170,667 research outputs found
Data driven approaches for smart city planning and design: a case scenario on urban data management
Purpose
Because of the use of digital technologies in smart cities, municipalities are increasingly facing issues related to urban data management and are seeking ways to exploit these huge amounts of data for the actualization of data driven services. However, only few studies discuss challenges related to data driven strategies in smart cities. Accordingly, the purpose of this study is to present data driven approaches (architecture and model), for urban data management needed to improve smart city planning and design. The developed approaches depict how data can underpin sustainable urban development.
Design/methodology/approach
Design science research is adopted following a qualitative method to evaluate the architecture developed based on top-level design using a case data from workshops and interviews with experts involved in a smart city project.
Findings
The findings of this study from the evaluations indicate that the identified enablers are useful to support data driven services in smart cities and the developed architecture can be used to promote urban data management. More importantly, findings from this study provide guidelines to municipalities to improve data driven services for smart city planning and design.
Research limitations/implications
Feedback as qualitative data from practitioners provided evidence on how data driven strategies can be achieved in smart cities. However, the model is not validated. Hence, quantitative data is needed to further validate the enablers that influence data driven services in smart city planning and design.
Practical implications
Findings from this study offer practical insights and real-life evidence to define data driven enablers in smart cities and suggest research propositions for future studies. Additionally, this study develops a real conceptualization of data driven method for municipalities to foster open data and digital service innovation for smart city development.
Social implications
The main findings of this study suggest that data governance, interoperability, data security and risk assessment influence data driven services in smart cities. This study derives propositions based on the developed model that identifies enablers for actualization of data driven services for smart cities planning and design.
Originality/value
This study explores the enablers of data driven strategies in smart city and further developed an architecture and model that can be adopted by municipalities to structure their urban data initiatives for improving data driven services to make cities smarter. The developed model supports municipalities to manage data used from different sources to support the design of data driven services provided by different enterprises that collaborate in urban environment.acceptedVersio
What working memory is for
Glenberg focuses on conceptualizations that change from
moment to moment, yet he dismisses the concept of working memory
(sect. 4.3), which offers an account of temporary storage and on-line
cognition. This commentary questions whether Glenberg's account
adequately caters for observations of consistent data patterns in
temporary storage of verbal and visuospatial information in healthy
adults and in brain-damaged patients with deficits in temporary
retention.</jats:p
Stream water age distributions controlled by storage dynamics and nonlinear hydrologic connectivity : Modeling with high-resolution isotope data
Peer reviewedPublisher PD
An Analysis of Service Ontologies
Services are increasingly shaping the worldâs economic activity. Service provision and consumption have been profiting from advances in ICT, but the decentralization and heterogeneity of the involved service entities still pose engineering challenges. One of these challenges is to achieve semantic interoperability among these autonomous entities. Semantic web technology aims at addressing this challenge on a large scale, and has matured over the last years. This is evident from the various efforts reported in the literature in which service knowledge is represented in terms of ontologies developed either in individual research projects or in standardization bodies. This paper aims at analyzing the most relevant service ontologies available today for their suitability to cope with the service semantic interoperability challenge. We take the vision of the Internet of Services (IoS) as our motivation to identify the requirements for service ontologies. We adopt a formal approach to ontology design and evaluation in our analysis. We start by defining informal competency questions derived from a motivating scenario, and we identify relevant concepts and properties in service ontologies that match the formal ontological representation of these questions. We analyze the service ontologies with our concepts and questions, so that each ontology is positioned and evaluated according to its utility. The gaps we identify as the result of our analysis provide an indication of open challenges and future work
Cognitive modeling of social behaviors
To understand both individual cognition and collective activity, perhaps the greatest opportunity today is to integrate the cognitive modeling approach (which stresses how beliefs are formed and drive behavior) with social studies (which stress how relationships and informal practices drive behavior). The crucial insight is that norms are conceptualized in the individual mind as ways of carrying out activities. This requires for the psychologist a shift from only modeling goals and tasks âwhy people do what they doâto modeling behavioral patternsâwhat people doâas they are engaged in purposeful activities. Instead of a model that exclusively deduces actions from goals, behaviors are also, if not primarily, driven by broader patterns of chronological and located activities (akin to scripts).
To illustrate these ideas, this article presents an extract from a Brahms simulation of the Flashline Mars Arctic Research Station (FMARS), in which a crew of six people are living and working for a week, physically simulating a Mars surface mission. The example focuses on the simulation of a planning meeting, showing how physiological constraints (e.g., hunger, fatigue), facilities (e.g., the habitatâs layout) and group decision making interact. Methods are described for constructing such a model of practice, from video and first-hand observation, and how this modeling approach changes how one relates goals, knowledge, and cognitive architecture. The resulting simulation model is a powerful complement to task analysis and knowledge-based simulations of reasoning, with many practical applications for work system design, operations management, and training
Who Cares about Axiomatization? Representation, Invariance, and Formal Ontologies
The philosophy of science of Patrick Suppes is centered on two important notions that are
part of the title of his recent book (Suppes 2002): Representation and Invariance.
Representation is important because when we embrace a theory we implicitly choose a way to
represent the phenomenon we are studying. Invariance is important because, since invariants
are the only things that are constant in a theory, in a way they give the âobjectiveâ meaning of
that theory.
Every scientific theory gives a representation of a class of structures and studies the invariant
properties holding in that class of structures. In Suppesâ view, the best way to define this class
of structures is via axiomatization. This is because a class of structures is given by a
definition, and this same definition establishes which are the properties that a single structure
must possess in order to belong to the class. These properties correspond to the axioms of a
logical theory.
In Suppesâ view, the best way to characterize a scientific structure is by giving a
representation theorem for its models and singling out the invariants in the structure.
Thus, we can say that the philosophy of science of Patrick Suppes consists in the application
of the axiomatic method to scientific disciplines.
What I want to argue in this paper is that this application of the axiomatic method is also at
the basis of a new approach that is being increasingly applied to the study of computer
science and information systems, namely the approach of formal ontologies.
The main task of an ontology is that of making explicit the conceptual structure underlying a
certain domain. By âmaking explicit the conceptual structureâ we mean singling out the most
basic entities populating the domain and writing axioms expressing the main properties of
these primitives and the relations holding among them.
So, in both cases, the axiomatization is the main tool used to characterize the object of
inquiry, being this object scientific theories (in Suppesâ approach), or information systems
(for formal ontologies).
In the following section I will present the view of Patrick Suppes on the philosophy of science
and the axiomatic method, in section 3 I will survey the theoretical issues underlying the work
that is being done in formal ontologies and in section 4 I will draw a comparison of these two
approaches and explore similarities and differences between them
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