A new modified sorting algorithm: A comparison with state of the art

Idrizi, Florim; Rustemi, Avni; Dalipi, Fisnik

doi:10.1109/meco.2017.7977252

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Con-135 sequently, the number of sorted elements at the top of the list increases while the rest remain unsorted. Merge sort, which is based on the divide-and-conquer principle, repeatedly divides the array into two halves and then combines them in a sorted manner For more details of classical sort 140 algorithms, such as quick sort and heap sort, we refer the reader to the comments in [17,7]. It is well known that computational cost and required memory are the primary concerns in sorting algorithms [18,19].…”

Section: Classical Sort Techniquesmentioning

confidence: 99%

Efficient algorithms for collecting the statistics of large-scale IP address data

Chen,

Mao

2021

Preprint

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Compiling the statistics of large-scale IP address data is an essential task in network traffic measurement. The statistical results are used to evaluate the potential impact of user behaviors on network traffic. This requires algorithms that are capable of storing and retrieving a high volume of IP addresses within time and memory constraints. In this paper, we present two efficient algorithms for collecting the statistics of large-scale IP addresses that balance time efficiency and memory consumption. The proposed solutions take into account the sparse nature of the statistics of IP addresses while building the hash function and maintain a dynamic balance among layered memory blocks. There are two layers in the first proposed method, each of which contains a limited number of memory blocks. Each memory block contains 256 elements of size 256 × 8 bytes for a 64-bit system. In contrast to built-in hash mapping functions, the proposed solution completely avoids expensive hash collisions while retaining the linear time complexity of hash-based solutions. Moreover, the mechanism dynamically determines the hash index length according to the range of IP addresses, and can balance the time and memory constraints. In addition, we propose an efficient parallel scheme to speed up the collection of statistics. The experimental results on several synthetic datasets show that the proposed method substantially outperforms the baselines with respect to time and memory space efficiency.

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Section: Classical Sort Techniquesmentioning

confidence: 99%

Efficient algorithms for collecting the statistics of large-scale IP address data

Chen,

Mao

2021

Preprint

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“…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]. Sorting has become very important due to extensively growing big data in different forms and with the growing types of applications, sorting is becoming more important [7]- [9]. Fast execution also depends on how sorting algorithm works [10] also efficient algorithm mechanism is more important than good hardware [11].…”

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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“…These algorithms belong to a class called "comparison sorts" where the sorted order is determined based only on comparisons between the input elements. It has also been proven that any comparison sort algorithm requires Ω(NlgN) comparisons in the worst [9]. i.e either we can try to identify the areas of improvement in the well established sorting methods or we can lok for completely new methods.…”

Section: Introductionmentioning

confidence: 99%

Efficient Data Sorting Introducing a New Method

Devidas¹,

Kulkarni²

2021

Preprint

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The efficiency of data sorting algorithms is the key aspect which determines the speed of data processing and searching. The best known efficiency of sorting algorithm has been Log (N) if there are N terms. All of the well-known sorting algorithms use various techniques to sort data. The basis for most of these are comparing the data terms with each other. In this manuscript, we are introducing a new approach for sorting data. This method is postulated to have the highest efficiency ever achieved by any of the sorting algorithms. We achieve this by sorting data without comparing the data terms. Or achieving results of data comparison without comparing the terms explicitly.

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A new modified sorting algorithm: A comparison with state of the art

Cited by 6 publications

References 7 publications

Efficient algorithms for collecting the statistics of large-scale IP address data

Efficient algorithms for collecting the statistics of large-scale IP address data

Preprocessing: A method For Reducing Time Complexity

Efficient Data Sorting Introducing a New Method

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