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Spatial Data Dynamic Balancing Distribution Method Based on the Minimum Spatial Proximity for Parallel Spatial Database

Yan Zhou, Qing Zhu, Yeting Zhang
2011 Journal of Software  
decomposing Hilbert space-filling curve code to allocate approximately even spatial data volume to parallel nodes in distributed network.  ...  Spatial data balancing distribution can evidently improve the performance of parallel spatial database in shared nothing parallel architecture.  ...  Data balancing distribution is one of the most important factors to improve the performance of parallel spatial database under shared nothing parallel architecture [2] .  ... 
doi:10.4304/jsw.6.7.1337-1344 fatcat:dz4esgdw7fhbndly3p34chxvze

Transaction Management in Distributed Scheduling Environment for High Performance Database Applications [chapter]

Sushant Goel, Hema Sharda, David Taniar
2003 Lecture Notes in Computer Science  
Though high performance database systems like distributed and parallel database systems distribute data to different sites, most of the systems tend to nominate a single node to manage all relevant information  ...  The problem of synchronisation increases many folds, as the nature of application becomes distributed or volume of data approaches to terabyte sizes.  ...  Introduction Continuously growing volume of data and expanding business needs have justified the needs of high performance database systems like Distributed and Parallel Database Systems (PDS) [2, 8,  ... 
doi:10.1007/978-3-540-24604-6_12 fatcat:ruq4cm34gjbkbkzrkxwucfkula

Just-In-Time Data Distribution for Analytical Query Processing [chapter]

Milena Ivanova, Martin Kersten, Fabian Groffen
2012 Lecture Notes in Computer Science  
In this paper, we explore an alternative approach that starts from a master node in control of the complete database, and a variable number of worker nodes for delegated query processing.  ...  Our experiments show that the proposed adaptive distributed architecture is a viable and flexible alternative for small scale MapReduce-type of settings.  ...  This work was partially supported by the Dutch research programme COMMIT and the European project TELEIOS.  ... 
doi:10.1007/978-3-642-33074-2_16 fatcat:aawgczggwvc2dadn6wo62pklma

Secure two-party k-means clustering

Paul Bunn, Rafail Ostrovsky
2007 Proceedings of the 14th ACM conference on Computer and communications security - CCS '07  
of software,Journal of Parallel and Distributed Computing, Volume 66 , ,Issue 9 (September 2006),Special issue: Security in grid and distributed systems,Pages: 1116 - 1128 ,Year of Publication: 2006  ...  "Query optimization distributed networks of autonomous database systems" Journal in Transactions on Database Systems. pp. 561-565. Volume 31, Issue 2 june 2006.  ... 
doi:10.1145/1315245.1315306 dblp:conf/ccs/BunnO07 fatcat:hv3vdom7kfhp7llmhvsvmdierm

Comparative Study of Apriori Algorithms for Parallel Mining of Frequent Itemsets

Avani M.Sakhapara, Bharathi H. N.
2014 International Journal of Computer Applications  
The parallel frequent itemsets mining algorithms addresses the issue of distributing the candidates among processors such that their counting and creation is effectively parallelized.  ...  Thus majority of parallel apriori algorithms focus on parallelizing the process of frequent item set discovery.  ...  PARALLEL APRIORI ALGORITHMS 4.1 Count Distribution Algorithm [10] Each processor generates the partial support of all candidate itemsets from its local database partition in parallel.  ... 
doi:10.5120/15594-4337 fatcat:flcabqaxenapvgyuw6rvrecfxy

Benchmarking SciDB data import on HPC systems

Siddharth Samsi, Laura Brattain, William Arcand, David Bestor, Bill Bergeron, Chansup Byun, Vijay Gadepally, Matthew Hubbell, Michael Jones, Anna Klein, Peter Michaleas, Lauren Milechin (+6 others)
2016 2016 IEEE High Performance Extreme Computing Conference (HPEC)  
This performance was achieved by using parallel inserts, a in-database merging of arrays as well as supercomputing techniques, such as distributed arrays and single-program-multiple-data programming.  ...  It is designed to be massively parallel and can run on commodity hardware in a high performance computing (HPC) environment.  ...  We would also like to thank David Martinez (Associate Division Head, Cyber Security and Information Sciences, MIT Lincoln Laboratory) and Dr.  ... 
doi:10.1109/hpec.2016.7761617 dblp:conf/hpec/SamsiBABBBGHJKM16 fatcat:swikmonoura5tkgwowidvutaqe

Data Dissemination and Parallel Processing Techniques Research Based on Massively Parallel Processing

Qiao SUN, Bu-qiao DENG, Xiao-bo NIE, Hui-yuan MA, Jia-song SUN
2017 DEStech Transactions on Computer Science and Engineering  
In the current distributed database system architecture enterprise-class, the massively parallel processing architecture is used frequently.  ...  Experimental results show that the proposed MPP data distribution and parallel processing scheme can support large volume of data processing, ensuring data consistency in the premise of improving query  ...  Figure 2 . 2 Basic Schematic Diagram of Distributed System Resource Management. Figure 3 . 3 Distributed MPP Data Distribution and Parallel Processing.  ... 
doi:10.12783/dtcse/wcne2016/5115 fatcat:yixho4cqera5tdq43brejx7hwq

Evolution of data management systems

Abdelkader Hameurlain
2012 Proceedings of the 14th International Conference on Information Integration and Web-based Applications & Services - IIWAS '12  
Valduriez, : "Parallel Database Systems: Open Problems and News Issues", in: Distributed and Parallel DB, Vol. 1, pp. 137--165, Kluwer Academic, (1993) • H.  ...  ., "Query Processing in Parallel Relational Database Systems", IEEE CS Press, 1994 • D. Taniar et al., "High Performance Parallel DB Processing and Grid Databases", Ed. Wiley, 2008 • A.  ... 
doi:10.1145/2428736.2428738 dblp:conf/iiwas/Hameurlain12 fatcat:ayoxhgsd7nfcrk6pbph3s6psy4

Design of Electric Energy Acquisition System on Hadoop

Yi Wu, Jianjun Zhou
2015 International Journal of Grid and Distributed Computing  
and parallel processed from the nodes.  ...  Recently, the environment of high performance distributed network parallel computation, represented by Hadoop architecture, is deployed step by step [4] .  ...  Under the architecture of Hadoop, Map Reduce programming pattern is a distributed parallel computing model on processing large volume data. It applies to the parallel computation of large scale data.  ... 
doi:10.14257/ijgdc.2015.8.5.04 fatcat:q4mcwengfzbobh63bkuk3am3ca

A Review on Need of MapReduce in Big Data Application

2015 International Journal of Science and Research (IJSR)  
The growing power of Big Data assume the significance of analyzing huge amount of data with a frequent and quick rate of growth and change in databases and data warehouses.  ...  The main fact of data analytics is scalability, due to the huge volume of data that need to be extracted, processed, and analyzed in a sensible fashion.  ...  Hadoop consist of 1) Hadoop Distributed File System (HDFS) and 2) Hadoop MapReduce: a software framework [4].  ... 
doi:10.21275/v4i12.nov152248 fatcat:y7qhomvdqnhw3hkli2iwfryt6i

Special issue on Data-driven Science

Tanu Malik
2021 Distributed and parallel databases  
The issue advances the state of statistical and scientific database management by providing data-driven guidance for knowledge discovery and analytics.  ...  This special issue on data-driven science explores the issues of data volume, data variety, and data velocity through a single lens.  ... 
doi:10.1007/s10619-021-07332-3 fatcat:bkicg7cfunfpbjcsa53a5ig3ji

The Design of an ODMG Compatible Parallel Object Database Server [chapter]

Paul Watson
1999 Lecture Notes in Computer Science  
Therefore we present an overview of the design of parallel relational database servers and investigate how their design choices could be adopted for a parallel object database server.  ...  We believe that it is important to build on experience gained in the design and usage of parallel relational database systems over the last ten years, as much is also relevant to parallel object database  ...  We would like to thank Francisco Pereira and Jim Smith (Newcastle), and Norman Paton (Manchester) for discussions which have contributed to the contents of this paper.  ... 
doi:10.1007/10703040_45 fatcat:qamywn7wdvbd5dwff4wopqauu4

Mining of Association Rules on Large Database Using Distributed and Parallel Computing

Anil Vasoya, Nitin Koli
2016 Procedia Computer Science  
Hence hybrid architecture is proposed which consists of integrated distributed and parallel computing concept.  ...  Later drawback of the Apriori algorithm is overcome by many algorithms / parallel algorithms (model) but those are also inefficient to find frequent item sets from large database with less time and with  ...  In such scenarios the task can be achieved in less time by using Distributed and Parallel Computing approaches. Fig. 2 shows Methodology used in proposed system and its key concerns.  ... 
doi:10.1016/j.procs.2016.03.029 fatcat:fo3ayddv45flrlqr7o3pg4guei

Power of Big Data System for Storing and Processing Huge Data

S. Natarajan, S. Rajarajesware, Suresh Ram R
2019 International Journal of Scientific Research in Science and Technology  
This challenging task of storing and managing huge volume of data is achieved in Big Data Systems.  ...  Nowadays the volume of data used by the people throughout the world is increasing enormously and exponentially.  ...  It has the scalability and programming flexibility of distributed MapReduce- like platforms with query optimization capabilities as in parallel databases.  ... 
doi:10.32628/ijsrst196422 fatcat:golivpxwpfb5licqfnpstrz67q

Review of Apriori Based Algorithms on MapReduce Framework [article]

Sudhakar Singh, Rakhi Garg, P. K. Mishra
2017 arXiv   pre-print
The problems with most of the distributed framework are overheads of managing distributed system and lack of high level parallel programming language.  ...  Now days many algorithms have been proposed on parallel and distributed platforms to enhance the performance of Apriori algorithm.  ...  CD and DD algorithms are categorized under data parallelism and task parallelism while candidate distribution algorithm is hybrid of data parallelism and task parallelism [38] .  ... 
arXiv:1702.06284v1 fatcat:khpoq35xcfhzfc4v7mm362bbyq
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