scholarly journals A Testbed for QOS-Based Data Analytic Service Selection in the Cloud

2021 ◽  
Author(s):  
Md Shahinur Rahman
Keyword(s):  
Service Selection ◽  
Selection Models ◽  
Data Mining Algorithms ◽  
Traditional Service ◽  
Monitoring Service ◽  
Mining Algorithms ◽  
Infrastructure Services ◽  
Software Services ◽  
Data Analytic

With the emergence of Big Data and Cloud Computing, more and more data analytic software services have become available through a Cloud platform. Compared to the traditional service selection problem, selecting this type of services has additional challenges, which requires new selection models being proposed. It is the purpose of this work to “create a testbed” to benefit the research community in this area so that different selection models with consideration of different performance-influencing factors such as algorithms implemented, datasets to be processed, hosting infrastructure, can be tested and compared. We created a cloud-based platform for publishing and invoking analytic services as well as monitoring service performance during invocation. We implemented various data mining algorithms from different packages as example analytic services and hosted them on different infrastructure services. We also ran these services on some real datasets to collect a sample dataset of their Quality of Service (QoS) values.

scholarly journals A Testbed for QOS-Based Data Analytic Service Selection in the Cloud

2021 ◽  
Author(s):  
Md Shahinur Rahman
Keyword(s):  
Service Selection ◽  
Selection Models ◽  
Data Mining Algorithms ◽  
Traditional Service ◽  
Monitoring Service ◽  
Mining Algorithms ◽  
Infrastructure Services ◽  
Software Services ◽  
Data Analytic

With the emergence of Big Data and Cloud Computing, more and more data analytic software services have become available through a Cloud platform. Compared to the traditional service selection problem, selecting this type of services has additional challenges, which requires new selection models being proposed. It is the purpose of this work to “create a testbed” to benefit the research community in this area so that different selection models with consideration of different performance-influencing factors such as algorithms implemented, datasets to be processed, hosting infrastructure, can be tested and compared. We created a cloud-based platform for publishing and invoking analytic services as well as monitoring service performance during invocation. We implemented various data mining algorithms from different packages as example analytic services and hosted them on different infrastructure services. We also ran these services on some real datasets to collect a sample dataset of their Quality of Service (QoS) values.

scholarly journals Learning analytics made in France: the METAL project

2019 ◽  
Vol 36 (4) ◽  
pp. 299-313 ◽  
Author(s):  
Armelle Brun ◽  
Geoffray Bonnin ◽  
Sylvain Castagnos ◽  
Azim Roussanaly ◽  
Anne Boyer
Keyword(s):  
Data Mining ◽  
Secondary School ◽  
Data Storage ◽  
Learning Analytics ◽  
Content Type ◽  
Data Mining Algorithms ◽  
School Based ◽  
Source Data ◽  
Mining Algorithms

Purpose The purpose of this paper is to present the METAL project, a French open learning analytics (LA) project for secondary school, that aims at improving the quality of teaching. The originality of METAL is that it relies on research through exploratory activities and focuses on all the aspects of a learning analytics environment. Design/methodology/approach This work introduces the different concerns of the project: collection and storage of multi-source data owned by a variety of stakeholders, selection and promotion of standards, design of an open-source LRS, conception of dashboards with their final users, trust, usability, design of explainable multi-source data-mining algorithms. Findings All the dimensions of METAL are presented, as well as the way they are approached: data sources, data storage, through the implementation of an LRS, design of dashboards for secondary school, based on co-design sessions data mining algorithms and experiments, in line with privacy and ethics concerns. Originality/value The issue of a global dissemination of LA at an institution level or at a broader level such as a territory or a study level is still a hot topic in the literature, and is one of the focus and originality of this paper, associated with the large spectrum of different concerns.

A Service Selection Algorithm Based on Heterogeneous QoS Model and Synthetic Weights

2015 ◽  
Vol 734 ◽  
pp. 459-462 ◽  
Author(s):  
Sen Zeng ◽  
Guo Qi Ni ◽  
Miao Miao Fan ◽  
Lin Zhang ◽  
Yuan Hua He
Keyword(s):  
Decision Making ◽  
Service Selection ◽  
Selection Problem ◽  
Final Decision ◽  
Selection Algorithm ◽  
Decision Making Problem ◽  
Different Types ◽  
Traditional Service ◽  
Multi Attribute Decision Making

Quality of Service (QoS) aware-based service selection problem is a multi-attribute decision making problem. In order to solve service selection problem with QoS indicators describe by different types of data, a service selection algorithm based on heterogeneous QoS model and synthetic weight (SSAoHS) is proposed. SSAoHS introduces real number, interval number and linguistic data to describe different QoS attributes, considers the subjective and objective weights wholly, and makes the final decision referring to the expectation and variance of QoS attributes after computing the synthetic scores. SSAoHS expands the traditional service selection and it is efficient and effective.

scholarly journals Toward a Standardized Strategy of Clinical Metabolomics for the Advancement of Precision Medicine

Metabolites ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 51 ◽  
Author(s):  
Nguyen Phuoc Long ◽  
Tran Diem Nghi ◽  
Yun Pyo Kang ◽  
Nguyen Hoang Anh ◽  
Hyung Min Kim ◽  
...  
Keyword(s):  
Internal Validity ◽  
Future Research ◽  
Data Mining Algorithms ◽  
Control Procedures ◽  
Comprehensive Metabolomics ◽  
Computational Resources ◽  
Five Phases ◽  
Mining Algorithms ◽  
Clinical Metabolomics

Despite the tremendous success, pitfalls have been observed in every step of a clinical metabolomics workflow, which impedes the internal validity of the study. Furthermore, the demand for logistics, instrumentations, and computational resources for metabolic phenotyping studies has far exceeded our expectations. In this conceptual review, we will cover inclusive barriers of a metabolomics-based clinical study and suggest potential solutions in the hope of enhancing study robustness, usability, and transferability. The importance of quality assurance and quality control procedures is discussed, followed by a practical rule containing five phases, including two additional “pre-pre-” and “post-post-” analytical steps. Besides, we will elucidate the potential involvement of machine learning and demonstrate that the need for automated data mining algorithms to improve the quality of future research is undeniable. Consequently, we propose a comprehensive metabolomics framework, along with an appropriate checklist refined from current guidelines and our previously published assessment, in the attempt to accurately translate achievements in metabolomics into clinical and epidemiological research. Furthermore, the integration of multifaceted multi-omics approaches with metabolomics as the pillar member is in urgent need. When combining with other social or nutritional factors, we can gather complete omics profiles for a particular disease. Our discussion reflects the current obstacles and potential solutions toward the progressing trend of utilizing metabolomics in clinical research to create the next-generation healthcare system.

scholarly journals Data dependent web service selection

2021 ◽  
Author(s):  
Navati Jain
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Selection Methods ◽  
Web Service Selection ◽  
Selection Approach ◽  
Meta Learning ◽  
Service Ranking ◽  
Selection Methodology ◽  
Data Analytic

Data mining applications and services are becoming increasingly important, especially in this age of Big Data. QoS (Quality of Service) properties such as latency, reliability, response time of such services can vary based on the characteristics of the dataset being processed. The existing QoS-based web service selection methods are not adequate for ranking these types of services since they do not consider these dataset characteristics. We have proposed a service selection methodology to predict the QoS values for data analytic services based on the attributes of the dataset involved by incorporating a meta-learning approach. Subsequently we rank the services according to the predicted QoS values. The outcome of our experiments proves the effectiveness of this approach with an improvement of above 20% in service ranking when compared to the traditional QoS selection approach.

scholarly journals Data dependent web service selection

2021 ◽  
Author(s):  
Navati Jain
Keyword(s):  
Web Service ◽  
Service Selection ◽  
Selection Methods ◽  
Web Service Selection ◽  
Selection Approach ◽  
Meta Learning ◽  
Service Ranking ◽  
Selection Methodology ◽  
Data Analytic

Data mining applications and services are becoming increasingly important, especially in this age of Big Data. QoS (Quality of Service) properties such as latency, reliability, response time of such services can vary based on the characteristics of the dataset being processed. The existing QoS-based web service selection methods are not adequate for ranking these types of services since they do not consider these dataset characteristics. We have proposed a service selection methodology to predict the QoS values for data analytic services based on the attributes of the dataset involved by incorporating a meta-learning approach. Subsequently we rank the services according to the predicted QoS values. The outcome of our experiments proves the effectiveness of this approach with an improvement of above 20% in service ranking when compared to the traditional QoS selection approach.

scholarly journals Research of algorithms of Data Mining

2020 ◽  
Vol 166 ◽  
pp. 05007
Author(s):  
Vitalii Levkivskyi ◽  
Nadiia Lobanchykova ◽  
Dmytro Marchuk
Keyword(s):  
Data Mining ◽  
Chronic Diseases ◽  
The Body ◽  
Data Mining Algorithms ◽  
Quality Of Patient Care ◽  
Regression Algorithms ◽  
Bayes Algorithm ◽  
Using Data ◽  
Mining Algorithms

The article explores data mining algorithms, which based on rules and calculations, that allow us to create a model that analyzes the data provided by searching for specific patterns and trends. The purpose of this work is to analyze correlation-regression algorithms on a statistical dataset of chronic diseases. Data mining allows building many models, multiple algorithms can be used within a single solution. The article explores the algorithms of clustering, correlation analysis, Naive Bayes algorithm for obtaining different views of data. Since diabetes is one of the most dangerous chronic diseases, the pathogenesis of which is a lack of insulin in the human body, which causes metabolic disorders and pathological changes in various organs and tissues. As a result, it leads to disability of all functional systems of the body. It was decided to investigate the data related to this disease. Also, the quality of the developed methods of information retrieval from the dataset was evaluated and the most informative features were identified. The developed methods were implemented in the system of intellectual data processing. Past studies show promise of using data mining methods to improve the quality of patient care.

scholarly journals PENERAPAN DATA MINING UNTUK ANALISIS PENGARUH LAMA STUDI MAHASISWA TEKNIK INFORMATIKA UIN SUNAN KALIJAGA YOGYAKARTA MENGGUNAKAN METODE APRIORI

2017 ◽  
Vol 1 (3) ◽  
pp. 153
Author(s):  
Gathut Cakra Sutradana ◽  
M Didik Rohmad Wahyudi
Keyword(s):  
College Students ◽  
Data Mining ◽  
Learning Process ◽  
Study Data ◽  
Study Time ◽  
Time Data ◽  
Data Mining Algorithms ◽  
Study Duration ◽  
Mining Algorithms

ABSTRACTThe accuracy of a long study of college students at a university becomes very important in demonstrating the quality of the learning process in college. There are many things that affect a student's study time. Data Mining offers a way to know the various aspects that may affect a student's study time. To know the various aspects that influence the duration of the study based on data graduation students are available, then the implementation of a Data Mining algorithms can be used. In this study, Data Mining algorithms used to find aspects that affect student study duration is Apriori algorithm.Keywords: graduation analysis, long studying, data mining, apriori algorithms  Ketepatan lama studi mahasiswa pada suatu perguruan tinggi menjadi hal yang sangat penting dalam menunjukkan kualitas proses pembelajaran di perguruan tinggi. Ada banyak hal yang mempengaruhi lama studi mahasiswa. Data Mining menawarkan suatu cara untuk mengetahui berbagai aspek yang dapat berpengaruh terhadap lama studi mahasiswa. Untuk mengetahui berbagai aspek yang mempengaruhi lama studi mahasiswa berdasarkan data kelulusan yang tersedia, maka implementasi suatu algoritma Data Mining dapat dipergunakan. Dalam penelitian ini, algoritma Data Mining yang dipergunakan untuk menemukan aspek yang mempengaruhi lama studi mahasiswa adalah algoritma Apriori.Katakunci : analisis kelulusan, lama studi, data mining, algoritma apriori

INOD: A Graph-Based Outlier Detection Algorithm

2013 ◽  
Vol 475-476 ◽  
pp. 1008-1012
Author(s):  
Li Hua Yang ◽  
Gui Lin Li ◽  
Shao Bin Zhou ◽  
Ming Hong Liao
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Algorithm ◽  
Typical Feature ◽  
Selection Algorithm ◽  
Data Set ◽  
Quality Of Results ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The outlier detection is to select uncommon data from a data set, which can significantly improve the quality of results for the data mining algorithms. A typical feature of the outliers is that they are always far away from a majority of data in the data set. In this paper, we present a graph-based outlier detection algorithm named INOD, which makes use of this feature of the outlier. The DistMean-neighborhood is used to calculate the cumulative in-degree for each data. The data, whose cumulative in-degree is smaller than a threshold, is judged as an outlier candidate. A KNN-based selection algorithm is used to determine the final outlier. Experimental results show that the INOD algorithm can improve the precision 80% higher and decrease the error rate 75% lower than the classical LOF algorithm.

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