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On an extension of fuzzy aggregate functions for databases.


Today, Fuzzy sets and fuzzy logic are used in many applications areas such as control, instrumentation, resource management, and databases just to name a few. In this paper, the authors present an extension of fuzzy aggregate functions in the context of fuzzy databases. The paper is not intended as a complete examination of relational databases and the SQL language.

Keywords - Fuzzy logic, Fuzzy sets, Aggregate, Fuzzy operator, Fuzzy relations.


The notion of fuzzy sets (Zadeh, 1965) has found ample and wide applications in a range of areas of finance, and control and management information systems. Fuzzy sets allow us to represent vague (or fuzzy) natural language concepts that may vary depending on the context in which they are used. Formally, the definition of a fuzzy set requires two basic components: a universe of discourse or domain and a function, called the membership function, which defines the "degree" to which a particular element of the domain belongs or not belongs to the set. More formally, we can define a fuzzy set, denoted by A, as follows:

[??]= {(x, [[mu].sub.A](x)) | x [member of] U}

where U is the universe of discourse and [[mu].sub.A]: U[right arrow] [0,1] is the membership function.

Fuzzy sets are an extension of the classic notion of sets (Bourbaki, 1968) called, for sake of understanding, crisp sets. A set A of the latter type can be defined, in terms of the membership function, as

[??]= {(x, [[mu].sub.A](x)) | x [member of] U}, [[mu].sub.A](x) = 0 if x /[member of] A and [[mu].sub.A](x) = 1 if x [member of] A

Observe that in the case of crisp set, the membership function can only takes the extreme values of the interval [0,1].

A Fuzzy set representation may vary even for similar concepts. Table 1, adapted from (Aguilera, 2011), shows examples of fuzzy sets where the domain is a discrete set of ages and the membership functions defined imprecise criteria such young, middle age, and old. It can be observed directly from the table that depending on the function a person may have different degrees of membership for the same universe of discourse. Figure 1 shows the trapezoidal membership functions associated with the values shown in the Table 1.



Several contributions on this area have appeared in the literature (Merigo, Fuller, Yager 2007). Here the authors define a new aggregate operators for fuzzy sets. They are as follows:

Definition 1. Let [??]= {(x, [[mu].sub.A](x)) | x [member of] U} a fuzzy set with membership function [[mu].sub.A](x). Let's define the scalar cardinality of A, denoted by | A |, as

| A | = [n.summation over (i=1)][[mu].sub.i](x)

Definition 2. The contribution denoted by w of an ith element ([x.sub.i], [[mu].sub.i](x)) in a fuzzy set is defined as

[w.sub.i] = [x.sub.i] * [[mu].sub.i](x)

where [w.sub.i] [member of] [??] . The set { [w.sub.i] } is totally ordered. Therefore, the minimum and a maximum element can be defined. We will do this next.

Definition 3. Let [??]= {(x, [[mu].sub.A](x)) | x [member of] U} be a fuzzy set. Let ([x.sub.i], [[mu].sub.i](x)) [member of] A and let wi be the contribution of this element.

([x.sub.i], [[mu].sub.i](x)) can be define as

a) Minimum of A is [w.sub.i] is minimum if [w.sub.i] [less than or equal to] [w.sub.k] (1 [less than or equal to] k [less than or equal to] n).

b) Maximum of A is [w.sub.i] is maximum if [w.sub.i] [less than or equal to] [w.sub.k] (1 [less than or equal to] i [less than or equal to] n).

This way, we take as maximum the element that "contributes the most" to the set. Likewise, we define as minimum the element that contributes "the least" to the set. These definitions clearly extend the classic concept of maximum and minimum of the set when the degree of membership is 1.

Definition 4. Let [??]= {(x, [[mu].sub.A](x)) | x [member of] U} be a fuzzy set; let [w.sub.i] be the contribution of an ith element on [??]. The weighed sum in [??], denoted by s, is defined as

s = [n.summation over (i=1)][w.sub.i]

Definition 5. Let [??]= {(x, [[mu].sub.A](x)) | x [member of] U} be a fuzzy set. Let s be a weighed sum and let | A | be the cardinality of A. The weighed average, denoted by a, is defined as



Traditional databases (Codd, 1970) can be formally defined, in mathematical terms, as consisting of a set of mappings in a given relation schema (Maier 83). A relation scheme, R, is a set of attribute names R={[A.sub.1], [A.sub.1],...[A.sub.n]} where [D.sub.i] =Domain ([A.sub.i]) 1 [less than or equal to] i [less than or equal to] n are arbitrary, non-empty sets, finite or countably infinite. If D = [[union].sup.n.sub.n=1][D.sub.i], a relation r (or table) on R is defined as a set of mappings {[t.sub.1], [t.sub.2],...[t.sub.p]} from R to D where for each mapping [t.sub.j][member of]r, t([A.sub.i])[member of][D.sub.i] with 1 [less than or equal to] j [less than or equal to] p. Each one of these mappings is called a tuple (or row). Following Maier (83) we have used mappings instead of the traditional concept of relations as subsets of the Cartesian Product of sets to avoid the explicit ordering of attribute names in the relational schema. We can extend the previous concept of relation to fuzzy sets with the understanding that for each for each mapping [t.sub.j][member of]r, [micro]([A.sub.i])[member of] [0,1].


In this paper, and following the approach of Cox (1995), the authors also focus on the use of fuzzy logic in the formulation of queries against a standard relational database using relational algebra. By the latter term we mean a formal system for manipulating relations. Here, the use of fuzzy predicates aims to widen the qualifications specifications of the aggregate or group functions to include approximate boundaries. Paraphrasing Cox (1995) we want to obtain what we "intend" to retrieve instead of what we must retrieve due to the mechanical...nature of the crisp aggregate" functions. Within the scope of this paper we are interested in the extensions of the crisp group functions or aggregate such as Average (avg), Minimum (min), Maximum (max), SUM (sum), and COUNT (count). Each of these functions operates on a set of rows and produces a single result. In general, and in terms of relational algebra, we can define an aggregate operation as follows:

G1, G2,...,Gn G F1(A1), F2(A2),...,Fn(An) (E) where

E is any relational-algebra expression

[G.sub.1], [G.sub.2] ..., [G.sub.n] is a list of attributes on which to group (can be empty)

Each [F.sub.i] is an aggregate function

Each [A.sub.i] is an attribute name

Table 3 shows the result of the query that counts "the number of instructors in each department" on the relation indicated in Table 2. Expressed in relational algebra, the query looks like this:

Dept_name G COUNT(instructor)

Based on the definitions 1 through 5 we can now define the fuzzy aggregate relational database operators shown next.


We can define this operator as follows:

Count([L.sub.i]) = [summation over (t[member of]r[conjunction].A[member of][L.sub.i]])[[mu].sub.[PSI]](t) where Li is an alias or label of the attribute t.A of the tuple t of the relation r and [PSI] is a fuzzy condition over the relation.

If we apply this definition to the data set of Table 1 given above we will have result given in Table 4. That is the quantity of young, middle ages and old people in the relation instructor.

Bosc (2010) also introduced a variation of the aggregate function COUNT() on fuzzy sets defined over a fuzzy partition defined as follows:


Where [L.sub.i] is the label or alias of an attribute, [[mu].sub.[omega]](t) is a fuzzy condition, and [[mu].sub.Li](t.A) defines the fuzzy condition on the column or attribute A for the t tuples of the relation.

To illustrate the use of this definition, consider, for example, the membership function on the salary attributes defined as low, medium and high of Figure 2 and Table 6. For the query: "what is the distribution over the different age classes of the employees whose salary is medium?" the result is shown in the Table 7.


6. MIN

We can define this operator as

MIN= (t.A, [[mu].sub.[PSI]](t)) where is (t.A, [[mu].sub.[PSI]](t)) is minimum according to the Definition 3 given above.

In other words, the minimum is the attribute, in a tuple, that contributes the least.

Example query: "what is the minimum age for young instructors?" According to Table 8 the minimum is MIN = (29, 0.1)

7. MAX

We can define this operator as

MAX= (t.A, [[mu].sub.[PSI]](t)) where is (t.A, [[mu].sub.[PSI]](t)) is maximu according to the Definition 3 given above.

In other words, the minimum is the attribute, in a tuple, that contributes the most.

Example query: "what is the maximum age for young instructors?" According to Table 8 the maximum is

MAX = (22, 0.8)

8. SUM

We can define this operator as

[n.summation over (i=1)][w.sub.i] where [w.sub.i] is the contribution defined according to the Definition 4 given above

9. AVG

We can define this operator as the SUM divided by the Cardinality (Definition 1 given above)


A first implementation attempt of the fuzzy aggregate COUNT was performed using the database management system PostgreSQL. The catalog was modified first to include the new aggregate functions. The catalog of a relational database management system stores the metadata of the relational schema such as all table related information (indexes, attributes, constraints, priviliges, etc). The parser was modified next. Here a new type of node was created. This new node is called A Partition and contains all relevant data of the partition such as is its domain, their labels, among other things. A pointer (UsingClause) to a list of nodes partition was added to the SelectStmt node. Within the module planner the tree search is verified and, from it, a fuzzy plan is created. Additional plans are also created within this module to calculate the necessary cardinalities to evaluate the fuzzy aggregate functions. The run-time manager uses the different execution plans in a recursive manner to obtain the required sets. The run-time manager also calculates the degrees of membership according to the fuzzy predicates that were defined.

An example of query for a fuzzy aggregate with fuzzy partition in a SQL expression is shown in Figure 3.
lab_marcha# select label (edad), count(*), count_p(*), count_prel(*)
            from estudlo where paso = gordo group by label (edad)
            using partition (edad) = {nino, adoleconte};

leabel (edad)  count  count_p    count_prel

adolecente     2      0.0600006  0.000766791
nino           4      3.5        0.00253531
(2 rows)

lab_marcha# select label (edad), count(*), count_p(*), count_prel(*)
            from estudlo where paso = flaco group by label (edad)
            using partition (edad) = {maduro, viejo};

leabel (edad)  count  count_p  count_prel

adolecente     15     11.8182  0.248092
viejo           3      1.7     0.0977011
(2 rows)

lab_marcha# select label (edad), count(*), count_p(*), count_prel(*)
            from estudlo where paso = bajo group by label (edad)
            using partition (edad) = {nino, adoleconte, maduro, viejo};

leabel (edad)  count  count_p    count_prel

adolecente      136     77.75    0.99361
nino           1432   1333.5     0.965954
maduro           60     46.8182  0.982825
viejo            15      8.9     0.511494
(4 rows)

Figure 3. Query result in PostgreSQL


The authors appreciate the financial support of the Fulbright program at James Madison University, USA and the CDCH of Carabobo University, Venezuela to Dr. Ana Aguilera.


Bosc, P. ; Pivert, O. On a fuzzy group-by clause in SQLf IEEE International Conference on Fuzzy Systems (FUZZ), Pp. 1-6, 2010.

Bourbaki, Nicolas. Elements of Mathematique, Theorie des Ensembles, Chap 2. Hermann, Paris, 1968

Codd, E.F. A Relational Model of Data for Large Shared Data Banks. CACM 13:6, June 1970, 377-387

Cox, Earl. Fuzzy Logic for Business and Industry. Charles River Media, Inc. 1995.

Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353. doi:10.1016/S0019-9958(65)90241-X

Merigo; J.M; Casanova, M. Generalized Aggregation Operators and Fuzzy Numbers in a Unified Model between the Weighted Average and the OWA Operator. Date unknown.

Fuller, R. OWA Operators in Decision Making

Yager, R. R. Final Report: Information Fusion and Aggregation for Cooperative Systems. 2007

Ana Aguilera (1)

Ramon Mata-Toledo (2)

Alberto Subero (1)

Morgan Monger (3)

Pranshu Gupta (4)

(1) Universidad de Carabobo (Valencia, Venezuela)

(2) James Madison University (Harrisonburg, Virginia, U.S.A.)

(3) Ellucian, Inc. (Fairfax, Virginia, U.S.A.)

(4) DeSales University (Center Valley, Pennsylvania, U.S.A.)
Age  [mu]Young  [mu]Middle Age  [mu]Old

51   0          0.9             0.1
40   0          1               0
24   0.6        0.4             0
39   0          1               0
28   0.2        0.8             0
54   0          0.6             0.4
29   0.1        0.9             0
61   0          0               1
22   0.8        0.2             0
62   0          0               1

Table 1. Membership function values for ages

Fuzzy Relational Aggregate Operators

ID  Instructor  Dept_name         Salary  Age

17  Crick       Biology           72000   51
76  Katz        Computer science  75000   40
26  Srinivasan  Computer science  65000   24
12  Brandt      Computer science  92000   39
19  Kim         Electric          80000   28
08  Wu          Finance           90000   54
31  Singh       Finance           80000   29
09  El Said     History           60000   61
44  Califieri   History           62000   22
23  Mozart      Music             40000   62

Table 2. Instructor relational table

Dept_name         count

Biology           1
Computer science  3
Electric          1
Finance           2
History           2
Music             1

Table 3. Query result

Label(age)  Count

young       0.8
middle-age  3.7
old         1.5

Table 4. Count

ID  Age  [mu]young  [mu]middle age  [mu]old  Salary  [mu]low

17  51   0          0.9             0.1      65      0
76  40   0          1               0        45      0
26  24   0.6        0.4             0        19      1
12  39   0          1               0        32      0.3
19  28   0.2        0.8             0        24      1
08  54   0          0.6             0.4      57      0
31  29   0.1        0.9             0        18      1
09  61   0          0               1        15      1
44  22   0.8        0.2             0        45      0
23  62   0          0               1        59      0

ID  [mu]Medium  [mu]High

17  0              1
76  1              0
26  0              0
12  0.7            0
19  0              0
08  0.8            0.2
31  0              0
09  0              0
44  1              0
23  0.6            0.4

Table 6. Membership values for ages and salaries

Label(age)  Count

young       0.8
middle-age  2.5
old         1

Table 7. Count defined by
Bosc (2010)

Age  [mu]Young  [w.sub.i]

51   0           0
40   0           0
24   0.6        14.4
39   0           0
28   0.2         5.6
54   0           0
29   0.1         2.9
61   0           0
22   0.8        17.6
62   0           0

Table 8. The contribution for the attribute age in a fuzzy set of young
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Author:Aguilera, Ana; Mata-Toledo, Ramon; Subero, Alberto; Monger, Morgan; Gupta, Pranshu
Publication:Journal of Information Systems & Operations Management
Article Type:Report
Date:May 1, 2013
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