Why is the snowflake schema a good data warehouse design?

Database design for data warehouses is based on the notion of the snowflake schema and its important special case, the star schema. The snowflake schema represents a dimensional model which is composed of a central fact table and a set of constituent dimension tables which can be further broken up into subdimension tables. We formalise the concept of a snowflake schema in terms of an acyclic database schema whose join tree satisfies certain structural properties. We then define a normal form for snowflake schemas which captures its intuitive meaning with respect to a set of functional and inclusion dependencies. We show that snowflake schemas in this normal form are independent as well as separable when the relation schemas are pairwise incomparable. This implies that relations in the data warehouse can be updated independently of each other as long as referential integrity is maintained. In addition, we show that a data warehouse in snowflake normal form can be queried by joining the relation over the fact table with the relations over its dimension and subdimension tables. We also examine an information-theoretic interpretation of the snowflake schema and show that the redundancy of the primary key of the fact table is zero

Bridging the Data Divide: Understanding State Agency and University Research Partnerships within SLDS

This report examines this question through an analysis of state agency-university researcher partnerships that exist in State Longitudinal Data Systems (SLDS). Building state agency-university researcher partnerships is an important value of SLDS. To examine state agency-university researcher partnerships within SLDS, our analysis is guided by the following set of questions based on 71 interviews conducted with individuals most directly involved with SLDS efforts in Virginia, Maryland, Texas and Washington. The findings from this analysis suggest that each state’s SLDS organization and governance structure includes university partners in differing ways. In general, stronger partnership efforts are driven by legislative action or executive-level leadership. Regardless of structure, the operation of these partnerships is shaped by the agency’s previous experience and cultural norms surrounding the value and inclusion of university researchers

Bridging the Data Divide: Understanding State Agency and University Research Partnerships within SLDS

This report examines this question through an analysis of state agency-university researcher partnerships that exist in State Longitudinal Data Systems (SLDS). Building state agency-university researcher partnerships is an important value of SLDS. To examine state agency-university researcher partnerships within SLDS, our analysis is guided by the following set of questions based on 71 interviews conducted with individuals most directly involved with SLDS efforts in Virginia, Maryland, Texas and Washington. The findings from this analysis suggest that each state’s SLDS organization and governance structure includes university partners in differing ways. In general, stronger partnership efforts are driven by legislative action or executive-level leadership. Regardless of structure, the operation of these partnerships is shaped by the agency’s previous experience and cultural norms surrounding the value and inclusion of university researchers

Implementing data-driven decision support system based on independent educational data mart

Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions

Factors in the Design and Development of a Data Warehouse for Academic Data.

Data warehousing is a relatively new field in the realm of information technology, and current research centers primarily around data warehousing in business environments. As new as the field is in these environments, only recently have educational institutions begun to embark on data warehousing projects, and little research has been done regarding the special considerations and characteristics of academic data, and the complexity of analyzing such data. Educational institutions measure success very differently from business-oriented organizations, and the analyses that are meaningful in such environments pose very unique and intricate problems in data warehousing. This research describes the process of developing a data warehouse for a community college, focusing on issues specific to academic data

Methods and models of next generation technology enhanced learning - White Paper

Our understanding of learning with technology is increasingly lagging behind technological advancements, such that it is no longer possible to fully understand learning with technologies without bringing together evidence from practice-based experiences and theoretical insight to inform research, design, policy and practice. Furthermore, whilst practical experiences and theoretical insights make significant contributions towards understanding learning with new technologies, the dynamic nature of learner practices and study contexts make it difficult to predict future requirements in terms of methods and models for next generation technology enhanced learning. We therefore require formal and comprehensive methods and models of learning with technology that accommodate theory and practice whilst allowing us to anticipate methodological innovations that capture future transitions and changes in learner practices and study contexts, in order to inform research, design, policy and practice. Workshop participants represented different communities of interest including research, design, evaluation and assessment. The overall objective was to anticipate methodological innovations in technology enhanced learning research and design over the next 5/10 years

Practitioner Track Proceedings of the 6th International Learning Analytics & Knowledge Conference (LAK16)

Practitioners spearhead a significant portion of learning analytics, relying on implementation and experimentation rather than on traditional academic research. Both approaches help to improve the state of the art. The LAK conference has created a practitioner track for submissions, which first ran in 2015 as an alternative to the researcher track. The primary goal of the practitioner track is to share thoughts and findings that stem from learning analytics project implementations. While both large and small implementations are considered, all practitioner track submissions are required to relate to initiatives that are designed for large-scale and/or long-term use (as opposed to research-focused initiatives). Other guidelines include: • Implementation track record The project should have been used by an institution or have been deployed on a learning site. There are no hard guidelines about user numbers or how long the project has been running. • Learning/education related Submissions have to describe work that addresses learning/academic analytics, either at an educational institution or in an area (such as corporate training, health care or informal learning) where the goal is to improve the learning environment or learning outcomes. • Institutional involvement Neither submissions nor presentations have to include a named person from an academic institution. However, all submissions have to include information collected from people who have used the tool or initiative in a learning environment (such as faculty, students, administrators and trainees). • No sales pitches While submissions from commercial suppliers are welcome; reviewers do not accept overt (or covert) sales pitches. Reviewers look for evidence that a presentation will take into account challenges faced, problems that have arisen, and/or user feedback that needs to be addressed. Submissions are limited to 1,200 words, including an abstract, a summary of deployment with end users, and a full description. Most papers in the proceedings are therefore short, and often informal, although some authors chose to extend their papers once they had been accepted. Papers accepted in 2016 fell into two categories. • Practitioner Presentations Presentation sessions are designed to focus on deployment of a single learning analytics tool or initiative. • Technology Showcase The Technology Showcase event enables practitioners to demonstrate new and emerging learning analytics technologies that they are piloting or deploying. Both types of paper are included in these proceedings

TLAD 2011 Proceedings:9th international workshop on teaching, learning and assesment of databases (TLAD)

This is the ninth in the series of highly successful international workshops on the Teaching, Learning and Assessment of Databases (TLAD 2011), which once again is held as a workshop of BNCOD 2011 - the 28th British National Conference on Databases. TLAD 2011 is held on the 11th July at Manchester University, just before BNCOD, and hopes to be just as successful as its predecessors.The teaching of databases is central to all Computing Science, Software Engineering, Information Systems and Information Technology courses, and this year, the workshop aims to continue the tradition of bringing together both database teachers and researchers, in order to share good learning, teaching and assessment practice and experience, and further the growing community amongst database academics. As well as attracting academics from the UK community, the workshop has also been successful in attracting academics from the wider international community, through serving on the programme committee, and attending and presenting papers.Due to the healthy number of high quality submissions this year, the workshop will present eight peer reviewed papers. Of these, six will be presented as full papers and two as short papers. These papers cover a number of themes, including: the teaching of data mining and data warehousing, databases and the cloud, and novel uses of technology in teaching and assessment. It is expected that these papers will stimulate discussion at the workshop itself and beyond. This year, the focus on providing a forum for discussion is enhanced through a panel discussion on assessment in database modules, with David Nelson (of the University of Sunderland), Al Monger (of Southampton Solent University) and Charles Boisvert (of Sheffield Hallam University) as the expert panel