Suchergebnisse

Some governmental organizations process a large number of research and development (R&D) projects simultaneously in their R&D program. For the planning of such a R&D program, decision makers can be supported by providing an overview that contains summaries of all currently running projects because they normally are not experts in all concerned R&D areas. A manual creation of such an overview is time consuming because the description of each project has been summarized in a homogeneous form. Further, each project summary has to be updated very often to consider changes within the project. Based on results of comprehensibility research, we identify a specific structure for the project summaries to ensure comprehensibility for a decision maker and usefulness for the R&D program planning. We introduce a new approach that enables a semi-automatic summarization of descriptions from R&D projects. It creates a summary in accordance to the proposed structure. A case study shows that the time taken by using the introduced approach is less than by creating a summary manually. As a result, the proposed methodology supports decision makers by planning an R&D program.

The aim of cross impact analysis (CIA) is to predict the impact of a first event on a second. For organizations strategic planning, it is helpful to identify the impacts among organizations internal events and to compare these impacts to the corresponding impacts of external events from organizations competitors. For this, literature has introduced compared cross impact analysis (CCIA) that depicts advantages and disadvantages of the relationships between organizations events to the relationships between competitors' events. However, CCIA is restricted to the use of patent data as representative for competitors events and it applies a knowledge structure based text mining approach that does not allow considering semantic aspects from highly unstructured textual information . In contrast to related work, we propose an internet based environmental scanning procedure to identify textual patterns represent competitors events. To enable processing of this highly unstructured textual information, the proposed methodology uses latent semantic indexing (LSI) to calculate the compared cross impacts (CCI) for an organization. A latent semantic subspace is built that consists of semantic textual patterns. These patterns are selected that represent organizations events. A web mining approach is used for crawling textual information from the internet based on keywords extracted from each selected pattern. This textual information is projected into the same latent semantic subspace. Based on the relationships between the semantic textual patterns in the su bspace, CCI is calculated for different events of an organization. A case study shows that the proposedapproach successfully calculates the CCI for technologies processed by a governmental organization. This enables decision makers to direct their investments more targeted.

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.

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.

We investigate a semi-automated identification of technical problems occurred by armed forces weapon systems during mission of war. The proposed methodology is based on a semantic analysis of textual information in reports from soldiers (war logs). Latent semantic indexing (LSI) with non-negative matrix factorization (NMF) as technique from multivariate analysis and linear algebra is used to extract hidden semantic textual patterns from the reports. NMF factorizes the term-by-war log matrix - that consists of weighted term frequencies – into two non-negative matrices. This enables natural parts-based representation of the report information and it leads to an easy evaluation by human experts because human brain also uses parts-based representation. For an improved research and technology planning, the identified technical problems are a valuable source of information. A case study extracts technical problems from military logs of the Afghanistan war. Results are compared to a manual analysis written by journalists of "Der Spiegel".

We investigate a robust and intelligent logistic system for emergency management where existing commercial logistic systems are loosely coupled with logistic systems of emergency management organizations and armed forces. This system is used to supply the population in case of a disaster where a high impact of environmental conditions on logistics can be seen. Very important are robustness as the ability of a logistic system to remain effective under these conditions and intelligent behavior for automated ad-hoc decisions facing unforeseen events. Scenario technique, roadmapping, as well as surveys are used as qualitative methodologies to identify current weaknesses in emergency management logistics and to forecast future development of loosely coupled logistic systems. Text mining and web mining analysis as quantitative methodologies are used to improve forecasting. As a result, options are proposed for governmental organizations and companies to enable such a loosely coupled logistic system within the next 20 years.