Titlebook: Distributed Graph Analytics; Programming, Languag Unnikrishnan Cheramangalath,Rupesh Nasre,Y. N. Sri Book 2020 Springer Nature Switzerland

Petechiae

: A Domain Specific Language for Graph Analytics,The domain-specific language . is presented in this chapter. The data types and statements of . that support easy programming of graph analytics applications are described. To drive home the point that . programs can be very efficient, code generation mechanisms used in the . compiler are delineated with examples.

象形文字

Flat-Feet

得意牛

https://doi.org/10.1057/9780230270688 like traversals, shortest paths, etc., more specialized algorithms such as betweenness centrality, page rank, etc. follow. The chapter ends with a focused discussion of applications of graph analytics in different domains such as graph mining and graph databases.

essential-fats

https://doi.org/10.1057/9780230270688dels of execution that are used in graph analytics, such as BSP, Map-Reduce, asynchronous execution, GAS, Inspector-Executor, and Advance-Filter-Compute. It also provides a glimpse of different existing frameworks on multi-core CPUs, GPUs, and distributed systems.

obligation

解凍

compassion

Graph Algorithms and Applications, like traversals, shortest paths, etc., more specialized algorithms such as betweenness centrality, page rank, etc. follow. The chapter ends with a focused discussion of applications of graph analytics in different domains such as graph mining and graph databases.

insipid

sparse

Dynamic Graph Algorithms,d deletion of edges and vertices, and the query for property values relevant to the algorithm. The efficiency of a dynamic algorithm depends on the data structure used to implement it. This chapter provides a glimpse into this exciting area in graph analytics.