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STATIC AND DYNAMIC BNP PARALLEL SCHEDULING ALGORITHMS FOR DISTRIBUTED DATABASE
2002
INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY
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The objective of this study is to analyzethe performance of static (HLFET) and dynamic (DLS) BNPparallel scheduling algorithm for allocating the tasks ofdistributed database over number of processors. ...
Parallel processing is usedto solve the complex problems that require vast amount ofprocessing time. Task scheduling is one of the major problemsof parallel processing. ...
and parallel processing (HLFET & DLS), when tasks in distributed environment are scheduled over three processors. ...
doi:10.24297/ijct.v1i1.2601
fatcat:d2ubgnmc5jdl3ofyxrr3xo7t3e
Big Data Management in the Cloud: Evolution or Crossroad?
[chapter]
2016
Communications in Computer and Information Science
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In this perspective, data management based on parallel and cloud (e.g. ...
MapReduce) systems are overviewed, and compared by relying on meeting software requirements (e.g. data independence, software reuse), high performance, scalability, elasticity, and data availability. ...
Parallel database systems have been developed for applications processing a large volume of data. Their main objectives are to obtain high performance and resource availability. ...
doi:10.1007/978-3-319-34099-9_2
fatcat:nj57rxfi5bhlxfwr7risc3tpcq
Superposition Enhanced Nested Sampling
2014
Physical Review X
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We also introduce a novel parallelization algorithm for nested sampling. ...
The theoretical analysis of many problems in physics, astronomy and applied mathematics requires an efficient numerical exploration of multimodal parameter spaces that exhibit broken ergodicity. ...
The key insight of nested sampling is that the volumes, Ω E≤ER , normalised by the total phase space volume, are distributed according to the Beta distribution, Beta(K − R + 1, R) [46] . ...
doi:10.1103/physrevx.4.031034
fatcat:b6a6caz355ahdint6b2raymssu
Main Memory Database Systems
2017
Foundations and Trends in Databases
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Editorial Scope Topics Foundations and Trends R in Databases covers a breadth of topics relating to the management of large volumes of data. ...
Kersten, and Hans Oerlemans. PRISMA
Database Machine: A Distributed, Main-Memory Approach. ...
doi:10.1561/1900000058
fatcat:j7dpcbszkbdqdarwrxdt3dv5ja
A Combined Horizontal Parallel Apriori Algorithm and Adaptive Frequent Pattern Growth Algorithm for Big Data Mining
2019
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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With the rapid increase in the database size, parallel and distributed computing systems can yield better benefits in the data mining applications. ...
In this paper, a combined Horizontal Parallel-Apriori (HP-Apriori) and Adaptive Frequent Pattern (FP) Growth algorithm is proposed to divide the database both horizontally and vertically into four sub-processes ...
The Data Distribution algorithm divides the entire database and assigns the candidate itemsets to various processors. ...
doi:10.35940/ijitee.b1133.1292s219
fatcat:eicabawvgrbvfgmhaxpjbwu2ni
Implementation and Evaluation of mpiBLAST-PIO on HPC Cluster
2014
International Journal of Computer Applications
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In mpiBLAST, the master process distributes the database fragments among worker nodes to compute the sequence search in parallel. ...
As merging and writing of the results is done sequentially by the master process, it would create performance bottleneck with increasing number of processors and varying database sizes. ...
As BLAST is both computationally intensive and parallelizes well, many parallel and distributed approaches of parallelizing BLAST have been proposed. ...
doi:10.5120/17131-7735
fatcat:trriihl3erainbw7ssxhcwdcyy
Parallel and Distributed Data Mining: An Introduction
[chapter]
2000
Lecture Notes in Computer Science
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This chapter presents a survey on large-scale parallel and distributed data mining algorithms and systems, serving as an introduction to the rest of this volume. ...
The explosive growth in data collection in business and scientific fields has literally forced upon us the need to analyze and mine useful knowledge from it. ...
We hope that this volume, representing the state-of-the-art in parallel and distributed mining methods, will be successful in bringing to surface the requirement and challenges in large-scale parallel ...
doi:10.1007/3-540-46502-2_1
fatcat:3mmcofbadbas7f7r5y5opxxwdy
Distributed Database Kriging for Adaptive Sampling (D2KAS)
2015
Computer Physics Communications
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We present an adaptive sampling method supplemented by a distributed database and a prediction method for multiscale simulations using the Heterogeneous Multiscale Method. ...
The cloud database Redis provides the plain table lookup, and with locality aware hashing we gather input data for our prediction scheme. ...
Rivera and E. Cieren for fruitful discussions at the beginning of this project as well as J. Mohd-Yusof for support with the CoMD code. ...
doi:10.1016/j.cpc.2015.03.006
fatcat:c46psndt4vc45akgxq6odjnory
Interactive Big Data Analytics Platform for Healthcare and Clinical Services
2018
Global Journal of Engineering Sciences
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The next step of the testing of the BDA platform will be to distribute and index the data to ten billion patient data rows across the database nodes, and then test the performance using the established ...
Afterwards, the study plan is to distribute the data across 100 nodes; Hadoop's HDFS and HBase are theoretically supposed to scale and function better with more nodes [14, 15] . ...
nodes across the computing cluster, and (2) high-throughput data processing via a batch processing framework and the Hadoop Distributed File System (HDFS) [38, [40] [41] [42] [43] . ...
doi:10.33552/gjes.2018.01.000502
fatcat:biiz2qnx4vckrcrmv4gcn5ydru
Scalability and Fuzzy Systems: What Parallelization Can Do
[chapter]
2013
Studies in Computational Intelligence
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In this paper, we discuss how the parallelization of fuzzy algorithms is crucial to tackle the problem of scalability and optimal performance in the context of database mining. ...
Fuzzy) Database management systems aim at providing tools for data storage and querying. ...
model (FRDBM) and into of the objectoriented database model (FOODBM) [33] [38] . ...
doi:10.1007/978-3-319-00954-4_13
fatcat:okkfilr5njdmrbijkdeyryuqca
A NOVEL TECHNIQUE IN NoSQL DATA EXTRACTION
2014
International journal of research - granthaalayah
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Results:Large-scale data processing (parallel processing over distributed systems); Embedded IR (basic machine-to-machine information look-up & retrieval); Exploratory analytics on semi-structured data ...
Conclusions:This study report motivation to provide an independent understanding of the strengths and weaknesses of various NoSQL database approaches to supporting applications that process huge volumes ...
The exponential growth of the volume of data generated by users, systems and sensors, further accelerated by the concentration of large part of this volume on big distributed systems like Amazon, Google ...
doi:10.29121/granthaalayah.v1.i1.2014.3086
fatcat:drzvgh6tcvflda52scih2dpqbi
Tagit
2017
Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis on - SC '17
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Data services such as search, discovery, and management in scalable distributed environments have traditionally been decoupled from the underlying file systems, and are often deployed using external databases ...
CCS CONCEPTS • Software and its engineering → File systems management; • Information systems → Distributed storage; ...
[38] . ...
doi:10.1145/3126908.3126929
dblp:conf/sc/SimKVVLB17
fatcat:db3r5tzh6veexgbr52qagli3le
Review of Apriori Based Algorithms on MapReduce Framework
[article]
2017
arXiv
pre-print
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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
A Survey of Data Mining Activities in Distributed Systems
2021
Asian Journal of Research in Computer Science
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Managing massive amounts of data and processing them with limited resources is difficult. Large volumes of data, for instance, are swiftly generated and stored in many locations. ...
distributed data mining methods, such as distributed common item-set mining, distributed frequent sequence mining, technical difficulties with distributed systems, distributed clustering, as well as privacy-protection ...
A large volume of data was given through the widespread usage of computers and advances in database technology [49] . ...
doi:10.9734/ajrcos/2021/v11i430267
fatcat:t4wcvxu2czd45imagjenzsvcqi
Turning points in systems architecture
1999
IBM Systems Journal
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processing services, databases, and networking. ...
An alternative approach for implementing a multiple processor complex is utilized in the S/390 Parallel Sysplex Cluster, which is the topic for the entire issue of Volume 36, Number 2, 1997 of the IBM ...
He was the recipient of IBM Outstanding Innovation Awards for his work on Enterprise Systems Architecture/370 and System/390 Parallel Sysplex Design. ...
doi:10.1147/sj.382.0335
fatcat:ldcu6dmos5ak3krkw4o5wtphgm
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