Search, Filter, Fork, and Link Open Data - The ADEQUATe platform: data- and community-driven quality improvements.

The present work describes the ADEQUATe platform: a framework to monitor the quality of (Governmental) Open Data catalogs, to re-publish improved and linked versions of the datasets and their respective metadata descriptions, and to include the community in the quality improvement process. The information acquired by the linking and (meta)data improvement steps is then integrated in a semantic search engine. In the paper, we first describe the requirements of the platform, which are based on focus group interviews and a web-based survey. Second, we use these requirements to formulate the goals and show the architecture of the overall platform, and third, we showcase the potential and relevance of the platform to resolve the requirements by describing exemplary user journeys exploring the system. The platform is available at: https://www.adequate.at/

Thermodynamic Dissipative Systems and Information Theory to Study the Social Component of a Smart City

In this article, we discuss the application of information theory and the theory of thermodynamic dissipative systems to smart cities. Specifically, we study how to model the interaction between a society and a smart city, under an information-theoretic approach. Because the smart city comprises both a social and a technological component, it then becomes possible to use information theory to study them both. In this paper, we discuss a model that applies the constraints from thermodynamic dissipative systems theory in order to study smart cities, and their associated social system, in their information processing capacity and in their evolution over time. Within the context of our model, we are allowed to study under what conditions a smart city would expand or contract, or to state that the smart city shrinks if its output greatly exceeds its input.Facultad de Informátic

Natural Language Processing in Geographic Information Systems - Some Trends and Open Issues -

The increasing ubiquity of information technology motivates situative interaction mechanisms for the benefit of standard and power users. Classical interaction paradigms in geographic information systems are currently subject to amendment or even to substitution by context-aware and technology-agnostic natural-language-based interfaces. This work compiles a recent selection of radically innovative concepts found in three selected domains of the open GIS literature including related research demands. The added value herein lies in the coherence of future options and challenges in that field

Improving the Computational Performance of Ontology-Based Classification Using Graph Databases

The increasing availability of very high-resolution remote sensing imagery (i.e., from satellites, airborne laser scanning, or aerial photography) represents both a blessing and a curse for researchers. The manual classification of these images, or other similar geo-sensor data, is time-consuming and leads to subjective and non-deterministic results. Due to this fact, (semi-) automated classification approaches are in high demand in affected research areas. Ontologies provide a proper way of automated classification for various kinds of sensor data, including remotely sensed data. However, the processing of data entities—so-called individuals—is one of the most cost-intensive computational operations within ontology reasoning. Therefore, an approach based on graph databases is proposed to overcome the issue of a high time consumption regarding the classification task. The introduced approach shifts the classification task from the classical Protégé environment and its common reasoners to the proposed graph-based approaches. For the validation, the authors tested the approach on a simulation scenario based on a real-world example. The results demonstrate a quite promising improvement of classification speed—up to 80,000 times faster than the Protégé-based approach

Openlaws.eu: open justice in Europe through open access to legal information

Legal texts represent a fundamental building block in all democratic states. As such, legal information must be accessible to all members of society to the widest possible extent, to aid inclusiveness and to enable participation in public decision-making. In recognition of this, the EU and its Member States work to make laws, court decisions, etc. publicly available online. The sheer mass of legal norms, instruments, and interpretations in court decisions, commentaries and other sources, makes it increasingly difficult for citizens, civil society, businesses, and all involved stakeholders in legal practices to locate the relevant law. The challenge is to interlink local legal information and to have structures in place to enrich this information through aggregation and mass customization. The technological possibilities to achieve this goal do exist. The European project openlaws.eu aims for initiating a platform and to develop a vision for Big Open Legal Data (BOLD): an open framework for legislation, case law, and legal literature from across Europe

Drivers of Human Migration: A Review of Scientific Evidence

While migration research is at the peak of its productivity, a substantial gap persists between scientific evidence and policy action. As societal complexity increases, migration theory loses track on the numerous factors of human migration; the information on the most relevant factors affecting human migration (i.e., migration drivers), essential for policy decision-making, are hidden and dispersed across the ever-growing literature. Introducing a novel approach to conducting a literature review, emphasizing an unbiased selection of literature and the approach to analysing literature by coding, we collect evidence on the most pertinent migration factors. The study establishes a methodology for a quick but rigorous, collaborative gathering of evidence, as well as an initial inventory and an interactive map of nearly 200 factors working at different migration corridors

Mapping Parallels between Outdoor Urban Environments and Indoor Manufacturing Environments

The concepts of “Smart Cities” and “Smart Manufacturing” are different data-driven domains, although both rely on intelligent information technology and data analysis. With the application of linked data and affordance-based approaches, both domains converge, paving the way for new and innovative viewpoints regarding the comparison of urban tasks with indoor manufacturing tasks. The present study builds on the work, who state that cities are scaled versions of each other, by extending this thesis towards indoor manufacturing environments. Based on their structure and complexity, these environments are considered to form ecosystems of their own, comparable to “small cities”. This conceptual idea is demonstrated by examining the process of human problem-solving in transportation situations from both perspectives (i.e., city-level and manufacturing-level). In particular, the authors model tasks of human operators that are used to support transportation processes in indoor manufacturing environments based on affordances and spatial-temporal data. This paper introduces the fundamentals of the transformation process of outdoor tasks and process planning activities to indoor environments, particularly to semiconductor manufacturing environments. The idea is to examine the mapping of outdoor tasks and applications to indoor environments, and vice-versa, based on an example focusing on the autonomous transportation of production assets in a manufacturing environment. The approach is based on a spatial graph database, populated with an indoor navigation ontology and instances of indoor and outdoor objects. The results indicate that human problem-solving strategies can be applied to indoor manufacturing environments to support decision-making in autonomous transportation tasks

An Extension of an Ontology-Based Land Cover Designation Approach for Fuzzy Rules. GI_Forum 2013 – Creating the GISociety|

Satellite image interpretation requires the assignment of sets of objects (or pixels) that share certain attribute values to object categories. This procedure requires expert intervention and knowledge. An approach has been developed that formalizes expert knowledge in the image interpretation procedure with ontologies. Ontologies provide a definition of object categories and associated attribute values that are known to represent these object categories. A classic ontology has the limitation that the definitions of object categories and their properties need to be crisp, i.e. not overlapping. Practical tests showed that less rigid definitions of class properties make the ontology-based approach more flexible and adaptable to different study areas and satellite images. This paper presents the extension of the ontology-based approach with fuzzy rules and discusses the advantages of this extension