hadoop map reduce project

Phase 2: Implement MR programs to solve unstructured data problems on the HDFS set up. In this phase you will implement the word co-occurrence MR algorithm

discussed in the Lin and Dyer’s book. You’ll select a data set from publications in any subject area you

are familiar with and prepare co-occurrence or co-author information from the publications. The stripes

method for co-occurrence may be better suited for this application. Map will have to parse and drop the

extra text in the publications. We need only the first author as key and rest of the authors as value and

number of occurrences in a given corpus.

Input: Many publications from an author.

Output: Author as the key and value is the associated array with the co-authors along with number of

occurrences as entry in the associated array.

Mandatory requirement: Every team has to have its own data set and cannot copy each other.

Taidot: Hadoop, Python, MapReduce

Näytä lisää: twitter clone hadoop map reduce, implement map tracker project, map reduce, hadoop, map reduce, hadoop, R, pintos project 2 user programs github, rehan garden phase 2 map, rehan garden lahore phase 2 map, project sunshine phase 2, buddhist project sunshine phase 2 report on may 24, buddhist project sunshine phase 2, project jasper phase 2, national standards project phase 2 pdf, second avenue subway phase 2 map, new city wah phase 2 map, new city phase 2 wah cantt map, project ubin phase 2, project seabird phase 2 was started in which of the following state, doha metro project phase 2, sanhita housing project phase 2, handri neeva project phase 2 map

Tietoa työnantajasta:
( 0 arvostelua ) Buffalo, United States

Projektin tunnus: #29921518