Recent Database Management Systems Research Articles - September 2020 International Journal of Database Management Systems (IJDMS) ISSN: 0975-5705 (Online); 0975-5985 (Print) http://airccse.org/journal/ijdms/index.html Contact us: ijdmsjournal@airccse.org

1. Recent Database Management Systems
Research Articles - September 2020
International Journal of Database
Management Systems (IJDMS)
ISSN: 0975-5705 (Online); 0975-5985 (Print)
http://airccse.org/journal/ijdms/index.html
Contact Us: ijdmsjournal@airccse.org

2. A XGBOOST RISK MODEL VIA FEATURE SELECTION AND BAYESIAN HYPER-
PARAMETER OPTIMIZATION
Yan Wang1
and Xuelei Sherry Ni2
1
Graduate College, Kennesaw State University, Kennesaw, USA
2
Department of Statistics and Analytical Sciences, Kennesaw State University, Kennesaw, USA
ABSTRACT
This paper aims to explore models based on the extreme gradient boosting (XGBoost) approach
for business risk classification. Feature selection (FS) algorithms and hyper-parameter
optimizations are simultaneously considered during model training. The five most commonly
used FS methods including weight by Gini, weight by Chi-square, hierarchical variable
clustering, weight by correlation, and weight by information are applied to alleviate the effect of
redundant features. Two hyper-parameter optimization approaches, random search (RS) and
Bayesian tree-structuredParzen Estimator (TPE), are applied in XGBoost. The effect of different
FS and hyper-parameter optimization methods on the model performance are investigated by the
Wilcoxon Signed Rank Test. The performance of XGBoost is compared to the traditionally
utilized logistic regression (LR) model in terms of classification accuracy, area under the curve
(AUC), recall, and F1 score obtained from the 10-fold cross validation. Results show that
hierarchical clustering is the optimal FS method for LR while weight by Chi-square achieves the
best performance in XG-Boost. Both TPE and RS optimization in XGBoost outperform LR
significantly. TPE optimization shows a superiority over RS since it results in a significantly
higher accuracy and a marginally higher AUC, recall and F1 score. Furthermore, XGBoost with
TPE tuning shows a lower variability than the RS method. Finally, the ranking of feature
importance based on XGBoost enhances the model interpretation. Therefore, XGBoost with
Bayesian TPE hyper-parameter optimization serves as an operative while powerful approach for
business risk modeling.
KEYWORDS
Extreme gradient boosting; XGBoost; feature selection; Bayesian tree-structured Parzen
estimator; risk modeling
Full Text: https://aircconline.com/ijdms/V11N1/11119ijdms01.pdf
The International Journal of Database Management Systems (IJDMS)
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5. AN INFECTIOUS DISEASE PREDICTION METHOD BASED ON K-NEAREST
NEIGHBOR IMPROVED ALGORITHM
Yaming Chen1
, Weiming Meng2
, Fenghua Zhang3
,Xinlu Wang4
and Qingtao Wu5
1,2&4
Computer Science and Technology, Henan University of Science and Technology, Luo
Yang, China
3
Computer Technology, Henan University of Science and Technology, Luo Yang, China
5
Professor, Henan University of Science and Technology, Luo Yang, China
ABSTRACT
With the continuous development of medical information construction, the potential value of a
large amount of medical information has not been exploited. Excavate a large number of medical
records of outpatients, and train to generate disease prediction models to assist doctors in
diagnosis and improve work efficiency.This paper proposes a disease prediction method based
on k-nearest neighbor improvement algorithm from the perspective of patient similarity analysis.
The method draws on the idea of clustering, extracts the samples near the center point generated
by the clustering, applies these samples as a new training sample set in the K-nearest neighbor
algorithm; based on the maximum entropy The K-nearest neighbor algorithm is improved to
overcome the influence of the weight coefficient in the traditional algorithm and improve the
accuracy of the algorithm. The real experimental data proves that the proposed k-nearest
neighbor improvement algorithm has better accuracy and operational efficiency.
KEYWORDS
Data Mining,KNN, Clustering,Maximum Entropy
Full Text: https://aircconline.com/ijdms/V11N1/11119ijdms02.pdf
The International Journal of Database Management Systems (IJDMS)
http://airccse.org/journal/ijdms/current2019.html

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8. A THEORETICAL EXPLORATION OF DATA MANAGEMENT AND INTEGRATION
IN ORGANISATION SECTORS
Chisom E. Offia and Malcolm Crowe
School of Computing, Engineering and Physical Sciences, University of the West of Scotland,
Paisley, Scotland
ABSTRACT
Big data development is a disturbing issue that will affect enterprise across various sectors. The
increase of data volume, high speed of data generation and increasing rate of different data from
heterogeneous sources have led to difficulties in data management. This paper first reviews
different aspects of big data management, including data integration and traditional data
warehouse, and their associated challenges. The problems include increase of redundant data,
data accessibility, time consumption in data modelling and data movement from heterogeneous
sources into a central database, especially in the big data environment. We then propose a logical
data management approach using RESTview technology to integrate and analyse data, without
fully adopting traditional ETL processes. Data that for governance, corporate, security or other
restriction reasons cannot be copied or moved, can easily be accessed, integrated and analysed,
without creating a central repository. Data can be kept in its original form and location,
eliminating the movement of data, significantly speeding up the process and allowing for live
data interrogation. It may not be the practical solution for every situation but, it is a feasible
solution that is comparably cost effective.
KEYWORDS
Big Data; Data Integration; Data warehouse; RESTView
Full Text: https://aircconline.com/ijdms/V11N1/11119ijdms03.pdf
The International Journal of Database Management Systems (IJDMS)
http://airccse.org/journal/ijdms/current2019.html

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11. HYBRID ENCRYPTION ALGORITHMS FOR MEDICAL DATA STORAGE
SECURITY IN CLOUD DATABASE
Fenghua Zhang1
，Yaming Chen2
，Weiming Meng3
and Qingtao Wu4
1
Computer Technology, Henan University of Science and Technology, Luo Yang, China
2,3
Computer Science and Technology, Henan University of Science and Technology, Luo Yang,
China
4
Professor, Henan University of Science and Technology, Luo Yang, China
ABSTRACT
Cloud database are derivatives of Cloud computing. At present, most medical institutions store
data in cloud database. Although the cloud database improves the efficiency of use, it also poses
a huge impact and challenge to the secure storage of data. The article proposes a hybrid
algorithm to solve the data security problem in the hospital cloud database. First, the AES
algorithm is improved. The improved algorithm is called P-AES algorithm. The P-AES
algorithm is then combined with the RSA algorithm, called a hybrid algorithm. The experimental
results show that the hybrid encryption algorithm has the advantages of fast encryption and
decryption speed, high security, good processing ability for longer data, and can solve the data
security problem in cloud database to a certain extent. The algorithm has been successfully
applied to hospital information management system.
KEYWORDS
Hospital System, AES, RSA, Hybrid Encryption
Full Text: https://aircconline.com/ijdms/V11N1/11119ijdms04.pdf
The International Journal of Database Management Systems (IJDMS)
http://airccse.org/journal/ijdms/current2019.html

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14. BRIDGING DATA SILOS USING BIG DATA INTEGRATION
Jayesh Patel
Senior Member
ABSTRACT
With cloud computing, cheap storage and technology advancements, an enterprise uses multiple
applications to operate business functions. Applications are not limited to just transactions,
customer service, sales, finance but they also include security, application logs, marketing,
engineering, operations, HR and many more. Each business vertical uses multiple applications
which generate a huge amount of data. On top of that, social media, IoT sensors, SaaS solutions,
and mobile applications record exponential growth in data volume. In almost all enterprises, data
silos exist through these applications. These applications can produce structured, semi-
structured, or unstructured data at different velocity and in different volume. Having all data
sources integrated and generating timely insights helps in overall decision making. With recent
development in Big Data Integration, data silos can be managed better and it can generate
tremendous value for enterprises. Big data integration offers flexibility, speed and scalability for
integrating large data sources. It also offers tools to generate analytical insights which can help
stakeholders to make effective decisions. This paper presents the overview on data silos,
challenges with data silos and how big data integration can help to stun them.
KEYWORDS
Data Silo, Big Data, Data Pipelines, Integration, Data Lake, Hadoop
Full Text: https://aircconline.com/ijdms/V11N3/11319ijdms01.pdf
The International Journal of Database Management Systems (IJDMS)
http://airccse.org/journal/ijdms/current2019.htm

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17. AUTHORIZATION FRAMEWORK FOR MEDICAL DATA
Geetha Madadevaiah1 , RV Prasad1
, Amogh Hiremath1
, Michel Dumontier2
, Andre Dekker3
1
Philips Research, Philips Innovation Campus, Philips India Ltd, Manyata Tech Park, Bangalore
2
Institute of Data Science, Maastricht University, Maastricht, The Netherlands
3
Department of Radiation Oncology (MAASTRO), GROW School for Oncology and
Developmental Biology, Maastricht University Medical Centre+, Dr Tanslaan 12, 6229ET,
Maastricht, The Netherlands
ABSTRACT
In this paper, the authors describe an approach for sharing sensitive medical data with the
consent of the data owner. The framework builds on the advantages of the Semantic Web
technologies and makes it secure and robust for sharing sensitive information in a controlled
environment. The framework uses a combination of Role-Based and Rule-Based Access Policies
to provide security to a medical data repository as per the FAIR guidelines. A lightweight
ontologywas developed, to collect consent from the users indicating which part of their data they
want to share with another user having a particular role. Here, the authors have considered the
scenario of sharing the medical data by the owner of data, say the patient, with relevant persons
such as physicians, researchers, pharmacist, etc. To prove this concept, the authors developed a
prototype and validated using the Sesame OpenRDF Workbench with 202,908 triples and a
consent graph stating consents per patient.
KEYWORDS
Access Policies, Semantic Web, RDF/SPARQL, Role Based, Rule Based, FAIR, Consent
Full Text: https://aircconline.com/ijdms/V11N3/11319ijdms02.pdf
The International Journal of Database Management Systems (IJDMS)
http://airccse.org/journal/ijdms/current2019.html

18. REFERENCES
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20. IMAGE STEGANOGRAPHY USING INHOMOGENEOUS IMAGES WITH
MODYFING VERNAM SCHEME
Huda H.Al.ghuraify1
, Dr.Ali A.Al-bakry2
, Dr. Ahmad T. Al-jayashi3
1
Engineering technical college,Al-furat Al-awsat university, Iraq
2
Dean of engineering technical college,Al-furat al-awsat university, Iraq
3
Assistance dean of engineering technical college,Al-furat al-awsat university, Iraq
ABSTRACT
Nowadays, due to the rapid development of the internet, it is prominent to guard mystery data
from cyberpunks through communicating. The steganography technique utilizes for trading
mystery data in an approach to stay away from doubt. This paper accomplishes a manner for
encryption each channel of RGB color image separately without the necessity to exchange an
encryption key utilizing the principle of modifying vernam scheme then camouflage it into a
grayscale cover image .On the other hand, encrypts a grayscale image without the necessity to
exchange an encryption key utilizing the principle of modifying vernam scheme then camouflage
it into a cover image of RGB color type . The simulation results revealed an offering of
extremely security for the image transmission.
KEYWORDS
Image steganography,Inhomogeneous images, Mystery data, Vernam scheme, Image
transmission
Full Text: https://aircconline.com/ijdms/V11N4/11419ijdms01.pdf
The International Journal of Database Management Systems (IJDMS)
http://airccse.org/journal/ijdms/current2019.html

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