INFORMATION ARCHITECTURE OF FRACTAL

INFORMATION SYSTEMS

Jānis Grabis, Mārīte Kirikova and Jānis Vanags

Faculty of Computer Science and Information Technology, Riga Technical University, Kalku 1, Riga, Latvia

Keywords: Fractal systems, Information architecture, Information systems design.

Abstract: The fractal approach has emerged as a promising method for development of loosely coupled, distributed

enterprise information systems. This paper investigates application of information architecture in

development of fractal information systems. Principles of designing the information architecture of fractal

information systems as well as rules for analyzing the information architecture are developed. These rules

are used to obtain problem-domain representations specifically suited for needs of individual fractal entities.

The usage of the information architecture in implementation of the fractal information system for the

university’s study programme development problem is demonstrated.

1 INTRODUCTION

Modern enterprises operate in close collaboration

with many other enterprises. Support for networking

and collaboration has become a vital requirement for

information systems (IS). That includes addressing

heterogeneity of systems, dealing with conflicting

objectives, accounting for dynamic changes in the

network, information sharing, knowledge exchange,

and providing different, specialized views of the

system. Despite elaboration of various technologies

(e.g. workflows, groupware, content and knowledge

management, virtual enterprises) addressing some

aspects of these issues, they remain of major

importance in enterprise computing and software

development. A fractal approach is also emerging as

a promising technology for designing multiple-

interrelated systems (Warneke, 1993; Hoverstadt,

2008). In the area of enterprise ISs, the fractal

approach appears suitable for dealing with ISs

development problems characterized by a relatively

loose coupling among entities involved in solving of

focused knowledge intensive problems without

highly elaborated and structured workflows.

Examples of such problems are operations of project

consortiums, academic institutions and distributed

product-design groups.

Fractal systems consist of self-similar, self-

optimizing, goal-oriented fractal (independently

acing organizational entities) arranged in a loosely

coupled hierarchical network. They are continuously

evolving and are characterized by rich information

exchange flows inside fractal entities, between

different levels of the fractal system and with

external environment (Ryu and Jung, 2003). Goal-

orientation allows balancing individual and system-

wide interests of all entities involved. Self-similarity

allows simplifying and structuring design of what

might appear as a chaotic system. Self-organization

allows finding ways for achieving goals without having

predefined processes. Information flows supported by

the fractal systems facilitate knowledge exchange.

Ryu and Jung (2003) and Kirikova (2008) discuss

general aspects of development of Fractal

Information Systems (FIS). This paper focuses on

information structuring and management issues in

FISs by means of elaboration of Information

Architecture of Fractal Information Systems

(IAFIS). Information Architecture (IA) describes the

structure of a system, i.e., the way information is

grouped, navigation methods and terminology used

within a system (Barker, 2005). That is particularly

important in fractal systems because a common,

easily accessible information basis is necessary for

fractal entities to achieve their and system-wide

objectives. Additionally, IA defines information

flows among fractal entities. From the ISs

development perspective, IA is used to develop self-

similar representations of the problem domain for

entities involved in the problem solving and to trace

information interdependencies.

150

Grabis J., Kirikova M. and Vanags J. (2009).

INFORMATION ARCHITECTURE OF FRACTAL INFORMATION SYSTEMS.

In Proceedings of the 11th International Conference on Enterprise Information Systems - Information Systems Analysis and Speciﬁcation, pages

150-155

DOI: 10.5220/0001988101500155

 SciTePress

Thus, the objective of this paper is to elaborate

methods for designing and analyzing IAFIS and to

demonstrate application of IAFIS in development of

FISs. It is assumed that each entity belonging to a

fractal system has its own problem representation,

which is based on a common goals and ontology of

the fractal systems. This representation suits needs

of a particular entity. IAFIS defines all Information

Elements (IE) characterizing the problem domain

and relationships among these elements. Analysis of

IAFIS yields IEs relevant to the problem

representation of individual entities. The

contribution of this paper is elaboration of rules for

analyzing IAFIS, as well as outlining of principles

for designing FISs on the basis of IAFIS. These

principles also can be applied in design of enterprise

content management systems and portals, which are

enterprise systems having limited support for

formalized development. Design, analysis and

application of IAFIS throughout the paper is presented

by using a problem of ISs development for Study

Programme (SP) development in Latvian universities.

2 FRACTAL IS

For purposes of this paper, the FIS is defined as a

problem-oriented IS shared by a network of

interrelated Organizational Entities (OE), where

each entity has its own representation of the problem

and information needs. Key elements of the FIS are

shown in Figure 1.



Figure 1: A fractal system and its IS. R

denotes problem

domain representation.

A FIS is created upon demand by a group of

entities involved in solving a common problem. The

common problem is defined by a set of goals and

core ontology. The core ontology defines initially

agreed concepts characterizing the problem domain

(Kirikova, 2009). However, it is likely that each

entity also has its own internal ontology, which to

some extent deviates from the core ontology because

usually it is not restricted just to the particular

problem domain. Entities might be arranged

hierarchically. However, they are relatively

independent, and entities at higher hierarchical

levels provide only problem-solving goals and

general framework while the choice of sub-goals and

particular problem-solving mechanisms is not

strictly regulated. An IS supports problem-solving. It

provides multiple views or representations of the

problem domain. These representations are suited

according to specific needs of each entity. At the

same time, they use the common core ontology and

common design principles. IAFIS is used to provide

a systematic framework for developing and

maintenance of these different representations.

3 IAFIS META-MODEL

In the FIS, an organization entity uses information to

complete its activities and achieve its objectives. It

either is an owner of this information or consumes

information provided by other OEs. IAFIS defines

Information Elements (IE) and relationships between

these elements. It allows to identify information

needs of each OE and to reason about change and

knowledge propagation inside the fractal system.

UML is used to describe IAFIS.

There are five types of nodes used to define

IAFIS (Figure 2). The central element is

InformationElement. It is used to describe any kind

of information unit (e.g., document, record, file)

relevant to a particular problem domain.

InformationElement may contain multiple

Parameter elements. The Parameter element

identifies data items of the IE what either

characterize this IE or have major importance in the

problem domain. It can be represented either as a

class or as an attribute. Parameters often are

numerical values, which can be used to quantify and

analyze the problem domain. IEs together form a

problem representation suitable for a particular OE.

Multiple OEs can share one representation. One

organizational can have multiple problem

representations. Definitions and meaning of IEs and

parameters are provided in either the core ontology

or ontologies owned by individual OEs.

Six types of relationships among IEs are defined.

Association is used to describe general connections

between IEs. A dependency relationship is used to

INFORMATION ARCHITECTURE OF FRACTAL INFORMATION SYSTEMS

151

InformationElement

Param e ter

OrganizationalUnit

Representation

Relationship

Pa rameterization

Summation

Owns

Uses

Figure 2: General representation of IAFIS.

Univ ersity

«OrganizationalEntity»

«InformationElement»

University: : StudyProgrammeRegulations

- MandatoryCoursesCredits: Parameter

«InformationElement»

Univ ersity::C ourseRegister

«Parameter»

Univ ersity::

TotalCreditHours

Insti tute

«OrganizationalEntity»

«InformationElement»

University::

ProjectWork Regulations

«InformationElement»

Institute::StudyProgramme

«InformationElement»

Institute::Course

«Parameter»

Institute::

TotalCreditHours

«Parameter»

Institute::

CreditHour

«InformationElement»

Institute::

StudyProgrammeDescription

«InformationElement»

Institute::Cou rceDescription

AVG

«Summation»

SUM

«Summation»

Explains

«Parameterization»

Figure 3: IA of SP development information system.

describe some kind of dependence of one IE upon

other IE. It is particularly used to show that content

of one IE is developed according to some

requirements provided by another IE. Aggregation

and composition relationships are used to describe

that an IE consists of other IEs. A parameterization

relationship indicates that a

Parameter element

belongs to the specified IE. A summation

relationship is used to describe data transformation

and aggregation relationships between different

problem domain representation.. IAFIS is developed

using the defined elements. A designer identifies

fractal OEs and IEs, assigns the IEs to the OEs,

parameterizes the IEs and establishes relationships

among the IEs. Thereafter, IAFIS is analyzed and

used in development of problem domain

representations for individual OEs and for

maintenance of the FIS by tracking change and

knowledge propagation.

The example of study program development IS is

used to illustrate IAFIS. SPs comply with general

guidelines set by the law. The SPs are accredited by

the Latvian Ministry of Education and Sciences. On

the basis of these legal requirements, each university

develops their internal regulations on development of

SPs. These regulations are more detailed and include

references to other administrative regulations and

documents (e.g., course register). SPs are developed

by university’s faculties.

A fragment of IA for the SP development IS is

shown in Figure 3. It consists of two OEs (depicted

using parallelogram) – university and faculty (other

hierarchical levels such are omitted). The SP

development at the university is governed by “SP

ICEIS 2009 - International Conference on Enterprise Information Systems

152

Development Regulations” represented by the

University::StudyProgrammeRegulations IE. There

are multiple data items characterizing this IE, and

these are presented using elements of type Parameter

(only one parameter is shown for the sake of

simplicity). For instance, the regulations mandate

the total number of credit hours required in a SP.

This is an important characteristic and therefore is

shown as a parameter. SPs are developed by

faculties, and IE

Faculty::Study Programme

belongs to the faculty. The dependency relationship

is used to show that the SP is developed according to

requirements provided by the SP development

regulations. The SP also has a parameter

characterizing the total number of credit hours. The

summation relationship is used to describe

information flows from one hierarchical level to

another. In this case, it is shown that the University

sets the bounds on the total number of credit hours

and that the University averages total credit hours

data received from faculties (this information can be

used to analyzed structure of SP and to judge about

necessary adjustments in regulations). The SP IE is

also shown to depend upon

University::

ProjectWorkRegulations

and indirectly upon

University::CourseRegister. There are also IEs,

which are unique to the faculty and do not directly

depend upon IEs belonging to other OEs.

4 ANALYSIS OF

ARCHITECTURE

IAFIS is analyzed in order to develop

representations of the problem domain for each OE.

The analysis yields several types of collections of

IEs gathered from different OEs. These collections

are aimed to contain all IEs and their parameters

necessary for an OE to address the particular

problem. The entity specific problem representations

consist of properly arranged collections. IAFIS is

also analyzed to understand information

interdependencies, to identify isolated IEs as well as

to investigate other features of fractal systems.

The most important task of the analysis is

identification of required IEs for each problem

representation. These IEs are grouped in collections.

The first collection C

includes all IEs owned by a

particular OE. The second collection C

includes all

IEs, which are suppliers in a dependency

relationship with IEs belonging to the first group.

The third collection C

consists of all IEs, which are

indirect suppliers of IEs. The fourth collection C

includes IEs, which have any other types of direct

relationships with IEs belonging to C

. These rules

are formally specified using OCL (Object

Constraints Language). Collections of IEs

subsequently can be used during the implementation of

the FIS for grouping and to establish hierarchy of IEs.

IEs in IAFIS are parameterized to highlight the

most important characteristics of the problem

domain. Relationships between parameters are

shown using the summation relationship. The

summation relationship is bidirectional. From a

supplier to a client, it describes what kind of

restrictions the supplier imposes on the client. From

a client to a supplier, it defines the supplier-side

processing of data provided by the client.

Parameterization and summation relationships

are also analyzed. That includes finding all para-

meters characterizing a particular IE. For each OE,

three groups of parameters are identified. The first

group P

includes parameters characterizing each IE

from collection C

. In the FIS, these are displayed

along the particular IE. The second group P

includes parameters directly or indirectly provided

by clients in the summation relationships. These can

be used as key performance indicators. The third

group P

includes parameters directly or indirectly

provided by suppliers in the summation

relationships. These can be used as the most

important problem-solving guidelines.

The analysis of IAFIS is also used to update the

ontology of the fractal system. IEs and parameters

used by OEs are matched against concepts defined

in the core ontology. If these elements are not found

in the core ontology, they are either identified as

candidates for inclusion in the core ontology or

inspected for correspondence to concepts already

included in the core ontology. An OE specific

problem representation contains only those elements

deemed explicitly necessary to problem-solving

though tracing capabilities also can be provided.

The analysis also is used to identify isolated IEs

and isolated clusters of IEs. These collections are

candidates for knowledge propagation and

modification of the core ontology in the case of

semantical inconsistencies.

5 UPDATING OF IAFIS

During maintenance of the FIS, IAFIS is updated

both automatically and manually in response to

changes in the fractal system and problem-domain.

The updating is classified as change propagation and

knowledge propagation. The FIS can be modified

according to the changes made in IAFIS.

INFORMATION ARCHITECTURE OF FRACTAL INFORMATION SYSTEMS

153

Figure 4: Sample implementation of SPDIS.

Change propagation deals with updating of IA in

the case of changes in its elements or relationships.

Three kinds of change propagation situations are

considered: 1) updating in the case of added IE; 2)

updating in the case of added parameter; and 3)

updating in the case of added relationships.

In the case of added IE, relationships between

the element and other elements owned by the

particular entity are manually established. The

ontology of the particular entity is also updated. If

the element is added at upper levels of the fractal

system, semantically related IEs belonging to lower

level entities are searched in the core ontology, and

the lower level entities are notified to consider

updating of their representations. Adding an element

at lower level entities often is performed in response

to changes in upper levels and dependency

relationships can be established. If the element

initially is added for internal used, it is defined in the

ontology of the particular entity and its further

evolution depends upon rules of knowledge

propagation.

In the case of added parameter, summation

relationships are added to the IA. Initially, all

parameters of directly or indirectly related IEs in all

other representations are checked to identify

semantically related parameters. The core ontology

is used in the identification process. Summation

relationships are established with semantically

related parameters. If semantically related

parameters are not found, a new parameter is added

to related IEs, a new IE and its parameters are added

or no action is taken. If new elements are added then

summation relationships are also established. In the

case of added relationships, there are no direct

changes in IAFIS but the analysis rules are

reevaluated and changes are resembled in the FIS.

Knowledge exchange is vital for fractal systems.

Knowledge can be propagated from individual

entities to the whole fractal system. Three types of

knowledge propagation mechanisms are identified:

1) promotion of IEs with high level of cohesion

); 2) best practice propagation (K

); and 3)

promotion of frequently used elements (K

). From

the IA perspective, an IE is of type K

if it is

involved in many direct or indirect relationships

what indicates that this element is important to the

problem domain. In the case of human directed

knowledge propagation, representations lacking

elements of type K

are analyzed to check relevance

of these elements. Automatically, elements from K

can be provided as recommendations (Montaner et

al., 2003) for inclusion in the representation.

The fractal system adopts best practice processes,

which have been successfully utilized by some of

fractal entities similarly as described by Steckuka et

al. (2008). IEs used in these processes are included

in problem-domain representations for those entities

adopting the processes. IEs of type K

are

determined by monitoring usage of the FIS. The

fractal system automatically recommends adding to

the representation IEs frequently used by other OEs

or IEs frequently requested using the trace function.

Similar knowledge mechanisms can also be

applied for propagating knowledge about

parameters.

ICEIS 2009 - International Conference on Enterprise Information Systems

154

6 SAMPLE IMPLEMENTATION

Application of IAFIS is demonstrated by

implementing a prototype of the information system

for the SP development problem (SPDIS). The IS is

implemented on the basis of commercial colla-

boration and content management system according

to IAFIS shown in Figure 3. Figure 4 shows the user

interface of SPDIS of the university. Markers are

used to indicate different parts of SPDIS. The first

part refers to problem domain representations for

different OEs (e.g., university and faculty). Part 2

contains links to IEs needed by the particular OE.

These elements are collected and structured accor-

ding to the rules established in Section 5.1. Part 3

contains the selected IE, in this case the regulations

on SP development. Part 4 lists parameters of the

selected IE. The list contains their title and value,

and summation value, which is computed from data

provided by clients in the summation relationship.

Part 5 lists all IEs from the collection C

. The

recommendations part (part 6) demonstrates

automatic knowledge propagation. The IEs in this

part are included according to the rules K

and K

specified in Section 6.2. Part 7 shows parameters,

which are used by the university in elaboration of

regulations on SP development. The parameters are

those included in group of parameters P

7 CONCLUSIONS

The paper has proposed IA and its analysis rules as a

tool for developing fractal systems. The problem

representations specifically suited for particular OEs

and built on the basis of IAFIS are self-similar what

ensures consistency in the relatively loose coupled

system and reduces systems development and

maintenance cost. At the same time, they are

adjusted to needs of particular OE, which have the

sufficient information basis to complete their tasks

with respect to common and individual goals. The

set of rules for change and knowledge propagation

enables updating of the FISs and facilitates

knowledge sharing among fractal entities. To our

knowledge, the proposed IA and its analysis rules

provide the first systematic framework for

information management in fractal systems.

Efficient utilization of IAFIS requires

parameterization of IEs. Concept modeling and

document mining techniques can be used for this

purpose. The fractal system can be designed in either

top-down or bottom-up manner. In the case of top-

down approach, a lead entity develops its problem

representation and this representation can be used as

a template for developing self-similar represent-

tations. In the case of bottom-up approach, fractal

entities have their own problem-representations,

which are continuously aligned during evolution of

the fractal system. Another question for future

research is integration of fractal systems with other

ISs because IAFIS and implementation of FIS

depends upon already existing models and systems.

REFERENCES

Adomavicius, G. Tuzhilin, A., 2005. Toward the next

generation of recommender systems: a survey of the

state-of-the-art and possible extensions. IEEE

Transactions on Knowledge and Data Engineering 17

(6), 734–749.

Barker, I., 2005. What is information architecture?

available at http://www.steptwo.com.au:80/

papers/kmc_whatisinfoarch/index.html.

Hoverstadt, P., 2008. The Fractal Organization: Creating

Sustainable Organizations with the Viable System

Model, Wiley, New York.

Kirikova M., 2008. Towards multifractal approach in IS

development. In Barry, C. et al. (eds). The Inter-

Networked World: ISD Theory, Practice, and

Education, ISD2007, Galway, Ireland, August 29-31,

2007, Springer-Verlag: New York.

Kirikova, M., 2009. Towards flexible information

architecture for fractal information systems. In The

proceedings of the Int. Conf. on Information, Process,

and Knowledge Management, eKNOW.

Montaner, M., Lopez, B., De la Rosa, J.L., 2005. A taxonomy

of recommender agents on the internet. Artificial

Intelligence Review 19 (4), 285–330.

Ryu, K., Jung, M., 2003. Fractal approach to managing

intelligent enterprises. Creating Knowledge Based

Organisations, Gupta, J.N.D., Sharma, S.K. (Eds.), Idea

Group, 312-348.

Stecjuka J., Makna J., Kirikova, M., 2008. Best practices

oriented business process operation and design. In

Proceedings of the BPMDS'08 Workshop, 9th

Workshop on BPM, Montpellier, France, June 16-17,

2008, 171-184.

Warneke, H.J., 1993. The Fractal Company, Springer

Verlag, New York.

INFORMATION ARCHITECTURE OF FRACTAL INFORMATION SYSTEMS

155