Innovation through pertinent patents research based on physical phenomena involved

One can find innovative solutions to complex industrial problems by looking for knowledge in patents. Traditional search using keywords in databases of patents has been widely used. Currently, different computational methods that limit human intervention have been developed. We aim to define a method to improve the search for relevant patents in order to solve industrial problems and specifically to deduce evolution opportunities. The non-automatic, semi-automatic, and automatic search methods use keywords. For a detailed keyword search, we propose as a basis the functional decomposition and the analysis of the physical phenomena involved in the achievement of the function to fulfill. The search for solutions to design a bi-phasic separator in deep offshore shows the method presented in this paper

An academic perspective on the entrepreneurship policy agenda: themes, geographies and evolution

Text mining is being increasingly used for the automatic analysis of different corpus of documents, either standalone or complementarily to other bibliometric techniques. The case of academic research into entrepreneurship policy is particularly interesting due to the increasing relevance of the topic and since the knowledge about the evolution of themes in this field is still rather limited. Consequently, this paper analyses the key topics, trends and shifts that have shaped the entrepreneurship policy research agenda to date using text mining techniques, cluster analysis and complementary bibliographic data to examine the evolution of a corpus of 1,048 academic papers focused on entrepreneurial-related policies and published during the period 1990-2016 in ten of the most relevant entrepreneurship journals. The results of the analysis show that inclusion, employment and regulation-related papers have largely dominated the research in the field, evolving from an initial classical approach about the relationship between entrepreneurship and employment to a wider and multidisciplinary perspective, including the relevance of management, geographies, and narrower topics such as agglomeration economics or internationalization instead of previous generic sectorial approaches. Overall, the text mining analysis reveals how entrepreneurship policy research has gained increasing attention and has become both more open, with a growing cooperation among researchers from different affiliations; and more sophisticated, with concepts and themes that moved forward the research agenda closer to the priorites of policies implementatio

An academic perspective on the entrepreneurship policy agenda: themes, geographies and evolution

Text mining is being increasingly used for the automatic analysis of different corpus of documents, either standalone or complementarily to other bibliometric techniques. The case of academic research into entrepreneurship policy is particularly interesting due to the increasing relevance of the topic and since the knowledge about the evolution of themes in this field is still rather limited. Consequently, this paper analyses the key topics, trends and shifts that have shaped the entrepreneurship policy research agenda to date using text mining techniques, cluster analysis and complementary bibliographic data to examine the evolution of a corpus of 1,048 academic papers focused on entrepreneurial-related policies and published during the period 1990-2016 in ten of the most relevant entrepreneurship journals. The results of the analysis show that inclusion, employment and regulation-related papers have largely dominated the research in the field, evolving from an initial classical approach about the relationship between entrepreneurship and employment to a wider and multidisciplinary perspective, including the relevance of management, geographies, and narrower topics such as agglomeration economics or internationalization instead of previous generic sectorial approaches. Overall, the text mining analysis reveals how entrepreneurship policy research has gained increasing attention and has become both more open, with a growing cooperation among researchers from different affiliations; and more sophisticated, with concepts and themes that moved forward the research agenda closer to the priorites of policies implementatio

Knowledge data discovery and data mining in a design environment

Designers, in the process of satisfying design requirements, generally encounter difficulties in, firstly, understanding the problem and secondly, finding a solution [Cross 1998]. Often the process of understanding the problem and developing a feasible solution are developed simultaneously by proposing a solution to gauge the extent to which the solution satisfies the specific requirements. Support for future design activities has long been recognised to exist in the form of past design cases, however the varying degrees of similarity and dissimilarity found between previous and current design requirements and solutions has restrained the effectiveness of utilising past design solutions. The knowledge embedded within past designs provides a source of experience with the potential to be utilised in future developments provided that the ability to structure and manipulate that knowledgecan be made a reality. The importance of providing the ability to manipulate past design knowledge, allows the ranging viewpoints experienced by a designer, during a design process, to be reflected and supported. Data Mining systems are gaining acceptance in several domains but to date remain largely unrecognised in terms of the potential to support design activities. It is the focus of this paper to introduce the functionality possessed within the realm of Data Mining tools, and to evaluate the level of support that may be achieved in manipulating and utilising experiential knowledge to satisfy designers' ranging perspectives throughout a product's development

What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)

Agile teams juggle multiple tasks so professionals are often assigned to multiple projects, especially in service organizations that monitor and maintain a large suite of software for a large user base. If we could predict changes in project conditions changes, then managers could better adjust the staff allocated to those projects.This paper builds such a predictor using data from 832 open source and proprietary applications. Using a time series analysis of the last 4 months of issues, we can forecast how many bug reports and enhancement requests will be generated next month. The forecasts made in this way only require a frequency count of this issue reports (and do not require an historical record of bugs found in the project). That is, this kind of predictive model is very easy to deploy within a project. We hence strongly recommend this method for forecasting future issues, enhancements, and bugs in a project.Comment: Accepted to 2018 International Conference on Software Engineering, at the software engineering in practice track. 10 pages, 10 figure

Modeling batch annealing process using data mining techniques for cold rolled steel sheets

The annealing process is one of the important operations in production of cold rolled steel sheets, which significantly influences the final product quality of cold rolling mills. In this process, cold rolled coils are heated slowly to a desired temperature and then cooled. Modelling of annealing process (prediction of heating and cooling time and trend prediction of coil core temperature) is a very sophisticated and expensive work. Modelling of annealing process can be done by using of thermal models. In this paper, Modelling of steel annealing process is proposed by using data mining techniques. The main advantages of modelling with data mining techniques are: high speed in data processing, acceptable accuracy in obtained results and simplicity in using of this method. In this paper, after comparison of results of some data mining techniques, feed forward back propagation neural network is applied for annealing process modelling. A good correlation between results of this method and results of thermal models has been obtained

Interests Diffusion in Social Networks

Understanding cultural phenomena on Social Networks (SNs) and exploiting the implicit knowledge about their members is attracting the interest of different research communities both from the academic and the business side. The community of complexity science is devoting significant efforts to define laws, models, and theories, which, based on acquired knowledge, are able to predict future observations (e.g. success of a product). In the mean time, the semantic web community aims at engineering a new generation of advanced services by defining constructs, models and methods, adding a semantic layer to SNs. In this context, a leapfrog is expected to come from a hybrid approach merging the disciplines above. Along this line, this work focuses on the propagation of individual interests in social networks. The proposed framework consists of the following main components: a method to gather information about the members of the social networks; methods to perform some semantic analysis of the Domain of Interest; a procedure to infer members' interests; and an interests evolution theory to predict how the interests propagate in the network. As a result, one achieves an analytic tool to measure individual features, such as members' susceptibilities and authorities. Although the approach applies to any type of social network, here it is has been tested against the computer science research community. The DBLP (Digital Bibliography and Library Project) database has been elected as test-case since it provides the most comprehensive list of scientific production in this field.Comment: 30 pages 13 figs 4 table

The future of technology enhanced active learning – a roadmap

The notion of active learning refers to the active involvement of learner in the learning process, capturing ideas of learning-by-doing and the fact that active participation and knowledge construction leads to deeper and more sustained learning. Interactivity, in particular learnercontent interaction, is a central aspect of technology-enhanced active learning. In this roadmap, the pedagogical background is discussed, the essential dimensions of technology-enhanced active learning systems are outlined and the factors that are expected to influence these systems currently and in the future are identified. A central aim is to address this promising field from a best practices perspective, clarifying central issues and formulating an agenda for future developments in the form of a roadmap