Abstract: This paper focuses on issues of ontology construction process, Computing Classification System and Data Science domain ontology all used to help not only IT-students but any IT-specialists from industry and academia also to tackle the problems addressing the Big Data and Data Science skills gap. We discuss some methodological aspects of ontology design process and enriching of existing free accessible ontologies and show how suggested methods and software tools help IT-specialists including master students to implement their research work and participate in real world projects. The role of visual data exploration tools for certain issues under discussion and some use cases are discussed.

Abstract: We describe the construction of a bilingual (English-Russian /Russian-English) semantic network covering basic concepts of computing. To construct the semantic network, we used the Computing Curricular series created during 2000-2015 under the aegis of ACM and IEEE and the current stan-dards of IT specialists training in Russia, as well as some other English language and Russian lan-guage sources. The resulting network can be used as a basic component in an intelligent information system that allows processing bilingual search queries while considering their semantics and to help support and guide automated translation efforts of academic texts from one language to the other. The network can also be useful to support comparative analysis and integration of the programs and teach-ing materials for Computing and IT education in Russia and English speaking countries. This network can support cross-lingual information retrieval, knowledge management and machine translation, which play an important role in e-learning personalization and retrieval in the computing domain, thus allowing to benefit from online educational resources that are available in both languages.

Abstract: The BBDTC (https://biobigdata.ucsd.edu) is a community-oriented platform to encourage high-quality knowledge dissemination with the aim of growing a well-informed biomedical big data community through collaborative efforts on training and education. The BBDTC collaborative is an e-learning platform that supports the biomedical community to access, develop and deploy open training materials. The BBDTC supports Big Data skill training for biomedical scientists at all levels, and from varied backgrounds. The natural hierarchy of courses allows them to be broken into and handled as modules. Modules can be reused in the context of multiple courses and reshuffled, producing a new and different, dynamic course called a playlist. Users may create playlists to suit their learning requirements and share it with individual users or the wider public. BBDTC leverages the maturity and design of the HUBzero content-management platform for delivering educational content. To facilitate the migration of existing content, the BBDTC supports importing and exporting course material from the edX platform. Migration tools will be extended in the future to support other platforms. Hands-on training software packages, i.e., toolboxes, are supported through Amazon EC2 and Virtualbox virtualization technologies, and they are available as: (i) downloadable lightweight Virtualbox Images providing a standardized software tool environment with software packages and test data on their personal machines, and (ii) remotely accessible Amazon EC2 Virtual Machines for accessing biomedical big data tools and scalable big data experiments. At the moment, the BBDTC site contains three open Biomedical big data training courses with lecture contents, videos and hands-on training utilizing VM toolboxes, covering diverse topics. The courses have enhanced the hands-on learning environment by providing structured content that users can use at their own pace. A four course biomedical big data series is planned for development in early 2016.

Abstract: We describe a service-based approach that provides a natural language interface to legacy information systems, built on top of relational database management systems. The long term goal is to make data management and analysis accessible to a wider range of users for a diverse range of purposes and to simplify the decision making process. We present an ontology-driven web-service, named Reply, that transforms traditional information systems into intelligent systems, endowed with a natural language interface, so that they can be queried by any novice user much like modern day search engines. The principal mechanism of our approach is turning a natural language query into a SQL-query for structured data sources by using Ontology-Based Data Access methods. We also outline how the proposed approach allows semantic searching of large structured, unstructured, or semi-structured data within the database or outside sources, thus helping bridge the talent gap in the case of Big Data Analytics used by researchers and postgraduate students.

Abstract: A principal component analysis is carried out on the undergraduate level “Stochastic Models” course. We determine that the first principal component has a positive correlation with the score of the final written cumulative exam. This could possible mean that the final exam could be eliminated from engineering curricula, but the variability is significant as measured by the correlation R statistic. We gathered a much larger sample and found that the variability increased, indicating changes in the course and students emphasis in learning activities. Therefore we concluded, that the evidence presented does not justify eliminating written cumulative final exams.