Comparing Rapid Sort with Some Existing Sorting Algorithms

Singh, Heisnam Rohen; Sarmah, Mriganka

doi:10.1007/978-81-322-2217-0_49

Cited by 7 publications

(2 citation statements)

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“…Various kinds of content such as integer and string data can be assigned to sorting methods for arranging them in the required order. Many conventional and advanced algorithms with different space and time complexities are available in the literature [5]. Every sorting method follows a unique technique and based on these techniques sorting methods can be classified as sorting by exchanging, insertion, selection, and merging [6].…”

Section: Introductionmentioning

confidence: 99%

Preprocessing: A method For Reducing Time Complexity

Mubarak

Iqbal

Rasool

et al. 2022

JCBI

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Data can be processed quickly if it is in some order, whereas unsequenced data can take more time to obtain results. Sorting is used for data arrangement. It is also one of the essential requirement for most applications and this step helps to boost performance. Sorting is also a prerequisite in several computer applications like databases. Over time computer scientists have not only introduced new sorting techniques considering various factors to be improved but they have also presented enhanced variants of existing sorting methods. The main objective has always been to reduce the execution time and space of the sorting algorithms. With every passing day, digital content is growing rapidly, which is a significant cause that encourages researchers to design new time-space efficient sorting algorithms. This paper presents some preprocessing strategies for quicksort and insertion sort to improve their performances. Tha main idea of using these preprocessings is to make input data more suitable for sorting algorithm, as most sorting function performs extraordinary for a specific type of input, such as insertion sort works better on nearyly sorted data. To authenticate the efficiency of existing sorting algorithms, these have been compared with proposed preprocessing strategies. The results with proposed techniqes outperforms the results of original sorting methods. It also helps to convert worst case into average case. By using this approch complexity of many algorithms can be reduced, therfore this is very important.

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Section: Introductionmentioning

confidence: 99%

Preprocessing: A method For Reducing Time Complexity

Mubarak

Iqbal

Rasool

et al. 2022

JCBI

View full text Add to dashboard Cite

show abstract

“…Furthermore, some primitive operations such as the classic sorting algorithms (e.g., quick sort, heap sort) cannot be implemented due to the restrictive constraint of storing all sorted data inside the storage during the sorting process. A sorting algorithm is the first important step of many algorithms (Cormen et al, 2009;Huang et al, 2019) such as searching and finding a closest pair (Singh and Sarmah, 2015;Tambouratzis, 1999).…”

Section: Introductionmentioning

confidence: 99%

Peer Review #1 of "Correct and stable sorting for overflow streaming data with a limited storage size and a uniprocessor (v0.2)"

2021

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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 can not 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 ) " role="presentation" style="position: relative;"> O ( n ) O ( n ) O(n) , where n " role="presentation" style="position: relative;"> n n n is the number of incoming data. The space complexity is O ( M ) " role="presentation" style="position: relative;"> O ( M ) O ( M ) O(M) , where M " role="presentation" style="position: relative;"> M M 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 proposedconcept can be practically applied to various applications in different fields where the data always overflowthe working storage and sorting process is needed.

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