Using NMF for analyzing war logs

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'

A Sentence Meaning Based Alignment Method for Parallel Text Corpora Preparation

Text alignment is crucial to the accuracy of Machine Translation (MT) systems, some NLP tools or any other text processing tasks requiring bilingual data. This research proposes a language independent sentence alignment approach based on Polish (not position-sensitive language) to English experiments. This alignment approach was developed on the TED Talks corpus, but can be used for any text domain or language pair. The proposed approach implements various heuristics for sentence recognition. Some of them value synonyms and semantic text structure analysis as a part of additional information. Minimization of data loss was ensured. The solution is compared to other sentence alignment implementations. Also an improvement in MT system score with text processed with described tool is shown.Comment: corpora filtration, text alignement, corpora improvement. arXiv admin note: text overlap with arXiv:1509.0888

Improved emergency management by a loosely coupled logistic system

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

Using text summarizing to support planning of research and development

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

Mining technologies in security and defense

In the last years, the rising asymmetrical threat is causing governments to pay more attention to security, especially in technological areas. New and ever more complex tasks in areas concerned with defense against these new types of threat require additional research and development of new techniques. For this reason, national and European governments are increasingly funding security and defense (S&D) based technological research. In this paper, we give an overview about the technological landscape of S&D by presenting different S&D-technologies and their relationships like described in (Geschka et al. (2005)) and (Reiß (2006)). Therefore we firstly identify technologies from different technological S&D-taxonomies and we secondly identify innovative S&D-research projects. The research projects are classified according to technologies and on that basis the relationships between technologies are presented. In detail, text documents are represented as vectors in vector space model using term frequency and corpus-based term co-occurrence data. We use Jaccard's coefficient (Ferber(2003)) to measure similarity and we use fuzzy alpha- cut method for classification. Structured documents (XML) are used as data source and drain. To realize this approach, we present a web application "S&D Technology Miner" for planning support to research program planners and to researchers, which acquire funding in this area but also for testing and evaluating the approach

Mining innovative ideas to support new product research and development

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

Mining Ideas from Textual Information

This approach introduces idea mining as process of extracting new and useful ideas from unstructured text. We use an idea definition from technique philosophy and we focus on ideas that can be used to solve technological problems. The rationale for the idea mining approach is taken over from psychology and cognitive science and follows how persons create ideas. To realize the processing, we use methods from text mining and text classification (tokenization, term filtering methods, Euclidean distance measure etc.) and combine them with a new heuristic measure for mining ideas. As a result, the idea mining approach extracts automatically new and useful ideas from a user given text. We present these problem solution ideas in a comprehensible way to support users in problem solving. This approach is evaluated with patent data and it is realized as a web-based application, named 'Technological Idea Miner' that can be used for further testing and evaluation.Idea Mining, Text Mining, Text Classification, Technology

Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing

We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts from textual information of existing customers’ websites can be used to uncover characteristics of websites of companies that will turn into profitable customers. Hence, the use of predictive analytics will help to identify new potential acquisition targets. Additionally, we show that a regression model based on these concepts is successful in the profitability prediction of new customers. In a case study, the acquisition process of a mail-order company is supported by creating a prioritized list of new customers generated by this approach. It is shown that the density of profitable customers in this list outperforms the density of profitable customers in traditional generated address lists (e. g. from list brokers). From a managerial point of view, this approach supports the identification of new business customers and helps to estimate the future profitability of these customers in a company. Consequently, the customer acquisition process can be targeted more effectively and efficiently. This leads to a competitive advantage for B2B companies and improves the acquisition process that is time- and cost-consuming with traditionally low conversion rates.B-to-B marketing, Text Mining, Web Mining, Acquisition, SVD

A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies

The planning of technological research and development (R&D) is demanding in areas with many relationships between technologies. To support decision makers of a government organization with R&D planning in these areas, a methodology to make the technology impact more transparent is introduced. The method shows current technology impact and impact trends from the R&D of an organization's competitors and compares these to the technology impact and impact trends from the organization's own R&D. This way, relative strength, relative weakness, plus parity of the organization's R&D activities in technology pairs can be identified. A quantitative cross impact analysis (CIA) approach is used to estimate the impact across technologies. Our quantitative CIA approach contrasts to standard qualitative CIA approaches that estimate technology impact by means of literature surveys and expert interviews. In this paper, the impact is computed based on the R&D information regarding the respective organization on one hand, and based on patent data representative regarding R&D information of the organization's competitors on the other hand. As an illustration, the application field 'defence' is used, where many interrelations and interdependencies between defence-based technologies occur. Firstly, an R&D-based and patent-based Compared Cross Impact (CCI) among technologies is computed. Secondly, characteristics of the CCI are identified. Thirdly, the CCI data is presented as a network to show the overall structure and the complex relationships between the technologies. Finally, changes of the CCI are analyzed over time. The results show that the proposed methodology generates useful insights for government organizations to direct technology investments.Compared cross impact, Cross impact analysis, Technological impact analysis, R&D, Patent analysis, Defence Taxonomy, Centroid Vector, Machine Learning, Multi Label Classification