Thesis supervisor: Gábor Magyar
Location of studies (in Hungarian): Távközlési és Médiainformatikai Tanszék Abbreviation of location of studies: TMIT
Description of the research topic:
Research objectives:
Big Data originates from several sources and in different forms (text, audio, video). Effective use of rapidly growing big data is challenging. Many real-life fields (like Health Care, Energy Management, Social Sciences, Engineering) need advanced methods and technology for interpreting and analysing often loosely-structured Big Data. To turn data into knowledge, data scientists need to effectively process and interpret data. To meet this challenges Big Data and semantic technologies could be combined in order to support knowledge discovery. Knowledge Processing and Acquisition are essential in a world rich with large heterogeneous data sources. The combination of Semantic Web and Big Data technologies are promising to enable the processing of semantically heterogeneous data sources and the capturing of new knowledge from those. For this, semantically-enriched data representation is required, e.g. in RDF. Transforming data from different formats into the RDF has novel research issues, especially maintaining (reducing) the complexity in parallel.
Open problems:
- knowledge enrichment in Big Data using semantic technologies
- transformation of heterogeneous data to semantically-enriched simplified data
- keep data integration, data quality and complexity manageable
Required language skills: english Further requirements: Requirements:
- skills in using markup languages, esp. XML
- basic database management skills (architectures, queries, SQL)
- experience in semantic technologies, esp. RDF,
- and some knowledge about ontologies
Number of students who can be accepted: 1
Deadline for application: 2019-06-14
2024. IV. 17. ODT ülés Az ODT következő ülésére 2024. június 14-én, pénteken 10.00 órakor kerül sor a Semmelweis Egyetem Szenátusi termében (Bp. Üllői út 26. I. emelet).
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