This paper designs and develops a framework on a distributed computing platform for massive multi-source spatial data using a column-oriented database(HBase).This platform consists of four layers including ETL(extract...This paper designs and develops a framework on a distributed computing platform for massive multi-source spatial data using a column-oriented database(HBase).This platform consists of four layers including ETL(extraction transformation loading) tier,data processing tier,data storage tier and data display tier,achieving long-term store,real-time analysis and inquiry for massive data.Finally,a real dataset cluster is simulated,which are made up of 39 nodes including 2 master nodes and 37 data nodes,and performing function tests of data importing module and real-time query module,and performance tests of HDFS's I/O,the MapReduce cluster,batch-loading and real-time query of massive data.The test results indicate that this platform achieves high performance in terms of response time and linear scalability.展开更多
The smart grid has caught great attentions in recent years, which is poised to transform a centralized, producer-controlled network to a decentralized, consumer- interactive network that＇s supported by fine-grained m...The smart grid has caught great attentions in recent years, which is poised to transform a centralized, producer-controlled network to a decentralized, consumer- interactive network that＇s supported by fine-grained monitoring. Large-scale WSNs （Wireless Sensor Networks） have been considered one of the very promising technologies to support the implementation of smart grid. WSNs are applied in almost every aspect of smart grid, including power generation, power transmission, power distribution, power utilization and power dispatch, and the data query processing of ＇WSNs in power grid＇ become an hotspot issue due to the amount of data of power grid is very large and the requirement of response time is very high. To meet the demands, top-k query processing is a good choice, which performs the cooperative query by aggregating the database objects＇ degree of match for each different query predicate and returning the best k matching objects. In this paper, a framework that can effectively apply top-k query to wireless sensor network in smart grid is proposed, which is based on the cluster-topology sensor network. In the new method, local indices are used to optimize the necessary query routing and process intermediate results inside the cluster to cut down the data traffic, and the hierarchical join query is executed based on the local results.Besides, top-k query results are verified by the clean-up process, and two schemes are taken to deal with the problem of node＇s dynamicity, which further reduce communication cost. Case studies and experimental results show that our algorithm has outperformed the current existing one with higher quality results and better efficiently.展开更多
基金Supported by the National Science and Technology Support Project(No.2012BAH01F02)from Ministry of Science and Technology of Chinathe Director Fund(No.IS201116002)from Institute of Seismology,CEA
文摘This paper designs and develops a framework on a distributed computing platform for massive multi-source spatial data using a column-oriented database(HBase).This platform consists of four layers including ETL(extraction transformation loading) tier,data processing tier,data storage tier and data display tier,achieving long-term store,real-time analysis and inquiry for massive data.Finally,a real dataset cluster is simulated,which are made up of 39 nodes including 2 master nodes and 37 data nodes,and performing function tests of data importing module and real-time query module,and performance tests of HDFS's I/O,the MapReduce cluster,batch-loading and real-time query of massive data.The test results indicate that this platform achieves high performance in terms of response time and linear scalability.
文摘The smart grid has caught great attentions in recent years, which is poised to transform a centralized, producer-controlled network to a decentralized, consumer- interactive network that＇s supported by fine-grained monitoring. Large-scale WSNs （Wireless Sensor Networks） have been considered one of the very promising technologies to support the implementation of smart grid. WSNs are applied in almost every aspect of smart grid, including power generation, power transmission, power distribution, power utilization and power dispatch, and the data query processing of ＇WSNs in power grid＇ become an hotspot issue due to the amount of data of power grid is very large and the requirement of response time is very high. To meet the demands, top-k query processing is a good choice, which performs the cooperative query by aggregating the database objects＇ degree of match for each different query predicate and returning the best k matching objects. In this paper, a framework that can effectively apply top-k query to wireless sensor network in smart grid is proposed, which is based on the cluster-topology sensor network. In the new method, local indices are used to optimize the necessary query routing and process intermediate results inside the cluster to cut down the data traffic, and the hierarchical join query is executed based on the local results.Besides, top-k query results are verified by the clean-up process, and two schemes are taken to deal with the problem of node＇s dynamicity, which further reduce communication cost. Case studies and experimental results show that our algorithm has outperformed the current existing one with higher quality results and better efficiently.
