Fusion-based knowledge logistics for intelligent decision support in network-centric environment

Smirnov, Alexander; Pashkin, Mikhail; Levashova, Tatiana; Chilov, Nikolai

doi:10.1080/03081070500360937

Cited by 15 publications

(9 citation statements)

References 9 publications

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“…The analysis of a number of knowledge fusion studies [14,35,[41][42][43][44][45][46]; as well as those mentioned above) has revealed the following kind of new knowledge produced as the knowledge fusion outcomes:…”

Section: Knowledge Fusion: State Of the Artmentioning

confidence: 99%

“…Based on this set a decision is made. The instantiation and the problem solving are carried out using a constraint solver (see, e.g., [35]). …”

mentioning

confidence: 99%

“…Some research efforts deal with knowledge fusion of multiple knowledge sources to build a new knowledge base [28][29][30][31]. Several knowledge fusion efforts are devoted to knowledge integration with the problem-solving purposes [27,[32][33][34][35]. A number of knowledge fusion applications deal with fusing information to obtain a model of the situation [36][37][38][39][40].…”

mentioning

confidence: 99%

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Context-Aware Knowledge Fusion for Decision Support

Smirnov

Levashova

Shilov

2016

Context-Enhanced Information Fusion

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The purpose of this chapter is to investigate knowledge fusion processes with reference to context-aware decision support. Various knowledge fusion processes and their possible outcomes are analyzed. A context-aware decision support system for emergency management serves as a possible application in which knowledge fusion processes go on. This system provides fused outputs from different knowledge sources. It relies upon context model, which is the key to fuse information/knowledge and to generate useful decisions. The discussion is complemented by examples from a fire response scenario.Keywords Context-aware decision support Á Constraint-based ontology Á Information fusion model Á Knowledge fusion Á Emergency management Á Fire response IntroductionInformation fusion deals with various forms of information integration (aggregation, union, merging, etc.) from multiple sources. Some time ago, sources of data and information (databases, sensors, etc.) [1] due to observed shortcomings. Currently, this model is the most popular among other information fusion models. Initially proposed for the military applications, now it is widely used in civil domains as well, such as business or medicine. The levels with the JDL/DFG model are: source pre-processing/subject assessment (level 0), object assessment (level 1), situation assessment (level 2), impact assessment/threat refinement (level 3), process refinement (level 4), and user refinement/cognitive refinement (level 5). Through its different levels, the model divides the fusion processes according to the different levels of abstraction of the data fused and the different problems the data fusion is applicable to (e.g., characteristic estimation vs. situation recognition and analysis). The model does not prescribe a strict ordering of the processes and the fusion levels, and the levels are not always discrete and may overlap. The JDL/DFG model is useful for visualizing the data fusion process, facilitating discussion and common understanding, and important for system-level information fusion design [2].Recently, research on information fusion extended the set of input sources with ontologies, text documents, the web, etc. That is, the focus of data and information fusion has changed to knowledge fusion.Information fusion and knowledge fusion are tightly related [3]. Referring to various perspectives on information fusion (e.g., [2,[4][5][6][7][8]), this technology is aimed at facilitation of situation awareness and improvement of decision making. The main result of information fusion is a new meaningful piece of information that is beneficial to decision making in less uncertainty, more preciseness, and/or more comprehensibility then the contributing parts [9][10][11].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 [12][13][14][15]. Knowledge f...

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Section: Knowledge Fusion: State Of the Artmentioning

confidence: 99%

“…Based on this set a decision is made. The instantiation and the problem solving are carried out using a constraint solver (see, e.g., [35]). …”

mentioning

confidence: 99%

mentioning

confidence: 99%

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Context-Aware Knowledge Fusion for Decision Support

Smirnov

Levashova

Shilov

2016

Context-Enhanced Information Fusion

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“…In dynamic environments correct decisions can only be made in the right context related to the current situation [17], [18]. Context is any information that can be used to characterize the situation of an entity where an entity is a person, place, or object that is considered relevant to the person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves [19].…”

Section: Introductionmentioning

confidence: 99%

Socio-Cyberphysical System for Parking Support

Smirnov¹,

Shilov²,

Gusikhin³

2016

IJFCC

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Abstract-The paper proposes an approach and its supporting technologies for building a socio-cyberphysical system aimed at intelligent driver support. The approach is based on usage of such technologies as smart space for communication possibilities, behavior analysis for personalized support and human decision foreseeing, and fuzzy constraint satisfaction for treating uncertainties arising in stochastic systems such as traffic. The application of the approach is illustrated via a parking spot searching case study.Index Terms-Socio-cyberphysical system, parking support, behavior analysis, uncertainty.

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“…The proposed methodology is based on the earlier developed concept of knowledge logistics [20] and includes such technologies as situation management, ontology management, profiling and intelligent agents [18]. Standards of information exchange (e.g., Web-service standards), negotiation protocols, decision making rules, etc.…”

Section: Introductionmentioning

confidence: 99%

Service-Based Business Network Modelling: Application to Dynamic Logistics

Smirnov

Shilov

2008

Lecture Notes in Business Information Processing

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Abstract. Appearance of business networks can be considered as a response to changes in global markets. Networks can be seen as a step beyond the linear supply chain topography. The paper proposes an approach to business network modelling based on service networks. Since the centralized control is not always possible, presented approach proposes decentralized communication and ad-hoc decision making based on the current situation state and its possible future development. An approach presented is based on application of such technologies as ontology and context management to knowledge sharing. Ontologies are used for description of knowledge domains. The paper also presents experience of application of the above approach to the area of dynamic logistics. The considered problem takes into account continuously changing problem environment and requires nearly real-time solving.

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Fusion-based knowledge logistics for intelligent decision support in network-centric environment

Cited by 15 publications

References 9 publications

Context-Aware Knowledge Fusion for Decision Support

Context-Aware Knowledge Fusion for Decision Support

Socio-Cyberphysical System for Parking Support

Service-Based Business Network Modelling: Application to Dynamic Logistics

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