Building digital twins of cities using the Inter Model Broker framework

Urban areas are becoming increasingly crowded, and as a result it is challenging to ensure that these areas remain liveable and accessible. Municipalities need to make strategic long-term decisions regarding urban planning, which is often a difficult and time-consuming task. Digital twins of cities can assist policy makers during this process,...

FSO: Food Safety Monitoring Ontology

Food safety is more than ever dependent on data collected about food samples by means of both laboratory analysis and, more recently, non-destructive sensors. The latter emerging technologies for food safety monitoring show great promise for ensuring the quality and suitability of food. Being non-destructive and quantitative as well as rapid and...

Quantum Approaches for Medoid Clustering

The k-medoids problem is an important problem in data clustering, which aims to partition a set of data points into k clusters, where each cluster is represented by a medoid, i.e., a data point that is the most centrally located in the cluster. Quantum annealing might be helpful in finding the solution to this problem faster. In this paper we...

Domain-independent data processing in an ontology based data access environment using the sosa ontology

Data pre-processing, analysis, and quality checks are constantly tailored to a specific data set. Ontology Based Data Access (OBDA) can provide the interoperability layer to apply the same procedure on heterogeneous data sets. The SOSA ontology, for example, provides a domain-independent generalization of sensor measurements, and is already...

Using Business Data in Customs Risk Management: Data Quality and Data Value Perspective

With the rise of data analytics use in government, government organizations are starting to explore the possibilities of using business data to create further public value. This process, however, is far from straightforward: key questions that governments need to address relate to the quality of this external data and the value it brings. In the...

Imputation methods outperform missing-indicator for data missing completely at random

Bridging the information gap of disaster responders by optimizing data selection using cost and quality

Natural disasters are chaotic and disruptive events, with compressed timelines and high levels of uncertainty. Comprehensive data on the impact becomes only available well into the response phase and data is scattered across organizations. Data heterogeneity issues are common. Consequently, responding organizations have difficulties finding data...

Data collection architecture for big data - A framework for a research agenda

As big data is expected to contribute largely to economic growth, scalability of solutions becomes apparent for deployment by organisations. It requires automatic collection and processing of large, heterogeneous data sets of a variety of resources, dealing with various aspects like improving quality, fusion and linking data sets to provide...