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This essay examines the "era of big data" through the lens of recent attempts at its application within the realm of public health. Arguing that debates about big data's newness or usefulness are misplaced, I suggest that big data is less about finding solutions to complex social problems than it is about discovering better — and more relevant — research questions.

It is challenging to standardize data; yet, the capabilities to draw upon data across information systems hold huge potential for improving defense acquisition and procurement. Acquisition planning and management involves many decision-making and action-taking processes that cover a complex environment including actual acquisition, contracting, fiscal, legal, personnel, and regulatory requirements. A sound decision-making process has to rely on data;high quality data. Often the available data is dirty, outdated, incomplete, or insufficient for the expert to make a decision. On the other hand, there are enormous amounts of data on the web that can be utilized to crystalize the needed information. These data repositories are often publicly accessible and from a variety of sources including websites, government reports, news, wikis, blogs, online forums, and social media. This paper investigates how to leverage the information in public data sources to complement the internal data in order to support effective acquisition planning and management. This research is based on publicly available government acquisition databases at usaspending.gov and fpds.gov. It takes a data science approach for analyzing acquisition databases and focuses on two major tasks: (1) research on leveraging the web data for quality assessment and improvement of federal acquisition data and (2) research on appropriate data analytic techniques to discover useful information that can potentially help federal acquisition management and planning process. ; Naval Postgraduate School Acquisition Research Program

AbstractIn this essay, I observe that data is valuable not only for what it is, but also for what it will become: that is, that data is a form of potential. I explore two aspects of this by drawing two comparisons with other forms of potential: ova and genes. First, building on ethnographic fieldwork with environmental scientists and technicians in the Brazilian Amazon, I compare data processing with ova donation in the United Kingdom in order to explore how data processing might be considered a form of reproductive labour. I then turn to emergent big data infrastructures in the environmental sciences, and compare the environmental sciences with genomics, in order to gesture towards some critical questions that need to be asked of such open data initiatives. I end with a reflection on comparison as a privileged means of drawing out the forms understood to be latent within data.

Over the past decade, the public awareness and availability as well as methods for the creation and use of spatial data on the Web have steadily increased. Besides the establishment of governmental Spatial Data Infrastructures (SDIs), numerous volunteered and commercial initiatives had a major impact on that development. Nevertheless, data isolation still poses a major challenge. Whereas the majority of approaches focuses on data provision, means to dynamically link and combine spatial data from distributed, often heterogeneous data sources in an ad hoc manner are still very limited. However, such capabilities are essential to support and enhance information retrieval for comprehensive spatial decision making. To facilitate spatial data fusion in current SDIs, this thesis has two main objectives. First, it focuses on the conceptualization of a service-based fusion process to functionally extend current SDI and to allow for the combination of spatial data from different spatial data services. It mainly addresses the decomposition of the fusion process into well-defined and reusable functional building blocks and their implementation as services, which can be used to dynamically compose meaningful application-specific processing workflows. Moreover, geoprocessing patterns, i.e. service chains that are commonly used to solve certain fusion subtasks, are designed to simplify and automate workflow composition. Second, the thesis deals with the determination, description and exploitation of spatial data relations, which play a decisive role for spatial data fusion. The approach adopted is based on the Linked Data paradigm and therefore bridges SDI and Semantic Web developments. Whereas the original spatial data remains within SDI structures, relations between those sources can be used to infer spatial information by means of Semantic Web standards and software tools. A number of use cases were developed, implemented and evaluated to underpin the proposed concepts. Particular emphasis was put on the use of established open standards to realize an interoperable, transparent and extensible spatial data fusion process and to support the formalized description of spatial data relations. The developed software, which is based on a modular architecture, is available online as open source. It allows for the development and seamless integration of new functionality as well as the use of external data and processing services during workflow composition on the Web. ; Die Entwicklung des Internet im Laufe des letzten Jahrzehnts hat die Verfügbarkeit und öffentliche Wahrnehmung von Geodaten, sowie Möglichkeiten zu deren Erfassung und Nutzung, wesentlich verbessert. Dies liegt sowohl an der Etablierung amtlicher Geodateninfrastrukturen (GDI), als auch an der steigenden Anzahl Communitybasierter und kommerzieller Angebote. Da der Fokus zumeist auf der Bereitstellung von Geodaten liegt, gibt es jedoch kaum Möglichkeiten die Menge an, über das Internet verteilten, Datensätzen ad hoc zu verlinken und zusammenzuführen, was mitunter zur Isolation von Geodatenbeständen führt. Möglichkeiten zu deren Fusion sind allerdings essentiell, um Informationen zur Entscheidungsunterstützung in Bezug auf raum-zeitliche Fragestellungen zu extrahieren. Um eine ad hoc Fusion von Geodaten im Internet zu ermöglichen, behandelt diese Arbeit zwei Themenschwerpunkte. Zunächst wird eine dienstebasierten Umsetzung des Fusionsprozesses konzipiert, um bestehende GDI funktional zu erweitern. Dafür werden wohldefinierte, wiederverwendbare Funktionsblöcke beschrieben und über standardisierte Diensteschnittstellen bereitgestellt. Dies ermöglicht eine dynamische Komposition anwendungsbezogener Fusionsprozesse über das Internet. Des weiteren werden Geoprozessierungspatterns definiert, um populäre und häufig eingesetzte Diensteketten zur Bewältigung bestimmter Teilaufgaben der Geodatenfusion zu beschreiben und die Komposition und Automatisierung von Fusionsprozessen zu vereinfachen. Als zweiten Schwerpunkt beschäftigt sich die Arbeit mit der Frage, wie Relationen zwischen Geodatenbeständen im Internet erstellt, beschrieben und genutzt werden können. Der gewählte Ansatz basiert auf Linked Data Prinzipien und schlägt eine Brücke zwischen diensteorientierten GDI und dem Semantic Web. Während somit Geodaten in bestehenden GDI verbleiben, können Werkzeuge und Standards des Semantic Web genutzt werden, um Informationen aus den ermittelten Geodatenrelationen abzuleiten. Zur Überprüfung der entwickelten Konzepte wurde eine Reihe von Anwendungsfällen konzipiert und mit Hilfe einer prototypischen Implementierung umgesetzt und anschließend evaluiert. Der Schwerpunkt lag dabei auf einer interoperablen, transparenten und erweiterbaren Umsetzung dienstebasierter Fusionsprozesse, sowie einer formalisierten Beschreibung von Datenrelationen, unter Nutzung offener und etablierter Standards. Die Software folgt einer modularen Struktur und ist als Open Source frei verfügbar. Sie erlaubt sowohl die Entwicklung neuer Funktionalität durch Entwickler als auch die Einbindung existierender Daten- und Prozessierungsdienste während der Komposition eines Fusionsprozesses.

The increasing development and adaptation of synthetic data raises critical concerns about the perpetuation of datafication logics. In examining some of synthetic data's core promises, this dialogue paper aims to uncover the potential harm of further de-politicizing synthetic data. With synthetic data, technological opportunities are introduced that promise to resolve a growing demand for data needed to train AI models. Furthermore, models trained on synthetic data are praised as more precise and effective while bring cheaper than collected data (Zewe 2022). With this dialogue paper, I aim to nuance the ways in which synthetic data complicate a critique directed at AI-driven technologies. I build my argument on two elements fundamental to the debate on the promises and perils of synthetic data. The first is the notion of data scarcity—often leveraged to argue for the implementation and further development of synthetic data to train bespoke models. Second, I discuss the concerns of data pollution and contamination with synthetic data. Through these entry points, I argue that synthetic data re-ignites issues previously raised by scholars in the field of critical data and surveillance studies. Therefore, the aim of this dialogue paper is to call for a critical understanding of synthetic data as living information, much like collected data, and to account for synthetic data and the conditions of its generation in the context of simulated environments.

Our world is being transformed by big data. The growth of the Internet and the rapid expansion of mobile communications and related technologies have created a massive flow of data--both structured and unstructured. The availability and use of that data has enormous implications for businesses and for the wider society. Used effectively, big data can drive businesses in the direction of more accurate analyses of vital information, leading ultimately to greater operational efficiencies, cost reductions, reduced risk, speedier innovations, and increased and new revenue. In this book, you'll find detailed instruction in big data strategy development and implementation, supported by numerous real-world business cases in ten distinct industries. You will learn what big data is and how to wield it--from calculating ROI and making a business case to developing overall and project-specific strategies that actually work. Each chapter answers key questions and will give you the skills you need to make your big data projects succeed

Data do not speak for themselves. Data must be narrated—put to work in particular contexts, sunk into narratives that give them shape and meaning, and mobilized as part of broader processes of interpretation and meaning-making. We examine these processes through the lens of ethnographic practice and, in particular, ethnography's attention to narrative processes. We draw on a particular case in which digital data must be animated and narrated by different groups in order to examine broader questions of how we might come to understand data ethnographically.