By Abdelkader Hameurlain, Josef Küng, Roland Wagner, Ladjel Bellatreche, Mukesh Mohania
The LNCS magazine Transactions on Large-Scale facts- and Knowledge-Centered platforms makes a speciality of info administration, wisdom discovery and data processing, that are middle and sizzling themes in desktop technology. because the Nineties, the web has turn into the most motive force in the back of program improvement in all domain names. a rise within the call for for source sharing throughout diversified websites hooked up via networks has resulted in an evolution of information- and knowledge-management structures from centralized platforms to decentralized platforms allowing large-scale dispensed purposes offering excessive scalability. present decentralized structures nonetheless specialize in facts and information as their major source. Feasibility of those platforms is predicated essentially on P2P (peer-to-peer) recommendations and the aid of agent platforms with scaling and decentralized regulate. Synergy among grids, P2P platforms and agent applied sciences is the main to info- and knowledge-centered structures in large-scale environments. This, the seventeenth factor of Transactions on Large-Scale facts- and Knowledge-Centered structures, comprises prolonged and revised types of 5 papers, chosen from the 24 complete and eight brief papers provided on the fifteenth foreign convention on info Warehousing and data Discovery, DaWaK 2013, held in Prague, The Czech Republic, in August 2013. Of the 5 papers, hide information warehousing elements relating to question processing optimization in complex systems, particularly Map lessen and parallel databases, and 3 disguise wisdom discovery, particularly the causal community inference challenge, dimensionality aid, and the quality-of-pattern-mining task.
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Additional resources for Transactions on Large-Scale Data- and Knowledge-Centered Systems XVII: Selected Papers from DaWaK 2013
This idea is partially inspired by the pipelined MapReduce . In the uncoupled version, a server is setup in each node which is responsible for receiving data from map tasks as shown in Fig. 5. When map tasks are generating intermediate output data, they will work as clients and push data to the servers. So there is no need to start reduce tasks before map tasks ﬁnish. – Partial data backup. Each node has a server for receiving data from map tasks. If some servers go wrong, the data pushed by map tasks will be lost.
6 Conclusion and Future Work Although there are a variety of optimizations to improve the localization rate of data in the MapReduce model, data transfer is inevitable. Reduce tasks need to pull intermediate data from map tasks, which will decrease the execution eﬃciency of jobs. In , it shows that shuﬄe can take about 33 % of the operating time in reduce tasks. Meanwhile, data transfer eﬃciency is very low, which is a system bottleneck . Since the MapReduce model is designed to be fault-tolerant, it’s costly to store the replicated ﬁle especially when the result is huge.
Master control Control data Fault Tolerant Slave Slave Data Transfer Fault Tolerant Data Compress Data Transfer Fault Tolerant Data Compress Fig. 6. 1 Master Control Module The master control module lies in the master node, responsible for monitoring and scheduling tasks, and coordinating other modules. This module should run all through the MapReduce job. Its functionalities are as follows. – Create the mapping relationship between reduce tasks and nodes to make sure that reduce tasks will be executed on the node where data stored.