=Paper=
{{Paper
|id=None
|storemode=property
|title=Towards Methodology for Design of Context-Aware Decision Support Systems based on
Knowledge Fusion Patterns
|pdfUrl=https://ceur-ws.org/Vol-1028/paper-06.pdf
|volume=Vol-1028
|dblpUrl=https://dblp.org/rec/conf/bir/Smirnov13
}}
==Towards Methodology for Design of Context-Aware Decision Support Systems based on
Knowledge Fusion Patterns==
https://ceur-ws.org/Vol-1028/paper-06.pdf
Towards Methodology for Design
of Context-Aware Decision Support Systems
based on Knowledge Fusion Patterns
Alexander Smirnov and Tatiana Levashova
St. Petersburg Institute for Informatics and Automation
of the Russian Academy of Sciences,
39, 14th line, St. Petersburg, 199178, Russian Federation
{smir, tatiana.levashova}iias.spb.su
Abstract. A pattern-based methodology for design of context-aware decision
support systems is proposed. The methodology is based on knowledge fusion
patterns for the knowledge fusion processes occurring at different stages of a
context-aware decision support system. The methodology as it stands focuses
on the context-aware stages of the system. For the knowledge fusion processes
ongoing at these stages the patterns’ elements relevant to the methodology are
specified. Usage of the patterns to propose system functionality depending on
the user needs is demonstrated.
Keywords: context aware decision support, ontology-based context, knowledge
fusion, knowledge fusion patterns, pattern-based methodology.
1 Introduction
Various methodologies for information system development [1], [2], [3], [4], [5]
consider information requirements as an important element of the system design. The
methodologies aim is to propose the system functionality suitable to the user.
Originally, the research presented in this paper was devoted to revealing of
knowledge fusion patterns for the processes occurring in a context-aware decision
support system (DSS) [6], [7]. The importance of knowledge fusion in context-aware
DSSs is reasoned by the intention of this technology. The objective of knowledge
fusion is to integrate information and knowledge from multiple sources into some
common knowledge that may be used for decision making and problem solving or
may provide a better insight and understanding of the situation under consideration
[8], [9], [10], i.e. knowledge fusion facilitates situational awareness.
The revealed patterns describe the knowledge fusion effects discovered in the DSS,
specify states of the knowledge sources involved in knowledge fusion, and lift the
effects at the ontology level. The main contribution of the present work is a pattern-
based specification of the DSS’ requirements to the knowledge sources involved in
the processes ongoing in this DSS. The specification is intended to describe the
system functionality.
Although the patterns can be used to describe the DSS’ requirements ensuring the
overall functionality of the system, this paper covers only context-aware phase of the
DSS. It is motivated by the ongoing research; the methodology has not been
developed to the full extent. It is believed that the ideas presented in this paper
provide a general conception according to that the methodology will be developed.
The rest of the paper is as follows. Section 2 gives an overview of possible
knowledge fusion effects and introduces the elements of the pattern description
language. Section 3 describes the conceptual framework that the DSS is based upon,
the stages of the DSS scenario, and presents the knowledge fusion effects produced at
these stages. The pattern-based methodology is presented in Section 4. The main
results and the future research are discussed in the Conclusion.
2 Knowledge Fusion Patterns
In this Section knowledge fusion patterns are revealed for the knowledge fusion
processes occurring in the context-aware DSS. Knowledge fusion effects found in this
DSS indicate presence of such processes.
2.1 Knowledge Fusion Effects
The main feature of knowledge fusion lies in creation of a synergic effect from the
integration of information/knowledge. Based on the analysis of knowledge fusion
studies, a number of knowledge fusion processes producing different effects have
been distinguished:
Intelligent fusion of huge amounts of heterogeneous data / information from a wide
range of distributed sources into a form which may be used by systems and humans
as the foundation for problem solving and decision making [8], [9]. The intelli-
gence assumes consideration of the semantic contents of the sources being fused.
Integration of knowledge from various knowledge sources resulting in a
completely different type of knowledge or new idea how to solve the problem [11],
[12], or integration of different types of knowledge (domain, procedural, derived,
presentation, etc.) resulting in a new knowledge type [10].
Combining knowledge from different autonomous knowledge sources in different
ways in different scenarios, which results in discovery of new relations between
the knowledge from different sources or/and between the entities this knowledge
represents [13], [14].
Integration of multiple knowledge sources into a new knowledge object, which is a
new knowledge source [15], [16].
Inference of explicit knowledge from information/knowledge hidden in knowledge
sources being integrated or fused [17].
Re-configuration of knowledge sources to achieve a new configuration with new
capabilities or competencies or knowledge exchange to improve capabilities or
competencies through learning, interactions, discussions, and practices [18].
Involving knowledge from various sources in problem solving, which results in a
solution [6].
From the analysis above it is noticed that different processes can produce the same
effect, and different effects may be outcomes of the same process. The following not
mutually exclusive kinds of new knowledge produced as the knowledge fusion effects
are distinguished: 1) a new type of knowledge; 2) a new knowledge source; 3) a new
knowledge created from data/information; 4) a new knowledge about the conceptual
scheme (new relations, concepts, properties, etc.); 5) a new explicit knowledge;
6) new capabilities/competencies of a knowledge object (an object that produces or
contains knowledge); 7) a new problem solving method or idea how to solve the
problem; 8) a solution for the problem.
Almost all the listed effects have been found in the DSS. An exception concerns
appearance of new ideas how to solve the problem. Such ideas may come as a result
of conscious interactions, discussions, and practices. These issues are not considered
in the research. Below, dimensions proposed to the generalization of the knowledge
fusion processes and elements of a pattern specification language are discussed.
2.2 Pattern Language
Knowledge fusion involves multiple sources in the integration processes. Autonomies
and structures of such sources have been chosen as the concepts in terms of which
knowledge fusion patterns are revealed. In the context-aware systems integration of
information/knowledge refers to the process of integration of their conceptual
structures. Therefore, source’s structure is an obligatory concept taken into account by
the integration. In this research, by sources’ structures the conceptual structures that
represent the knowledge in the knowledge sources are meant.
Autonomy creates awareness of the reliability of the information/knowledge repre-
sented in the sources. The consideration of the knowledge source autonomy concept is
reduced to the detection of relations existing between knowledge sources regardless
of their structures. Autonomous knowledge source is an independent source having no
relationships with other sources. Such source can get changed at any time, at that, the
changes in this source produce no changes in other sources. On the contrary, non-au-
tonomous source is linked to other (non-autonomous) sources. Changes in a non-
autonomous source produce appropriate changes in the related sources.
The DSS operates in a dynamic environment. Information and knowledge
represented in environmental sources that are related to the internal system sources
(environmental and system sources are non-autonomous) are considered to be more
reliable than information/knowledge represented in the autonomous environmental
sources. An argument in favor of this is any changes in the linked (non-autonomous)
environmental sources are reflected in the system sources.
The patterns measure knowledge fusion outcomes in terms of preservation/change
of the structures and autonomies of the initial and target knowledge sources, and in
terms of the effects the knowledge fusion processes produce in the DSS.
Initial knowledge sources are the sources that are integrated leading to the
emergence of a new knowledge (producing some knowledge fusion effect). The
sources resulting from the knowledge fusion or enclosing the knowledge fusion result
are referred to as target knowledge sources. The environmental sources (below,
resources) include sources of data/information/knowledge. In this sense the
environmental resources belong to the collection of knowledge sources.
The knowledge fusion patterns are described using a pattern description language
[7]. The detailed presentation of the patterns elements is as follows:
Name: a name to refer to the pattern
Problem: a problem the knowledge fusion process solves
Solution: a meaningful description of the knowledge fusion process
Initial knowledge source(s): knowledge sources(s) that are integrated leading to
producing some knowledge fusion effect
Target knowledge source(s): knowledge sources(s) resulting from the knowledge
fusion or enclosing the knowledge fusion result
Related pattern (may be omitted): an alternative pattern that can be used instead of
the described one or in parallel or after termination of the described pattern
Exception (may be omitted): a description of cases when the pattern is not applicable
Autonomy pre-states: the degree of autonomy of knowledge sources before the
knowledge fusion process. Three degrees are provided for: autonomous, non-
autonomous, and n/a (for a non-existing knowledge source)
Effect in DSS: the effect the knowledge fusion process produces in the DSS
Effect in ontology terms: ontology-based generalization of the effect produced
Post-states: the knowledge source autonomy and structures preservation degrees after
the knowledge fusion process completes. For the knowledge source autonomies
the degrees introduced in pre-state descriptions are kept on. Three degrees of
knowledge object structure preservations are provided for: preserved, changed,
and new (for a new knowledge object)
Schematic representation: the knowledge fusion process represented schematically
DSS stage: the stage of the DSS scenario where the knowledge fusion process occurs.
In this work one of the possible pattern applications, which is the pattern-based
methodology for design of context-aware DSSs, is offered. Prior to present this
methodology, the conceptual framework the DSS is based upon and the context-aware
stages of the DSS scenario are described.
3 Context-Aware Decision Support System
The DSS is intended for support of decisions on planning emergency response
actions. A two-level representation of emergency situation is used in the DSS. At the
first level the situation is represented by abstract context that is an ontology-based
intensional model of the situation. At the second level the emergency situation is
represented by operational context that is an instantiation of the abstract context for
the actual circumstances.
Environmental resources produce the operational context and solve the problem of
planning emergency response actions based on this context. For this the resources
organize a resource network. Nodes of this network represent the resources; network
arcs signify the order of the nodes execution.
The problem solution is a set of alternative emergency response actions feasible in
the current emergency situation. The decision maker chooses an alternative from the
set of feasible ones. The chosen alternative is considered as the decision. According to
this decision the response actions are undertaken.
Once the interactions of the decision maker with the DSS have been finished, the
abstract context, the operational context, the decision, and the resources' representa-
tions are saved in a context archive. At that, the operational context and the resources'
representations are saved in their states at the instant of the alternatives generation.
The DSS scenario follows two main phases: preliminary and executive. At the
preliminary phase an application ontology (AO), which describes knowledge of the
emergency management domain, is built. This ontology specifies knowledge to
describe the emergency situations happening in this domain along with problems
requiring solutions in these situations. The AO is a knowledge source fusing two
types of knowledge: domain and problem-solving. The executive phase concerns
support of the decision maker with alternative decisions, decision implementation,
and archiving. The executive phase is the focus of this paper since at this phase con-
text-aware functions of the DSS come into operation. Several stages are distinguished
at this phase. At each of them one or more knowledge fusion effects manifest.
3.1 Context-Aware Stages of DSS
This Section focuses on the knowledge fusion processes going on at the context-
aware stages of the DSS and the knowledge fusion effects produced at these stages.
The processes are generalized in the patterns terms. In the paper only pattern elements
that are relevant to the discussion of the proposed methodology are presented. They
are the states for the autonomies and structures of the knowledge sources.
3.1.1 Abstract Context Creation
The abstract context represents knowledge relevant for decision making in the
emergency situation. The represented knowledge is captured from the AO based on
the type of emergency event. A (smart) sensor reads the type of event and sends it to
the DSS or the user enters it in the system.
The knowledge fusion effect produced at the stage of abstract context creation is a
new knowledge source of the same type as the initial knowledge source (the AO). The
AO preserves its structure and autonomy; the abstract context becomes an autono-
mous knowledge source with a proper structure. The processes going on at the stage
of abstract context refinement are generalized by the simple fusion pattern (Table 1).
Table 1. Simple fusion
Pattern element Initial knowledge source Target knowledge source
Knowledge source application ontology abstract context
Autonomy pre-state autonomous n/a
Structure post-state preserved new
Autonomy post-state autonomous autonomous
3.1.2 Abstract Context Refinement
In the abstract context the captured knowledge may result in discovery of new
relationships between the knowledge unrelated in the AO. These relationships are the
result of deductive inference. Generally, any kind of knowledge representation items
can be inferred. The inferred items are considered as a knowledge fusion effect that is
the new knowledge. This knowledge is introduced in the abstract context, hereby
changing its structure. In the case of the abstract context refinement the abstract
context plays the roles of the initial and target knowledge source at the same time.
The processes taking place at the stage of the abstract context refinement are
generalized by the extension pattern (Table 2).
Table 2. Extension
Pattern element Initial knowledge source Target knowledge source
Knowledge source abstract context abstract context
Autonomy pre-state autonomous autonomous
Structure post-state changed changed
Autonomy post-state autonomous autonomous
3.1.3 Abstract Context Reuse
The abstract contexts are reusable components of the DSS. The reuse of an abstract
context in settings when the available resources are not intended to solve the problems
specified in this context may result in finding alternative resources. For instance, one
unavailable method can be substituted for a sequence of methods providing by the
available resources. This leads to a new configuration of the resource network.
At the stage of abstract context reuse the knowledge fusion effect is twofold: a new
(alternative) problem solving method and a new configuration of the resource net-
work. A specification of the new method(s) is introduced in the abstract context. The
context structure is changed, at that this context remains autonomous. The autonomies
and structures of the resources representing the new method(s) are preserved.
In the case of abstract context reuse the knowledge fusion pattern is nested. The
main pattern "configured fusion" (Table 3) includes the extension pattern. The
extension pattern corresponds to introducing the new specification in the context.
Table 3. Configured fusion (the main pattern)
Pattern element Initial knowledge source Target knowledge source
Knowledge source abstract context resource network
Autonomy pre-state autonomous autonomous
Structure post-state changed preserved
Autonomy post-state autonomous autonomous
3.1.4 Operational Context Producing
An operational context is produced through the semantic fusion of data/information
from multiple environmental resources within the ontological structure of the abstract
context. Initially the operational context is a copy of the abstract context. As soon as
the resources start instantiating this copy, they lose their autonomies. When the copy
is fully instantiated it becomes the operational context. At that, information from the
resources is constantly coming into this context. Therefore, the operational context
and the environmental resources are related over the period of decision making and
implementation. In practice, the operational context represents the map of the area
around the emergency event where the situation dynamic is represented (the mobile
responders are moving, the traffic situation is changing, etc.).
The knowledge fusion effects had at the stage of the operational context producing
are 1) the operational context is a new knowledge source; 2) this context is a
knowledge source created from data/information; and 3) the operational context rep-
resents knowledge of a new dynamic type. The abstract context preserves its structure
and autonomy when the operational context is produced. The operational context is a
new non-autonomous knowledge source. The instantiated fusion pattern (Table 4)
generalizes the processes going on at the stage of operational context producing.
Table 4. Instantiated fusion
Pattern element Initial knowledge source Target knowledge source
Knowledge source abstract context operational context
Autonomy pre-state autonomous n/a
Structure post-state preserved new
Autonomy post-state autonomous non-autonomous
3.1.5 Problem Solving
As it is said above, the result of problem solving is a set of feasible emergency
response plans. An emergency response plan is a set of emergency responders with
required helping services, schedules for the responders’ activities, and transportation
routes for the mobile responders. In the plans earlier independent entities become
related, i.e. new relations between these entities have arisen.
The plans are represented in the picture of the operational context. In this way, the
operational context and the results of problem solving are fused forming, at that, a
new knowledge source. This new source represents knowledge of a new type (the
instantiated knowledge fused with the solution set).
At the problem solving stage the operational context dissolves within the new
knowledge source and does not preserve the structure and autonomy. At time of alter-
natives generation and decision making the environmental resources and the opera-
tional context are related (non-autonomous). As soon as the decision has been made
the new knowledge source and the environmental resources become autonomous. The
knowledge fusion effects produced at this stage are 1) new relations between entities,
2) a problem solution, and 3) a new knowledge source of a new type. Table 5 presents
a fragment of the flat fusion pattern for the processes at the problem solving stage.
Table 5. Flat fusion
Pattern element Initial knowledge source Target knowledge source
Knowledge source operational context knowledge source fusing the operational
context and the set of alternatives
Autonomy pre-state non-autonomous n/a
Structure post-state changed n/a
Autonomy post-state n/a autonomous
3.1.6 Decision Implementation
The decision is a solution that the decision maker has chosen from the set of alterna-
tive ones. This decision is made at a certain time instant. The situation may change
from the moment the decision was made to the moment of its implementation. The
responders whom the decision is delivered may be unable to implement it in the
changed circumstances. In some cases, the activities assigned to the responders who
become unable to operate can be delegated to or redistributed between other respond-
ers participating in the decision implementation. As a result of this, the responders
that are ready to take the assignments gain new capabilities / competencies.
For instance, an emergency team trained to rescue operations has failed in the
course of actions because of a road destruction, ambulance blockage, etc. In certain
cases these operations can be delegated to available teams. In the DSS the emergency
responders are represented by their profiles. In the case of consent, the plan (the
decision) is adjusted accordingly and the profiles of the teams agreed to take part in
the rescue operations are extended with the new capability.
At the time of the decision implementation, the responders taking part in the
response plan are not autonomous. Moreover, in the course of the response actions the
structures of their profiles as well as the decision structure may change. The changed
decision structure results in changing the structure of the knowledge source
containing the set of solutions. This knowledge source is not autonomous until the
decision is implemented. The knowledge fusion effect produced at the decision
implementation stage consists in gaining new capabilities / competencies by the
emergency responders. The adaptation pattern (Table 6) generalizes this case.
Table 6. Adaptation
Pattern element Initial knowledge sources Target knowledge sources
Knowledge source knowledge source representing the decision
profiles of the emergency responders
Autonomy pre-state non-autonomous non-autonomous
Structure post-state changed changed
Autonomy post-state non-autonomous non-autonomous
3.1.7 Archival Knowledge Management
The stage of archival context management deals with the management of knowledge
contained in the archived components. The main intention of such management is
inference of new knowledge based on the accumulated one.
For example, an emergency team participated in different emergency response
actions. Some operational contexts in which this team appeared and then participated
in corresponding actions represent the same hospital. Based on a comparative analysis
of these operational contexts it can be judged that most probably the team is a part of
the hospital found together with this team in different contexts. The part-of relation
between the emergency team and hospital is the new revealed relation. The revealing
of a new knowledge based on a set of observations is a kind of inductive inference.
In the archive, the operational contexts, the representations of environmental
resources, the responders' profiles, and the knowledge object representing the decision
are related. I.e., in the archive all the listed knowledge sources are non-autonomous.
As a result of archival knowledge management, new knowledge about conceptual
schemes of the archived knowledge sources can be inferred. This new knowledge gets
specified in the AO. As a result of this, the structure of the AO gets changed but its
autonomy is preserved. The knowledge fusion effect produced is a new knowledge
about the conceptual scheme. Table 7 shows a fragment of the historical fusion
pattern for the processes occurring at the stage of archival knowledge management.
Table 7. Historical fusion
Pattern element Initial knowledge sources Target knowledge source
Knowledge source operational contexts application ontology
Autonomy pre-state non-autonomous autonomous
Structure post-state preserved changed
Autonomy post-state non-autonomous autonomous
4 Pattern-Based Methodology
The offered methodology follows four steps: 1) specification of the system
information requirements; 2) capturing the user requirements; 3) matching the system
requirements against the user requirements; 4) finding available system functionality.
The proposed patterns enable to formulate the system requirements in terms of the
patterns' inputs/outputs (Table 8). Further on in this paper only the patterns' inputs are
taken into account to formulate the requirements. The patterns' inputs and outputs
jointly are planned to be used to track the information flows across the DSS' stages
and between the patterns. These flows manifest interrelationships between the stages
and thereupon allow ones to specify explicitly in the methodology what output
scenario components can serve as the input at what stage.
The system requirements (Table 8) are formulated for the particular DSS. The
patterns' parameters are presented in the way they are used in this DSS. These
parameters generalized are presented in Table 9 (the columns "System
requirements"). These columns formalize possible parameters’ values for general
cases and formulate general conditions for the patterns applicability. In the table the
following notation is used: “a” – autonomous knowledge objects, “na” – non-
autonomous, “m” – modifiable, “nm” – not-modifiable, “/” – logical OR, “&” –
logical AND. The resource network is considered as a single knowledge object.
Modifiable resource network means that this network is reconfigurable, at that any
changes inside the network nodes are not supposed (the structures of the resource
organizing the network are not changed).
User requirements are demonstrated by an example. For instance, the user does not
possess any AO. Though, he/she has an unalterable abstract context representing the
abstract situation this user usually deals with and a set of resources authorized for
his/her needs. As well, this user can manage the actors’ profiles as at he/she
discretion. The example of the user requirements is presented in the column "User
requirements" (Table 9). "Not defined" parameter value means that the user has no
specific requirements to the input component. The system designers can manipulate
such a component by their choice. Here it is supposed that the system provides the
user with the components resulted in the system scenario execution with the
parameters required for the overall system functionality.
The system requirements are matched against the user requirements to determine
the patterns applicability for this user (the column "Pattern applicability" of Table 9).
From Table 9 seen, that the fully applicable patterns are flat fusion and adaptation.
Table 8. DSS requirements
Stage Input Output Functionality Pattern
Abstract context Autonomous AO Autonomous Creation of a non- Simple
creation abstract context instantiated ontol- fusion
ogy-based model
of the situation
Abstract context Autonomous Autonomous Inference of new Extension
refinement modifiable abstract abstract context (contextual)
context knowledge
Abstract context Autonomous Autonomous Reconfiguration of Configured
reuse modifiable abstract abstract context the resource net- fusion
context Autonomous work according to
Autonomous recon- resource network the current circum-
figurable resource stances
network
Operational Autonomous ab- Non-autonomous Creation of a near Instantiated
context stract context modifiable oper- real-time picture of fusion
producing Resources able to ational context the situation
lose their Non-autonomous
autonomies resource network
Problem solving Non-autonomous New autonomous Providing the Flat fusion
modifiable opera- knowledge decision maker
tional context source with a set of
Resources able to Autonomous alternative
lose their resource network decisions
autonomies
Decision Non-autonomous modifiable knowledge Gaining new Adaptation
implementation source representing the decision capabilities /
Non-autonomous modifiable actors' competencies by
profiles actors
Archival context Non-autonomous Autonomous AO Inductive inference Historical
management operational contexts of new knowledge fusion
Autonomous
modifiable AO
The configured fusion, instantiated fusion, and historical fusion patterns are
applicable partly. The applicability of the configured fusion and instantiated fusion
patterns covers management of the resource network. The historical fusion pattern
allows for some inductive inference. The remaining patterns are inapplicable.
The analysis of the applicable patterns results in the following system functional-
ity. The system can reuse the existing abstract context. As introducing any knowledge
into this context is not allowed, this context can be reused if the previously used set of
environmental resources is available. At that, any new network configurations are
senseless as they cannot be specified in the context. Having the abstract context and
the resource network the system can provide the user with an operational context (the
dynamic picture of the situation) and a set of feasible decisions in this situation. As
the user is able to manage the actors’ profiles, the system provides he/her with the
ability to manage the decision implementation. The operational contexts produced
based on the reusable abstract context can be archived, inductive inference over them
can be supported but the inference results cannot be retained.
Table 9. Pattern-based requirements
Pattern System requirements User requirements Pattern
Input(s) Parameter Parameter value applicability
value
Simple fusion AO a/na/m/nm unavailable n/a
Extension Abstract context a/na&m nm n/a
Configured Abstract context a/na&m nm n/a
fusion Resource network a/na/m/nm a/na&m applicable
Instantiated Abstract context a/na/m/nm nm n/a
fusion Resource network na&m/nm a/na&m applicable
Flat fusion Operational context na&m not defined = na&m applicable
Resource network na&m/nm a/na&m applicable
Adaptation Decision na&m not defined = na&m applicable
Actors’ profiles na&m na/a&m applicable
Historical Operational contexts a/na/m not defined = a/na/m applicable
fusion AO a/na&m unavailable n/a
The proposed functionality means that the user can deal with the situation he/she
usually deals with, solve repetitive problems with different values for the problems'
variables, and manage the decision implementation. For instance, applying to the
emergency management domain, the system can support decisions on the emergency
situations caused by one and the same type of event. At that, such events are supposed
to happen in some area where the system has access to a fixed and the same set of
environmental resources.
5 Conclusion
The possible knowledge fusion effects were described. These effects were found in
the context-aware DSS for the emergency response domain. Knowledge fusion
patterns specifying these effects along with the states of knowledge sources involved
in the knowledge fusion processes were revealed. One of the possible pattern
applications, which is the pattern-based methodology for design of context-aware
DSSs, was offered. In the methodology the patterns were used to specify the system
requirements ensuring the full system functionality.
So far, the methodology covers the executive phase of the DSS. Some future
research is needed to specify the overall set of the system requirements and to
describe information/knowledge flows across the DSS' stages and between the
patterns. Such a specification will enable to find out dependencies between the system
stages and available functionalities, and to specify more accurately the applicability of
the knowledge fusion patterns to different user's requirements.
Acknowledgments. The research was supported partly by projects funded by grants
11-07-00045, 11-07-00058, 12-07-00298 of the Russian Foundation for Basic
Research, the project 213 of the research program “Information, control, and
intelligent technologies & systems” of the Russian Academy of Sciences (RAS), and
the project 2.2 of the Nano- & Information Technologies Branch of RAS.
References
1. Sage, A.P.: Decision Support Systems Engineering. John Wiley & Sons, New York (1991)
2. Vessey, I., Conger, S.: Learning to Specify Information Requirements: The Relationship
between Application and Methodology. JMIS, 10 (2) 177–201 (1993)
3. Elliott G., Strachan, J.: Global Business Information Technology: an Integrated Systems
Approach. Addison Wesley, Harlow (2004)
4. MDA – the Architecture of Choice for a Changing World, OMG Model Driven
Architecture, http://www.omg.org/mda/ (access date: 05.07.2013)
5. Shahbazian, E., Pigeon, L., Krtaft, J., Bossé, É.: Building Technology-Enabled Decision
Support Applications. In: Bossé, É., Shahbazian, E., Rogova G. (eds.) Prediction and
Recognition of Privacy Efforts Using Collaborative Human-Centric Information Systems.
IOS Press, Amsterdam, pp. 98–108 (2013)
6. Smirnov, A., Pashkin, M., Chilov, N. Levashova T., Haritatos, F.: Knowledge Source
Network Configuration Approach to Knowledge Logistics. Int. J. Gen. Syst. 32 (3) 251–
269 (2003)
7. Smirnov, A., Levashova T., Shilov, N.: Context-Based Knowledge Fusion Patterns in
Decision Support System for Emergency Response. In: Comes, T., Fiedrich, F., Fortier S.,
Geldermann J., Műller, T. (eds.) 10th International ISCRAM Conference, Baden-Baden,
Germany, pp. 597–606 (2013)
8. Alun, P., Hui, K., Gray, A., Marti, P., Bench-Capon, T., Cui, Z., Jones, D.: Kraft: an
Agent Architecture for Knowledge Fusion. Int. J. Coop. Inf. Syst. 10 (1–2) 171–195
(2001)
9. Scherl R., Ulery D.L.: Technologies for Army Knowledge Fusion. Final report no. ARL-
TR-3279, Computer Science Department, Monmouth University, Monmouth (2004)
10. Holsapple C.W., Whinston A.B.: Building Blocks for Decision Support Systems. In:
Ariav G., Clifford J. (eds.) New Directions for Database Systems. Ablex Publishing Corp.,
Norwood, pp. 66–86 (1986)
11. Lee, K.-R.: Patterns and Processes of Contemporary Technology Fusion: The Case of
Intelligent Robots. Asian J. Technol. Innov. 15 (2) 45–65 (2007)
12. Grebla, H.A., Cenan C.O., Stanca, L.: Knowledge Fusion in Academic Networks. BRAIN:
Broad Res. Artif. Intell. Neurosci. 1 (2) http://www.edusoft.ro/brain/index.php/
brain/article/download/60/145 (2010)
13. Laskey K.B., Costa P., Janssen T.: Probabilistic Ontologies for Knowledge Fusion. In:
11th IEEE International Conference on Information Fusion, http://ieeexplore.ieee.org/xpl/-
freeabs_all.jsp?arnumber=4632375 (2008)
14. Jonquet, C., LePendu, P., Falconer, S., Coulet, A., Noy, N.F., Musen, M.A., Shah, N.H.:
NCBO Resource Index: Ontology-Based Search and Mining of Biomedical Resources. J.
Web Semant. 9 (3) 316–324 (2011)
15. Kuo, T.-T., Tseng, S.-S., Lin, Y.-T.: Ontology-Based Knowledge Fusion Framework
Using Graph Partitioning. In: Chung, P.W.H., Hinde, C.J., Ali M. (eds.) IEA/AIE 2003.
LNAI, vol. 2718, pp. 11–20 (2003)
16. Gou J., Yang J., Chen Q.: Evolution and Evaluation in Knowledge Fusion System. In:
Mira J., Alvarez, J.R. (eds.) IWINAC 2005. LNCS, vol. 3562, pp. 192–201 (2005)
17. Roemer, M.J., Kacprzynski, G.J., Orsagh, R.F.: Assessment of Data and Knowledge
Fusion Strategies for Prognostics and Health Management. In: 2001 IEEE Aerospace
Conference, vol. 6, pp. 2979–2988 (2001)
18. Lin, L.Y., Lo, Y.J.: Knowledge Creation and Cooperation Between Cross-Nation R&D
Institutes. Int. J. Electron. Bus. Manag. 8 (1) 9–19 (2010)