EAI Endorsed Transactions on Scalable Information Systems

Issue 22, 2019

Editor(s)-in-Chief: Hua Wang, Xiaohua Jia and Manik Sharma

Articles
Information

An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval
Appears in:
sis 19(22): e1

Authors:
Shikha Bhardwaj, Gitanjali Pandove, Pawan Kumar Dahiya

Abstract:
Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository. Texture, Color and Shape are among the most prominent features of any CBIR system. Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet tran…Content-based image retrieval (CBIR) is a methodology used to search indistinguishable images across any vast repository. Texture, Color and Shape are among the most prominent features of any CBIR system. Two texture descriptors namely Gray level Co-occurence matrix (GLCM) and Discrete wavelet transform (DWT) have been utilized here for the formation of a hybrid texture descriptor, denoted as (Co-DGLCM). To enhance the retrieval accuracy of the proposed system, a framework of an Extreme learning machine (ELM) with Relevance feedback (RF) has also been used. This technique provides simultaneously spatial relationship and information related to frequency in co-occuring local patterns of an image. Two benchmark texture databases namely Brodatz and MIT-Vistex have been tested and results are obtained in terms of accuracy, total average recall and total average precision which is 96.35% and 97.34% respectively on the two databases.
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Escalation of Forecasting Accuracy through Linear Combiners of Predictive Models
Appears in:
sis 19(22): e2

Author:
Sarat Chandra Nayak

Abstract:
Precise and proficient modelling and forecasting financial time series has been paying attention of researchers, which leads to the development of various statistical and machine learning based models. Accuracy of a particular method is problem and domain specific, hence identifying best method i…Precise and proficient modelling and forecasting financial time series has been paying attention of researchers,
which leads to the development of various statistical and machine learning based models. Accuracy of a particular
method is problem and domain specific, hence identifying best method is controversial. To boost up overall
accuracies and minimizing risk of model selection, combination of outputs of different models has been
recommended in the literature. This work presents a linear combiner of five predictive models i.e. ARIMA,
RBFNN, MLP, SVM, and FLANN for improving prediction accuracy. Four statistical methods i.e. trimmed mean,
simple average, median, and an error based method are used for suitable choice of combining weights. The
individual forecasts and the linear combiner are used separately to predict closing price of five stock markets and
exchange rate of five global markets. Extensive simulation work demonstrates the feasibility and supremacy of the
linear combiner.
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Scalable Source Code Similarity Detection in Large Code Repositories
Appears in:
sis 19(22): e3

Authors:
Firas Alomari, Muhammed Harbi

Abstract:
Source code similarity are increasingly used in application development to identify clones, isolate bugs, and find copy-rights violations. Similar code fragments can be very problematic due to the fact that errors in the original code must be fixed in every copy. Other maintenance changes, such as …Source code similarity are increasingly used in application development to identify clones, isolate bugs, and find copy-rights violations. Similar code fragments can be very problematic due to the fact that errors in the original code must be fixed in every copy. Other maintenance changes, such as extensions or patches, must be applied multiple times. Furthermore, the diversity of coding styles and flexibility of modern languages makes it difficult and cost ineffective to manually inspect large code repositories. Therefore, detection is only feasible by automatic techniques. We present an efficient and scalable approach for similar code fragment identification based on source code control flow graphs fingerprinting. The source code is processed to generate control flow graphs that are then hashed to create a unique fingerprint of the code capturing semantics as well as syntax similarity. The fingerprints can then be efficiently stored and retrieved to perform similarity search between code fragments. Experimental results from our prototype implementation supports the validity of our approach and show its effectiveness and efficiency in comparison with other solutions.
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Cloud Services Ranking by measuring Multiple Parameters using AFIS
Appears in:
sis 19(22): e4

Authors:
Sagheer Abbas, Tahir Alyas, Atifa Athar, Muhammad Adnan Khan, Areej Fatima, Waseem Ahmad Khan

Abstract:
Assigning a level to a number of choices is referred to as ranking. The concept of ranking is applied in many situations, wherein, team rankings, player rankings, university rankings, and country rankings are commonly used these days. Similarly, in cloud standardization, ranking the web services …Assigning a level to a number of choices is referred to as ranking. The concept of ranking is applied in many
situations, wherein, team rankings, player rankings, university rankings, and country rankings are commonly
used these days. Similarly, in cloud standardization, ranking the web services is a principal concern, as it is a relatively new approach, assigning ranks to cloud facilities has gained significant attention from researchers across the globe. Furthermore, cloud services standardization is an important idea as it is necessary if it is required to assign ranking for cloud services. There are few limitations in cloud standardization as there is no technique to check valid services and its classifications, wherein, the standardization of cloud services will play a major role in controlling the redundancy of cloud services. In this article, a new cloud service ranking method is proposed using an Adaptive Fuzzy Inference System (AFIS).
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Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams
Appears in:
sis 19(22): e5

Authors:
Najam us Sahar, Muhammad Sohail Irshad, Muhammad Adnan Khan

Abstract:
Today the computational study of people’s opinion expressed in free form written text is called the field of sentiment analysis and opinion mining. Various research areas such as Natural Language Processing, Data Mining, Text Mining lie in field of Sentiment Analysis and is also becoming major part…Today the computational study of people’s opinion expressed in free form written text is called the field of sentiment analysis and opinion mining. Various research areas such as Natural Language Processing, Data Mining, Text Mining lie in field of Sentiment Analysis and is also becoming major part of importance to organizations because of online commerce is included in their operational strategy. Due to excess of user’s comments, feedback on web there is a need to analyze the user generated text. This research focuses on aspect level sentiment analysis in which identification of aspects and their related sentiments
is being done. Opinion analysis helps to identify the polarity of the text and feature extraction. This study is being done to provide an effective and efficient framework to calculate the sentiments of written text by using Naïve Bayes approach. For sentiment analysis dataset of 1060 reviews of different restaurants from online website TripAdvisor.com is being used. The
outcome achieved good accuracy 80.833 percent.
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Privacy-Preserving Data Sharing using Multi-layer Access Control Model in Electronic Health Environment
Appears in:
sis 19(22): e6

Authors:
Shekha Chenthara, Khandakar Ahmed, Frank Whittaker

Abstract:
Electronic Health Data (EHD) is an emerging health information exchange model that facilitates healthcare providers and patients to efficiently store and share their private healthcare information from any place and at any time as per demand. Generally, Cloud services provide the infrastructure by…Electronic Health Data (EHD) is an emerging health information exchange model that facilitates healthcare
providers and patients to efficiently store and share their private healthcare information from any place and at any time as per demand. Generally, Cloud services provide the infrastructure by reducing the cost of storing, processing and updating information with improved efficiency and quality. However, the privacy of Electronic Health Records (EHR) is a significant hurdle while outsourcing private health data in the cloud because there is a higher peril of leaking health information to unauthorized parties. Several existing techniques are able to analyse the security and privacy issues associated with e-healthcare services. These methods are designed for single database, or databases, with an authentication centre and thus cannot adequately protect the data from
insider attacks. Therefore, this research study mainly focusses on how to ensure the patient privacy while
sharing the sensitive data between same or different organisations as well as healthcare providers in a cloud
environment. This paper proposes a multi-layer access control mechanism named MLAC Model to construct a
secure and privacy-preserving EHR system that enables patients to share their data with stakeholders. In this paper, we use a Dual layer access control model named Pseudo-Role Attribute based access control (PR-ABAC) mechanism that integrates attributes with roles for the secure sharing of EHR between multiple collaborators. The proposed framework also uses the concept of Provenance to ensure the Integrity of patient data. This work is expected to provide a foundation for developing security solutions against cyber-attacks, and thus contribute to the robustness of healthcare information sharing environments.
more »
Improving Performance in Component Based Distributed Systems
Appears in:
sis 19(22): e7

Authors:
Fahd N. Al-Wesabi, Huda G. Iskandar, Mokhtar M. Ghilan

Abstract:
Assuring high performance is one of the most important factors when building computerized systems. In distributed component-based systems different configurations and workloads are common. Over the years, many approaches were proposed to analyse, predict, measure and evaluate the performance of com…Assuring high performance is one of the most important factors when building computerized systems. In distributed component-based systems different configurations and workloads are common. Over the years, many approaches were proposed to analyse, predict, measure and evaluate the performance of component-based distributed systems in an attempt
to improve its performance. Higher performance results in better utilization of system resources, high throughput and quick response time of user requests. In this paper, we extend the work of the E-Avala approach to improve the overall performance
of the proposed approach by adding power processing capabilities. The proposed approach has been implemented and compared with previous approach using Arena simulation with varying configuration parameters. Comparative results show that the proposed approach has given better performance with different levels of processing powers.
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Ambient Air Quality Estimation using Supervised Learning Techniques
Appears in:
sis 19(22): e8

Authors:
Jasleen Kaur Sethi, Mamta Mittal

Abstract:
The exponential increase of population in the urban areas has led to deforestation and industrialization that greatly affects the air quality. The polluted air affects the human health. Due to this concern, the prediction of air quality has become a potential research area. For the assessment of …The exponential increase of population in the urban areas has led to deforestation and industrialization that
greatly affects the air quality. The polluted air affects the human health. Due to this concern, the prediction of
air quality has become a potential research area. For the assessment of air quality an important indicator is Air Quality Index (AQI). The objective of this paper is to build prediction models using supervised learning.
Supervised Learning is broadly classified into: classification, regression and ensemble techniques. This study has been carried out using various techniques of classification, regression and ensemble learning. It has been observed from experimental work that Decision Trees from classification, Support Vector Regression from regression and Stacking Ensemble from ensemble techniques work more effectively and efficiently than the rest of the other techniques that fall under these categories.
more »

Scope

As the data volumes continue to increase and the ways of information dispersion across the globe continue to diversify, new scalable methods and structures are needed for efficiently processing those distributed and autonomous data. Grid computing, P2P technology, distributed information retrieval …

Topics

The scope of the journal includes:

Scalable distributed information systems
Scalable grid information systems
Parallel information processing and systems
Web information searching and retrieval
Data mining
Content delivery networks (CDN)
VLDB
P2P systems
Scalable mobile…

The scope of the journal includes:

Scalable distributed information systems
Scalable grid information systems
Parallel information processing and systems
Web information searching and retrieval
Data mining
Content delivery networks (CDN)
VLDB
P2P systems
Scalable mobile and wireless database systems
Large scale sensor network systems
Index compression methods
Architectures for scalability
Scalable information system applications
Evaluation metrics for scalability
Information security

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Editors-in-Chief
Hua Wang, Victoria University, Australia
Xiaohua Jia, City University of Hong Kong
Editorial board
Abhishek Shukla, IntraEdge, United States
Ajay Kattepur, Tata Consultancy Services, India
Arianna D'Ulizia, Consiglio Nazionale delle Ricerche (CNR), Italy
Bish…

Editors-in-Chief
Hua Wang, Victoria University, Australia
Xiaohua Jia, City University of Hong Kong
Editorial board
Abhishek Shukla, IntraEdge, United States
Ajay Kattepur, Tata Consultancy Services, India
Arianna D'Ulizia, Consiglio Nazionale delle Ricerche (CNR), Italy
Bishnu Prasad Gautam, Kanazawa Gakuin University, Japan
Chai Tong Yuen, Universiti Tunku Abdul Rahman, Malaysia
Chandu Thota, University of Nicosia, Cyprus
Chang Choi, Chosun University, South Korea
Chi Lin, Dalian University of Technology, China
Chin-Feng Lee, Chaoyang University of Technology, Taiwan
Chin-Ling Chen, Chaoyang University of Technology, Taiwan
Cho-Li Wang, University of Hong Kong, China
Chuanlong Wang, Taiyuan Normal University, China
Chun-Hao Chen, National Kaohsiung University of Science and Technology, Taiwan
Daniel S. Katz, University of Chicago, USA
Fabrizio Silvestri, ISTI – CNR, Italy
G. Reza Nasiri, Alzahra University, Tehran, Iran
Hamed Taherdoost, University Canada West, Canada
Haris M. Khalid, University of Dubai, UAE
Heng Tao Shen, University of Queensland, Australia
Hiep Xuan Huynh, Can Tho University, Vietnam
Houbing Song, University of Maryland Balitmore, USA
Hsing-Chung Chen (Jack Chen), Asia University, Taiwan
Hsiu-Ming Wu, National Taipei University of Technology, Taiwan
Hui Ma, Victoria University of Wellington, New Zealand
Huiliang Zhao, Shanghai University of Engineering Science, China
J Amudhavel, KL University, India
Jianming Yong, University of Southern Queensland, Australia
José Manuel Machado, University of Minho, Portugal
Jose Merseguer, Universidad de Zaragoza, Spain
Ji Zhang, University of Southern Queensland, Australia
Jie Li, University of Tsukuba, Japan
Jin Wang, Institute of Applied Physics and Computational Mathematics, China
Karolj Skala, Ruđer Bošković Institute, Croatia
Lakshmana Kumar R, Tagore IET, India
Le Sun, Nanjing University of Information Science and Technology, China
Limei Peng, Kyungpook National University, South Korea
Lin Yun, Harbin Engineering University, China
Mahesh Jangid, Manipal University Jaipur, India
Manik Sharma, DAV University, India
Manish Kumar, The Indian Institute of Information Technology, Allahabad, India
Mera Kartika Delimayanti, Politeknik Negeri Jakarta, Indonesia
Michael Bewong, Charles Sturt University, Australia
Mudasir Mohd, University of Kashmir, India
Mustafa Bin Man, Universiti Malaysia Terengganu, Malaysia
Muzammil Hussain, University of Management and Technology, Lahore, Pakistan
Nageswara Rao Moparthi, KL University, India
Nhlanhla B.W. Mlita, University of South Africa, South Africa
Nuno M. Garcia, Faculty of Sciences, University of Lisbon, Portugal
Irvanizam, Universitas Syiah Kuala, Indonesia
Phan Cong Vinh, Nguyen Tat Thanh University, Vietnam
Pon Harshavardhanan, VIT Bhopal University, India
Punitha Palanisamy, Tagore IET, India
Rafiqul Islam, Australian Institute of Higher Education, Australia
Raj Gururajan, University of Southern Queensland, Australia
Robertas Damaševičius, Kaunas University of Technology (KTU), Lithuania
Rose Quan, Northumbria University, UK
Sathya Priya. S, SRM Institute of Science and Technology, India
Saadi Slami, Ziane Achour University of Djelfa, Algeria
Shabir Ahmad, Gachon University, Korea
Sherman Chow, Chinese University of Hong Kong
Shi Dong, Zhoukou Normal University, China
Silva Fábio, University of Minho, Portugal
Siuly Siuly, Victoria University, Australia
Sivaparthipan C B, Bharathiar University, India
Steve Beitzel, Telcordia, USA
Thaier Hayajneh, Fordham University, USA
Ting-Chia Hsu, National Taiwan Normal University, Taiwan
Tzung-Pei Hong, National University of Kaohsiung, Kaohsing City, Taiwan
Venkatesan Subramanian, Indian Institute of Information Technology, Allahabad, India
Vijayakumar Ponnusamy, SRM University, India
Vu Nguyen, University of Science, Vietnam
Wang-Chien Lee, The Pennsylvania State University, USA
Weili Wu, The University of Texas at Dallas, USA
Weiwei Jiang, Beijing University of Posts and Telecommunications, China
Wen-Yang Lin, National University of Kaohsiung, Taiwan
Xueyan Tang, Nanyang Technological University, Singapore
Xiao-Zhi Gao, University of Eastern Finland, Finland
Xiaodi Huang, Charles Sturt University, Australia
Xiaohui Tao, University of Southern Queensland, Australia
Xiaoming Fu, Georg-August-University of Goettingen, Germany
Yawar Abbas Bangash, Sunway University, Malaysia
Ye Wang, National University of Defense Technology, China
Yingshu Li, Georgia State University, USA
Yogeshwar Vijayakumar Navandar, National Institute of Technology, India
Zhengyi Chai, Tiangong University in China, China
Zhisheng Huang, Vrije University of Amsterdam, Netherlands

Journal Blurb

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/sis/index

Publisher: EAI
ISSN: 2032-9407
Volume: 6

Published: 2019-07-29

EUDL

An Intelligent Multi-resolution and Co-occuring local pattern generator for Image Retrieval

Escalation of Forecasting Accuracy through Linear Combiners of Predictive Models

Scalable Source Code Similarity Detection in Large Code Repositories

Cloud Services Ranking by measuring Multiple Parameters using AFIS

Bayesian Sentiment Analytics for Emerging Trends in Unstructured Data Streams

Privacy-Preserving Data Sharing using Multi-layer Access Control Model in Electronic Health Environment

Improving Performance in Component Based Distributed Systems

Ambient Air Quality Estimation using Supervised Learning Techniques

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