A Volunteer Design Methodology of Data Warehouses

Abstract

In the context of Volunteered Geographic Information (VGI), volunteers are not involved in the decisional processes. Moreover, VGI systems do not offer advanced historical analysis tools. Therefore, in this work, we propose to use Data Warehouse (DW) and OLAP systems to analyze VGI data, and we define a new DW design methodology that allows involving volunteers in the definition of analysis needs over VGI data. We validate it using a real biodiversity case study.

A profile-aware methodological framework for collaborative multidimensional modeling

“…To the best of our knowledge, only [7] investigates the collaborative design of cube schemata in crowdsourcing scenarios taking the profile of volunteers into account. In particular, the authors propose a DW design methodology where three groups of users have been identified: volunteers, committers, and BI experts.…”

“…So far, not much has been said in the literature about this. In an attempt to fill this gap, the authors of [7] investigated the collaborative design of cube schemata, proposing a methodology that relies on three groups of users: volunteers (nonauthoritative users who express some preliminary analysis needs), committers (authoritative users who collaboratively validate the volunteers proposals), and DW experts (who take care of all technical issues related to the development of the DW). Volunteers can give their feedback about the cube schemata; to achieve a consensus about the elements of these schemata, they use a Group Decision Support System (GDSS), i.e., a software tool that allows the management of group meetings with mathematical methods for finding solutions to problems that are unstructured in nature [8].…”

“…However, some experiments carried out on a real case study have highlighted some limitations of such a methodology, mainly related to the difficulty in coping with the varying skills of users in the definition of analysis requirements [7,9]. Some relevant issues that remain open are:…”

“…3. Taking as a reference the most challenging methodological scenario, we propose a profile-aware methodology that extends the one in [7] by taking into account the varying skills of the users involved (Section 7). To this end we identify (i) a metric aimed at assessing how trustworthy each schema element is, also considering the reliability of volunteer end-users (Section 5), and (ii) a set of quality attributes 1 aimed at supporting decision-makers without multidimensional modeling skills in inspecting cube schemata and detecting errors (Section 6).…”

Requirements-driven data warehouse design based on enhanced pivot tables

“…In terms of DW design, this corresponds to the fusion of a set of multidimensional schemata, for which some approaches have been proposed in the literature. We adopt the one proposed in [47], which produces the schema shown in Fig. 15 starting from the ones in Figs.…”