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https://doi.org/10.1080/1331677X.2015.1095110

Processing unstructured documents and social media using Big Data techniques

Vlad Diaconita   ORCID icon orcid.org/0000-0002-5169-9232

Puni tekst: engleski, pdf (483 KB) str. 981-993 preuzimanja: 386* citiraj
APA 6th Edition
Diaconita, V. (2015). Processing unstructured documents and social media using Big Data techniques. Economic research - Ekonomska istraživanja, 28 (1), 981-993. https://doi.org/10.1080/1331677X.2015.1095110
MLA 8th Edition
Diaconita, Vlad. "Processing unstructured documents and social media using Big Data techniques." Economic research - Ekonomska istraživanja, vol. 28, br. 1, 2015, str. 981-993. https://doi.org/10.1080/1331677X.2015.1095110. Citirano 23.01.2020.
Chicago 17th Edition
Diaconita, Vlad. "Processing unstructured documents and social media using Big Data techniques." Economic research - Ekonomska istraživanja 28, br. 1 (2015): 981-993. https://doi.org/10.1080/1331677X.2015.1095110
Harvard
Diaconita, V. (2015). 'Processing unstructured documents and social media using Big Data techniques', Economic research - Ekonomska istraživanja, 28(1), str. 981-993. https://doi.org/10.1080/1331677X.2015.1095110
Vancouver
Diaconita V. Processing unstructured documents and social media using Big Data techniques. Economic research - Ekonomska istraživanja [Internet]. 2015 [pristupljeno 23.01.2020.];28(1):981-993. https://doi.org/10.1080/1331677X.2015.1095110
IEEE
V. Diaconita, "Processing unstructured documents and social media using Big Data techniques", Economic research - Ekonomska istraživanja, vol.28, br. 1, str. 981-993, 2015. [Online]. https://doi.org/10.1080/1331677X.2015.1095110

Sažetak
Big Data technologies can be very useful when it comes to storing and processing using sophisticated algorithms, terabytes or petabytes of data. With the latest advancements, such as Hadoop YARN, processing can be done not only in batch but also in real time. In this paper, we detail a methodology followed by a case study that investigates the power of machine learning algorithms used in a Hadoop environment in classifying unstructured data. We also investigate how to capture
geolocated messages from social networks and how kriging can be used to see if there is a strong relationship between two or more such datasets.

Ključne riječi
Hadoop; MapReduce; k-NN; social media; geolocated messages; large data sets

Hrčak ID: 171608

URI
https://hrcak.srce.hr/171608

Posjeta: 469 *