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Interaction Between Record Matching and Data Repairing

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Interaction Between Record Matching and Data Repairing Interaction between Record Matching and Data Repairing Wenfei Fan1;2 Jianzhong Li2 Shuai Ma3 Nan Tang1 Wenyuan Yu1 1University of Edinburgh 2Harbin Institute of Technology 3Beihang University fwenfei@inf., ntang@inf., [email protected] [email protected] [email protected] Abstract cost: it costs us businesses 600 billion dollars each year [16]. Central to a data cleaning system are record matching and With this comes the need for data cleaning systems. As data repairing. Matching aims to identify tuples that re- an example, data cleaning tools deliver \an overall business fer to the same real-world object, and repairing is to make a value of more than 600 million GBP" each year at BT [31]. In database consistent by fixing errors in the data by using con- light of this, the market for data cleaning systems is grow- straints. These are treated as separate processes in current ing at 17% annually, which substantially outpaces the 7% data cleaning systems, based on heuristic solutions. This pa- average of other IT segments [22]. per studies a new problem, namely, the interaction between There are two central issues about data cleaning: ◦ record matching and data repairing. We show that repair- Recording matching is to identify tuples that refer to ing can effectively help us identify matches, and vice versa. the same real-world entity [17, 26]. ◦ To capture the interaction, we propose a uniform frame- Data repairing is to find another database (a candidate work that seamlessly unifies repairing and matching oper- repair) that is consistent and minimally differs from ations, to clean a database based on integrity constraints, the original data, by fixing errors in the data [4, 21]. matching rules and master data. We give a full treatment Most data cleaning systems in the market support record of fundamental problems associated with data cleaning via matching, and some also provide the functionality of data matching and repairing, including the static analyses of con- repairing. These systems treat matching and repairing as straints and rules taken together, and the complexity, termi- separate and independent processes. However, the two pro- nation and determinism analyses of data cleaning. We show cesses typically interact with each other: repairing helps us that these problems are hard, ranging from NP- or coNP- identify matches and vice versa, as illustrated below. complete, to PSPACE-complete. Nevertheless, we propose Example 1.1: Consider two databases Dm and D from efficient algorithms to clean data via both matching and re- a UK bank: Dm maintains customer information collected pairing. The algorithms find deterministic fixes and reliable when credit cards are issued, and is treated as clean master fixes based on confidence and entropy analysis, respectively, data [28]; D consists of transaction records of credit cards, which are more accurate than possible fixes generated by which may be dirty. The databases are specified by schemas: heuristics. We experimentally verify that our techniques sig- card(FN; LN; St; city; AC; zip; tel; dob; gd), nificantly improve the accuracy of record matching and data tran(FN; LN; St; city; AC; post; phn; gd; item; when; where). repairing taken as separate processes, using real-life data. Here a card tuple specifies a UK credit card holder identified Categories and Subject Descriptors by first name (FN), last name (LN), address (street (St), city, zip code), area code (AC), phone (tel), date of birth (dob) H.2 [Database Management]: General|integrity and gender (gd). A tran tuple is a record of a purchased item paid by a credit card at place where and time when, by a UK General Terms customer who is identified by name (FN; LN), address (St, Theory, Algorithms, Experimentation city, post code), AC, phone (phn) and gender (gd). Example instances of card and tran are shown in Figures 1(a) and 1(b), Keywords which are fractions of Dm and D, respectively (the cf rows in Fig. 1(b) will be discussed later). conditional functional dependency, matching dependency, Following [18, 19], we use conditional functional depen- data cleaning dencies (CFDs [18]) '1{'4 to specify the consistency of tran 1. Introduction data D, and a matching dependency (MD [19]) as a rule for matching tuples across D and master card data D : It has long been recognized that data residing in a m database is often dirty [32]. Dirty data inflicts a daunting '1: tran([AC = 131] ! [city = Edi]), '2: tran([AC = 020] ! [city = Ldn]), '3: tran([city; phn] ! [St; AC; post]), '4: tran([FN = Bob] ! [FN = Robert]), Permission to make digital or hard copies of all or part of this work for : tran[LN; city; St; post] = card[LN; city; St; zip] ^ personal or classroom use is granted without fee provided that copies are tran[FN] ≈ card[FN] ! tran[FN; phn] card[FN; tel], not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to where (1) the CFD '1 (resp. '2) asserts that if the area code republish, to post on servers or to redistribute to lists, requires prior specific is 131 (resp. 020), the city must be Edi (resp. Ldn); (2) CFD permission and/or a fee. SIGMOD’11, June12–16, 2011, Athens, Greece. '3 is a traditional functional dependency (FD) asserting that Copyright 2011 ACM 978•1•4503•0661•4/11/06 ...$10.00. city and phone number uniquely determine street, area code FN LN St city AC zip tel dob gd s1: Mark Smith 10 Oak St Edi 131 EH8 9LE 3256778 10/10/1987 Male s2: Robert Brady 5 Wren St Ldn 020 WC1H 9SE 3887644 12/08/1975 Male (a) Master data Dm: An instance of schema card FN LN St city AC post phn gd item when where t1: M. Smith 10 Oak St Ldn 131 EH8 9LE 9999999 Male watch, 350 GBP 11am 28/08/2010 UK cf (0.9) (1.0) (0.9) (0.5) (0.9) (0.9) (0.0) (0.8) (1.0) (1.0) (1.0) t2: Max Smith Po Box 25 Edi 131 EH8 9AB 3256778 Male DVD, 800 INR 8pm 28/09/2010 India cf (0.7) (1.0) (0.5) (0.9) (0.7) (0.6) (0.8) (0.8) (1.0) (1.0) (1.0) t3: Bob Brady 5 Wren St Edi 020 WC1H 9SE 3887834 Male iPhone, 599 GBP 6pm 06/11/2009 UK cf (0.6) (1.0) (0.9) (0.2) (0.9) (0.8) (0.9) (0.8) (1.0) (1.0) (1.0) t4: Robert Brady null Ldn 020 WC1E 7HX 3887644 Male necklace, 2,100 USD 1pm 06/11/2009 USA cf (0.7) (1.0) (0.0) (0.5) (0.7) (0.3) (0.7) (0.8) (1.0) (1.0) (1.0) (b) Database D: An instance of schema tran Figure 1: Example master data and database and postal code; (3) the CFD '4 is a data standardization rules consisting of CFDs Σ and matching rules Γ, the data rule: if the first name is Bob, then it should be \normalized" cleaning problem is to find a repair Dr of D such that (a) as Robert; and (4) the MD assures that for any tuple in D Dr is consistent (i.e., satisfying the CFDs Σ), (b) no more and any tuple in Dm, if they have the same last name and tuples in Dr can be matched to master tuples in Dm by rules address, and moreover, if their first names are similar, then of Γ, and (c) Dr minimally differs from the original data D. their phone and FN attributes can be identified. As opposed to record matching and data repairing, the Consider tuples t3 and t4 in D. The bank suspects that the data cleaning problem aims to fix errors in the data by unify- two refer to the same person. If so, then these transaction ing matching and repairing, and by leveraging master data. records show that the same person made purchases in the UK Here master data (a.k.a. reference data) is a single reposi- and in the US at about the same time (taking into account tory of high-quality data that provides various applications the 5-hour time difference between the two countries). This with a synchronized, consistent view of its core business en- indicates that a fraud has likely been committed. tities [28]. It is being widely used in industry, supported Observe that t3 and t4 are quite different in their FN; city, by, e.g., IBM, SAP, Microsoft and Oracle. To identify tuples St; post and Phn attributes. No rule allows us to identify the from D and Dm, we use matching rules that are an exten- two directly. Nonetheless, they can indeed be matched by a sion of MDs [19] by supporting negative rules (e.g., a male sequence of interleaved matching and repairing operations: and female may not refer to the same person) [3, 37]. 0 0 (a) get a repair t3 of t3 such that t3[city]=Ldn via the CFD 0 (2) We propose a uniform framework for data cleaning. We '2, and t3[FN]=Robert by normalization with '4; 0 treat both CFDs and MDs as cleaning rules, which tell us (b) match t3 with s2 of Dm, to which can be applied; 00 how to fix errors. This yields a rule-based logical frame- (c) as a result of the matching operation, get a repair t3 00 work, which allows us to seamlessly interleave repairing and of t3 by correcting t3 [phn] with the master data s2[tel]; 0 00 matching operations.
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