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QA
data
Raw
data
FA
data
Fig. 2. Example of a vector space formed by dimensions FA data, QA data, Raw data
is associated with a property value p j
P i , the corresponding vector dimension
π j (
v a ) has value 1; otherwise, the dimension π j (
v a )hasvalue0:
v a )= 1 ,
props i ( a );
0 , otherwise.
For process model m in Fig. 1, activities Prepare data for quick analysis and
Prepare data for full analysis correspond, respectively, to vectors
if p j
π j (
v 1 =(0 , 1 , 1)
and
v 2 =(1 , 0 , 1) in the vector space with dimensions FA data, QA data, Raw
data , see Fig. 2.
Similarity of two vectors in the space is defined by the angle between these
vectors: the larger the angle, the more distant the activities are. Typically, the
cosine of the angle between two vectors is used as a vector similarity measure:
v a 2
v a 1 v a 2
v a 1 ·
sim ( a 1 ,a 2 )= cos ( v a 1 ,v a 2 )=
(1)
Then, the distance between two activities is:
dist ( a 1 ,a 2 )=1
sim ( a 1 ,a 2 )
(2)
By construction the vector dimension values are non-negative. Hence, the activity
similarity and activity distance measures vary within the interval [0 , 1].
For a process model collection c =( M, A, P, σ ) we distinguish two types of
vector spaces. On the one hand, a vector space can be formed by the dimen-
sions corresponding to the activity property values disregard their type, i.e., all
elements of P . We reference such spaces as heterogeneous vector spaces .Anexam-
ple of a heterogeneous vector space is a space with 6 dimensions Analyst, Clerk,
FA data, QA data, Raw data ,and Senior analyst . On the other hand, a vector
space can be formed by the dimensions corresponding to the activity property
values of a particular type. Given an activity property type t ,suchaspaceis
formally defined by the set P t =
. We refer to such spaces
as homogeneous vector spaces . Fig. 2 provides an example of a homogeneous
vector space formed by activity properties of type Data object .Wedenotethe
{∀
p
P : type ( p )= t
}
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