A Scalable and Skew-insensitive Algorithm for Join Operations using Map/Reduce Model

In this paper we consider the implementation of the Partition Based Spatial Merge Join [13] provided by SpatialHadoop, de- noted as Sjmr, which is the only spatial join algorithm

The systems mentioned in this last subsection appear to be using a common technique in order to handle iteration in streaming data or queries: they employ some form of tagging in

In particu- lar, we focus on the decomposition of the query posed by the user, which is given in the form of a query graph, into star subqueries and propose a two-phase,

To this end, an additional map-phase (without any intermediate shuffle or reduce phase) is initialized with the previously computed mappings as input. Since these mappings reside in

L’algorithme MR permet de traiter en parall` ele une quantit´ e importante de donn´ ees en divisant les donn´ ees en blocs, puis en appliquant une fonction sur chaque bloc de donn´

Ressource Manager : Scheduler (planifie les taches) et Application Manager (ordonnancer des taches, s’occupe de l’ex´ ecution, n´ egocie avec les Application Masters). Node Manager

On souhaite r´ ealiser un programme map/reduce avec hadoop fournissant pour chacune de ces villes une liste de coordonn´ ees GPS (celles de chacun des bureaux de poste associ´ es). o`

Our study shows that in the case where the size of the alpha- bet for the NFA’s is large and we have a large number of reducers available, an algorithm that distributes the