Difference between revisions of "Graph An"

From Master Projects
Jump to: navigation, search
(New page: {{Projectproposal |Contact person=Peter Boncz |Master areas=High Performance Distributed Computing, Parallel and Distributed Computer Systems |Project page=www.cwi.nl/~boncz |Fulfilled=No ...)
 
 
Line 3: Line 3:
 
|Master areas=High Performance Distributed Computing, Parallel and Distributed Computer Systems
 
|Master areas=High Performance Distributed Computing, Parallel and Distributed Computer Systems
 
|Project page=www.cwi.nl/~boncz
 
|Project page=www.cwi.nl/~boncz
|Fulfilled=No
+
|Fulfilled=yes
 
}}
 
}}
 
Graph Analysis on large datasets is a hot topic in Big data processing.
 
Graph Analysis on large datasets is a hot topic in Big data processing.
  
 
Graph Programming frameworks like Giraph and Graphlab and associated languages such as Green Marl currently heither have non-scalable or relatively slow back-ends. This project aims to see if high-performance database query processing techniques could be used to power a graph programming framework. A frst question would be to code and test a number of graph algorithms (PageRank,APSP,Clustering) in terms of iterative dtabase queries. This chould bpreferably be developed and tested on scalable parallel database systems, such as Impala or Vectorwise-on-Hadoop. A second question would be whether a language like Green Marl could be automatically translated to such query plans, i.e. the task would be to create a graph query compiler.
 
Graph Programming frameworks like Giraph and Graphlab and associated languages such as Green Marl currently heither have non-scalable or relatively slow back-ends. This project aims to see if high-performance database query processing techniques could be used to power a graph programming framework. A frst question would be to code and test a number of graph algorithms (PageRank,APSP,Clustering) in terms of iterative dtabase queries. This chould bpreferably be developed and tested on scalable parallel database systems, such as Impala or Vectorwise-on-Hadoop. A second question would be whether a language like Green Marl could be automatically translated to such query plans, i.e. the task would be to create a graph query compiler.

Latest revision as of 11:27, 4 December 2014


About Graph An

Description

Graph Analysis on large datasets is a hot topic in Big data processing.

Graph Programming frameworks like Giraph and Graphlab and associated languages such as Green Marl currently heither have non-scalable or relatively slow back-ends. This project aims to see if high-performance database query processing techniques could be used to power a graph programming framework. A frst question would be to code and test a number of graph algorithms (PageRank,APSP,Clustering) in terms of iterative dtabase queries. This chould bpreferably be developed and tested on scalable parallel database systems, such as Impala or Vectorwise-on-Hadoop. A second question would be whether a language like Green Marl could be automatically translated to such query plans, i.e. the task would be to create a graph query compiler.