Ashokan ashwathy (8 risultati)

Condizione: New. pp. 104.

Condizione: New.

Taschenbuch. Condizione: Neu. Hotspots Of Topics From Time Stamped Document | The Mapreduce Way | Ashwathy Ashokan (u. a.) | Taschenbuch | 104 S. | Englisch | 2013 | LAP LAMBERT Academic Publishing | EAN 9783659479106 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. …

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Hotspots of a word/topic are time periods with a burst of activities in a time stamped document set. Identifying and analyzing hot spots of topics has been an important area of research. Finding hot spots of topics requires processing of contents of documents which is often time consuming. In this thesis, we explore MapReduce style algorithms for computing hot spots of topics. MapReduce is a distributed parallel programming model and an associated implementation for processing and analyzing large datasets. User specifies a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model and this thesis explores the feasibility of implementing the hotspot algorithm using MapReduce. We design map and reduce functions appropriate for preprocessing of documents, and the hot spot computation. We implement the functions in Hadoop (a MapReduce framework for Apache Foundation) and conduct several experiments to assess the benefits of MapReduce style implementation versus simple sequential implementation. 104 pp. Englisch.…

Condizione: New. Print on Demand pp. 104 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 104.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Hotspots of a word/topic are time periods with a burst of activities in a time stamped document set. Identifying and analyzing hot spots of topics has been an important area of research. Finding hot spots of topics requires processing of contents of documents which is often time consuming. In this thesis, we explore MapReduce style algorithms for computing hot spots of topics. MapReduce is a distributed parallel programming model and an associated implementation for processing and analyzing large datasets. User specifies a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model and this thesis explores the feasibility of implementing the hotspot algorithm using MapReduce. We design map and reduce functions appropriate for preprocessing of documents, and the hot spot computation. We implement the functions in Hadoop (a MapReduce framework for Apache Foundation) and conduct several experiments to assess the benefits of MapReduce style implementation versus simple sequential implementation.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.…

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Hotspots of a word/topic are time periods with a burst of activities in a time stamped document set. Identifying and analyzing hot spots of topics has been an important area of research. Finding hot spots of topics requires processing of contents of documents which is often time consuming. In this thesis, we explore MapReduce style algorithms for computing hot spots of topics. MapReduce is a distributed parallel programming model and an associated implementation for processing and analyzing large datasets. User specifies a map function that processes a key/value pair to generate a set of intermediate key/value pairs, and a reduce function that merges all intermediate values associated with the same intermediate key. Many real world tasks are expressible in this model and this thesis explores the feasibility of implementing the hotspot algorithm using MapReduce. We design map and reduce functions appropriate for preprocessing of documents, and the hot spot computation. We implement the functions in Hadoop (a MapReduce framework for Apache Foundation) and conduct several experiments to assess the benefits of MapReduce style implementation versus simple sequential implementation.…