In this paper we investigate the problem of processing multiway interval joins on map-reduce platform. We look at join queries formed by interval predicates as defined by Allen’s interval algebra. These predicates can be classified in two groups: colocation based predicates and sequence based predicates. A colocation predicate requires two intervals to share at least one common point while a se...

We present a novel query language for large-scale analysis of XML data on a map-reduce environment, called MRQL, that is expressive enough to capture most common data analysis tasks and at the same time is amenable to optimization. Our evaluation plans are constructed using a small number of higher-order physical operators that are directly implementable on existing map-reduce systems, such as ...

The Affinity Propagation (AP) is a clustering algorithm that does not require pre-set K cluster numbers. We improve the original AP to Map/Reduce Affinity Propagation (MRAP) implemented in Hadoop, a distribute cloud environment. The architecture of MRAP is divided to multiple mappers and one reducer in Hadoop. In the experiments, we compare the clustering result of the proposed MRAP with the K-...

Map Reduce has gained remarkable significance as a prominent parallel data processing tool in the research community, academia and industry with the spurt in volume of data that is to be analyzed. Map Reduce is used in different applications such as data mining, data analytics where massive data analysis is required, but still it is constantly being explored on different parameters such as perf...

MapReduce is a parallel programming model and an associated implementation introduced by Google. In the programming model, a user specifies the computation by two functions, Map and Reduce. The underlying MapReduce library automatically parallelizes the computation, and handles complicated issues like data distribution, load balancing and fault tolerance. Massive input, spread across many machi...

We consider a class of workflows, which we call generalized map and reduce workflows (GMRWs), where input data sets are processed by an acyclic graph of map and reduce functions to produce output results. We show how data provenance (also sometimes called lineage) can be captured for map and reduce functions transparently. The captured provenance can then be used to support backward tracing (fi...