scholarly journals A Microservice Decomposition Method Through Using Distributed Representation of Source Code

2021 ◽  
Vol 22 (1) ◽  
pp. 39-52
Author(s):  
Omar Al-Debagy ◽  
Peter Martinek
Keyword(s):  
Decomposition Method ◽  
Clustering Algorithm ◽  
Source Code ◽  
Decomposition Methods ◽  
Quality Characteristics ◽  
Domain Model ◽  
Test Cases ◽  
Hierarchical Clustering Algorithm ◽  
Similar Code ◽  
Domain Level

This research proposed a novel decomposition method for refactoring monolithic applications into microservices applications using a neural network model (code2vec) for creating code embeddings from the monolithic application source code. As a Result, semantically similar code embeddings are clustered through a hierarchical clustering algorithm to produce microservices candidates to resemble the domain model more efficiently. The quality characteristics of the results were measured using two metrics for measuring cohesion. These metrics were Cohesion at Message Level (CHM) and Cohesion at Domain Level (CHD). Also, four applications were used as test cases with different sizes ranging from small to big applications. The proposed method showed promising results in terms of cohesion when compared to other decomposition methods. The proposed method scored better scores in 5 out of 8 tests compared to other methods. Also, averaged CHD and CHM results were 0.52 and 0.76, respectively, for the proposed method, better results when compared to the other methods.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

Nonlinear singular one dimensional thermo-elasticity coupled system and double Laplace decomposition methods

Filomat ◽  
2017 ◽  
Vol 31 (20) ◽  
pp. 6269-6280
Author(s):  
Hassan Gadain
Keyword(s):  
Laplace Transform ◽  
Decomposition Method ◽  
Adomian Decomposition Method ◽  
Coupled System ◽  
Decomposition Methods ◽  
One Dimensional ◽  
Convergence Proof ◽  
Double Laplace Transform ◽  
Adomian Decomposition

In this work, combined double Laplace transform and Adomian decomposition method is presented to solve nonlinear singular one dimensional thermo-elasticity coupled system. Moreover, the convergence proof of the double Laplace transform decomposition method applied to our problem. By using one example, our proposed method is illustrated and the obtained results are confirmed.

scholarly journals A Note on the Decomposition Methods for Support Vector Regression

2002 ◽  
Vol 14 (6) ◽  
pp. 1267-1281 ◽  
Author(s):  
Shuo-Peng Liao ◽  
Hsuan-Tien Lin ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Regression ◽  
Decomposition Method ◽  
Decomposition Methods ◽  
Support Vector ◽  
Dual Formulation ◽  
Working Set ◽  
Number Of Iterations

The dual formulation of support vector regression involves two closely related sets of variables. When the decomposition method is used, many existing approaches use pairs of indices from these two sets as the working set. Basically, they select a base set first and then expand it so all indices are pairs. This makes the implementation different from that for support vector classification. In addition, a larger optimization subproblem has to be solved in each iteration. We provide theoretical proofs and conduct experiments to show that using the base set as the working set leads to similar convergence (number of iterations). Therefore, by using a smaller working set while keeping a similar number of iterations, the program can be simpler and more efficient.

scholarly journals Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 370
Author(s):  
Shuangsheng Wu ◽  
Jie Lin ◽  
Zhenyu Zhang ◽  
Yushu Yang
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Hierarchical Clustering ◽  
Research Gaps ◽  
Judicial Practice ◽  
Linguistic Term ◽  
Clustering Effect ◽  
Hierarchical Clustering Algorithm ◽  
Fuzzy Linguistic

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

scholarly journals Transmission Path Tracking of Maritime COVID-19 Pandemic via Ship Sailing Pattern Mining

2021 ◽  
Vol 13 (3) ◽  
pp. 1089
Author(s):  
Hailin Zheng ◽  
Qinyou Hu ◽  
Chun Yang ◽  
Jinhai Chen ◽  
Qiang Mei
Keyword(s):  
Clustering Algorithm ◽  
Pattern Mining ◽  
Transportation Networks ◽  
Automatic Identification ◽  
Identification System ◽  
Classification Models ◽  
Transmission Path ◽  
The Hierarchical Structure ◽  
Hierarchical Clustering Algorithm ◽  
And Control

Since the spread of the coronavirus disease 2019 (COVID-19) pandemic, the transportation of cargo by ship has been seriously impacted. In order to prevent and control maritime COVID-19 transmission, it is of great significance to track and predict ship sailing behavior. As the nodes of cargo ship transportation networks, ports of call can reflect the sailing behavior of the cargo ship. Accurate hierarchical division of ports of call can help to clarify the navigation law of ships with different ship types and scales. For typical cargo ships, ships with deadweight over 10,000 tonnages account for 95.77% of total deadweight, and 592,244 berthing ships’ records were mined from automatic identification system (AIS) from January to October 2020. Considering ship type and ship scale, port hierarchy classification models are constructed to divide these ports into three kinds of specialized ports, including bulk, container, and tanker ports. For all types of specialized ports (considering ship scale), port call probability for corresponding ship type is higher than other ships, positively correlated with the ship deadweight if port scale is bigger than ship scale, and negatively correlated with the ship deadweight if port scale is smaller than ship scale. Moreover, port call probability for its corresponding ship type is positively correlated with ship deadweight, while port call probability for other ship types is negatively correlated with ship deadweight. Results indicate that a specialized port hierarchical clustering algorithm can divide the hierarchical structure of typical cargo ship calling ports, and is an effective method to track the maritime transmission path of the COVID-19 pandemic.

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