59,943 research outputs found
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
Web Data Extraction, Applications and Techniques: A Survey
Web Data Extraction is an important problem that has been studied by means of
different scientific tools and in a broad range of applications. Many
approaches to extracting data from the Web have been designed to solve specific
problems and operate in ad-hoc domains. Other approaches, instead, heavily
reuse techniques and algorithms developed in the field of Information
Extraction.
This survey aims at providing a structured and comprehensive overview of the
literature in the field of Web Data Extraction. We provided a simple
classification framework in which existing Web Data Extraction applications are
grouped into two main classes, namely applications at the Enterprise level and
at the Social Web level. At the Enterprise level, Web Data Extraction
techniques emerge as a key tool to perform data analysis in Business and
Competitive Intelligence systems as well as for business process
re-engineering. At the Social Web level, Web Data Extraction techniques allow
to gather a large amount of structured data continuously generated and
disseminated by Web 2.0, Social Media and Online Social Network users and this
offers unprecedented opportunities to analyze human behavior at a very large
scale. We discuss also the potential of cross-fertilization, i.e., on the
possibility of re-using Web Data Extraction techniques originally designed to
work in a given domain, in other domains.Comment: Knowledge-based System
CHORUS Deliverable 4.3: Report from CHORUS workshops on national initiatives and metadata
Minutes of the following Workshops:
• National Initiatives on Multimedia Content Description and Retrieval, Geneva, October 10th, 2007.
• Metadata in Audio-Visual/Multimedia production and archiving, Munich, IRT, 21st – 22nd November 2007
Workshop in Geneva 10/10/2007
This highly successful workshop was organised in cooperation with the European Commission. The event brought together
the technical, administrative and financial representatives of the various national initiatives, which have been established
recently in some European countries to support research and technical development in the area of audio-visual content
processing, indexing and searching for the next generation Internet using semantic technologies, and which may lead to an
internet-based knowledge infrastructure. The objective of this workshop was to provide a platform for mutual information
and exchange between these initiatives, the European Commission and the participants. Top speakers were present from
each of the national initiatives. There was time for discussions with the audience and amongst the European National
Initiatives. The challenges, communalities, difficulties, targeted/expected impact, success criteria, etc. were tackled. This
workshop addressed how these national initiatives could work together and benefit from each other.
Workshop in Munich 11/21-22/2007
Numerous EU and national research projects are working on the automatic or semi-automatic generation of descriptive and
functional metadata derived from analysing audio-visual content. The owners of AV archives and production facilities are
eagerly awaiting such methods which would help them to better exploit their assets.Hand in hand with the digitization of
analogue archives and the archiving of digital AV material, metadatashould be generated on an as high semantic level as
possible, preferably fully automatically. All users of metadata rely on a certain metadata model. All AV/multimedia search
engines, developed or under current development, would have to respect some compatibility or compliance with the
metadata models in use. The purpose of this workshop is to draw attention to the specific problem of metadata models in the
context of (semi)-automatic multimedia search
A Dynamic Knowledge Management Framework for the High Value Manufacturing Industry
Dynamic Knowledge Management (KM) is a combination of cultural and technological factors, including the cultural factors of people and their motivations, technological factors of content and infrastructure and, where these both come together, interface factors. In this paper a Dynamic KM framework is described in the context of employees being motivated to create profit for their company through product development in high value manufacturing. It is reported how the framework was discussed during a meeting of the collaborating company’s (BAE Systems) project stakeholders. Participants agreed the framework would have most benefit at the start of the product lifecycle before key decisions were made. The framework has been designed to support organisational learning and to reward employees that improve the position of the company in the market place
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