Over the past few years, the scale of sensor networks has greatly expanded. This generates extended spatiotemporal datasets, which form a crucial information resource in numerous fields, ranging from sports and healthcare to environmental science and surveillance. Unfortunately, these datasets often contain missing values due to systematic or inadvertent sensor misoperation. This incompleteness hampers the subsequent data analysis, yet addressing these missing observations forms a challenging problem. This is especially the case when both the temporal correlation of timestamps within a single sensor and the spatial correlation between sensors are important. Here, we apply and evaluate 12 imputation methods to complete the missing values in a dataset originating from large-scale environmental monitoring. As part of a large citizen science project, IoT-based microclimate sensors were deployed for six months in 4400 gardens across the region of Flanders, generating 15-min recordings of temperature and soil moisture. Methods based on spatial recovery as well as time-based imputation were evaluated, including Spline Interpolation, MissForest, MICE, MCMC, M-RNN, BRITS, and others. The performance of these imputation methods was evaluated for different proportions of missing data (ranging from 10% to 50%), as well as a realistic missing value scenario. Techniques leveraging the spatial features of the data tend to outperform the time-based methods, with matrix completion techniques providing the best performance. Our results therefore provide a tool to maximize the benefit from costly, large-scale environmental monitoring efforts.

The role of metadata is gaining importance due to today's growth of multimedia content. Currently, XML is the standard for data interchange. However, as XML Schemas do not express semantics but rather the document structure, there is a lack of semantic interoperability regarding current (XML-based) metadata standards. By using semantic Web technologies, ontologies can be created to describe the semantics of a particular metadata format. A problem is that the existing XML data (compliant with a particul.ar metadata format) cannot be used by an ontology, implying the need for a conversion of XML data to RDF instances. In this paper, a generic approach is proposed for the transformation of XML data into RDF instances in an ontology-dependent way. By means of a mapping document, the link is described between an XML Schema (describing the structure of particular XML data) and an OWL ontology. Our approach is illustrated by applying it to the DIG35 specification, which is an XML-based metadata standard for the description of digital images.

Incorporating structured data in the Linked Data cloud is still complicated, despite the numerous existing tools. In particular, hierarchical structured data (e.g., JSON) are underrepresented, due to their processing complexity. A uniform mapping formalization for data in different formats, which would enable reuse and exchange between tools and applied data, is missing. This paper describes a novel approach of mapping heterogeneous and hierarchical data sources into RDF using the RML mapping language, an extension over R2RML (the W3C standard for mapping relational databases into RDF). To facilitate those mappings, we present a toolset for producing RML mapping files using the Karma data modelling tool, and for consuming them using a prototype RML processor. A use case shows how RML facilitates the mapping rules' definition and execution to map several heterogeneous sources.

The concept of Linked Data has made its entrance in the cultural heritage sector due to its potential use for the integration of heterogeneous collections and deriving additional value out of existing metadata. However, practitioners and researchers alike need a better understanding of what outcome they can reasonably expect of the reconciliation process between their local metadata and established controlled vocabularies which are already a part of the Linked Data cloud. This paper offers an in-depth analysis of how a locally developed vocabulary can be successfully reconciled with the Library of Congress Subject Headings (LCSH) and the Arts and Architecture Thesaurus (AAT) through the help of a general-purpose tool for interactive data transformation (OpenRefine). Issues negatively affecting the reconciliation process are identified and solutions are proposed in order to derive maximum value from existing metadata and controlled vocabularies in an automated manner.