Multi-keyword Parallel Search Algorithm for Streaming RDF Data

2018 ◽  
pp. 494-511
Author(s):  
Jian Guan ◽  
Jingbin Wang ◽  
Long Yu
Keyword(s):  
Search Algorithm ◽  
Parallel Search ◽  
Rdf Data

Effective ACPS-Based Rescheduling of Parallel Batch Processing Machines with MapReduce

2014 ◽  
Vol 575 ◽  
pp. 820-824
Author(s):  
Bin Zhang ◽  
Jia Jin Le ◽  
Mei Wang
Keyword(s):  
Big Data ◽  
Parallel Computing ◽  
Ant Colony Algorithm ◽  
Search Algorithm ◽  
Ant Colony ◽  
Search Problem ◽  
Parallel Search ◽  
Good Efficiency ◽  
Heuristic Scheduling ◽  
Low Efficiency

MapReduce is a highly efficient distributed and parallel computing framework, allowing users to readily manage large clusters in parallel computing. For Big data search problem in the distributed computing environment based on MapReduce architecture, in this paper we propose an Ant colony parallel search algorithm (ACPSMR) for Big data. It take advantage of the group intelligence of ant colony algorithm for global parallel search heuristic scheduling capabilities to solve problem of multi-task parallel batch scheduling with low efficiency in the MapReduce. And we extended HDFS design in MapReduce architecture, which make it to achieve effective integration with MapReduce. Then the algorithm can make the best of the scalability, high parallelism of MapReduce. The simulation experiment result shows that, the new algorithm can take advantages of cloud computing to get good efficiency when mining Big data.

scholarly journals Prime Box Parallel Search Algorithm: Searching Dynamic Dictionary in O(lg m) Time

2016 ◽  
Vol 04 (04) ◽  
pp. 134-145
Author(s):  
Amit Pandey ◽  
Berhane Wolde-Gabriel ◽  
Elias Jarso
Keyword(s):  
Search Algorithm ◽  
Parallel Search

Concept and design of exhaustive-parallel search algorithm for Network-on-Chip

2011 ◽  
Author(s):  
Meganathan Deivasigamani ◽  
Shaghayeghsadat Tabatabaei ◽  
Naveed Mustafa ◽  
Hamza Ijaz ◽  
Haris Bin Aslam ◽  
...  
Keyword(s):  
Search Algorithm ◽  
Network On Chip ◽  
Parallel Search ◽  
On Chip

scholarly journals Parallel implementation of the primer search algorithm for loop-mediated isothermal amplification

2021 ◽  
Vol 2131 (2) ◽  
pp. 022004
Author(s):  
L U Akhmetzianova ◽  
T M Davletkulov ◽  
I M Gubaidullin ◽  
A R Islamgulov
Keyword(s):  
Dna Sequence ◽  
Parallel Implementation ◽  
Search Algorithm ◽  
Isothermal Amplification ◽  
Parallel Search ◽  
Specific Primers ◽  
Loop Mediated Isothermal Amplification ◽  
Search Result

Abstract In the paper, the implementation of an algorithm of search for primers in a DNA sequence with a size varying between one nucleotide and multiple million nucleotides was discussed. This analysis was done within the objective of finding a set of six specific primers that are used for a conduction of the loop-mediated isothermal amplification (LAMP). For the fastest search result possible, a parallel search for the primer with the use of the Rabin-Karp Algorithm which enables the search for a primer’s entry in DNA sequence in each thread was proposed. A new software for search for primers was developed using Python with BioPython library which implements the algorithm.

scholarly journals Parallel AND/OR Search for Marginal MAP

2020 ◽  
Vol 34 (06) ◽  
pp. 10226-10234
Author(s):  
Radu Marinescu ◽  
Akihiro Kishimoto ◽  
Adi Botea
Keyword(s):  
Graphical Models ◽  
State Of The Art ◽  
Search Algorithm ◽  
Search Space ◽  
Parallel Search ◽  
Sequential Search ◽  
Limited Memory ◽  
Mixed Inference ◽  
First Time ◽  
Inference Task

Marginal MAP is a difficult mixed inference task for graphical models. Existing state-of-the-art algorithms for solving exactly this task are based on either depth-first or best-first sequential search over an AND/OR search space. In this paper, we explore and evaluate for the first time the power of parallel search for exact Marginal MAP inference. We introduce a new parallel shared-memory recursive best-first AND/OR search algorithm that explores the search space in a best-first manner while operating with limited memory. Subsequently, we develop a complete parallel search scheme that only parallelizes the conditional likelihood computations. We also extend the proposed algorithms into depth-first parallel search schemes. Our experiments on difficult benchmarks demonstrate the effectiveness of the parallel search algorithms against current sequential methods for solving Marginal MAP exactly.

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