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Scalable, distributed database system architecture is composed of three tiers: web service client (front-end), web service and broker (middleware), and agents and a collection of databases (back-end).
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Many scientific fields routinely generate huge datasets. In many cases, these datasets are not static but rapidly grow in size. Handling these types of datasets, as well as allowing sophisticated queries necessitates efficient distributed database systems that allow geographically dispersed users to access resources and to use machines simultaneous...
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... this paper we discuss data scalability in the distributed database system with the software and hardware architecture, using a collection of more 17 million 3D chemical structures. Figure 1 shows a broad 3-tier architecture view for our scalable distributed database system built on multicore systems. The scalable, distributed database system architecture is composed of three tiers -the web service client (front-end), a web service and message service system (middleware), agents and a collection of databases (back-end). ...
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... Figure 1, the database agent (DBA) is used as a proxy for database server (PostgreSQL). The DBA accepts query requests from front-end users via middleware, translates the requests to be understood by database server and retrieves the results from the database server. ...
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... reduce the latency with increasing data locality in using the data clustering method, we combined the data clustering method with the horizontal partitioning method to maximize the use of multicore with independent threads of execution by concurrently running multiple databases, one on each thread associated with each core in a multicore server. Figure 9, 10 and 11 show the experimental results with data clustering, horizontal partitioning, and the combination of both methods respectively. Our experimental results show there is a data locality vs. latency tradeoff. ...
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... experimental results show there is a data locality vs. latency tradeoff. Compare the query processing time in Figure 9 with that in Figure 10, with Table 3. Also while the figures show that the query processing cost increases as the distance R increases, the cost becomes a smaller portion of overall cost than the transit cost in the distribution of data over multicore servers, with increasing data locality and decreasing query processing cost as shown in Figure 11. ...
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... the query processing time in Figure 9 with that in Figure 10, with Table 3. Also while the figures show that the query processing cost increases as the distance R increases, the cost becomes a smaller portion of overall cost than the transit cost in the distribution of data over multicore servers, with increasing data locality and decreasing query processing cost as shown in Figure 11. This result shows our distributed database system is scalable with the partitioning of database over multicore servers by data clustering method for increasing data locality, and with multithreads of executions associated with multiple databases split by horizontal partitioning in each cluster for decreasing query processing cost, and thus the system improves overall performance as well as query processing performance. ...
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Citations
... In many cases, these datasets are not static but rapidly grow in size. Patrick in [12] proposed a query processing based on compressed intermediates. ...
This paper proposed an enhanced Top-k query processing in a real time distributed database system. The system employs a Particle Swarm Optimizer (PSO) based Geno-Generative iSWAN Model technique that enhances and allows multi-task concurrent query processing in a real time co-simulation data acquisition platform and as part of refinement to an existing Top-k query processing Technique. In this paper, the proposed system is compared for efficiency with the Top-K Query Algorithm, which is emerging as an alternative to more conventional technique for real time query processing in distributed databases. Dynamic simulations were performed with a real time small testbed comprising of physical and non-physical devices to test and evaluate the performance and efficiency of the two systems. Considering the estimated and expected temperatures, the result of simulation study proves that the Intelligent Swarming Network (iSWAN) Geno-Generative Model is more preferred over Top-K Query Algorithm due to its 70% accuracy over the Top-K Model, which reported a lower accuracy level of 40%.
