Open business intelligence: on the importance of data quality awareness in user-friendly data mining

Citizens demand more and more data for making decisions in their daily life. Therefore, mechanisms that allow citizens to understand and analyze linked open data (LOD) in a user-friendly manner are highly required. To this aim, the concept of Open Business Intelligence (OpenBI) is introduced in this position paper. OpenBI facilitates non-expert users to (i) analyze and visualize LOD, thus generating actionable information by means of reporting, OLAP analysis, dashboards or data mining; and to (ii) share the new acquired information as LOD to be reused by anyone. One of the most challenging issues of OpenBI is related to data mining, since non-experts (as citizens) need guidance during preprocessing and application of mining algorithms due to the complexity of the mining process and the low quality of the data sources. This is even worst when dealing with LOD, not only because of the different kind of links among data, but also because of its high dimensionality. As a consequence, in this position paper we advocate that data mining for OpenBI requires data quality-aware mechanisms for guiding non-expert users in obtaining and sharing the most reliable knowledge from the available LOD.

Data analysis using circular causality in networks

Complex systems in causal relationships are known to be circular rather than linear; this means that a particular result is not produced by a single cause, but rather that both positive and negative feedback processes are involved. However, although interpreting systemic interrelationships requires a language formed by circles, this has only been developed at the diagram level, and not from an axiomatic point of view. The first difficulty encountered when analysing any complex system is that usually the only data available relate to the various variables, so the first objective was to transform these data into cause-and-effect relationships. Once this initial step was taken, our discrete chaos theory could be applied by finding the causal circles that will form part of the system attractor and allow their behavior to be interpreted. As an application of the technique presented, we analyzed the system associated with the transcription factors of inflammatory diseases.

Comparing Common Data Environment Platforms for Student Collaborative Working: A Case Study from Ulster University

PAS1192-2 (2013) outlines the “fundamental principles of Level 2 information modeling”, one of these principles is the use of what is commonly referred to as a Common Data Environment (CDE). A CDE could be described as an internet-enabled cloudhosting platform, accessible to all construction team members to access shared project information. For the construction sector to achieve increased productivity goals, the next generation of industry professionals will need to be educated in a way that provides them with an appreciation of Building Information Modelling (BIM) working methods, at all levels, including an understanding of how data in a CDE should be structured, managed, shared and published. This presents a challenge for educational institutions in terms of providing a CDE that addresses the requirements set out in PAS1192-2, and mirrors organisational and professional working practices without causing confusion due to over complexity. This paper presents the findings of a two-year study undertaken at Ulster University comparing the use of a leading industry CDE platform with one derived from the in-house Virtual Learning Environment (VLE), for the delivery of a student BIM project. The research methodology employed was a qualitative case study analysis, focusing on observations from the academics involved and feedback from students. The results of the study show advantages for both CDE platforms depending on the learning outcomes required.