RESTful Wireless Sensor Networks

Sensor networks have diverse structures and generally employ proprietary protocols to gather useful information about the physical world. This diversity generates problems to interact with these sensors since custom APIs are needed which are tedious, error prone and have steep learning curve. In this thesis, I present RESThing, a lightweight REST framework for wireless sensor networks to ease the process of interacting with these sensors by making them accessible over the Web. I evaluate the system and show that it is feasible to support widely used and standard Web protocols in wireless sensor networks. Being able to integrate these tiny devices seamlessly into the global information medium, we can achieve the Web of Things

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

Corporate Smart Content Evaluation

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches

Web-based Implementation of Winter Maintenance Decision Support System Using GIS and Remote Sensing, May 2005

Winter maintenance, particularly snow removal and the stress of snow removal materials on public structures, is an enormous budgetary burden on municipalities and nongovernmental maintenance organizations in cold climates. Lately, geospatial technologies such as remote sensing, geographic information systems (GIS), and decision support tools are roviding a valuable tool for planning snow removal operations. A few researchers recently used geospatial technologies to develop winter maintenance tools. However, most of these winter maintenance tools, while having the potential to address some of these information needs, are not typically placed in the hands of planners and other interested stakeholders. Most tools are not constructed with a nontechnical user in mind and lack an easyto-use, easily understood interface. A major goal of this project was to implement a web-based Winter Maintenance Decision Support System (WMDSS) that enhances the capacity of stakeholders (city/county planners, resource managers, transportation personnel, citizens, and policy makers) to evaluate different procedures for managing snow removal assets optimally. This was accomplished by integrating geospatial analytical techniques (GIS and remote sensing), the existing snow removal asset management system, and webbased spatial decision support systems. The web-based system was implemented using the ESRI ArcIMS ActiveX Connector and related web technologies, such as Active Server Pages, JavaScript, HTML, and XML. The expert knowledge on snow removal procedures is gathered and integrated into the system in the form of encoded business rules using Visual Rule Studio. The system developed not only manages the resources but also provides expert advice to assist complex decision making, such as routing, optimal resource allocation, and monitoring live weather information. This system was developed in collaboration with Black Hawk County, IA, the city of Columbia, MO, and the Iowa Department of transportation. This product was also demonstrated for these agencies to improve the usability and applicability of the system

Automatic Extraction and Assessment of Entities from the Web

The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval

Statistics of the Self: Shaping the Self Through Quantified Self-Tracking

Self-tracking practices are growing in popularity worldwide. From heart-rate monitoring to mood tracking, many believe that wearable technologies are making their users more mindful in exclusively positive ways. However, I will argue that consistent and deliberate self-tracking (with or without portable devices) necessitates a particular understanding of the self with consequences that have yet to be fully explored. Through an analysis of forum posts on a popular self-tracking discussion and informational site, QuantifiedSelf.com, I will claim that self-trackers approach the creation of self-knowledge in a manner that is particular to today’s society. I will discuss how the ubiquitous conflation of numerical identities with objective reasoning feeds into a mindset that supports quantification of the self, and how the views of self exhibited by these self-trackers can be considered a version of creating a “scientific self.” The notion of the scientific self supports both an individual and societal shift in the practice of “being”—a shift that carries with it many possible repercussions that have yet to be widely analyzed. This analysis, I will argue, is key to limiting the destructive potential of understanding people in terms of data, while simultaneously enabling new conceptualizations of self to be practiced in modern society

From Data to Decision: An Implementation Model for the Use of Evidence-based Medicine, Data Analytics, and Education in Transfusion Medicine Practice

Healthcare in the United States is underperforming despite record increases in spending. The causes are as myriad and complex as the suggested solutions. It is increasingly important to carefully assess the appropriateness and cost-effectiveness of treatments especially the most resource-consuming clinical interventions. Healthcare reimbursement models are evolving from fee-for-service to outcome-based payment. The Patient Protection and Affordable Care Act has added new incentives to address some of the cost, quality, and access issues related to healthcare, making the use of healthcare data and evidence-based decision-making essential strategies. However, despite the great promise of these strategies, the transition to data-driven, evidence-based medical practice is complex and faces many challenges. This study aims to bridge the gaps that exist between data, knowledge, and practice in a healthcare setting through the use of a comprehensive framework to address the administrative, cultural, clinical, and technical issues that make the implementation and sustainability of an evidence-based program and utilization of healthcare data so challenging. The study focuses on promoting evidence-based medical practice by leveraging a performance management system, targeted education, and data analytics to improve outcomes and control costs. The framework was implemented and validated in transfusion medicine practice. Transfusion is one of the top ten coded hospital procedures in the United States. Unfortunately, the costs of transfusion are underestimated and the benefits to patients are overestimated. The particular aim of this study was to reduce practice inconsistencies in red blood cell transfusion among hospitalists in a large urban hospital using evidence-based guidelines, a performance management system, recurrent reporting of practice-specific information, focused education, and data analytics in a continuous feedback mechanism to drive appropriate decision-making prior to the decision to transfuse and prior to issuing the blood component. The research in this dissertation provides the foundation for implementation of an integrated framework that proved to be effective in encouraging evidence-based best practices among hospitalists to improve quality and lower costs of care. What follows is a discussion of the essential components of the framework, the results that were achieved and observations relative to next steps a learning healthcare organization would consider

Machine-actionable assessment of research data products

Research data management is a relevant topic for academic research which is why many concepts and technologies emerge to face the challenges involved, such as data growth, reproducibility, or heterogeneity of tools, services, and standards. The basic concept of research data management is a research data product; it has three dimensions: the data, the metadata describing them, and the services providing both. Traditionally, the assessment of a research data product has been carried out either manually via peer-review by human experts or automated by counting certain events. We present a novel mechanism to assess research data products. The current state-of-the-art of machine-actionable assessment of research data products is based on the assumption that its quality, impact, or relevance are linked to the likeliness of peers or others to interact with it: event-based metrics include counting citations, social media interactions, or usage statistics. The shortcomings of event-based metrics are systematically discussed in this thesis; they include dependance on the date of publication and the impact of social effects. In contrast to event-based metrics benchmarks for research data products simulate technical interactions with a research data product and check its compliance with best practices. Benchmarks operate on the assumption that the effort invested in producing a research data product increases the chances that its quality, impact, or relevance are high. This idea is translated into a software architecture and a step-by-step approach to create benchmarks based on it. For a proof-of-concept we use a prototypical benchmark on more than 795,000 research data products deposited at the Zenodo repository to showcase its effectiveness, even with many research data products. A comparison of the benchmark’s scores with event-based metrics indicate that benchmarks have the potential to complement event-based metrics and that both weakly correlate under certain circumstances. These findings provide the methodological basis for a new tool to answer scientometric questions and to support decision-making in the distribution of sparse resources. Future research can further explore those aspects of benchmarks that allow to improve the reproducibility of scientific findings.Dass das Management von Forschungsdaten ein relevantes Thema ist, zeigt sich an der Vielzahl an konzeptioneller und technischer Antworten auf die damit einhergehenden Herausforderungen, wie z.B. Datenwachstum, Reproduzierbarkeit oder Heterogenität der genutzten Tools, Dienste und Standards. Das Forschungsdatenprodukt ist in diesem Kontext ein grundlegender, dreiteilig aufgebauter Begriff: Daten, Metadaten und Dienste, die Zugriffe auf die beiden vorgenannten Komponenten ermöglichen. Die Beurteilung eines Forschungsdatenprodukts ist bisher händisch durch den Peer Review oder durch das Zählen von bestimmten Ereignissen realisiert. Der heutige Stand der Technik, um automatisiert Qualität, Impact oder Relevanz eines Forschungsdatenprodukts zu beurteilen, basiert auf der Annahme, dass diese drei Eigenschaften mit der Wahrscheinlichkeit von Interaktionen korrelieren. Event-basierte Metriken umfassen das Zählen von Zitationen, Interaktionen auf sozialen Medien oder technische Zugriffe. Defizite solcher Metriken werden in dieser Arbeit systematisch erörtert; besonderes Augenmerk wird dabei auf deren Zeitabhängigkeit und den Einfluss sozialer Mechanismen gelegt. Benchmarks sind Programme, die Interaktionen mit einem Forschungsdatenprodukt simulieren und dabei die Einhaltung guter Praxis prüfen. Benchmarks operieren auf der Annahme, dass der Aufwand, der in die Erzeugung und Wartung von Forschungsdatenprodukte investiert wurde, mit deren Qualität, Impact und Relevanz korreliert. Diese Idee wird in dieser Arbeit in eine Software-Architektur gegossen, für deren Implementierung geeignete Hilfsmittel bereitgestellt werden. Ein prototypischer Benchmark wird auf mehr als 795.000 Datensätzen des Zenodo Repositorys evaluiert, um die Effektivität der Architektur zu demonstrieren.Ein Vergleich zwischen Benchmark Scores und event-basierten Metriken legt nahe, dass beide unter bestimmten Umständen schwach korrelieren. Dieses Ergebnis rechtfertigt den Einsatz von Benchmarks als neues szientrometrisches Tool und als Entscheidungshilfe in der Verteilung knapper Ressourcen. Der Einsatz von Benchmarks in der Sicherstellung von reproduzierbaren wissenschaftlichen Erkenntnissen ist ein vielversprechender Gegenstand zukünftiger Forschung

Fog assisted application support for animal behaviour analysis and health monitoring in dairy farming

With the exponential growth rate of technology, the future of all activities, including dairy farming involves an omnipresence of widely connected devices. Internet of things (IoT), fog computing, cloud computing and data analytics together offer a great opportunity to increase productivity in the dairy industry. In this paper, we present a fog computing assisted application system for animal behaviour analysis and health monitoring in a dairy farming scenario. The sensed data from sensors is sent to a fog based platform for data classification and analysis, which includes decision making capabilities. The solution aims towards keeping track of the animals' well-being by delivering early warning alerts generated through behavioural analytics, thus aiding the farmer to monitor the health of their livestock and the capability to identify potential diseases at an early stage, thereby also helping in increasing milk yield and productivity. The proposed system follows a service based model, avoids vendor lock-in, and is also scalable to add new features such as the detection of calving, heat, and issues like lameness

Towards a human-centric data economy

Spurred by widespread adoption of artificial intelligence and machine learning, “data” is becoming a key production factor, comparable in importance to capital, land, or labour in an increasingly digital economy. In spite of an ever-growing demand for third-party data in the B2B market, firms are generally reluctant to share their information. This is due to the unique characteristics of “data” as an economic good (a freely replicable, non-depletable asset holding a highly combinatorial and context-specific value), which moves digital companies to hoard and protect their “valuable” data assets, and to integrate across the whole value chain seeking to monopolise the provision of innovative services built upon them. As a result, most of those valuable assets still remain unexploited in corporate silos nowadays. This situation is shaping the so-called data economy around a number of champions, and it is hampering the benefits of a global data exchange on a large scale. Some analysts have estimated the potential value of the data economy in US$2.5 trillion globally by 2025. Not surprisingly, unlocking the value of data has become a central policy of the European Union, which also estimated the size of the data economy in 827C billion for the EU27 in the same period. Within the scope of the European Data Strategy, the European Commission is also steering relevant initiatives aimed to identify relevant cross-industry use cases involving different verticals, and to enable sovereign data exchanges to realise them. Among individuals, the massive collection and exploitation of personal data by digital firms in exchange of services, often with little or no consent, has raised a general concern about privacy and data protection. Apart from spurring recent legislative developments in this direction, this concern has raised some voices warning against the unsustainability of the existing digital economics (few digital champions, potential negative impact on employment, growing inequality), some of which propose that people are paid for their data in a sort of worldwide data labour market as a potential solution to this dilemma [114, 115, 155]. From a technical perspective, we are far from having the required technology and algorithms that will enable such a human-centric data economy. Even its scope is still blurry, and the question about the value of data, at least, controversial. Research works from different disciplines have studied the data value chain, different approaches to the value of data, how to price data assets, and novel data marketplace designs. At the same time, complex legal and ethical issues with respect to the data economy have risen around privacy, data protection, and ethical AI practices. In this dissertation, we start by exploring the data value chain and how entities trade data assets over the Internet. We carry out what is, to the best of our understanding, the most thorough survey of commercial data marketplaces. In this work, we have catalogued and characterised ten different business models, including those of personal information management systems, companies born in the wake of recent data protection regulations and aiming at empowering end users to take control of their data. We have also identified the challenges faced by different types of entities, and what kind of solutions and technology they are using to provide their services. Then we present a first of its kind measurement study that sheds light on the prices of data in the market using a novel methodology. We study how ten commercial data marketplaces categorise and classify data assets, and which categories of data command higher prices. We also develop classifiers for comparing data products across different marketplaces, and we study the characteristics of the most valuable data assets and the features that specific vendors use to set the price of their data products. Based on this information and adding data products offered by other 33 data providers, we develop a regression analysis for revealing features that correlate with prices of data products. As a result, we also implement the basic building blocks of a novel data pricing tool capable of providing a hint of the market price of a new data product using as inputs just its metadata. This tool would provide more transparency on the prices of data products in the market, which will help in pricing data assets and in avoiding the inherent price fluctuation of nascent markets. Next we turn to topics related to data marketplace design. Particularly, we study how buyers can select and purchase suitable data for their tasks without requiring a priori access to such data in order to make a purchase decision, and how marketplaces can distribute payoffs for a data transaction combining data of different sources among the corresponding providers, be they individuals or firms. The difficulty of both problems is further exacerbated in a human-centric data economy where buyers have to choose among data of thousands of individuals, and where marketplaces have to distribute payoffs to thousands of people contributing personal data to a specific transaction. Regarding the selection process, we compare different purchase strategies depending on the level of information available to data buyers at the time of making decisions. A first methodological contribution of our work is proposing a data evaluation stage prior to datasets being selected and purchased by buyers in a marketplace. We show that buyers can significantly improve the performance of the purchasing process just by being provided with a measurement of the performance of their models when trained by the marketplace with individual eligible datasets. We design purchase strategies that exploit such functionality and we call the resulting algorithm Try Before You Buy, and our work demonstrates over synthetic and real datasets that it can lead to near-optimal data purchasing with only O(N) instead of the exponential execution time - O(2N) - needed to calculate the optimal purchase. With regards to the payoff distribution problem, we focus on computing the relative value of spatio-temporal datasets combined in marketplaces for predicting transportation demand and travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and New York we show that the value of data is different for each individual, and cannot be approximated by its volume. Our results reveal that even more complex approaches based on the “leave-one-out” value, are inaccurate. Instead, more complex and acknowledged notions of value from economics and game theory, such as the Shapley value, need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms. However, the Shapley value entails serious computational challenges. Its exact calculation requires repetitively training and evaluating every combination of data sources and hence O(N!) or O(2N) computational time, which is unfeasible for complex models or thousands of individuals. Moreover, our work paves the way to new methods of measuring the value of spatio-temporal data. We identify heuristics such as entropy or similarity to the average that show a significant correlation with the Shapley value and therefore can be used to overcome the significant computational challenges posed by Shapley approximation algorithms in this specific context. We conclude with a number of open issues and propose further research directions that leverage the contributions and findings of this dissertation. These include monitoring data transactions to better measure data markets, and complementing market data with actual transaction prices to build a more accurate data pricing tool. A human-centric data economy would also require that the contributions of thousands of individuals to machine learning tasks are calculated daily. For that to be feasible, we need to further optimise the efficiency of data purchasing and payoff calculation processes in data marketplaces. In that direction, we also point to some alternatives to repetitively training and evaluating a model to select data based on Try Before You Buy and approximate the Shapley value. Finally, we discuss the challenges and potential technologies that help with building a federation of standardised data marketplaces. The data economy will develop fast in the upcoming years, and researchers from different disciplines will work together to unlock the value of data and make the most out of it. Maybe the proposal of getting paid for our data and our contribution to the data economy finally flies, or maybe it is other proposals such as the robot tax that are finally used to balance the power between individuals and tech firms in the digital economy. Still, we hope our work sheds light on the value of data, and contributes to making the price of data more transparent and, eventually, to moving towards a human-centric data economy.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Georgios Smaragdakis.- Secretario: Ángel Cuevas Rumín.- Vocal: Pablo Rodríguez Rodrígue