26,246 research outputs found
Methodological considerations concerning manual annotation of musical audio in function of algorithm development
In research on musical audio-mining, annotated music databases are needed which allow the development of computational tools that extract from the musical audiostream the kind of high-level content that users can deal with in Music Information Retrieval (MIR) contexts. The notion of musical content, and therefore the notion of annotation, is ill-defined, however, both in the syntactic and semantic sense. As a consequence, annotation has been approached from a variety of perspectives (but mainly linguistic-symbolic oriented), and a general methodology is lacking. This paper is a step towards the definition of a general framework for manual annotation of musical audio in function of a computational approach to musical audio-mining that is based on algorithms that learn from annotated data. 1
Attribute oriented induction with star schema
This paper will propose a novel star schema attribute induction as a new
attribute induction paradigm and as improving from current attribute oriented
induction. A novel star schema attribute induction will be examined with
current attribute oriented induction based on characteristic rule and using non
rule based concept hierarchy by implementing both of approaches. In novel star
schema attribute induction some improvements have been implemented like
elimination threshold number as maximum tuples control for generalization
result, there is no ANY as the most general concept, replacement the role
concept hierarchy with concept tree, simplification for the generalization
strategy steps and elimination attribute oriented induction algorithm. Novel
star schema attribute induction is more powerful than the current attribute
oriented induction since can produce small number final generalization tuples
and there is no ANY in the results.Comment: 23 Pages, IJDM
The contribution of data mining to information science
The information explosion is a serious challenge for current information institutions. On the other hand, data mining, which is the search for valuable information in large volumes of data, is one of the solutions to face this challenge. In the past several years, data mining has made a significant contribution to the field of information science. This paper examines the impact of data mining by reviewing existing applications, including personalized environments, electronic commerce, and search engines. For these three types of application, how data mining can enhance their functions is discussed. The reader of this paper is expected to get an overview of the state of the art research associated with these applications. Furthermore, we identify the limitations of current work and raise several directions for future research
The Hidden Web, XML and Semantic Web: A Scientific Data Management Perspective
The World Wide Web no longer consists just of HTML pages. Our work sheds
light on a number of trends on the Internet that go beyond simple Web pages.
The hidden Web provides a wealth of data in semi-structured form, accessible
through Web forms and Web services. These services, as well as numerous other
applications on the Web, commonly use XML, the eXtensible Markup Language. XML
has become the lingua franca of the Internet that allows customized markups to
be defined for specific domains. On top of XML, the Semantic Web grows as a
common structured data source. In this work, we first explain each of these
developments in detail. Using real-world examples from scientific domains of
great interest today, we then demonstrate how these new developments can assist
the managing, harvesting, and organization of data on the Web. On the way, we
also illustrate the current research avenues in these domains. We believe that
this effort would help bridge multiple database tracks, thereby attracting
researchers with a view to extend database technology.Comment: EDBT - Tutorial (2011
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