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http://hdl.handle.net/1942/15684
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DC Field | Value | Language |
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dc.contributor.advisor | NEVEN, Frank | - |
dc.contributor.author | KETSMAN, Bas | - |
dc.date.accessioned | 2013-10-01T14:48:01Z | - |
dc.date.available | 2013-10-01T14:48:01Z | - |
dc.date.issued | 2013 | - |
dc.identifier.uri | http://hdl.handle.net/1942/15684 | - |
dc.description.abstract | In the current big data era, in which we are overloaded with huge amounts of data, there is a large demand for alternatives to traditional querying systems. In our context, big data refers to the petabyte scale data analysis to which industry and academia are ex- posed today; and, where hundreds or thousands of machines, running in parallel, are required to finish computations in a reasonable amount of time. However, the current data landscape also has a complex and non-traditional structure, which typically fits well into the graph model. In semi-structured data, the traditional relational query languages fall short. Hence, we consider the conjunctive regular path queries (CRPQs); a simple, but reasonably expressive language for querying graph data. This thesis is about the eval- uation of CRPQs in MapReduce, a framework that provides a programming abstraction that enables the design of algorithms that can be executed automatically on a cluster of machines in a fault-tolera | - |
dc.format.mimetype | Application/pdf | - |
dc.language | nl | - |
dc.language.iso | en | - |
dc.publisher | tUL | - |
dc.title | Conjunctive Regular Path Queries in MapReduce | - |
dc.type | Theses and Dissertations | - |
local.bibliographicCitation.jcat | T2 | - |
dc.description.notes | master in de informatica-databases | - |
local.type.specified | Master thesis | - |
item.accessRights | Open Access | - |
item.contributor | KETSMAN, Bas | - |
item.fulltext | With Fulltext | - |
item.fullcitation | KETSMAN, Bas (2013) Conjunctive Regular Path Queries in MapReduce. | - |
Appears in Collections: | Master theses |
Files in This Item:
File | Description | Size | Format | |
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08265582012197.pdf | 1.02 MB | Adobe PDF | View/Open |
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