, Jianzhong

Ma, Shuai

Tang, Nan

Yu, Wenyuan

English

We present CerFix, a data cleaning system that finds certain fixes for tuples at the point of data entry, i.e., fixes that are guaranteed correct. It is based on master data, editing rules and certain regions. Given some attributes of an in-put tuple that are validated (assured correct), editing rules tell us what other attributes to x and how to correct them with master data. A certain region is a set of attributes that, if validated, warrant a certain x for the entire tuple. We demonstrate the following facilities provided by Cer-Fix: (1) a region finder to identify certain regions; (2) a data monitor to find certain fixes for input tuples, by guiding users to validate a minimal number of attributes; and(3) an auditing module to show what attributes are fixed and where the correct values come from

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CerFix: A System for Cleaning Data with Certain FixesWenfei Fan 1,2 Jianzhong Li2 Shuai Ma3 Nan Tang1 Wenyuan Yu11University of Edinburgh 2Harbin Institute of Technology 3Beihang University{wenfei@inf., ntang@inf., wenyuan.yu@}ed.ac.uk lijzh@hit.edu.cn mashuai@act.buaa.edu.cnAbstractWe present CerFix, a data cleaning system that finds cer-tain fixes for tuples at the point of data entry, i.e., fixes thatare guaranteed correct. It is based on master data, editingrules and certain regions. Given some attributes of an in-put tuple that are validated (assured correct), editing rulestell us what other attributes to fix and how to correct themwith master data. A certain region is a set of attributesthat, if validated, warrant a certain fix for the entire tuple.We demonstrate the following facilities provided by Cer-Fix: (1) a region finder to identify certain regions; (2) adata monitor to find certain fixes for input tuples, by guid-ing users to validate a minimal number of attributes; and(3) an auditing module to show what attributes are fixedand where the correct values come from.1. IntroductionIt has long been recognized that real-life data is oftendirty [10]. Dirty data is costly: it costs us businesses 600billion dollars each year [5]. This highlights the need fordata cleaning tools, to help users detect errors in the dataand moreover, repair the data, i.e., correct the errors.Most data cleaning systems on the market are ETL tools(extraction, transformation, loading; see [8] for a survey).To detect semantic errors in the data, there have also beena host of approaches on data repairing based on integrityconstraints [1, 2, 4, 11]. A variety of constraints have beenstudied for data repairing, such as traditional functional,inclusion and full dependencies [1,2], as well as their exten-sions (e.g., conditional functional dependencies [4, 11]).Integrity constraints are capable of detecting the presenceof errors in the data, i.e., determining whether the data isdirty or not. However, they do not tell us which attributesof a tuple have errors and how we could correct the errors.Example 1: Consider an input tuple:t: (FN = Bob, LN =Brady, AC = 020, phn =079172485, type =2,str = 501 Elm St, city = Edi, zip = EH8 4AH, item = CD)It specifies a uk customer: name (fn, ln), phone number(area code AC and phone phn), address (street str, city, zipcode) and item purchased. Here phn is either home phoneor mobile phone, indicated by type (1 or 2, respectively).Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies arenot made or distributed for profit or commercial advantage and that copiesbear this notice and the full citation on the first page. To copy otherwise, torepublish, to post on servers or to redistribute to lists, requires prior specificpermission and/or a fee. Articles from this volume were invited to presenttheir results at The 37th International Conference on Very Large Data Bases,August 29th - September 3rd 2011, Seattle, Washington.Proceedings of the VLDB Endowment, Vol. 4, No. 12Copyright 2011 VLDB Endowment 2150-8097/11/08... $ 10.00.To specify the semantics of the input data, one may definethe following conditional functional dependencies (cfds):ψ1: AC = 020 → city = Ldn, ψ2: AC = 131 → city = Edi.These cfds state that if AC is 020, then city should be Ldn(ψ1), and when AC is 131, then city must be Edi (ψ2). Theydetect the presence of errors in tuple t: t[AC] = 020 butt[city] = Edi. However, they do not tell us which of t[AC]and t[city] is wrong, and to what value it should be changed.In light of this, previous constraint-based methods useheuristics: they do not guarantee correct fixes in data repair-ing. Worse still, they may introduce new errors when tryingto repair the data. Indeed, the correct values of t[AC, city]are (131, Edi). Indeed, all these previous methods may optto change t[city] to Ldn; this does not fix the erroneous t[AC]and worse, messes up the correct attribute t[city]. 2In practice, one often wants to find certain fixes for thedata, i.e., fixes that are guaranteed to be correct. In otherwords, if a data repairing process changes the value of anattribute t[A] to v, then v should be the true value of t[A]. Inaddition, it should ensure that no new errors are introducedin the process. The need for certain fixes is particularlyevident when repairing critical data, e.g., medical data, inwhich a seemingly minor error may mean life or death.An approach to finding certain fixes was proposed in [7],based on master data, editing rules and certain regions.Master data (a.k.a. reference data) is a single repository ofhigh-quality data that provides various applications in anenterprise with a synchronized, consistent view of its corebusiness entities [9]. It is increasingly common for enter-prises to maintain master data nowadays, and master datamanagement (MDM) is being developed by IBM, SAP, Mi-crosoft and Oracle. In contrast to integrity constraints, edit-ing rules aim to tell us which attributes of a tuple are wrongand what values from master data they should take, pro-vided that master data is available and that some other at-tributes are validated (assured correct). With respect to aset of editing rules and a master relation, a certain regionis a set A of attributes and a pattern tableau, such that forany input tuple t, if t[A] is validated and t[A] bears a pat-tern in the tableau, then the editing rules guarantee to finda (unique) certain fix for all the attributes of t.Example 2: Suppose that master data contains a tuple:s: (FN = Robert,¡ LN =Brady,¡ AC = 131, Hphn = 6884563,Mphn =079172485, str = 501 Elm St, city = Edi,zip = EH8 4AH, DoB = 11/11/55, gender = M)An editing rule is given as follows:ϕ1: ((zip, zip) → (AC,AC), tp1 = ())It states that for an input tuple t and a master tuple s,if they have identical zip code (i.e., t[zip] = s[zip]), then tcould be updated by t[AC] := s[AC], provided that t[zip] is1375Master Data ManagerDmRule EngineEditing RulesData AuditingData MonitorRegion FinderCertain regionsinteractData cleaning moduleWeb interfaceData moduleNew suggestionData repairingData Explorertupletuple tFigure 1: The CerFix Architecture.correct (either ensured by the users or verified automati-cally). When the master data is accurate, the editing ruleis assured valid, and the region t[zip] is correct, the fix tot[AC] with the master data value is certainly correct. 2Following [7], we develop CerFix, a data cleaning systemthat finds certain fixes for input tuples at the point of dataentry. It differs from other systems that also ask for userfeedback (e.g., [3]) in what feedback is requested and howthe feedback is used. Below we first present CerFix, andthen outline what functionalities we shall demonstrate.2. The CerFix SystemThe architecture of CerFix is depicted in Fig. 1. CerFixmaintains a collection of master data (master data manager)and a set of editing rules (rule engine). With respect to themaster data and editing rules, it computes a set of certainregions (region finder). It inspects and repairs input tuplesvia a data monitor, which interacts with the users to findcertain fixes for input tuples. It also provides a Web inter-face (data explorer) and a data auditing module for users tomanage editing rules and trace changes to data, respectively.Below we briefly present the key components of CerFix.We refer the interested readers to [7] for details about editingrules, certain regions and their computation.Rule engine. It maintains a set of editing rules (eRs) thatspecify (a) whether an input tuple t can match a master tu-ple s via a pattern tuple (e.g., (zip, zip) together with tp1 inrule ϕ1 of Example 2), and (b) which attribute values of t canbe changed and what correct values they should take fromthe master data. The engine also implements static analysistechniques developed in [7]. In particular, it supports thefollowing. (1) It checks the consistency of editing rules, i.e.,whether the given rules are dirty themselves. Moreover, (2)provided that some attributes of a tuple are correct, it au-tomatically derives what other attributes can be validated(assured correct) by using editing rules and master data.Editing rules can be either explicitly specified by the users,or derived from integrity constraints, e.g., cfds and match-ing dependencies [6] for which discovery algorithms are al-ready in place. CerFix currently only supports manualspecification of editing rules via the Web interface.Master data manager. It maintains master data, whichis assumed consistent and accurate [9].Region finder. A region is a pair (Z, Tc), where Z is a listof attributes of an input tuple and Tc is a pattern tableauconsisting of a set of pattern tuples with attributes in Z.Figure 2: Management of editing rules.A region (Z, Tc) is a certain region w.r.t. a set of editingrules and master data if for any input tuple t, as long ast[Z] is correct and t[Z] matches a pattern in Tc, the editingrules warrant to find a certain fix for t. Based on the algo-rithms in [7], top-k certain regions are pre-computed thatare ranked ascendingly by the number of attributes, and arerecommended to users as (initial) suggestions.Data monitor. This is the most important module of Cer-Fix. It interacts with the users and finds certain fixes forinput tuples, as follows. (1) Initial suggestions. It recom-mends the set of certain regions computed by region finderto the users as suggestions. For each input tuple t, if theusers ensure that t[Z] is correct and matches a pattern in Tcfor any region (Z, Tc) in the set, then a certain repair for tis warranted. (2) Data repairing. For an input tuple t, theusers may respond with a set t[S] of attributes that is cor-rect, where S may not be any of the certain regions. Datamonitor iteratively employs editing rules and master datato fix as many attributes in t as possible, and expands thecorrect attribute set S by including those attributes thatare validated via the inference system of the rule engine.(3) New suggestion. If not all attributes of t have been vali-dated, data monitor computes a new suggestion, i.e., a min-imal number of attributes, which are recommended to theusers. If the users ensure the correctness of these attributesin t, data monitor will find a certain fix for t. The processof steps (2) and (3) repeats until a certain fix of t is reached.CerFix ensures that each fix is correct with editing rulesand master data. It also minimizes users’ effort by identify-ing a minimal number of attributes for users to validate.Data auditing. This module keeps track of changes toeach tuple, incurred either by the users or automatically bydata monitor with editing rules and master data. Statisticsabout the changes can be retrieved upon users’ requests.1376(a) Providing suggestions (b) Fixing data (c) Achieving certain fixesFigure 3: Data monitor.3. Demonstration OverviewWe next describe various aspects of CerFix in more de-tail, and explain the aims of our demonstration. More specif-ically, we show the following: (1) how users manage editingrules with the aid of the Web interface (data explorer inFig. 1); (2) how CerFix interacts with the users for datamonitoring, to detect and fix errors in input tuples at thepoint of their entry; and (3) how data auditing works, tokeep track of which attributes are fixed and where the cor-rect values come from, and provide statistics about the per-centage of data that is fixed by user efforts or by CerFix.Initialization. The users are required to configure an in-stance, which consists of two parts: (a) a data connec-tion with JDBC url, username, password and the correspond-ing JDBC driver provided by users; and (b) specifying theschema of input (dirty) tuples and that of the master data.We illustrate these with the master data and the inputdata shown in Fig. 2. Note that they have different schemas.Master data. Each tuple in the master data specifies a per-son in the uk in terms of the name (fn, ln), area code (AC),home phone (Hphn), mobile phone (Mphn), address (streetstr, city and zip code), date of birth (DOB) and gender.Input tuples. Each tuple specifies a uk customer, as illus-trated in Example 1.Editing rule management. Figure 2 displays the Webinterface for managing eRs. In the demonstration we shallshow how the users can manage (view/modify/add/delete)eRs using the data explorer. The system currently only sup-ports to import eRs manually via the rule manager, wherethe eRs may either be designed by experts or be discoveredfrom cfds or mds. For instance, Figure 2 shows nine editingrules ϕ1–ϕ9, for the id’s 1–9, respectively.◦ ϕ1 (resp. ϕ2 and ϕ3) states that if an input tuple t anda master tuple s have the same zip code and if t[zip] isalready validated, then t can be updated by t[zip] :=s[zip] (resp. str and city).◦ ϕ4 (resp. ϕ5) states that if the phn of a tuple t matchesthe Mphn of a master tuple s, and if t[phn] is validated,then t[FN] := s[FN] (resp. LN). These eRs pose a con-straint (a pattern tuple) t[type] = 2, requiring that phnis mobile phone. This rule can be viewed or edited byclicking the view/edit frame for the pattern.◦ ϕ6 (resp. ϕ7 and ϕ8) tells us that if the (AC, phn)attributes of an input tuple t match the (AC, Hphn)values of a master tuple s, and if t[AC, phn] are vali-dated, then t[str] := s[str] (resp. city and zip). TheseeRs have a pattern t[type] = 1, i.e., phn is home phone.◦ ϕ9 states that when the AC value of an input tuple tis not 0800 (toll free, non-geographic), if it agrees witha master tuple s on its AC attribute, and moreover,if t[AC] has been validated, then t should be updatedwith t[city] := s[city]. As shown in Fig. 2, the pattern“ 6= 0800” can be edited via a pop-up frame.CerFix automatically tests whether the specified eRsmake sense w.r.t. master data, i.e., the rules do not con-tradict each other and will lead to a unique fix for any inputtuple. Furthermore, given certain attributes that are vali-dated, it automatically derives what other attributes can bevalidated by eR and master data, via an inference system.Data monitor. We shall demonstrate how CerFix inter-acts with users to find a certain fix for each input tuple.1. CerFix suggests a set of attributes for the users to val-idate. The users may either validate these attributes,or opt to assure that some other attributes are correct.The initial suggestions are computed by region finder.◦ As shown in Fig. 3(a), the attributes suggested byCerFix are highlighted in yellow, i.e., area codeAC, phone number phn, phone type, and productitem. The values of the attributes assigned by theusers are 201, 075568485, Mobile phone (type 2),and DVD, respectively.2. If the users opt to validate these attributes, CerFixiteratively applies editing rules and master data to thedata, and expands the set of attributes validated.◦ As shown in Fig. 3(b), all attributes that havebeen validated are now highlighted in green.These include attributes first name FN, last name1377Figure 4: Data auditing.LN, and city, for which the correctness is validatedby CerFix. For instance, the value of FN is nor-malized from ‘M.’ to ‘Mark’ by eR ϕ4 with theFN value of the second master tuple in Fig. 2.The users may decide to validate attributes other thanthose suggested. CerFix reacts by fixing data withediting rules and master data in the same way, basedon the attributes selected and validated by the users.3. If some attributes of the input tuple are still not val-idated, CerFix computes a new suggestion and goesback to step 1, to interact with the users by providingthe new suggestion. In each interaction, both the usersand CerFix expand the attributes that are validated.◦ As shown in Fig. 3(b), CerFix suggests the usersto validate zip code. After two rounds of inter-actions, all the attributes are validated. This isshown in Fig. 3(c) with all attributes in green.When fixing the data, the most time-consuming procedureis to compute suggestions. To reduce the cost, CerFix pre-computes a set of certain regions with region finder (seeFig. 1), which are provided to the users as initial suggestions,and are referenced when computing new suggestions.We remark that data monitor of CerFix is quite generic,i.e., it does not depend on any particular system. Indeed,it supports several interfaces to access data, which could bereadily integrated with other database applications.Data auditing. CerFix provides a data auditing facilitysuch that after a stream of input tuples is fixed, the usersmay inspect the changes made to those tuples.The users may inspect attributes of an individual tuple.For instance, as shown in Fig. 4, when the users select the FNattribute of a tuple (highlighted in yellow), CerFix showsthat it has been fixed by normalizing the first name ‘M.’ to‘Mark’. It further presents what master tuples and editingrules have been employed to make the change.The users may also want to inspect each attribute (col-umn) of the input tuples. As shown in Fig. 4, when FN isselected, CerFix presents the statistics about the attributeFN, namely, the percentage of FN values that were validatedby the users and the percentage of values that were automat-ically fixed by CerFix. Our experimental study indicatesthat in average, 20% of values are validated by users whileCerFix automatically fixes 80% of the data.Summary. The demonstration aims to exhibit the strengthof editing rules and important functionalities of CerFix. (1)Editing rules. As opposed to integrity constraints that onlydetect the presence of errors in the data, editing rules iden-tify what attributes are erroneous and tell us how to correctthe errors with master data. (2) Region finder. It tells usto validate an input tuple, what minimal sets of attributeshave to be assured correct. (3) Data monitor. It interactswith the users to find certain fixes, while minimizing hu-man efforts by suggesting a minimal number of attributesfor the users to validate. (4) Data auditing. It helps theusers understand better the quality of input data sets.Acknowledgments. Fan is supported in part by the RSE-NSFC Joint Project Scheme and an IBM scalable data ana-lytics for a smarter planet innovation award. Li is supportedin part by NGFR 973 grant 2006CB303000 and NSFC grant60533110. Shuai is supported in part by NGFR 973 grant2011CB302602 and NSFC grants 90818028 and 60903149.4. References[1] M. Arenas, L. E. Bertossi, and J. Chomicki. Consistentquery answers in inconsistent databases. TPLP, 3(4-5):393–424, 2003.[2] P. Bohannon, W. Fan, M. Flaster, and R. Rastogi. Acost-based model and effective heuristic for repairingconstraints by value modification. In SIGMOD, pages143–154, 2005.[3] K. Chen, H. Chen, N. Conway, J. M. Hellerstein, andT. S. Parikh. Usher: Improving data quality with dy-namic forms. In ICDE, pages 321–332, 2010.[4] G. Cong, W. Fan, F. Geerts, X. Jia, and S. Ma. Improv-ing data quality: Consistency and accuracy. In VLDB,pages 315–326, 2007.[5] W. W. Eckerson. Data Quality and the Bottom Line:Achieving Business Success through a Commitment toHigh Quality Data. In The Data Warehousing Institute,2002.[6] W. Fan, X. Jia, J. Li, and S. Ma. Reasoning aboutrecord matching rules. PVLDB, pages 407–418, 2009.[7] W. Fan, J. Li, S. Ma, N. Tang, and W. Yu. Towards cer-tain fixes with editing rules and master data. PVLDB,pages 173–184, 2010.[8] T. N. Herzog, F. J. Scheuren, and W. E. Winkler. DataQuality and Record Linkage Techniques. Springer, 2009.[9] D. Loshin. Master Data Management. Knowledge In-tegrity, Inc., 2009.[10] T. Redman. The impact of poor data quality on thetypical enterprise. Commun. ACM, 2:79–82, 1998.[11] M. Yakout, A. K. Elmagarmid, J. Neville, M. Ouzzani,and I. F. Ilyas. Guided data repair. 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A cost-based model and eective heuristic for repairing constraints by value modi

Consistent query answers in inconsistent databases.

Data Quality and Record Linkage Techniques.

Data Quality and the Bottom Line: Achieving Business Success through a Commitment to High Quality Data.

CerFix: A System for Cleaning Data with Certain Fixes

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