46,955 research outputs found
Open semantic service networks
Online service marketplaces will soon be part of the economy to scale the provision of specialized multi-party services through automation and standardization. Current research, such as the *-USDL service description language family, is already defining the basic building blocks to model the next generation of business services. Nonetheless, the developments being made do not target to interconnect services via service relationships. Without the concept of relationship, marketplaces will be seen as mere functional silos containing service descriptions. Yet, in real economies, all services are related and connected. Therefore, to address this gap we introduce the concept of open semantic service network (OSSN), concerned with the establishment of rich relationships between services. These networks will provide valuable knowledge on the global service economy, which can be exploited for many socio-economic and scientific purposes such as service network analysis, management, and control
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
Transdisciplinarity seen through Information, Communication, Computation, (Inter-)Action and Cognition
Similar to oil that acted as a basic raw material and key driving force of
industrial society, information acts as a raw material and principal mover of
knowledge society in the knowledge production, propagation and application. New
developments in information processing and information communication
technologies allow increasingly complex and accurate descriptions,
representations and models, which are often multi-parameter, multi-perspective,
multi-level and multidimensional. This leads to the necessity of collaborative
work between different domains with corresponding specialist competences,
sciences and research traditions. We present several major transdisciplinary
unification projects for information and knowledge, which proceed on the
descriptive, logical and the level of generative mechanisms. Parallel process
of boundary crossing and transdisciplinary activity is going on in the applied
domains. Technological artifacts are becoming increasingly complex and their
design is strongly user-centered, which brings in not only the function and
various technological qualities but also other aspects including esthetic, user
experience, ethics and sustainability with social and environmental dimensions.
When integrating knowledge from a variety of fields, with contributions from
different groups of stakeholders, numerous challenges are met in establishing
common view and common course of action. In this context, information is our
environment, and informational ecology determines both epistemology and spaces
for action. We present some insights into the current state of the art of
transdisciplinary theory and practice of information studies and informatics.
We depict different facets of transdisciplinarity as we see it from our
different research fields that include information studies, computability,
human-computer interaction, multi-operating-systems environments and
philosophy.Comment: Chapter in a forthcoming book: Information Studies and the Quest for
Transdisciplinarity - Forthcoming book in World Scientific. Mark Burgin and
Wolfgang Hofkirchner, Editor
A unified view of data-intensive flows in business intelligence systems : a survey
Data-intensive flows are central processes in today’s business intelligence (BI) systems, deploying different technologies to deliver data, from a multitude of data sources, in user-preferred and analysis-ready formats. To meet complex requirements of next generation BI systems, we often need an effective combination of the traditionally batched extract-transform-load (ETL) processes that populate a data warehouse (DW) from integrated data sources, and more real-time and operational data flows that integrate source data at runtime. Both academia and industry thus must have a clear understanding of the foundations of data-intensive flows and the challenges of moving towards next generation BI environments. In this paper we present a survey of today’s research on data-intensive flows and the related fundamental fields of database theory. The study is based on a proposed set of dimensions describing the important challenges of data-intensive flows in the next generation BI setting. As a result of this survey, we envision an architecture of a system for managing the lifecycle of data-intensive flows. The results further provide a comprehensive understanding of data-intensive flows, recognizing challenges that still are to be addressed, and how the current solutions can be applied for addressing these challenges.Peer ReviewedPostprint (author's final draft
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