Similarity recognition using context-based pattern for cyber-society

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13440%2F16%3A43886725" target="_blank" >RIV/44555601:13440/16:43886725 - isvavai.cz</a>

Result on the web

<a href="http://link.springer.com/article/10.1007%2Fs00500-015-1763-9" target="_blank" >http://link.springer.com/article/10.1007%2Fs00500-015-1763-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00500-015-1763-9" target="_blank" >10.1007/s00500-015-1763-9</a>

Result language

angličtina

Original language name

Similarity recognition using context-based pattern for cyber-society

Original language description

To measure the similarity of contexts in smart devices, comparison is made of the user-defined contexts and another context which is defined in a server or in a network device that a user has (Segev and Toch IEEE Trans Serv Comput 2(3):210-222, 2009). While it processes to compare, if they find some users who have similarities with them, they surely may be interested in the users, because they know that they can share their information without the time wasting to search, finally getting what they want. However, according to the characteristics of the registered contexts, they are classified into two types, a rank-definitional context and a rank-undefined context. Also, the users usually want to use the two types to get quickly what they want at the same time. It means that another algorithm may be needed to get the similarity depending on the contexts, because the existing similarity search algorithms usually perform the similarity process without the contexts' characteristics analysis. They assume that all contexts have the same features when they process. Now, the existing methods that find the similarity usually have an accuracy problem. Low accuracy gives invisible services to users. This paper has suggested named context-based pattern measurement method including weight defines for higher accuracy. As a result, it would be able to get accuracy similarity by applying to the proposed algorithms about 69.072 % without weight and also 95.322 % accuracy in case it has a specific weight.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

IN - Informatics

OECD FORD branch

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Project

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Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Soft Computing

ISSN

1432-7643

e-ISSN

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Volume of the periodical

2016

Issue of the periodical within the volume

20

Country of publishing house

US - UNITED STATES

Number of pages

9

Pages from-to

4565-4573

UT code for WoS article

000385246200026

EID of the result in the Scopus database

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