Automatic Entity Extraction from an Instance of a Relational Database in .NET

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4.2 Automatic Entity Extraction from an Instance of a Relational Database
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Assume that you start with the instance of a database in Table 23.4. This table links three different entities: suppliers, projects and parts. We assume that initially, we ignore these entities and would like to discover these entities automatically. We can notice that an entity is defined as a subset of attributes. In this example, we have the following attributes: {S#, SNAME, STATUS, SCITY, P#, PNAME, COLOR, WEIGHT, PCITY, J#, JNAME, JCITY, QTY }.
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Table 23.4 An instance SPJ of a relational database.
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S# SNAME STATUS SCITY S1 S1 S2 S2 S2 S2 S2 S2 S2 S2 S3 S3 S4 S4 S5 S5 S5 S5 S5 S5 S5 S5 S5 S5 Smith Smith Durand Durand Durand Durand Durand Durand Durand Durand Dupont Dupont Clark Clark Kurt Kurt Kurt Kurt Kurt Kurt Kurt Kurt Kurt Kurt 20 20 10 10 10 10 10 10 10 10 30 30 20 20 30 30 30 30 30 30 30 30 30 30 London London Paris Paris Paris Paris Paris Paris Paris Paris Paris Paris London London Athens Athens Athens Athens Athens Athens Athens Athens Athens Athens P# PNAME COLOR WEIGHT PCITY p1 p1 p3 p3 p3 p3 p3 p3 p3 p5 p3 p4 p6 p6 p1 p2 p2 p3 p5 p5 p5 p4 p6 p6 Nut Nut Screw Screw Screw Screw Screw Screw Screw Cam Screw Screw Cog Cog Nut Bolt Bolt Screw Cam Cam Cam Cam Cam Cam Red Red Blue Blue Blue Blue Blue Blue Blue Blue Blue Red Red Red Red Green Green Blue Blue Blue Blue Red Red Red 12 12 17 17 17 17 17 17 17 12 17 14 19 19 12 17 17 17 12 12 12 14 19 19 London London Rome Rome Rome Rome Rome Rome Rome Paris Rome London London London London Paris Paris Rome Paris Paris Paris London London London J# JNAME J1 J4 J1 J2 J3 J4 J5 J6 J7 J2 J1 J2 J3 J7 J4 J4 J2 J4 J5 J4 J7 J4 J2 J4 Sorter Console Sorter Punch Reader Console Collator Terminal Tape Punch Reader Tape Console Console Punch Console Collator Console Tape Console Punch Console Punch Console JCITY Paris Athens Paris Rome Athens Athens London Oslo London Rome Paris Rome Athens London Athens Athens Rome Athens London Athens London Athens Rome Athens QTY 200 700 400 200 200 500 600 400 800 100 200 500 300 300 1000 100 200 1200 500 400 100 800 200 500
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The entity extraction algorithm [18 20] is composed of the following steps: 1. We define elementary data as a pair of (attribute, value) written as attribute.value . The elementary data set is: {S#.S1, S#.S2, S#.S3, S#.S4, S#.S5, SNAME.Clark, SNAME.Kurt, SNAME.Dupont, SNAME.Smith, SNAME.Durand, STATUS.10, STATUS.20, STATUS.30, SCITY.Paris, SCITY.Athens, SCITY.London, P#.p1, P#.p2, P#.p3, P#.p4, P#.p5, P#.p6, etc }. 2. We then create a binary relation R relating elementary data using the following definition. We say that a pair of items X x Y y belongs to binary relation R if and only if x is the value for attribute X, and y is the value for attribute Y , for the same row in the instance SPJ in Table 23.4. We then extract the first optimal concept REopt as shown in Figure 23.11. 3. From REopt we may extract two disjoint sets of attributes: set Sleft , which contains the attributes which appear in dom REopt , and set Sright , which contains the attributes which appear in cod REopt . Sleft = S# SNAME, STATUS, SCITY, P# PNAME, COLOR, WEIGHT, PCITY Sright = J# JNAME, JCITY, QUANTITY
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{S#.S2, SNAME.Durand, STATUS.10, SCITY.Paris, PNAME.Screw, COLOR.Blue, JCITY.Rome, WEIGHT.17} X {J#.J1; JNAME.Sorter; JCITY.Paris, QTY.200, J#.J4, JNAME.Console, JCITY.Athens; QTY.500, J#.J2, JNAME.Punch, JCITY.Rome; J#.J3; JNAME.Reader, QTY.200, J#.J5, Collator.10, JCITY.London, QTY.600, J#.J6, JNAME.Terminal, JCITY.Oslo; J#.J7, JNAME.Tape, QTY.800};
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