Advances in Web technology and the proliferation of mobile devices and sensors connected to the Internet have resulted in immense processing and storage requirements. Cloud computing has emerged as a paradigm that promises to meet these requirements. This work focuses on the storage aspect of cloud computing, specifically on data management in cloud environments. Traditional relational databases were designed in a different hardware and software era and are facing challenges in meeting the performance and scale requirements of Big Data. NoSQL and NewSQL data stores present themselves as alternatives that can handle huge volume of data. Because of the large number and diversity of existing NoSQL and NewSQL solutions, it is difficult to comprehend the domain and even more challenging to choose an appropriate solution for a specific task. Therefore, this paper reviews NoSQL and NewSQL solutions with the objective of: (1) providing a perspective in the field, (2) providing guidance to practitioners and researchers to choose the appropriate data store, and (3) identifying challenges and opportunities in the field. Specifically, the most prominent solutions are compared focusing on data models, querying, scaling, and security related capabilities. Features driving the ability to scale read requests and write requests, or scaling data storage are investigated, in particular partitioning, replication, consistency, and concurrency control. Furthermore, use cases and scenarios in which NoSQL and NewSQL data stores have been used are discussed and the suitability of various solutions for different sets of applications is examined. Consequently, this study has identified challenges in the field, including the immense diversity and inconsistency of terminologies, limited documentation, sparse comparison and benchmarking criteria, and nonexistence of standardized query languages.

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Data management in cloud environments: NoSQL and NewSQL data stores

This paper investigates the usability of Future Internet technologies (aka “Generic Enablers of the Future Internet”) in the context of environmental applications. The paper incorporates the best aspects of the state-of-the-art in environmental informatics with geospatial solutions and scalable processing capabilities of Internet-based tools. It specifically targets the promotion of the “Environmental Observation Web” as an observation-centric paradigm for building the next generation of environmental applications. In the Environmental Observation Web, the great majority of data are considered as observations. These can be generated from sensors (hardware), numerical simulations (models), as well as by humans (human sensors). Independently from the observation provenance and application scope, data can be represented and processed in a standardised way in order to understand environmental processes and their interdependencies. The development of cross-domain applications is then leveraged by technologies such as Cloud Computing, Internet of Things, Big Data Processing and Analytics. For example, “the cloud” can satisfy the peak-performance needs of applications which may occasionally use large amounts of processing power at a fraction of the price of a dedicated server farm. The paper also addresses the need for Specific Enablers that connect mainstream Future Internet capabilities with sensor and geospatial technologies. Main categories of such Specific Enablers are described with an overall architectural approach for developing environmental applications and exemplar use cases.

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Future Internet technologies for environmental applications

Novel digital data sources allow us to attain enhanced knowledge about locations and mobilities of people in space and time. Already a fast-growing body of literature demonstrates the applicability and feasibility of mobile phone-based data in social sciences for considering mobile devices as proxies for people. However, the implementation of such data imposes many theoretical and methodological challenges. One major issue is the uneven spatial resolution of mobile phone data due to the spatial configuration of mobile network base stations and its spatial interpolation. To date, different interpolation techniques are applied to transform mobile phone data into other spatial divisions. However, these do not consider the temporality and societal context that shapes the human presence and mobility in space and time. The paper aims, first, to contribute to mobile phone-based research by addressing the need to give more attention to the spatial interpolation of given data, and further by proposing a dasymetric interpolation approach to enhance the spatial accuracy of mobile phone data. Second, it contributes to population modelling research by combining spatial, temporal and volumetric dasymetric mapping and integrating it with mobile phone data. In doing so, the paper presents a generic conceptual framework of a multi-temporal function-based dasymetric MFD interpolation method for mobile phone data. Empirical results demonstrate how the proposed interpolation method can improve the spatial accuracy of both night-time and daytime population distributions derived from different mobile phone data sets by taking advantage of ancillary data sources. The proposed interpolation method can be applied for both location-and person-based research, and is a fruitful starting point for improving the spatial interpolation methods for mobile phone data. We share the implementation of our method in GitHub as open access Python code.

https://tandfonline.com/doi/pdf/10.1080/13658816.2017.1287369

Enhancing spatial accuracy of mobile phone data using multi-temporal dasymetric interpolation

Inferring Migrations: Traditional Methods and New Approaches based on Mobile Phone, Social Media, and other Big Data: Feasibility study on Inferring (labour) mobility and migration in the European Union from big data and social media data

With the rise of social media platforms, crowdsourcing became a powerful tool for mobilizing the public. Events such as the earthquake in Haiti or the downfall of governments in Libya and Egypt indicate its potential in crisis situations. In the scope of this paper, we discuss the relevance of crowdsourcing in the area of crisis and disaster management (CDM). Starting with a general overview of the topic, we distinguish between different types of crowds and crowdsourcing and define what is meant by crowdtasking in the area of CDM. After considering technological, societal and ethical challenges for using crowdsourcing in crisis management, applications of crowdsourcing tools in ongoing projects are described and future developments outlined.

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Crowdsourcing in Crisis and Disaster Management – Challenges and Considerations

In this paper ongoing developments regarding the conceptual setup and subsequent implementation logic of a seamless spatio-temporal population dynamics model are presented. The DynaPop model aims at serving as basic input for social impact evaluation in crisis management. In addition to providing the starting point for assessing population exposure dynamics, i.e. the location and number of affected people at different stages during an event, knowledge of spatio-temporal population distribution patterns is also considered crucial for a set of other related aspects in disaster risk and crisis management including evacuation planning and casualty assessment. DynaPop is implemented via a gridded spatial disaggregation approach and integrates previous efforts on spatio-temporal modeling that account for various aspects of population dynamics such as human mobility and activity patterns that are particularly relevant in picturing the highly dynamic daytime situation.

DynaPop - Population distribution dynamics as basis for social impact evaluation in crisis management

A great majority of the EU citizens already owns a cellular phone. An increasing part of these phones are smartphones with a broadband internet connection. This growing network of smart internet enabled devices could act as a dense sensing network, as well as a tool for individual informing and tasking of mobile citizens and volunteers.

/pdf/robust-and-trusted-crowd-sourcing-and-crowd-tasking-in-the-o3g98gyy9p.pdf

Robust and Trusted Crowd-Sourcing and Crowd-Tasking in the Future Internet

Migration is a major challenge for the European Union, resulting in early preparedness being an imperative for target states and their stakeholders such as border police forces. This preparedness is necessary for multiple reasons, including the provision of adequate search and rescue measures. To support preparedness, there is a need for early indicators for detection of developing migratory push-factors related to imminent migration flows. To address this need, we have investigated the daily number of geo-referenced Tweets in three regions of Ukraine and the whole of Japan from August 2014 until October 2014. This analysis was done by using the data handling tool Ubicity. Additionally, we have identified days when relevant natural, civil or political events took place in order to identify possible event triggered changes of the daily number of Tweets. In all the examined Ukrainian regions a considerable increase in the number of daily Tweets was observed for the election day of a new parliament. Furthermore, we identified a significant decrease in the number of daily Tweets for the Crimea for the whole examined period which could be related to the political changes that took place. The natural disasters identified in Japan do not show a clear relationship with the changes in the degree of use of the social media tool Twitter. The results are a good basis to use communication patterns as future key indicator for migration analysis.

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On the Volume of Geo-referenced Tweets and Their Relationship to Events Relevant for Migration Tracking

Effektives Katastrophenmanagement erfordert moglichst genaue raum-zeitliche Informationen sowohl uber Strukturen als auch uber betroffene Personen. Wahrend physische Strukturen sich in der Regel nur langsam verandern und daher auf kurze Sicht weniger zeitkritisch in der Erfassung sind, ist die raumliche Verteilung der Bevolkerung hochgradig variabel uber die Zeit (Freire und Aubrecht 2012). Die direkte Erfassung potentiell betroffener Personen bei Katastrophen ist nicht oder nur sehr eingeschrankt moglich, daher wird bei der Planung vorbeugender Masnahmen und der Abschatzung des Gefahrenpotentials die Bevolkerungsverteilung modelliert. 
Zensusdaten stellen dabei eine standardisierte Datengrundlage dar, die aber in zweierlei Hinsicht limitiert ist. Zum einen ist die raumliche Ausdehnung der Zahlsprengel meist zu gros und unregelmasig um auf lokaler Ebene ausreichend genaue Aussagen uber die betroffenen Personen machen zu konnen, zum anderen reprasentiert der Zensus die Verteilung der Wohnbevolkerung, also den Ort, wo sich die Personen in der Regel in der Nacht aufhalten. Naturgefahren (und deren Auswirkungen) konnen raumlich sehr fein differenziert, sowie teils ohne Vorwarnung zu einem beliebigen Zeitpunkt auftreten. Es besteht daher die Notwendigkeit, Information zur Bevolkerungsverteilung sowohl in einem raumlichen Kontext zu verfeinern als auch die zeitliche Komponente mitzuberucksichtigen (Aubrecht et al. 2012).

Modellierung raum-zeitlicher Bevölkerungsverteilungsmuster im Katastrophenmanagementkontext

In the context of proactive disaster risk as well as immediate situational crisis management knowledge of locational social aspects in terms of spatio-temporal population distribution dynamics is considered among the most important factors for disaster impact minimization (Aubrecht et al., 2013a). This applies to both the pre-event stage for designing appropriate preparedness measures and to acute crisis situations when an event chain actually unfolds for efficient situation-aware response. The presented DynaPop population dynamics model is developed at the interface of those interlinked crisis stages and aims at providing basic input for social impact evaluation and decision support in crisis management. The model provides the starting point for assessing population exposure dynamics – thus here labeled as DynaPop-X – which can either be applied in a sense of illustrating the changing locations and numbers of affected people at different stages during an event or as ex-ante estimations of probable and maximum expected clusters of affected population (Aubrecht et al., 2013b; Freire & Aubrecht, 2012). DynaPop is implemented via a gridded spatial disaggregation approach and integrates previous efforts on spatio-temporal modeling that account for various aspects of population dynamics such as human mobility and activity patterns that are particularly relevant in picturing the highly dynamic daytime situation (Ahola et al., 2007; Bhaduri, 2008; Cockings et al., 2010). We will present ongoing developments particularly focusing on the implementation logic of the model using the emikat software tool, a data management system initially designed for inventorying and analysis of spatially resolved regional air pollutant emission scenarios. This study was performed in the framework of the EU CRISMA project. CRISMA is funded from the European Community’s Seventh Framework Programme FP7/2007-2013 under grant agreement no. 284552.

Hermann Huber

Papers

DynaPop - Population distribution dynamics as basis for social impact evaluation in crisis management

Robust and Trusted Crowd-Sourcing and Crowd-Tasking in the Future Internet

On the Volume of Geo-referenced Tweets and Their Relationship to Events Relevant for Migration Tracking

Modellierung raum-zeitlicher Bevölkerungsverteilungsmuster im Katastrophenmanagementkontext

DynaPop-X: A population dynamics model applied to spatio-temporal exposure assessment - Implementation aspects from the CRISMA project