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Thorleuchter D.,Fraunhofer Institute for Technological Trend Analysis | Van Den Poel D.,Ghent University
Advances in Intelligent and Soft Computing | Year: 2012

In literature, idea mining is introduced as an approach that extracts interesting ideas from textual information. Idea mining research shows that the quality of the results strongly depends on the domain. This is because ideas from different domains consist of different properties. Related research has identified the idea properties for the medical domain and the social behavior domain. Based on these results, idea mining has been applied successfully in these two domains. In contrast to previous research, this work identifies the idea properties from a general technological domain to show that this domain differs from the two above mentioned domains and to show that idea mining also can applied successfully in a technological domain. Further, idea properties are identified by use of backward selection as main approach in stepwise regression, which is in contrast to previous research. Predictive variables are selected considering their statistical significance and a grid search is used to adapt the parameters of the idea mining algorithm. Microsystems technology is selected for a case study. It covers a wide range of different technologies because it is widely used in many technological areas. The case study shows that idea mining is successful in extracting new ideas from that domain. © 2012 Springer-Verlag GmbH.


Thorleuchter D.,Fraunhofer Institute for Technological Trend Analysis | Van Den Poel D.,Ghent University
Expert Systems with Applications | Year: 2012

We analyze the impact of textual information from e-commerce companies' websites on their commercial success. The textual information is extracted from web content of e-commerce companies divided into the Top 100 worldwide most successful companies and into the Top 101 to 500 worldwide most successful companies. It is shown that latent semantic concepts extracted from the analysis of textual information can be adopted as success factors for a Top 100 e-commerce company classification. This contributes to the existing literature concerning e-commerce success factors. As evaluation, a regression model based on these concepts is built that is successful in predicting the commercial success of the Top 100 companies. These findings are valuable for e-commerce websites creation. © 2012 Elsevier Ltd. All rights reserved.


Thorleuchter D.,Fraunhofer Institute for Technological Trend Analysis | Van Den Poel D.,Ghent University
Expert Systems with Applications | Year: 2012

Multilevel security (MLS) is specifically created to protect information from unauthorized access. In MLS, documents are assigned to a security label by a trusted subject e.g. an authorized user and based on this assignment; the access to documents is allowed or denied. Using a large number of security labels lead to a complex administration in MLS based operating systems. This is because the manual assignment of documents to a large number of security labels by an authorized user is time-consuming and error-prone. Thus in practice, most MLS based operating systems use a small number of security labels. However, information that is normally processed in an organization consists of different sensitivities and belongs to different compartments. To depict this information in MLS, a large number of security labels is necessary. The aim of this paper is to show that the use of latent semantic indexing is successful in assigning textual information to security labels. This supports the authorized user by his manual assignment. It reduces complexity by the administration of a MLS based operating system and it enables the use of a large number of security labels. In future, the findings probably will lead to an increased usage of these MLS based operating systems in organizations. © 2012 Elsevier Ltd. All rights reserved.


Thorleuchter D.,Fraunhofer Institute for Technological Trend Analysis | Van Den Poel D.,Ghent University
Expert Systems with Applications | Year: 2014

Cross impact analysis (CIA) consists of a set of related methodologies that predict the occurrence probability of a specific event and that also predict the conditional probability of a first event given a second event. The conditional probability can be interpreted as the impact of the second event on the first. Most of the CIA methodologies are qualitative that means the occurrence and conditional probabilities are calculated based on estimations of human experts. In recent years, an increased number of quantitative methodologies can be seen that use a large number of data from databases and the internet. Nearly 80% of all data available in the internet are textual information and thus, knowledge structure based approaches on textual information for calculating the conditional probabilities are proposed in literature. In contrast to related methodologies, this work proposes a new quantitative CIA methodology to predict the conditional probability based on the semantic structure of given textual information. Latent semantic indexing is used to identify the hidden semantic patterns standing behind an event and to calculate the impact of the patterns on other semantic textual patterns representing a different event. This enables to calculate the conditional probabilities semantically. A case study shows that this semantic approach can be used to predict the conditional probability of a technology on a different technology. © 2012 Elsevier B.V. All rights reserved.


Thorleuchter D.,Fraunhofer Institute for Technological Trend Analysis | Van Den Poel D.,Ghent University
Expert Systems with Applications | Year: 2013

In recent years, governmental and industrial espionage becomes an increased problem for governments and corporations. Especially information about current technology development and research activities are interesting targets for espionage. Thus, we introduce a new and automated methodology that investigates the information leakage risk of projects in research and technology (R&T) processed by an organization concerning governmental or industrial espionage. Latent semantic indexing is applied together with machine based learning and prediction modeling. This identifies semantic textual patterns representing technologies and their corresponding application fields that are of high relevance for the organization's strategy. These patterns are used to estimate organization's costs of an information leakage for each project. Further, a web mining approach is processed to identify worldwide knowledge distribution within the relevant technologies and corresponding application fields. This information is used to estimate the probability that an information leakage occur. A risk assessment methodology calculates the information leakage risk for each project. In a case study, the information leakage risk of defense based R&T projects is investigated. This is because defense based R&T is of particularly interest by espionage agents. Overall, it can be shown that the proposed methodology is successful in calculation the espionage information leakage risk of projects. This supports an organization by processing espionage risk management. © 2012 Elsevier Ltd. All rights reserved.


Thorleuchter D.,Fraunhofer Institute for Technological Trend Analysis | Van Den Poel D.,Ghent University
Expert Systems with Applications | Year: 2013

Many national and international governments establish organizations for applied science research funding. For this, several organizations have defined procedures for identifying relevant projects that based on prioritized technologies. Even for applied science research projects, which combine several technologies it is difficult to identify all corresponding technologies of all research-funding organizations. In this paper, we present an approach to support researchers and to support research-funding planners by classifying applied science research projects according to corresponding technologies of research-funding organizations. In contrast to related work, this problem is solved by considering results from literature concerning the application based technological relationships and by creating a new approach that is based on latent semantic indexing (LSI) as semantic text classification algorithm. Technologies that occur together in the process of creating an application are grouped in classes, semantic textual patterns are identified as representative for each class, and projects are assigned to one of these classes. This enables the assignment of each project to all technologies semantically grouped by use of LSI. This approach is evaluated using the example of defense and security based technological research. This is because the growing importance of this application field leads to an increasing number of research projects and to the appearance of many new technologies. © 2012 Elsevier Ltd. All rights reserved.


Thorleuchter D.,Fraunhofer Institute for Technological Trend Analysis | Van Den Poel D.,Ghent University
Expert Systems with Applications | Year: 2013

We investigate an automated identification of weak signals according to Ansoff to improve strategic plan- ning and technological forecasting. Literature shows that weak signals can befound in the organization's environment and that they appear indifferent contexts.We use internet information to represent orga- nization 'senvironment and we select these websites that are related to a given hypothesis.In contrast to related research, a methodology is provided that uses latent semantic indexing (LSI) for the identification of weak signals.This improves existing knowledge based approaches because LSI considers the aspects of meaning and thus, it is able to identify similar textual patterns indifferent contexts.A new weak signal maximization approach is introduced that replaces the commonly used prediction modeli ngapproach in LSI. It enables to calculate the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions.A case study identifies and analyses weak signals to predict trends in the field of on-site medical oxygen production.This supports the planning of research and develop- ment (R&D) for a medical oxygen supplier.As a result, it is shown that the proposed methodology enables organizations to identify weak signals from the internet for a given hypothesis.This helps strategic plan- ners to react ahead of time. © 2013 Elsevier Ltd. All rights reserved.


Thorleuchter D.,Fraunhofer Institute for Technological Trend Analysis | Van Den Poel D.,Ghent University
Expert Systems with Applications | Year: 2013

The internet is a valuable source of information where many ideas can be found dealing with different topics. A few numbers of ideas might be able to solve an existing problem. However, it is time-consuming to identify these ideas within the large amount of textual information in the internet. This paper introduces a new web mining approach that enables an automated identification of new technological ideas extracted from internet sources that are able to solve a given problem. It adapts and combines several existing approaches from literature: approaches that extract new technological ideas from a user given text, approaches that investigate the different idea characteristics in different technical domains, and multi-language web mining approaches. In contrast to previous work, the proposed approach enables the identification of problem solution ideas in the internet considering domain dependencies and language aspects. In a case study, new ideas are identified to solve existing technological problems as occurred in research and development (R&D) projects. This supports the process of research planning and technology development. © 2012 Elsevier Ltd. All rights reserved.


Gusarov A.,Belgian Nuclear Research Center | Hoeffgen S.K.,Fraunhofer Institute for Technological Trend Analysis
IEEE Transactions on Nuclear Science | Year: 2013

Fiber Bragg and long period gratings are photonic components that find numerous applications in telecommunication and sensing. In some cases, such as space, high-energy physics, and nuclear industry, those applications include the presence of ionizing radiation. It is therefore essential to evaluate their radiation response. In this paper, we review radiation effects on various types of fiber gratings. © 1963-2012 IEEE.


Thorleuchter D.,Fraunhofer Institute for Technological Trend Analysis
Studies in Classification, Data Analysis, and Knowledge Organization | Year: 2010

Here, we present an approach for automatically identifying the innovative potential of new technological ideas extracted from textual information. The starting point of each innovation is a good and new idea. Unfortunately, a high percentage of innovations fail, which means many ideas do not have the potential to become an innovation in future. The innovation process from a new idea as starting point via research, development, and production activities through to an innovative product is very cost- and time-consuming. Therefore, the aim of our work is to identify the innovative potential of new technological ideas to improve the performance of the innovation process. We extract new technological ideas from provided textual information. We also identify innovative technology fields by analysing relationships among technologies. All identified ideas are assigned to innovative technology fields by using text mining and text classification methods. Technological ideas in these fields are presented to the user as innovative ideas and might be used as starting point for new product research and development divisions. © Springer-Verlag Berlin Heidelberg 2010.

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