Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.
Data Mining / Nada Lavrac and Marko Grobelnik --2. Text and Web Mining / Dunja Mladenic and Marko Grobelnik --3. Decision Support / Marko Bohanec --4. Integration of Data Mining and Decision Support / Nada Lavrac and Marko Bohanec --5. Collaboration in a Data Mining Virtual Organization / Steve Moyle, Jane McKenzie and Alipio Jorge. Series Title:
Data mining offers the promise of increased business intelligence, and also improved user experiences, leading to increased participation and greater quality in the knowledge that is captured, both of which are central objectives in Mass Collaboration. In this talk, I will introduce Mass Collaboration and discuss some important data mining related issues.
Text and data mining As a publisher we believe it is our job to help meet the needs of researchers and we are committed to reducing the barriers to mining content. We actively collaborate with researchers and institutes to facilitate text and data mining by enabling access and by developing our platforms, tools and services to support researchers.
data. In recent times, the explosion in the availability of various kinds of data has triggered tremendous oppor-tunities for collaboration, in particularcollaborationin data mining. The following is some realistic scenarios: 1. Multiple competing supermarkets, each having an extra large set of data records of its customers' buying behaviors
Data Mining, Collaboration, and Institutional Infrastructure for Transforming Research and Teaching in the Human Sciences and Beyond. Cathy N. Davidson, Duke University The first generation of the digital humanities was all about data. The excitement and impetus of digital humanities throughout much of the 1990s and continuing to the present was that massive data bases could be digitized
Data mining deals with finding patterns in data that are by user-definition, interesting and valid. It is an interdisciplinary area involving databases, machine learning, pattern recognition, statistics, visualization and others. Decision support focuses on developing systems to help decision-makers solve problems. Decision support provides a selection of data analysis, simulation
Data Mining is also known as Data Analytics, Data Science and Machine Learning. Huge amount of data are being collected in every sector of life today, making data mining an extremely important research area. It is useful for data collection, data pre-processing and cleansing, knowledge discovery, event detection, future prediction and policy/strategy development. We develop novel techniques
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Data Mining, or Knowledge Discovery in Databases (KDD) as it is also known, is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. Data mining refers to "using a variety of techniques to identify nuggets of information or decision-making knowledge in bodies of data, and extracting these in such a way that they can be put to use in the
TRIBE: collaboration and data mining. Posted by Rudi Pillich. May 7, 2015. Tribe is a collaborative platform that allows mining of big data across bioinformatics systems. Here at the NDEx Project, we are excited to find a system with a similar philosophy and complementary content. Tribe has features that make it an interesting resource for both biology and bioinformatics researchers and we
Oracle Data Mining (ODM) offers powerful data mining algorithms that allow data analysts to uncover meaningful insights and make predictions. In short, with the help of ODM, you can target your best customers, predict customer behavior, create customer profiles, detect different anomalies, and spot new selling opportunities.
In unserem kostenlosen Webinar „Effizienzsteigerung in der Abschlussprfung durch Einsatz von Process Mining" am 21.09.2018 in der Zeit von 10.00 – 11.00 Uhr zeigt Dr. Dominique Hoffmann, Warth Klein Grant Thornton AG, zunchst auf, wie Sie Process Mining im Rahmen der Jahresabschlussprfung sinnvoll und effizient einsetzen knnen. Anschlieend prsentiert Costel
In this project, in collaboration with the SQL Server Product Group, we identify opportunities for new abstractions and interfaces that enable integration of data mining. Our joint work resulted in defining OLE-DB DM, an extension of OLE-DB that exposes data mining functionality.
18.06.2014Second, we develop a pattern mining approach based on sequence mining and graph mining. Third, using time-dependent Cox regression, our approach derives business insights from real-world collaboration data that are directly applicable to managerial actions. Our empirical study identifies collaboration patterns that can lead to more efficient teamwork. It also shows that the effects of
Data Mining. Einen Weg finden oder einen schaffen. Jeder Softwareentwickler lst tglich viele kleine und grosse Probleme. Whrend die kleinen Probleme schnell behoben sind, besteht bei grsseren oft die Gefahr, einfach die erstbeste Idee umzusetzen – mit negativen Folgen in der Zukunft bezglich effizienter Wartbarkeit und Erweiterbarkeit. Verwaltung. Mit der Digitalisierung steigen
Abstract Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. Two research communities--Educational Data
Two research communities -- Educational Data Mining (EDM) and Learning Analytics and Knowledge (LAK) have developed separately to address this need. This paper argues for increased and formal communication and collaboration between these communities in order to share research, methods, and tools for data mining and analysis in the service of developing both LAK and EDM fields.
It is also likely to spur the research collaboration between the Semantic Web community (represented by the linked data sub-community as its practice-oriented segment) and the Data Mining community. Track B addresses the domain of scientific research collaboration, in particular cross-disciplinary collaboration. While collaboration between