Web Intelligence: A New Paradigm for Virtual Communities and Web Science

2007 ◽  
Author(s):  
Abdel-Badeeh M. Salem
Keyword(s):  
Virtual Communities ◽  
New Paradigm ◽  
Web Intelligence ◽  
Web Science

Big Data and Data Analytics Research

Web Services ◽  
2019 ◽  
pp. 2161-2171
Author(s):  
Miltiadis D. Lytras ◽  
Vijay Raghavan ◽  
Ernesto Damiani
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Internet Technology ◽  
Cognitive Computing ◽  
New Paradigm ◽  
Web Science ◽  
New Business ◽  
Value Propositions

The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the same moment communities out of web science need to realize the potential of this new paradigm with the support of new sound business models and a critical shift in the perception of decision making. In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years. A number of research directions are outlined as well as a new roadmap towards the evolution of Big Data to Smart Decisions and Cognitive Computing. The authors do hope that the readers would like to react and to propose their own value propositions for the domain initiating a scientific dialogue beyond self-fulfilled expectations.

scholarly journals Crowdsourcing - a New Paradigm of Organisational Learning of Public Organisation

2018 ◽  
Author(s):  
Regina Lenart-Gansiniec ◽  
Łukasz Sułkowski
Keyword(s):  
Virtual Communities ◽  
Business Processes ◽  
Collective Intelligence ◽  
Organisational Learning ◽  
Structured Interviews ◽  
New Paradigm ◽  
Cross Sectional ◽  
Public Organisation ◽  
The Subject ◽  
Public Organisations

Crowdsourcing is one of the new themes that has appeared in the last decade. Considering its potential, more and more organisations reach for it. It is perceived as an innovative method that can be used for problem solving, improving business processes, creating open innovations, building a competitive advantage, and increasing transparency and openness of the organisation. Crowdsourcing is also conceptualised as a source of a knowledge-based organisation. The importance of crowdsourcing for organisational learning is seen as one of the key themes in the latest literature in the field of crowdsourcing. Since 2008, there has been an increase in the interest of public organisations in crowdsourcing and including it in their activities. This article is a response to the recommendations in the subject literature, which states that crowdsourcing in public organisations is a new and exciting research area. The aim of the article is to present a new paradigm that combines crowdsourcing levels with the levels of learning. The research methodology is based on an analysis of the subject literature and exemplifications of organisations which introduce crowdsourcing. This article presents a cross-sectional study of four Polish municipal offices that use four types of crowdsourcing, according to the division by J. Howe: collective intelligence, crowd creation, crowd voting, and crowdfunding. Semi-structured interviews were conducted with the management personnel of those municipal offices. The research results show that knowledge acquired from the virtual communities allows the public organisation to anticipate changes, expectations, and needs of citizens and to adapt to them. It can therefore be considered that crowdsourcing is a new and rapidly developing organisational learning paradigm.

Scientific Web Intelligence

2011 ◽  
pp. 1714-1719
Author(s):  
Mike Thelwall
Keyword(s):  
Data Mining ◽  
Research Field ◽  
Data Sources ◽  
Web Pages ◽  
Web Intelligence ◽  
Research Fields ◽  
Web Science ◽  
Patterns Of Communication ◽  
The Impact ◽  
Citation Databases

Scientific Web Intelligence (SWI) is a research field that combines techniques from data mining, web intelligence and scientometrics to extract useful information from the links and text of academic-related web pages, using various clustering, visualization and counting techniques. Its origins lie in previous scientometric research into mining offline academic data sources such as journal citation databases, in contrast to Web Science, which focuses on engineering an effective Web (Berners-Lee et al., 2006). Typical scientometric objectives are either evaluative: assessing the impact of research; or relational: identifying patterns of communication within and between research fields. From scientometrics, SWI also inherits a need to validate its methods and results so that the methods can be justified to end-users and the causes of the results can be found and explained.

scholarly journals Some assembly required: leveraging Web science to understand and enable team assembly

2013 ◽  
Vol 371 (1987) ◽  
pp. 20120385 ◽  
Author(s):  
Noshir Contractor
Keyword(s):  
Recommender Systems ◽  
Virtual Communities ◽  
Ad Hoc ◽  
Contemporary Society ◽  
Theoretical Understanding ◽  
Web Science ◽  
The World ◽  
Team Assembly ◽  
Creativity And Innovation ◽  
The Web

Recent advances on the Web have generated unprecedented opportunities for individuals around the world to assemble into teams. And yet, because of the Web, the nature of teams and how they are assembled has changed radically. Today, many teams are ad hoc, agile, distributed, transient entities that are assembled from a larger primordial network of relationships within virtual communities. These assemblages possess the potential to unleash the high levels of creativity and innovation necessary for productively addressing many of the daunting challenges confronting contemporary society. This article argues that Web science is particularly well suited to help us realize this potential by making a substantial interdisciplinary intellectual investment in (i) advancing theories that explain our socio-technical motivations to form teams, (ii) the development of new analytic methods and models to untangle the unique influences of these motivations on team assembly, (iii) harvesting, curating and leveraging the digital trace data offered by the Web to test our models, and (iv) implementing recommender systems that use insights gleaned from our richer theoretical understanding of the motivations that lead to effective team assembly.

Big Data and Data Analytics Research

2017 ◽  
Vol 13 (1) ◽  
pp. 1-10 ◽  
Author(s):  
Miltiadis D. Lytras ◽  
Vijay Raghavan ◽  
Ernesto Damiani
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Business Models ◽  
Science Research ◽  
Internet Technology ◽  
Cognitive Computing ◽  
New Paradigm ◽  
Scientific Dialogue ◽  
Web Science

The Big Data and Data Analytics is a brand new paradigm, for the integration of Internet Technology in the human and machine context. For the first time in the history of the human mankind we are able to transforming raw data that are massively produced by humans and machines in to knowledge and wisdom capable of supporting smart decision making, innovative services, new business models, innovation, and entrepreneurship. For the Web Science research, this is a new methodological and technological spectrum of advanced methods, frameworks and functionalities never experienced in the past. At the same moment communities out of web science need to realize the potential of this new paradigm with the support of new sound business models and a critical shift in the perception of decision making. In this short visioning article, the authors are analyzing the main aspects of Big Data and Data Analytics Research and they provide their own metaphor for the next years. A number of research directions are outlined as well as a new roadmap towards the evolution of Big Data to Smart Decisions and Cognitive Computing. The authors do hope that the readers would like to react and to propose their own value propositions for the domain initiating a scientific dialogue beyond self-fulfilled expectations.

Solar Filaments as Tracers of Subsurface Processes

2000 ◽  
Vol 179 ◽  
pp. 177-183
Author(s):  
D. M. Rust
Keyword(s):  
Magnetic Fields ◽  
Magnetic Flux ◽  
Coronal Mass Ejections ◽  
Interplanetary Space ◽  
Intermediate Stage ◽  
Coronal Plasma ◽  
Magnetic Helicity ◽  
The Sun ◽  
New Paradigm ◽  
Solar Filaments

AbstractSolar filaments are discussed in terms of two contrasting paradigms. The standard paradigm is that filaments are formed by condensation of coronal plasma into magnetic fields that are twisted or dimpled as a consequence of motions of the fields’ sources in the photosphere. According to a new paradigm, filaments form in rising, twisted flux ropes and are a necessary intermediate stage in the transfer to interplanetary space of dynamo-generated magnetic flux. It is argued that the accumulation of magnetic helicity in filaments and their coronal surroundings leads to filament eruptions and coronal mass ejections. These ejections relieve the Sun of the flux generated by the dynamo and make way for the flux of the next cycle.

Does the Motor System Facilitate Spatial Imagery?

2017 ◽  
Vol 49 (3) ◽  
pp. 127-137 ◽  
Author(s):  
Markus Krüger ◽  
Horst Krist
Keyword(s):  
Motor System ◽  
Third Graders ◽  
External Support ◽  
New Paradigm ◽  
Perceptual Condition ◽  
Imagery Condition ◽  
Passive Rotation ◽  
Perception Condition ◽  
Motor Condition ◽  
Dynamic Imagery

Abstract. Recent studies have ascertained a link between the motor system and imagery in children. A motor effect on imagery is demonstrated by the influence of stimuli-related movement constraints (i. e., constraints defined by the musculoskeletal system) on mental rotation, or by interference effects due to participants’ own body movements or body postures. This link is usually seen as qualitatively different or stronger in children as opposed to adults. In the present research, we put this interpretation to further scrutiny using a new paradigm: In a motor condition we asked our participants (kindergartners and third-graders) to manually rotate a circular board with a covered picture on it. This condition was compared with a perceptual condition where the board was rotated by an experimenter. Additionally, in a pure imagery condition, children were instructed to merely imagine the rotation of the board. The children’s task was to mark the presumed end position of a salient detail of the respective picture. The children’s performance was clearly the worst in the pure imagery condition. However, contrary to what embodiment theories would suggest, there was no difference in participants’ performance between the active rotation (i. e., motor) and the passive rotation (i. e., perception) condition. Control experiments revealed that this was also the case when, in the perception condition, gaze shifting was controlled for and when the board was rotated mechanically rather than by the experimenter. Our findings indicate that young children depend heavily on external support when imagining physical events. Furthermore, they indicate that motor-assisted imagery is not generally superior to perceptually driven dynamic imagery.

Self-Prioritization Beyond Perception

2015 ◽  
Vol 62 (6) ◽  
pp. 415-425 ◽  
Author(s):  
Sarah Schäfer ◽  
Dirk Wentura ◽  
Christian Frings
Keyword(s):  
The Self ◽  
Stimulus Category ◽  
Abstract Concept ◽  
Conceptual Level ◽  
New Paradigm ◽  
Stimulus Features ◽  
Relevant Word ◽  
Word Shape

Abstract. Recently, Sui, He, and Humphreys (2012) introduced a new paradigm to measure perceptual self-prioritization processes. It seems that arbitrarily tagging shapes to self-relevant words (I, my, me, and so on) leads to speeded verification times when matching self-relevant word shape pairings (e.g., me – triangle) as compared to non-self-relevant word shape pairings (e.g., stranger – circle). In order to analyze the level at which self-prioritization takes place we analyzed whether the self-prioritization effect is due to a tagging of the self-relevant label and the particular associated shape or due to a tagging of the self with an abstract concept. In two experiments participants showed standard self-prioritization effects with varying stimulus features or different exemplars of a particular stimulus-category suggesting that self-prioritization also works at a conceptual level.

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