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

Data mining in manufacturing: a review based on the kind of knowledge

In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, including product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection etc. Data mining has emerged as an important tool for knowledge acquisition from the manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with a special emphasis on the type of functions to be performed on the data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been used on the abstracts and keywords of 150 papers to identify the research gaps and find the linkages between knowledge area, knowledge type and the applied data mining tools and techniques

How to Create an Innovation Accelerator

Too many policy failures are fundamentally failures of knowledge. This has become particularly apparent during the recent financial and economic crisis, which is questioning the validity of mainstream scholarly paradigms. We propose to pursue a multi-disciplinary approach and to establish new institutional settings which remove or reduce obstacles impeding efficient knowledge creation. We provided suggestions on (i) how to modernize and improve the academic publication system, and (ii) how to support scientific coordination, communication, and co-creation in large-scale multi-disciplinary projects. Both constitute important elements of what we envision to be a novel ICT infrastructure called "Innovation Accelerator" or "Knowledge Accelerator".Comment: 32 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Intelligent data analysis from the financial execution of research projects at University of Minho

Dissertação de mestrado em Engenharia InformáticaThe number of research and development (R&D) projects underway has increased substantially in recent years, which derives from the recognition of the importance of these projects for the future success of the University of Minho and its scientific partners, not only from a financial perspective but also innovation and search for knowledge. Any Higher Education Institution (HEI) needs a solid management base for many areas that are part of and complete its global organization, such as the area related to R&D projects. A large part of the financial man agement carried out by the University of Minho is intrinsically linked to project management, whose budgets are often in the thousands of euros. The data used by the most diverse entities and support centers at the University of Minho are available to those responsible for them in an unintuitive and dispersed way. This dispersion, besides making access to information very difficult, does not sympathize with the organization that a higher education unit needs. Therefore, getting detailed and reliable information is the key to success, both for researchers, who are directly responsible, and for the regulatory bodies that are implanted in the university. Thus, it was proposed to create a Data Visualization (DV) platform based on project execution data sources from the Financial and Patrimonial Services Unit (USFP) of the University of Minho to provide an organized and coherent data visualization platform, according to the needs of its stakeholders. With the creation of this platform, through an Intelligent Data Analysis System, using a temporal and detailed observation of the data, it is possible to draw conclusions about the investments made in research projects that have occurred until now and to help in future investment decisions crucial to the healthy functioning of the educational institution. Thus, this analysis seeks not only to improve the financial management of the area in question but also to understand the extent to which the use of Machine Learning techniques can be useful in analyzing data related to the financial execution of R&D projects. Furthermore, an area that is highly related to research projects, and cannot be ignored, is scientific production. The dissemination of scientific knowledge is an essential part of the research work carried out in any area, so this topic was also studied and introduced within the scope of this dissertation.O número de projetos de investigação e desenvolvimento (I&D) em execução tem vindo a aumentar substan cialmente nos últimos anos, o que deriva do reconhecimento da importância destes projetos para o sucesso futuro da Universidade do Minho e seus parceiros científicos, não só numa perspetiva financeira, mas também de inovação e procura pelo conhecimento. Qualquer instituição de ensino superior necessita de uma base sólida de gestão para todos os tipos de áreas que fazem parte e completam a sua organização global, como é o caso da área relacionada com os projetos de I&D. Uma grande parte da gestão financeira realizada pela Universidade do Minho está intrinsecamente ligada à gestão de projetos, cujos orçamentos rondam, muitas vezes, os milhares de euros. Os dados utilizados pelas mais diversas entidades e centros de apoio da Universidade do Minho encontram se à disposição dos responsáveis das mesmas de uma forma pouco intuitiva e dispersa. Esta dispersão, para além de dificultar bastante o acesso à informação, não se compadece com a organização que uma unidade de ensino superior necessita. Neste sentido, a obtenção de informação detalhada e fidedigna é a chave do sucesso, tanto para os in vestigadores, responsáveis diretos, como para as entidades reguladoras que se encontram implementadas na universidade. Assim, foi proposta a criação de uma plataforma de visualização de dados a partir de fontes de dados de execução de projetos provenientes da Unidade de Serviços Financeiro e Patrimonial (USFP) da Uni versidade do Minho com o intuito de fornecer uma plataforma de visualização de dados organizada e coerente, conforme as necessidades dos seus stakeholders. Com a criação desta plataforma, através de um sistema de Análise Inteligente de Dados, isto é, fazendo uso de uma observação temporal e detalhada dos dados, é possível retirar conclusões sobre os investimentos feitos nos projetos de investigação ocorridos até à data e ajudar nas futuras decisões de investimento cruciais ao funcionamento saudável da instituição de ensino. Assim, com esta análise procura-se, não só melhorar a gestão financeira da área em questão, mas também perceber até que ponto a utilização de técnicas de Machine Learning pode ser útil na análise de dados relativos à execução financeira de projetos de I&D. Para além disso, uma área que está altamente relacionada com os projetos de investigação, não podendo ficar alheia à mesma, é a produção científica. A disseminação de conhecimento científico é uma parte essencial do trabalho de investigação levado a cabo em qualquer área, pelo que é extremamente importante que também este tema seja estudado e introduzido no âmbito desta dissertação

Mission Dependency Index of Air Force Built Infrastructure: Knowledge Discovery with Machine Learning

Mission Dependency Index (MDI) is a metric developed to capture the relative criticality of infrastructure assets with respect to organizational missions. The USAF adapted the MDI metric from the United States Navy’s MDI methodology. Unlike the Navy’s MDI data collection process, the USAF adaptation of the MDI metric employs generic facility category codes (CATCODEs) to assign MDI values. This practice introduces uncertainty into the MDI assignment process with respect to specific missions and specific infrastructure assets. The uncertainty associated with USAF MDI values necessitated the MDI adjudication process. The MDI adjudication process provides a mechanism for installation civil engineer personnel to lobby for accurate MDI values for specific infrastructure assets. The MDI adjudication process requires manual identification of MDI discrepancies, documentation, and extensive coordination between organizations. Given the existing uncertainty with USAF MDI values and the effort required for the MDI adjudication process, this research pursues machine learning and the knowledge discovery in databases (KDD) process to identify and understand relationships between real property data and mission critical infrastructure. Furthermore, a decision support tool is developed for the MDI adjudication process. Specifically, supervised learning techniques are employed to develop a classifier that can identify potential MDI discrepancies. This automation effort serves to minimize the manual MDI review process by identifying a subset of facilities for potential adjudication

A Literature Review on Predictive Monitoring of Business Processes

Oleme läbi vaadanud mitmesuguseid ennetava jälgimise meetodeid äriprotsessides. Prognoositavate seirete eesmärk on aidata ettevõtetel oma eesmärke saavutada, aidata neil valida õige ärimudel, prognoosida tulemusi ja aega ning muuta äriprotsessid riskantsemaks. Antud väitekirjaga oleme hoolikalt kogunud ja üksikasjalikult läbi vaadanud selle väitekirja teemal oleva kirjanduse. Kirjandusuuringu tulemustest ja tähelepanekutest lähtuvalt oleme hoolikalt kavandanud ennetava jälgimisraamistiku. Raamistik on juhendiks ettevõtetele ja teadlastele, teadustöötajatele, kes uurivad selles valdkonnas ja ettevõtetele, kes soovivad neid tehnikaid oma valdkonnas rakendada.The goal of predictive monitoring is to help the business achieve their goals, help them take the right business path, predict outcomes, estimate delivery time, and make business processes risk aware. In this thesis, we have carefully collected and reviewed in detail all literature which falls in this process mining category. The objective of the thesis is to design a Predictive Monitoring Framework and classify the different predictive monitoring techniques. The framework acts as a guide for researchers and businesses. Researchers who are investigating in this field and businesses who want to apply these techniques in their respective field

A Process to Implement an Artificial Neural Network and Association Rules Techniques to Improve Asset Performance and Energy Efficiency

In this paper, we address the problem of asset performance monitoring, with the intention of both detecting any potential reliability problem and predicting any loss of energy consumption e ciency. This is an important concern for many industries and utilities with very intensive capitalization in very long-lasting assets. To overcome this problem, in this paper we propose an approach to combine an Artificial Neural Network (ANN) with Data Mining (DM) tools, specifically with Association Rule (AR) Mining. The combination of these two techniques can now be done using software which can handle large volumes of data (big data), but the process still needs to ensure that the required amount of data will be available during the assets’ life cycle and that its quality is acceptable. The combination of these two techniques in the proposed sequence di ers from previous works found in the literature, giving researchers new options to face the problem. Practical implementation of the proposed approach may lead to novel predictive maintenance models (emerging predictive analytics) that may detect with unprecedented precision any asset’s lack of performance and help manage assets’ O&M accordingly. The approach is illustrated using specific examples where asset performance monitoring is rather complex under normal operational conditions.Ministerio de Economía y Competitividad DPI2015-70842-

Sustainability Model from Cradle-to-Gate: Residential Buildings in Palestine

Aim of study is to develop a sustainable model for residential building sector in Palestine by tracking the sector from cradle to end of construction process. In the sustainability model, all input resources to the model will be specified and connect to the outputs of the building process involving all dynamic contributors; labor, water, and energy. Based on the sustainability supply chain a model is implemented for a 100 m2 residential building. For the implemented case study all, necessary costs have been included showing the total cost of this building. The Value Creation Index of the investigated case has been estimated showing the ratio between the revenues gained by the local contracting companies and cost incurred in the building. The large consumed resources indicate the importance and need for creating a sustainability model for the sector.