The paper describes the key properties of the collaborative systems. There are presented main quality characteristics for the collaborative systems. The paper analyzes different types of indicators. They represent the base for further metrics definition. There are described the indicators most important characteristics as sensitivity, non catastrophic, non compensatory and representatives.collaborative systems

Adrian VISOIU

Cristian CIUREA

Ion IVAN

English

Research Papers in Economics

PROPERTIES OF THE COLLABORATIVE SYSTEMS METRICS 
 
ION IVAN, PhD 
ACADEMY OF ECONOMIC STUDIES BUCHAREST 
Email: ionivan@ase.ro 
 
Cristian CIUREA 
ACADEMY OF ECONOMIC STUDIES BUCHAREST 
Email: cristian.ciurea@ie.ase.ro 
 
Adrian VISOIU 
ACADEMY OF ECONOMIC STUDIES BUCHAREST 
Email: adrian.visoiu@ie.ase.ro 
 
Abstract. The paper describes the key properties of the collaborative systems. There are presented main 
quality characteristics for the collaborative systems. The paper analyzes different types of indicators. 
They represent the base for further metrics definition. There are described the indicators most important 
characteristics as sensitivity, non catastrophic, non compensatory and representatives. 
 
1.  INTRODUCTION 
 
A collaborative system is one where multiple users or agents are engaged in a shared activity, 
usually from remote locations. In the large family of distributed applications, collaborative systems are 
distinguished by the fact that the agents from the system are working together towards a common goal 
and have a critical need to interact closely with each other. 
The informatics collaborative system is like a distribution firm that has the objective to sell more 
and more quantities of their products and for that has commercial agents, which go to various retail shops 
in order to convince them to close a distribution contract. 
The properties of the collaborative systems are an important subject of our days, and an important 
part of the human activities is involved in this problem. The complexity of this subject, but also the huge 
number of the applications makes impossible to have a large presentation in a note, but we would 
underline some of the main aspects. 
Collaborative systems represent a new interdisciplinary domain at the intersection of economics, 
computer science, management, sociology, etc. From the implementation viewpoint, the collaborative 
systems represent software entities that are developed during a life cycle process that starts with the 
problem analysis and ends with the implementation of a fully functional software system. Implementing a 
collaborative system is accomplished using software instruments that allow the development of 
distributed software applications. 
Science has great impact on the development of different types of collaborative systems from 
various activity fields. The medical field in which modern communication technologies allow doctors 
from around the world to work on the same patient gives one important domain that was one of the first 
fields presenting great interest in implementing complex collaborative systems. In a chirurgical operation 
each person from the group of doctors has distinct roles. In this example it is analyzed a collaborative 
system model representing a training on different chirurgical activities that is done in a virtual medium. 
The training is based on the scenario in which the instructor and the trainee are on different locations. The 
instructor and the trainee share a common virtual space that contains various three-dimensional 
anatomical models. Each person interacts with the other one through the virtual space and a medical 
simulation engine describes the physical and logical behavior of objects present on the virtual scene. The 
interaction is maintained by a multi-modal interface that uses visual 2D and 3D data, voices and audio 
simulation. Each person is in front of a working table that has a monitor and stereo active pair of glasses. All of these generate a three-dimensional desktop. For collaborative use, it has been implemented a mini 
broadband system that allows creating a videoconference between persons. 
 
 
 
Fig. 1. Collaborative system model representing training on different chirurgical activities [STEV05] 
 
The interaction between the instructor and the trainee is based on voice, gestures and chirurgical 
demonstrative actions, step-by-step tutorial and simultaneous actions.  
The chirurgical training from this example, suppose a high level of interaction between the two 
persons. In opposition with training systems developed only for chirurgical dexterity, this process is 
concentrated also on procedures that target the understanding of the patient anatomy. The learning 
process is enhanced by the demonstration, the dialog and the show how activities. 
 
2. THE QUALITY CHARACTERISTICS SYSTEM FOR COLLABORATIVE SYSTEMS 
 
  The collaborative systems represent, from the implementation viewpoint, software entities that 
are developed during a life cycle process that starts with the problem analysis and ends with the 
implementation of a fully functional software system. 
The quality is a main characteristic of a collaborative system and contains the followings 
properties: maintainability, reliability, efficiency, usability, portability and functionality. 
This characteristic may be analyzed also from the viewpoint of the length of the track the message 
is taking from the source component to the destination one. On this way, the system must take care the 
messages are not lost in the system or they aren’t altered. 
When for each quality characteristic C1, C2, …, Cn are established the normal areas in which are 
enclosed, delimited like subintervals [bi, 1] with 0 < bi < 1, i=1..n, on represent on the nomogram the 
standard diagram of the collaborative system functionality:  
 
Fig. 2. The standard diagram for the functionality of a usual collaborative system 
 
Is defined below the indicator HS like: 
 
2
1
S
S
HS = , where S1 and S2 are the surfaces delimited in the figure 2. 
  If HS = 0, then the collaborative system is working properly and very well and if HS = 1, the 
collaborative system is working very bad. 
The most important quality characteristics for the collaborative systems are enumerated below: 
C1 – complexity; 
C2 – structurability; 
C3 – stability; 
C4 – adaptability; 
C5 – operationality; 
C6 – integrability. 
All the indicators associated with these characteristics are defined with values from the interval 
[0;1]. Experimentally, was established the following intervals: 
[0; 0,78) – the interval in which the collaborative system is working unsatisfactorily for the 
characteristic Ci, i=1..6. 
[0,78; 0,92] – the interval in which the collaborative system is working well for the characteristic 
Ci, i=1..6. 
(0,92; 1]  –  the interval in which the collaborative system  is working very well for the 
characteristic Ci, i=1..6. 
 
3. THE QUALITY INDICATORS 
  The main quality indicators of the collaborative systems are the indicators like complexity, 
reliability, portability and maintainability.  
  The complexity is a measure for the interdependencies between components and their links and 
also for the diversity of different types of input and output constructions. This characteristic describes the 
density of fluxes between the components of the system. 
  The McCabe complexity was implemented with the next formula: 
CC = na – nn + 2, where: 
- na is the number of relations between the components of the collaborative system; 
- nn is the number of collaborative system components. 
The system reliability is determined by analyzing the number of problems solved by the system 
and the total number of specified problems. 
The reliability for the software component of a collaborative system is defined like:   r total
  succes r 
    fiab   I   = , where:  
- I fiab is the reliability indicator; 
- r succes is the number of successfully executions of the program 
- r total is the total number of program executions. 
System reliability is a very important quality indicator because: 
- it value is directly determined by the number of processes and activities that give correct and 
complete results; 
-  allows particular approaches for determining models of quality estimation; taking into 
consideration the hypothesis that once the causes that generates unwanted errors and system failures are 
eliminated it is possible to increase its levels and directly the system quality; 
- its value influences the entire collaborative system project; 
The portability for the software component of a collaborative system is: 
LI
LE LM LA
  -   1     portab G 
+ +
= , where: 
- G portab is the portability degree indicator; 
- LA represents the number of added instructions; 
- LM represents the number of modified instructions; 
- LE represents the number of instructions eliminated from the program; 
- LI represent the total number of program instructions; 
The maintainability of a collaborative system is defined like: 
dezv   T
modif   T
  ment    I = , where: 
- I ment is the maintainability indicator; 
- T modif represent the necessary time for the realization of the modifications in the system in 
order to keep them in current use; 
- T dezv is the necessary time for the system development. 
The maintainability is a process particular to software products that have a complex development 
process and that are intended to be used for a long time, meaning more than three years. In this category 
are included also products like the collaborative systems. 
The quality-aggregated indicator for a collaborative system is defined like: 
q
Y X
Y X
p
B A
B A
Icalit *
) , max(
) , min(
*
) , max(
) , min(
+ = , where: 
- A is the level of the planned quantity; 
- B is the level of the realized quantity; 
- X is the level of the planned quality; 
- Y is the level of the realized quality; 
- p represent the percentage of quantitative characteristics and has, generally, the value 0,4; 
- q represent the percentage of qualitative characteristics and has, generally, the value 0,6. 
 
 
4. THE INDICATORS PROPERTIES 
 
  The analytical forms of the indicators must be built such as the indicators simultaneously assure 
the following conditions. They must be: 
-  sensitive, that is at small variations of the influence factors the result variable has small 
variations; at big variations of the influence factors the result variable has big variations; 
-  non-compensatory, that is at different variation sets of the factors, small values of the result 
variable are not obtained; -  non-catastrophic, that is at small variations of the factors, big variations of the result variable 
have not to obtain; 
-  representative, it represents the quality to be accepted by users in analysis making assuring the 
significance of the results. 
The analytical form of an indicator used to measured quantitative levels for collaborative systems 
qualitative characteristics is based on 
y = f(x1, x2, …,xnfc), 
where: 
nfc   – number of identified factors which have impact on the evolution of analyzed phenomena; 
xi   – measured level for the i
th influence factor of the case study; 
f( )   – an analytical real form used to represent the dependency between the influence factors 
and result variables; it is used to describe and to study the phenomenon; 
y   – result variable that describe an existing situation in the phenomenon evolution. 
 
In case of the type I indicators, which have analytical forms as:  
B
A
I = , 
the catastrophic character is the result of the very high variation of metrics value while the value 
of B factor is converging to zero. 
 
The indicator KT = N1log2N1 + N2log2N2 is sensitive, because the variations from N1 to N1’ = 
N1 + γ, respectively from N2 to N2’ = N2 + Δ determines: 
KT’ = N1’log2N1’+ N2’log2N2’ = (N1+γ) log2(N1+γ) + (N2+Δ) log2(N2+Δ) = 
N1 * log2(N1+γ) + N2* log2(N2+Δ) + γ * log2(N1+γ) + Δ * log2(N2+Δ) > KT + γ * log2(N1+γ) + Δ * 
log2(N2+Δ)  
 
  In the case of n variables x1, x2, …, xn, a generalized indicator for collaborative systems are: 
In = f(x1, x2, …, xn) = 
   xn} ,    x2, max{x1,
   xn} ,    x2, min{x1,
…
…
 
For this indicator, the maximal value are 1 when x1=x2=…=xn,  with the condition that xi>0, 
i=1..n.  The sensitive property for this indicator are verified, but the non-compensatory property is not 
verified, because at different variation sets of x1, x2, …, xn factors, the In value is the same.  
   
The indicator is sensitive in case in which the levels associated to the influence factors have a 
variation such as it obtains the same general type of variation for the analyzed collaborative system. 
 
5. CONCLUSIONS 
  The field of collaborative systems is a domain that has many published papers and that has 
acquired in the last period a great volume of theoretical knowledge. This provides the methods and 
techniques to analyze the problem, to identify the resulting variables, the influence factors and in the end 
to define the model. 
  The properties of the collaborative systems metrics have great impact on the number of factors 
and as result on the scale of the model. In the end, it must be reached equilibrium between the model 
dimension and its capability to give significant results. The metrics must be not too complicated because 
it will use lots of resources when implemented and also it must be not too simple because the measured 
levels will loose relevance.  
 
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PROPERTIES OF THE COLLABORATIVE SYSTEMS METRICS

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