Map-Reduce-Join-Locate: a Data Processing Framework for Decreasing the Processing Cost on Large Data

Big Data Processing with Hadoop Map-reduce

The amount of data in our world has been exploding, and analyzing large data sets—so-called big data—will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus. The increasing volume and detail of information captured by enterprises, the rise of multimedia, social media, and the Internet of Things will fuel exponential growth in data ...

Multilevel Data Clustering for Spatial Join Processing

Thc spatial join is a cuminon spalid query typc that requires n high processing cost due lo thc largc volume of spatial detil and the computntion-iiitcnsivc spatial opcrntions [ I , 12, 13, 141. A spalial join consists in testing cvcry two possible pairs of objects hdoinging to two spatia? daln sets against n spalial predicatc [ 1 I ] . This prcdicnte might be flveriiip, cross, wilhiit distaric...

Performance Enhancement of Big Data Processing in Hadoop Map/reduce

P2P Join Query Processing over Data Streams

De nombreuses applications distribuées partagent la même nécessité de traiter des flux de données de façon continue, par ex. la surveillance de réseau ou la gestion de réseaux de capteurs. Dans ce contexte, un problème important et difficile concerne le traitement de requêtes continues de jointure qui nécessite de maintenir une fenêtre glissante sur les données la plus grande possible, afin de ...

Data processing on a large scale

Within the last decade the high costs and complexity of Next Generation Sequencing (NGS) data organization put pressure on NGS data centres to organize convenient IT service infrastructures for automatic data management, processing and analyses. Our market analysis showed that existing applications processing NGS data were insufficiently documented, not extensible or strongly dependent on the u...