Wyniki wyszukiwania - BazTech

Ograniczanie wyników

2 Computer Science

2 2012

Powiadomienia systemowe

Sesja wygasła!

Znaleziono wyników: 2

Liczba wyników na stronie

Wyniki wyszukiwania

Sortuj według:

Ogranicz wyniki do:

Using advanced data mining and integration in environmental prediction scenarios

Habala O., Hluchy L., Tran V., Krammer P., Seleng M.

Computer Science

2012

Vol. 13 (1)

5-16

We present one of the meteorological and hydrological experiments performed in the FP7 project ADMIRE. It serves as an experimental platform for hydrologists, and we have used it also as a testing platform for a suite of advanced data integration and data mining (DMI) tools, developed within ADMIRE. The idea of ADMIRE is to develop an advanced DMI platform accessible even to users who are not familiar with data mining techniques. To this end, we have designed a novel DMI architecture, supported by a set of software tools, managed by DMI process descriptions written in a specialized high-level DMI language called DISPEL, and controlled via several different user interfaces, each performing a different set of tasks and targeting different user group.

Distributed web-scale infrastructure for crawling, indexing and search with semantic support

Dlugolinsky S., Seleng M., Laclavik M., Hluchy L.

Computer Science

2012

Vol. 13 (4)

5-19

In this paper, we describe our work in progress in the scope of web-scale information extraction and information retrieval utilizing distributed computing. We present a distributed architecture built on top of the MapReduce paradigm for information retrieval, information processing and intelligent search supported by spatial capabilities. Proposed architecture is focused on crawling documents in several different formats, information extraction, lightweight semantic annotation of the extracted information, indexing of extracted information and finally on indexing of documents based on the geo-spatial information found in a document. We demonstrate the architecture on two use cases, where the first is search in job offers retrieved from the LinkedIn portal and the second is search in BBC news feeds and discuss several problems we had to face during the implemen-tation. We also discuss spatial search applications for both cases because both LinkedIn job offer pages and BBC news feeds contain a lot of spatial information to extract and process.