21st Century Simulation: Exploiting High Performance Computing and Data Analysis

This paper identifies, defines, and analyzes the limitations imposed on Modeling and Simulation by outmoded paradigms in computer utilization and data analysis. The authors then discuss two emerging capabilities to overcome these limitations: High Performance Parallel Computing and Advanced Data Analysis. First, parallel computing, in supercomputers and Linux clusters, has proven effective by providing users an advantage in computing power. This has been characterized as a ten-year lead over the use of single-processor computers. Second, advanced data analysis techniques are both necessitated and enabled by this leap in computing power. JFCOM's JESPP project is one of the few simulation initiatives to effectively embrace these concepts. The challenges facing the defense analyst today have grown to include the need to consider operations among non-combatant populations, to focus on impacts to civilian infrastructure, to differentiate combatants from non-combatants, and to understand non-linear, asymmetric warfare. These requirements stretch both current computational techniques and data analysis methodologies. In this paper, documented examples and potential solutions will be advanced. The authors discuss the paths to successful implementation based on their experience. Reviewed technologies include parallel computing, cluster computing, grid computing, data logging, OpsResearch, database advances, data mining, evolutionary computing, genetic algorithms, and Monte Carlo sensitivity analyses. The modeling and simulation community has significant potential to provide more opportunities for training and analysis. Simulations must include increasingly sophisticated environments, better emulations of foes, and more realistic civilian populations. Overcoming the implementation challenges will produce dramatically better insights, for trainees and analysts. High Performance Parallel Computing and Advanced Data Analysis promise increased understanding of future vulnerabilities to help avoid unneeded mission failures and unacceptable personnel losses. The authors set forth road maps for rapid prototyping and adoption of advanced capabilities. They discuss the beneficial impact of embracing these technologies, as well as risk mitigation required to ensure success

E-finance-lab at the House of Finance : about us

The financial services industry is believed to be on the verge of a dramatic [r]evolution. A substantial redesign of its value chains aimed at reducing costs, providing more efficient and flexible services and enabling new products and revenue streams is imminent. But there seems to be no clear migration path nor goal which can cast light on the question where the finance industry and its various players will be and should be in a decade from now. The mission of the E-Finance Lab is the development and application of research methodologies in the financial industry that promote and assess how business strategies and structures are shared and supported by strategies and structures of information systems. Important challenges include the design of smart production infrastructures, the development and evaluation of advantageous sourcing strategies and smart selling concepts to enable new revenue streams for financial service providers in the future. Overall, our goal is to contribute methods and views to the realignment of the E-Finance value chain. ..

Data Mining Applications in Higher Education and Academic Intelligence Management

Higher education institutions are nucleus of research and future development acting in a competitive environment, with the prerequisite mission to generate, accumulate and share knowledge. The chain of generating knowledge inside and among external organizations (such as companies, other universities, partners, community) is considered essential to reduce the limitations of internal resources and could be plainly improved with the use of data mining technologies. Data mining has proven to be in the recent years a pioneering field of research and investigation that faces a large variety of techniques applied in a multitude of areas, both in business and higher education, relating interdisciplinary studies and development and covering a large variety of practice. Universities require an important amount of significant knowledge mined from its past and current data sets using special methods and processes. The ways in which information and knowledge are represented and delivered to the university managers are in a continuous transformation due to the involvement of the information and communication technologies in all the academic processes. Higher education institutions have long been interested in predicting the paths of students and alumni (Luan, 2004), thus identifying which students will join particular course programs (Kalathur, 2006), and which students will require assistance in order to graduate. Another important preoccupation is the academic failure among students which has long fuelled a large number of debates. Researchers (Vandamme et al., 2007) attempted to classify students into different clusters with dissimilar risks in exam failure, but also to detect with realistic accuracy what and how much the students know, in order to deduce specific learning gaps (Piementel & Omar, 2005). The distance and on-line education, together with the intelligent tutoring systems and their capability to register its exchanges with students (Mostow et al., 2005) present various feasible information sources for the data mining processes. Studies based on collecting and interpreting the information from several courses could possibly assist teachers and students in the web-based learning setting (Myller et al., 2002). Scientists (Anjewierden et al., 2007) derived models for classifying chat messages using data mining techniques, in order to offer learners real-time adaptive feedback which could result in the improvement of learning environments. In scientific literature there are some studies which seek to classify students in order to predict their final grade based on features extracted from logged data ineducational web-based systems (Minaei-Bidgoli & Punch, 2003). A combination of multiple classifiers led to a significant improvement in classification performance through weighting the feature vectors. The author’s research directions through the data mining practices consist in finding feasible ways to offer the higher education institutions’ managers ample knowledge to prepare new hypothesis, in a short period of time, which was formerly rigid or unachievable, in view of large datasets and earlier methods. Therefore, the aim is to put forward a way to understand the students’ opinions, satisfactions and discontentment in the each element of the educational process, and to predict their preference in certain fields of study, the choice in continuing education, academic failure, and to offer accurate correlations between their knowledge and the requirements in the labor market. Some of the most interesting data mining processes in the educational field are illustrated in the present chapter, in which the author adds own ideas and applications in educational issues using specific data mining techniques. The organization of this chapter is as follows. Section 2 offers an insight of how data mining processes are being applied in the large spectrum of education, presenting recent applications and studies published in the scientific literature, significant to the development of this emerging science. In Section 3 the author introduces his work through a number of new proposed directions and applications conducted over data collected from the students of the Babes-Bolyai University, using specific data mining classification learning and clustering methods. Section 4 presents the integration of data mining processes and their particular role in higher education issues and management, for the conception of an Academic Intelligence Management. Interrelated future research and plans are discussed as a conclusion in Section 5.data mining,data clustering, higher education, decision trees, C4.5 algorithm, k-means, decision support, academic intelligence management

Intellectual Property Management in Health and Agricultural Innovation: Executive Guide

Prepared by and for policy-makers, leaders of public sector research establishments, technology transfer professionals, licensing executives, and scientists, this online resource offers up-to-date information and strategies for utilizing the power of both intellectual property and the public domain. Emphasis is placed on advancing innovation in health and agriculture, though many of the principles outlined here are broadly applicable across technology fields. Eschewing ideological debates and general proclamations, the authors always keep their eye on the practical side of IP management. The site is based on a comprehensive Handbook and Executive Guide that provide substantive discussions and analysis of the opportunities awaiting anyone in the field who wants to put intellectual property to work. This multi-volume work contains 153 chapters on a full range of IP topics and over 50 case studies, composed by over 200 authors from North, South, East, and West. If you are a policymaker, a senior administrator, a technology transfer manager, or a scientist, we invite you to use the companion site guide available at http://www.iphandbook.org/index.html The site guide distills the key points of each IP topic covered by the Handbook into simple language and places it in the context of evolving best practices specific to your professional role within the overall picture of IP management

Dynamically Assessing the Intertwined Influences of ISD Project Risk Factors

This study aims to adopt an approach for assessing the mutual influences of risk factors on information system development project transferring from initialization to the control phases. Given that risks evolve dynamically, the variations of the degrees of risk influences throughout the development process of information system project must be analyzed so that effective risk management strategies can be devised in a cost-effective way at the right stage. Therefore, our study applies Decision Making Trial and Evaluation Laboratory to quantitatively assess the interdependencies among the risk factors for each project development phase. An application conducted in a private, medium-scale university in Taiwan is demonstrated. The results suggest the directions for possible improvements of risk management during university information system development process

A typology of technology market intermediaries

Technology Market Intermediaries (TMI) are currently emerging on the markets for technologies attempting to realize business opportunities and facilitate the technology and IP transactions supporting firms and other markets actors (e.g. universities). They aim to support open innovation, respectively facilitate more economically technology and particularly IP transactions. However, our understanding of TMIs and their roles needs to be considered incomplete. In this paper I provide evidence on the growing number of TMIs and derived a conceptual basis for a further understanding of TMIs. The inherent difficulties of intellectual property monetization present a challenge for technology based enterprises and business opportunities for IP firms. Following a literature review, I develop a typology for TMIs. Having carried out a review of the literature I compiled a mix of primary and secondary data on about 70 TMIs. Applying the 'nine business model building blocks' from Osterwalder (2004) I identify 12 different TMI types which I then consolidate into six TMI archetypes using the framework for 'business models archetypes' of Herman and Malone (2003). --typology,type,Technology Market Intermediaries

Global Strategy and the Acquisition of Local Knowledge How MNCs Enter Regional Knowledge Clusters

The paper addresses two recent interrelated phenomena: High- tech regional knowledge clusters, and globalization of R&D activities by multinational corporations (MNCs). Combining MNC literature; regional development literature; and literature on social networks, the paper discusses determinants of entry modes used by MNCs that localize R&D units in regional knowledge clusters. The paper states that the entry mode used by a MNC depends upon the type of agglomeration economies the latter seeks to appropriate: Those related to network relations; to local labor market specialization; or to institutional specialization. The paper adds theoretical insight into advantages and disadvantages of different entry modes with respect to appropriation of agglomeration economies, and special attention is dedicated to discussing acquisition. Through the use of an empirical case ¾ the entry of five MNCs into the Danish telecommunications cluster in Aalborg, the paper exemplifies its theoretical observations, but also points to how the evolution of a knowledge cluster may be severely affected by MNCs that enter through acquisition.MNCs; entry mode; acquisition; explorative R&D; regional clusters; localized learning; networks; telecommunications industry; North Jutland; Denmark.