Going Beyond Local Search in Alliance Networks: Technological Knowledge Base and Network Position as Predictors of Clique Spanning Ties

2008 ◽  
Author(s):  
Michiel Pieters ◽  
Wim Vanhaverbeke ◽  
John Hagedoorn ◽  
Vareska Van de Vrande
2014 ◽  
Vol 5 (2) ◽  
pp. 39-53 ◽  
Author(s):  
Bachir Bahamida ◽  
Dalila Boughaci

Due to a growing number of intrusion events, organizations are increasingly implementing various intrusion detection systems that classify network traffic data as normal or anomaly. In this paper, three intrusion detection systems based fuzzy meta-heuristics are proposed. The first one is a fuzzy stochastic local search (FSLS). The second one is a fuzzy tabu search (FTS) and the third one is a fuzzy deferential evolution (FDE). These classifiers are built on a knowledge base modelled as a fuzzy rule “if-then”. The main purpose of these methods is to get the highest quality solutions by optimizing the fuzzy rules generation. The proposed classifiers FSLS, FTS and FDE are tested on the benchmark KDD'99 intrusion dataset and compared with some well-known existing techniques for intrusion detection. The results show the efficiency of the proposed approaches in the intrusion detection field.


2017 ◽  
Vol 62 (2) ◽  
pp. 715-720 ◽  
Author(s):  
K. Regulski

AbstractThe process of knowledge formalization is an essential part of decision support systems development. Creating a technological knowledge base in the field of metallurgy encountered problems in acquisition and codifying reusable computer artifacts based on text documents. The aim of the work was to adapt the algorithms for classification of documents and to develop a method of semantic integration of a created repository. Author used artificial intelligence tools: latent semantic indexing, rough sets, association rules learning and ontologies as a tool for integration. The developed methodology allowed for the creation of semantic knowledge base on the basis of documents in natural language in the field of metallurgy.


2021 ◽  
pp. 1-31
Author(s):  
Peter Persoon ◽  
Rudi Bekkers ◽  
Floor Alkemade

Abstract Technological cumulativeness is considered one of the main mechanisms for technological progress, yet its exact meaning and dynamics often remain unclear. To develop a better understanding of this mechanism we approach a technology as a body of knowledge consisting of interlinked inventions. Technological cumulativeness can then be understood as the extent to which inventions build on other inventions within that same body of knowledge. The cumulativeness of a technology is therefore characterized by the structure of its knowledge base, which is different from, but closely related to, the size of its knowledge base. We analytically derive equations describing the relation between the cumulativeness and the size of the knowledge base. In addition, we empirically test our ideas for a number of selected technologies, using patent data. Our results suggest that cumulativeness increases proportionally with the size of the knowledge base, at a rate which varies considerably across technologies. Furthermore, this rate is inversely related to the rate of invention over time. This suggests that the cumulativeness increases relatively slow in rapidly growing technologies. In sum, the presented approach allows for an in depth, systematic analysis of cumulativeness variations across technologies and the knowledge dynamics underlying technology development. Peer Review https://publons.com/publon/10.1162/qss_a_00140


2016 ◽  
Vol 17 (3) ◽  
pp. 243-250 ◽  
Author(s):  
Alicja E. Gudanowska

The purpose of the article was to exhibit the technology mapping method as one of the methods which may be used in foresight research. Foresight studies was described in the context of technology as well as technology analysis. The main part of the article is the presentation of an original proposal of a technology mapping method enabling to diagnose the current state of technology. The execution of the method should allow to maximise the resources of knowledge on specific technologies. A list of technological knowledge base elements which might emerge as a result of the process was also described.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jian Li ◽  
Yue Yu

Purpose Although collaborative research is believed to be an important means of accessing external knowledge, research on whether taking a strategic network position benefits new product development (NPD) is inconclusive. This study aims to unravel the conditions under which taking a strategic position within a collaborative research network is conducive for a firm’s NPD. Design/methodology/approach Drawing on social network theory, absorptive capacity theory and knowledge recombinant studies, this study examines how strategic network positions (i.e. degree centrality and structural holes) and knowledge base cohesion (i.e. local and global cohesion) in tandem affect a firm’s NPD. A panel data set of 366 firms in the Chinese automobile sector (2002–2010) is empirically analyzed, using the panel negative binomial approach with random effects and several alternate estimation approaches. Findings This study reveals that, rather than the volume of a firm’s knowledge base, its cohesion determines how it absorbs and uses knowledge accrued from collaborative research for NPD. Specifically, this paper finds that centrally positioned firms have greater NPD when their knowledge bases are locally cohesive, while firms spanning structural holes have more NPD when their knowledge bases are globally cohesive. Originality/value Successfully transferring collaborative research outcomes into product innovation is difficult. This study contributes to the literature on strategic network positions and NPD. The findings advance the understanding of knowledge base cohesion’s moderating role in explaining how firms absorb and exploit external knowledge for internal innovation. The findings also have important implications for managers who wish to promote product innovation by engaging in collaborative research with external partners.


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