scholarly journals Correct and stable sorting for overflow streaming data with a limited storage size and a uniprocessor

2021 ◽  
Vol 7 ◽  
pp. e355
Author(s):  
Suluk Chaikhan ◽  
Suphakant Phimoltares ◽  
Chidchanok Lursinsap
Keyword(s):  
Data Storage ◽  
Time Complexity ◽  
Streaming Data ◽  
Sorting Algorithm ◽  
Storage Size ◽  
The Past ◽  
Sorting Algorithms ◽  
Sorting Time ◽  
Sorting Process ◽  
Numeric Data

Tremendous quantities of numeric data have been generated as streams in various cyber ecosystems. Sorting is one of the most fundamental operations to gain knowledge from data. However, due to size restrictions of data storage which includes storage inside and outside CPU with respect to the massive streaming data sources, data can obviously overflow the storage. Consequently, all classic sorting algorithms of the past are incapable of obtaining a correct sorted sequence because data to be sorted cannot be totally stored in the data storage. This paper proposes a new sorting algorithm called streaming data sort for streaming data on a uniprocessor constrained by a limited storage size and the correctness of the sorted order. Data continuously flow into the storage as consecutive chunks with chunk sizes less than the storage size. A theoretical analysis of the space bound and the time complexity is provided. The sorting time complexity is O (n), where n is the number of incoming data. The space complexity is O (M), where M is the storage size. The experimental results show that streaming data sort can handle a million permuted data by using a storage whose size is set as low as 35% of the data size. This proposed concept can be practically applied to various applications in different fields where the data always overflow the working storage and sorting process is needed.

scholarly journals A Novel Sorting Method for Real and Integer Numbers: An Extension of Counting Sort

2020 ◽  
Vol 8 (6S4) ◽  
pp. 36-39
Keyword(s):  
Data Structures ◽  
Time Complexity ◽  
Sorting Algorithm ◽  
Real Numbers ◽  
Sorting Method ◽  
Sorting Algorithms ◽  
Counting Sort ◽  
Sort Algorithm ◽  
Positive Integers

Sorting is an essential conceptin the study of data structures. There are many sorting algorithms that can sort elements in a given array or list. Counting sort is a sorting algorithm that has the best time complexity. However, the counting sort algorithm only works for positive integers. In this paper, an extension of the counting sort algorithm is proposed that can sort real numbers and integers (both positive and negative).

scholarly journals Design and Analysis of Optimized Stooge Sort Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 1669-1673
Keyword(s):  
Time Complexity ◽  
Large Time ◽  
Sorting Algorithm ◽  
Data Element ◽  
Running Time ◽  
Sorting Algorithms ◽  
Sorting Technique ◽  
Sort Algorithm ◽  
The Given ◽  
Data Sorting

Data sorting hasmany advantages and applications in software and web development. Search engines use sorting techniques to sorttheresult before itispresented totheuser.Thewordsinadictionary are insorted ordersothatthewords canbe found easily.There aremanysorting algorithms that areused in many domains to perform some operation and obtain the desired output. But there are some sorting algorithms that take large time in sorting the data. This huge time can be vulnerable to the operation. Every sorting algorithm has the different sorting technique to sort the given data, Stooge sort is asorting algorithm which sorts the data recursively. Stooge sort takes comparatively more time as compared tomany othersorting algorithms.Stooge sortworks recursively to sort the data element but the Optimized Stooge sort does not use recursive process. In this paper, we propose Optimized Stooge sort to reduce the time complexity of the Stooge sort. The running time of Optimized Stooge sort is very much reduced as compared to theStooge sort algorithm. The existing researchfocuses onreducing therunning time of Stooge sort. Our results show that the Optimized Stooge sort is faster than the Stooge sort algorithm.

A Quick Sorting Algorithm Adaptive to Massive Data with High Repetition Rate

2013 ◽  
Vol 834-836 ◽  
pp. 1002-1005
Author(s):  
Bao Ping Chen
Keyword(s):  
Experimental Data ◽  
Theoretical Analysis ◽  
Repetition Rate ◽  
Time Complexity ◽  
High Repetition Rate ◽  
Sorting Algorithm ◽  
Massive Data ◽  
Recursive Algorithms ◽  
High Repetition ◽  
Sorting Algorithms

Quick sorting is one of the sorting algorithms with good performance. However, there is a bottleneck of its performance in dealing with massive data with high repetition rate. Therefore, a new effective quick sorting algorithm is proposed in this study. This approach possesses the advantage of conciseness of quick sorting algorithms while avoiding the disadvantages of recursive algorithms. The time complexity is O(n), and the space complexity is O(1). Theoretical analysis and experimental data have shown that its performance is superior to the original quick sorting algorithm, and it is applicable to the processing of massive data with high repetition rate.

scholarly journals SETNDS: A SET-Based Non-Dominated Sorting Algorithm for Multi-Objective Optimization Problems

2020 ◽  
Vol 10 (19) ◽  
pp. 6858
Author(s):  
Lingling Xue ◽  
Peng Zeng ◽  
Haibin Yu
Keyword(s):  
Time Complexity ◽  
Optimization Problems ◽  
Sorting Algorithm ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Worst Case ◽  
Multi Objective ◽  
Sorting Algorithms ◽  
Dominant Set ◽  
The One

Non-dominated sorting, used to find pareto solutions or assign solutions to different fronts, is a key but time-consuming process in multi-objective evolutionary algorithms (MOEAs). The best-case and worst-case time complexity of non-dominated sorting algorithms currently known are O(MNlogN) and O(MN2); M and N represent the number of objectives and the population size, respectively. In this paper, a more efficient SET-based non-dominated sorting algorithm, shorted to SETNDS, is proposed. The proposed algorithm can greatly reduce the number of comparisons on the promise of ensuring a shorter running time. In SETNDS, the rank of a solution to be sorted is determined by only comparing with the one with the highest rank degree in its dominant set. This algorithm is compared with six generally existing non-dominated sorting algorithms—fast non-dominated sorting, the arena’s principle sort, the deductive sort, the corner sort, the efficient non-dominated sort and the best order sort on several kinds of datasets. The compared results show that the proposed algorithm is feasible and effective and its computational efficiency outperforms other existing algorithms.

Analisis Kinerja Algoritma Quick Double Merge Sort Paralel Menggunakan openMP

2020 ◽  
Vol 11 (2) ◽  
pp. 95-102
Author(s):  
I Nyoman Aditya Yudiswara ◽  
Abba Suganda
Keyword(s):  
Parallel Algorithms ◽  
The Other ◽  
Sorting Algorithm ◽  
Data Parallelism ◽  
Parallel Sorting ◽  
Sequential Algorithms ◽  
Sorting Algorithms ◽  
Merge Sort ◽  
Sort Algorithm ◽  
Quick Sort

Processor technology currently tends to increase the number of cores more than increasing the clock speed. This development is very useful and becomes an opportunity to improve the performance of sequential algorithms that are only done by one core. This paper discusses the sorting algorithm that is executed in parallel by several logical CPUs or cores using the openMP library. This algorithm is named QDM Sort which is a combination of sequential quick sort algorithm and double merge algorithm. This study uses a data parallelism approach to design parallel algorithms from sequential algorithms. The data used in this study are the data that have not been sorted and also the data that has been sorted is integer type which is stored in advance in a file. The parameter measured to determine the performance of the QDM Sort algorithm is speedup. In a condition where a large amount of data is above 4096 and the number of threads in QDM Sort is the same as the number of logical CPUs, the QDM Sort algorithm has a better speedup compared to the other parallel sorting algorithms discussed in this study. For small amounts of data it is still better to use sequential sorting algorithm.

Evaluation of Reliable Data Storage in Cloud Using an Efficient Encryption Technique

2021 ◽  
pp. 758-772
Author(s):  
Saswati Sarkar ◽  
Anirban Kundu ◽  
Ayan Banerjee
Keyword(s):  
Power Consumption ◽  
Data Storage ◽  
Time Complexity ◽  
Storage Time ◽  
Reliable Data ◽  
Time Analysis ◽  
Physical Size ◽  
Time Consumption ◽  
Current Consumption ◽  
Storage Technique

Cloud-based reliable and protected data storage technique is proposed in this chapter. The proposed technique encrypts and protects data with less time consumption. Power consumption of storage is dependent upon capacity of storage and physical size of storage. Time analysis is presented graphically in this chapter. Reliable data storage is represented in cloud based proposed approach. Data is encrypted with minimum time complexity due to usage of proposed cloud-based reliable data storage. The competent ratio of time complexity is graphically observed in proposed data storage technique. Power consumption of storage has been typically dependent on the basis of capacity of storage and amount of storage. A ratio of power consumption and capacity of storage is presented in cloud-based approach. An efficient usage of energy is shown depending on current consumption and voltage in proposed reliable approach.

A Method for Real-Time Radar Signal Main Sorting

2013 ◽  
Vol 760-762 ◽  
pp. 567-571
Author(s):  
Hong Chao Wu ◽  
Wei Hua Xiao ◽  
Jian Feng Pu
Keyword(s):  
Real Time ◽  
Pulse Sequence ◽  
High Accuracy ◽  
Radar Signal ◽  
Sorting Algorithm ◽  
The Real ◽  
Sorting Algorithms ◽  
Key Technologies ◽  
Simulation Results ◽  
Sequence Simulation

The real-time radar signal sorting is one of the key technologies for electronic reconnaissance, first analyzes the defects of traditional main sorting algorithms, and then proposes a comprehensive main sorting algorithm. The method first uses the SDIF algorithm to sort PRI fixed and PRI stagger radar signal, then uses dynamic expansion association method to search PRI jitter radar signal. When using the SDIF algorithm, in order to improve the efficiency of extraction of PRI, first taking amplitude pretreatment, then only accumulate the signals whose amplitude meet certain requirements. After extracting PRI, extract to the original full pulse sequence. Simulation results show that the method of sorting has high accuracy and good real-time.

Clock Skew Scheduling with Optimizing-Potential Prediction

2012 ◽  
Vol 198-199 ◽  
pp. 621-625
Author(s):  
Kai Huang ◽  
Jin Guo Zhang ◽  
Zhi Kuang Cai
Keyword(s):  
Time Complexity ◽  
Scheduling Algorithm ◽  
Past Research ◽  
Half Time ◽  
Clock Skew ◽  
Scheduling Scheme ◽  
The Past ◽  
Clock Skew Scheduling

This paper addresses the issue of increasing the efficiency of the clock skew scheduling. The past research focuses on reducing the time complexity of clock skew scheduling algorithm. However, even if an algorithm has time complexity close to linear, the flow iterations still consumes a lot of time. In this paper, a novel clock skew scheduling scheme with the feature of optimization-potential prediction is proposed. With this feature, the algorithm has timing complexity close to linear, and the number of flow iterations is decreased. The experiment results show that the proposed scheme consumes about half time and achieves almost the same optimization strength (13% highest frequency improvement of ARM1136J-FS) compared to the traditional.

Research on Search Sorting Algorithm Based on Multi-Dimensional Matching

2014 ◽  
Vol 926-930 ◽  
pp. 3195-3199
Author(s):  
Xiao Yan Yuan
Keyword(s):  
Semantic Similarity ◽  
Temporal Information ◽  
Sorting Algorithm ◽  
Search Term ◽  
Ontology Concept ◽  
Average Recall ◽  
Search Terms ◽  
Sorting Algorithms

Since the current search sorting algorithms cannot find the desirable webpages quickly and accurately, a novel search sorting algorithm based on multi-dimensional matching is proposed in this study. This algorithm computes the semantic similarity of search terms based on ontology concept, and then the relevance of temporal information of those search terms with time of webpage. As a result, the relevance of the search term with the content of the webpage is calculated, hence realizing most appropriate webpage sorting. Finally, several methods are compared in terms of their average precisions and average recall ratios.

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