Search (8 results, page 1 of 1)

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Abstract

The present challenge faced by scientists working with Big Data comes in the overwhelming volume and level of detail provided by current data sets. Exceeding traditional empirical approaches, Big Data opens a new perspective on scientific work in which data comes to play a role in the development of the scientific problematic to be developed. Addressing this reconfiguration of our relationship with data through readings of Wittgenstein, Macherey, and Popper, we propose a picture of science that encourages scientists to engage with the data in a direct way, using the data itself as an instrument for scientific investigation. Using GIS as a theme, we develop the concept of cyber-human systems of thought and understanding to bridge the divide between representative (theoretical) thinking and (non-theoretical) data-driven science. At the foundation of these systems, we invoke the concept of the "semantic pixel" to establish a logical and virtual space linking data and the work of scientists. It is with this discussion of the relationship between analysts in their pursuit of knowledge and the rise of Big Data that this present discussion of the philosophical foundations of Big Data addresses the central questions raised by social informatics research.

Date

7. 3.2019 16:32:22

Type

a

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Abstract

Many authors propose that open source software (OSS) is a good strategy to bring information and communication technologies to developing countries. Nevertheless, the use of OSS needs to be more than just adopting Linux as the standard for operating systems. Adoption of OSS is not only a choice of software, but also a means of acquiring knowledge. Developing countries have to use OSS as a way to gain knowledge about the technology itself and as a way of creating technology products that fit their specific needs. In this article, the authors introduce a model of OSS based on its essential characteristics to understand how developing countries may use OSS to achieve their development goals. The authors argue that there are two defining properties of any open source software. The first property is the potential for shared conceptualization and the second is the potential for modularity. By assessing how each OSS project satisfies these two conditions, a taxonomy is built for open source projects. This taxonomy will help the development of more sensible policies to promote the use of open source in developing countries.

Type

a

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Abstract

In philosophy, Ontology is the basic description of things in the world. In information science, an ontology refers to an engineering artifact, constituted by a specific vocabulary used to describe a certain reality. Ontologies have been proposed for validating both conceptual models and conceptual schemas; however, these roles are quite dissimilar. In this article, we show that ontologies can be better understood if we classify the different uses of the term as it appears in the literature. First, we explain Ontology (upper case O) as used in Philosophy. Then, we propose a differentiation between ontologies of information systems and ontologies for information systems. All three concepts have an important role in information science. We clarify the different meanings and uses of Ontology and ontologies through a comparison of research by Wand and Weber and by Guarino in ontology-driven information systems. The contributions of this article are twofold: (a) It provides a better understanding of what ontologies are, and (b) it explains the double role of ontologies in information science research.

Type

a

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Abstract

There is one point which should be made to set the context of this talk before I get into the body of the text. Most participants at this conference are working in classification research at a theoretical level, in an academic environment. They deal with questions of the "universe of knowledge". The project I will present deals with problems and issues in records and archives management, and attempts to provide a pragmatic solution to a very concrete problem. This difference of mission and focus should be borne in mind

Type

a

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Abstract

The explosion of the infosphere has led to a proliferation of metadata and formal ontology artefacts for information systems. Information scientists are creating ontologies and metadata in order to facilitate the sharing of meaningful information rather than similarly structured information. Formal ontologies are a complex form of metadata that specify the underlying concepts and their relationships that comprise the information of and for an information system. The most common understanding of ontology in computer and information sciences is Gruber's specification of a conceptualization. However, formal ontologies are problematic in that they simultaneously crystallize and decontextualize information, which in order to be meaningful must be adaptive in context. In trying to construct a correct taxonomical system, formal ontologies are focused on syntactic precision rather than meaningful exchange of information. Smith describes accurately the motivation and practice of ontology creation: It becomes a theory of the ontological content of certain representations . The elicited principles may or may not be true, but this, to the practitioner . is of no concern, since the significance of these principles lies elsewhere - for instance in yielding a correct account of the taxonomical system used by speakers of a given language or by scientists working in a given discipline. It is not fair to claim that syntax is irrelevant, but the meaning we make of information is dependent upon more than its syntactic structure.

Type

a

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Abstract

In recent years, the term "semantics" has been widely used in various fields of research and particularly in areas related to information technology. One of the motivators of such an appropriation is the vision of the Semantic Web, a set of developments underway, which might allow one to obtain better results when querying on the web. However, it is worth asking what kind of semantics we can find in the Semantic Web, considering that studying the subject is a complex and controversial endeavor. Working within this context, we present an account of semantics, relying on the main linguist approaches, in order to then analyze what semantics is within the scope of information technology. We critically evaluate a spectrum, which proposes the ordination of instruments (models, languages, taxonomic structures, to mention but a few) according to a semantic scale. In addition to proposing a new extended spectrum, we suggest alternative interpretations with the aim of clarifying the use of the term "semantics" in different contexts. Finally, we offer our conclusions regarding the semantic in the Semantic Web and mention future directions and complementary works.

Type

a

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Abstract

An influx of mechanisms for the collection of large sets of data has prompted widespread consideration of the impact that data analytic methods can have on a number of disciplines. Having an established record of the use of a unique mixture of empirical methods, the work of understanding and designing for user behavior is well situated to take advantage of the advances claimed by "big data" methods. Beyond any straightforward benefit of the use of large sets of data, such an increase in the scale of empirical evidence has far-reaching implications for the work of empirically guided design. We develop the concept of "peak empiricism" to explain the new role that large-scale data comes to play in design, one in which data become more than a simple empirical tool. In providing such an expansive empirical setting for design, big data weakens the subjective conditions necessary for empirical insight, pointing to a more performative approach to the relationship between a designer and his or her work. In this, the work of design is characterized as "thinking with" the data in a partnership that weakens not only any sense of empiricism but also the agentive foundations of a classical view of design work.

Type

a

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Abstract

Data Science can be considered a technique or a science. As a technique, it is more interested in the "what" than in the "why" of data. It does not need theories that explain how things work, it just needs the results. As a science, however, working strictly from data and without theories contradicts the post-empiricist view of science. In this view, theories come before data and data is used to corroborate or falsify theories. Nevertheless, one of the most controversial statements about Data Science is that it is a science that can work without theories. In this conceptual paper, we focus on the science aspect of Data Science. How is Data Science as a science? We propose a three-phased view of Data Science that shows that different theories have different roles in each of the phases we consider. We focus on when theories are used in Data Science rather than the controversy of whether theories are used in Data Science or not. In the end, we will see that the statement "Data Science works without theories" is better put as "in some of its phases, Data Science works without the theories that originally motivated the creation of the data."

Type

a