• Corpus ID: 17007748

A Recent Survey on Unstructured Data to Structured Data in Distributed Data Mining

@inproceedings{Hemalatha2014ARS,
  title={A Recent Survey on Unstructured Data to Structured Data in Distributed Data Mining},
  author={M. Hemalatha},
  year={2014}
}
The organization of unstructured data is recognized as one of the major uncertain problems in the information industry and data mining paradigm. It will be in the form of computerized information that moreover, does not have a data model and there are not simply used by data mining. The task of managing unstructured data signifies possibly the major data management opportunity for our community subsequently managing relational data. The communities users such as KDD, Semantic web, AI and web… 
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References

SHOWING 1-10 OF 21 REFERENCES
The Case for a Structured Approach to Managing Unstructured Data
TLDR
Drawing on the lessons learned while managing relational data, a structured approach to managing unstructured data is outlined and the potential implications of this approach to manage other kinds of non-relational data are discussed.
Semantic Tool for Analysing Unstructured Data
TLDR
This paper proposes an interactive component that extracts pertinent information from unstructured data and presents to the user through an innovative graph mec hanism, namely the spring graph, that makes use of newly emerging semantic and NLP technologies for extracting and representing structured data from un Structured data.
Information Sharing on the Semantic Web
TLDR
Addressing problems like missing conceptual models, unclear system boundaries, and heterogeneous representations, the authors design a framework for ontology-based information sharing in weakly structured environments like the Semantic Web.
Tools for privacy preserving distributed data mining
TLDR
This paper presents some components of a toolkit of components that can be combined for specific privacy-preserving data mining applications, and shows how they can be used to solve several Privacy preserving data mining problems.
Privacy Preserving Data Mining
TLDR
This work considers a scenario in which two parties owning confidential databases wish to run a data mining algorithm on the union of their databases, without revealing any unnecessary information, and proposes a protocol that is considerably more efficient than generic solutions and demands both very few rounds of communication and reasonable bandwidth.
Semantic systems biology: enabling integrative biology via semantic web technologies
TLDR
An overview of the projects associated with the Semantic Systems Biology initiative, which augment the systems biology approach with semantic web technologies to enable smooth data integration, rigorous knowledge representation, efficient querying, and hypothesis generation.
The Ontology Extraction & Maintenance Framework Text-To-Onto
TLDR
The paper presents a framework for semi-automatically learning ontologies from domainspecific texts by applying machine learning techniques, and presents the TEXT-TO-ONTO framework, which integrates manual engineering facilities to follow a balanced cooperative modelling paradigm.
A Protégé Plug-In for Ontology Extraction from Text Based on Linguistic Analysis
TLDR
A plug-in for the widely used Protege ontology development tool that supports the interactive extraction and/or extension of ontologies from text and provides an environment for the integration of linguistic analysis in ontology engineering through the definition of mapping rules.
Privacy-preserving anonymization of set-valued data
TLDR
A new version of the k-anonymity guarantee is defined, the km-Anonymity, to limit the effects of the data dimensionality and two efficient algorithms to transform the database are proposed.
Background knowledge for ontology construction
TLDR
A word weighting schema is introduced to be used in the document representation of OntoGen based on the background knowledge provided by user, which is than used byOntoGen's machine learning and text mining algorithms.
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