Using a Competitive Clustering Algorithm to Comprehend Web Applications

2006 ◽  
Author(s):  
Andrea De Lucia ◽  
Giuseppe Scanniello ◽  
Genoveffa Tortora
Keyword(s):  
Clustering Algorithm ◽  
Web Applications

scholarly journals Workload Aware Incremental Repartitioning of NoSQL for Online Transactional Processing Applications

2018 ◽  
Vol 7 (1) ◽  
pp. 54
Author(s):  
Anagha Bhunje ◽  
Swati Ahirrao
Keyword(s):  
Relational Databases ◽  
Clustering Algorithm ◽  
Web Applications ◽  
Work Load ◽  
Fast Response ◽  
Data Partitioning ◽  
Graph Representation ◽  
Fuzzy C Means Clustering ◽  
System Data ◽  
And Migration

<p><span lang="EN-US">Numerous applications are deployed on the web with the increasing popularity of internet. The applications include, 1) Banking applications,<br /> 2) Gaming applications, 3) E-commerce web applications. Different applications reply on OLTP (Online Transaction Processing) systems. OLTP systems need to be scalable and require fast response. Today modern web applications generate huge amount of the data which one particular machine and Relational databases cannot handle. The E-Commerce applications are facing the challenge of improving the scalability of the system. Data partitioning technique is used to improve the scalability of the system. The data is distributed among the different machines which results in increasing number of transactions. The work-load aware incremental repartitioning approach is used to balance the load among the partitions and to reduce the number of transactions that are distributed in nature. Hyper Graph Representation technique is used to represent the entire transactional workload in graph form. In this technique, frequently used items are collected and Grouped by using Fuzzy C-means Clustering Algorithm. Tuple Classification and Migration Algorithm is used for mapping clusters to partitions and after that tuples are migrated efficiently.</span></p>

Clustering Columns of the Wide-Table in Cloud Computing

2012 ◽  
Vol 433-440 ◽  
pp. 5129-5135
Author(s):  
Bin Huang ◽  
Yu Xing Peng
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Web Applications ◽  
Second Law Of Thermodynamics ◽  
Access Time ◽  
Materialized Views ◽  
It Use ◽  
Consistent Hashing ◽  
Single Column ◽  
And Storage

Various data-centric web applications are becoming the developing trend of information society. Cloud computing currently adopt column-oriented storage wide table to represent the heterogeneous structured data of these applications. The wide table reduces the waste of storage space, but slows down query efficiency. The paper implements the hybrid partition on access frequent (HPAF) to horizontally and vertically partition a wide table. It uses a variant of consistent hashing to dynamically horizontally partition a wide table across multiple storage nodes on each node’s performance; It use entropy to represent the number of reducing access data block from the table with N columns than from N column-oriented storage tables. According to the second law of thermodynamics, the paper designs an entropy increasing clustering algorithm to classify the columns of a wide table. The algorithm finds a cluster with multiple classes which save maximum access time. The paper implements an algorithm for structured query across multiple materialized views too. Lastly the paper demonstrates the query performance and storage efficiency of our strategy compared to single column storage.

Identifying similar pages in Web applications using a competitive clustering algorithm

2007 ◽  
Vol 19 (5) ◽  
pp. 281-296 ◽  
Author(s):  
Andrea De Lucia ◽  
Giuseppe Scanniello ◽  
Genoveffa Tortora
Keyword(s):  
Clustering Algorithm ◽  
Web Applications

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

DESIGNING E-LEARNING RESOURCES FOR TEACHING MATHEMATICS IN PRIMARY SCHOOL WITH ADOBE FLASH AND HTML5 SYSTEMS

2018 ◽  
pp. 49-57
Author(s):  
N. A. Gluzman
Keyword(s):  
Primary School ◽  
Web Applications ◽  
Special Importance ◽  
Learning Resources ◽  
Multimedia Presentations ◽  
Adobe Flash ◽  
E Learning ◽  
High Level ◽  
And Training ◽  
Training Practice

In the modern educational space regarding the realities of the information society special importance is attached to issues related to the provision of a high level of informatization of education, which implies teachers’ mastering the necessary competencies and the ability to introduce e-learning resources into educational and training practice. Adobe Flash as one of the platforms for creating web applications and multimedia presentations enjoys greatest popularity with users including teachers. However, in connection with the announcement of discontinuing Adobe Flash support in 2020, the issue of choosing an analog to create web applications and presentations for use in teaching purposes is becoming particularly relevant. The article provides a comprehensive analysis of developing electronic educational resources by teachers using Adobe Flash and HTML5 for teaching math in primary school.

A Survey on various Security Issues in Health Data in Web Applications

2020 ◽  
Vol 5 (3) ◽  
pp. 84-89
Author(s):  
Maragathavalli P. ◽  
Seshankkumar M. ◽  
Dhivakaran V. ◽  
Ravindran S.
Keyword(s):  
Web Applications ◽  
Health Data ◽  
Security Issues

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%

K-MEANS CLUSTERING ALGORITHM BASED CLASSIFICATION OF SOIL FERTILITY IN NORTH WEST NIGERIA

2020 ◽  
Vol 4 (2) ◽  
pp. 780-787
Author(s):  
Ibrahim Hassan Hayatu ◽  
Abdullahi Mohammed ◽  
Barroon Ahmad Isma’eel ◽  
Sahabi Yusuf Ali
Keyword(s):  
Soil Fertility ◽  
Crop Yield ◽  
Clustering Algorithm ◽  
Soil Samples ◽  
North West ◽  
R Programming ◽  
Available Information ◽  
Northwest Region ◽  
The Relationship

Soil fertility determines a plant's development process that guarantees food sufficiency and the security of lives and properties through bumper harvests. The fertility of soil varies according to regions, thereby determining the type of crops to be planted. However, there is no repository or any source of information about the fertility of the soil in any region in Nigeria especially the Northwest of the country. The only available information is soil samples with their attributes which gives little or no information to the average farmer. This has affected crop yield in all the regions, more particularly the Northwest region, thus resulting in lower food production.  Therefore, this study is aimed at classifying soil data based on their fertility in the Northwest region of Nigeria using R programming. Data were obtained from the department of soil science from Ahmadu Bello University, Zaria. The data contain 400 soil samples containing 13 attributes. The relationship between soil attributes was observed based on the data. K-means clustering algorithm was employed in analyzing soil fertility clusters. Four clusters were identified with cluster 1 having the highest fertility, followed by 2 and the fertility decreases with an increasing number of clusters. The identification of the most fertile clusters will guide farmers on where best to concentrate on when planting their crops in order to improve productivity and crop yield.

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