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EAI Endorsed Transactions on Scalable Information Systems

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

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 …

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 technology and networking technology all must be merged to address the scalability concern.

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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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Special Issues

Special issue editor: J. Amudhavel

Special issue editor: J. Amudhavel

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Editorial Board

  • Steve Beitzel (Telcordia)
  • Cho-Li Wang (University of Hong Kong)
  • Daniel S. Katz (University of Chicago)
  • Fabrizio Silvestri (ISTI – CNR, Italy)
  • Hamed Taherdoost (Hamta Business Solution Snd)

  • Heng Tao Shen (University of Queensland)</li…

  • Steve Beitzel (Telcordia)
  • Cho-Li Wang (University of Hong Kong)
  • Daniel S. Katz (University of Chicago)
  • Fabrizio Silvestri (ISTI – CNR, Italy)
  • Hamed Taherdoost (Hamta Business Solution Snd)
  • Heng Tao Shen (University of Queensland)
  • José Machado (Uniiversity of Minho)
  • Jie Li (University of Tsukuba)
  • Phan Cong Vinh (Nguyen Tat Thanh University)
  • Raj Gururajan (University of Southern Queensland)
  • Wang-Chien Lee (The Pennsylvania State University)
  • Weili Wu (The University of Texas at Dallas)
  • Xueyan Tang (Nanyang Technological University)
  • Yingshu Li (Georgia State University)
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Submission Instructions
Publication Ethics and Malpractice Statement
Editors and Editorial Board
Publisher
EAI
ISSN
2032-9407
Volume
4
Published
25th Sep 2017

  • Welcome message from the Editor-in-Chief and Co-Editor-in-Chief

    Editorial in EAI Endorsed Transactions on Scalable Information Systems 17(14): e1

    Xiaohua Jia

    Abstract

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  • A Comparative Analysis of Feature Extraction Methods for Classifying Colon Cancer Microarray Data

    Research Article in EAI Endorsed Transactions on Scalable Information Systems 17(14): e2

    M.O. Arowolo, R.M. Isiaka, S.O. Abdulsalam, Y.K. Saheed, K.A. Gbolagade

    Abstract
    Feature extraction is a proficient method for reducing dimensions in the analysis and prediction of cancer classification. Microarray procedure has shown great importance in fetching informative gene…Feature extraction is a proficient method for reducing dimensions in the analysis and prediction of cancer classification. Microarray procedure has shown great importance in fetching informative genes th at needs enhancement in diagnosis. Microarray data is a challenging task due to high dimensional-low sample dataset with a lot of noisy or irrelevant genes and missing data. In this paper, a comparative study to demonstrate the effectiveness of feature ext raction as a dimensionality reduction process is proposed, and concludes by investigating the most efficient approach that can be used to enhance classification of microarray. Principal Component Analysis (PCA) as an unsupervised technique and Partial Least Square (PLS) as a supervised technique are considered, Support Vector Machine (SVM) classifier were applied on the dataset. The overall result shows that PLS algorithm provides an improved performance of about 95.2% accu racy compared to PCA algorithms.
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  • An Unsupervised Approach of Knowledge Discovery from Big Data in Social Network

    Research Article in EAI Endorsed Transactions on Scalable Information Systems 17(14): e3

    Mohiuddin Ahmed

    Abstract
    Social network is a common source of big data. It is becoming increasingly difficult to understand what is happening in the network due to the volume. To gain meaningful information or identifying the …Social network is a common source of big data. It is becoming increasingly difficult to understand what is happening in the network due to the volume. To gain meaningful information or identifying the underlying patterns from social networks, summarization is an useful approach to enhance understanding of the pattern from big data. However, existing clustering and frequent item-set based summarization techniques lack the ability to produce meaningful summary and fails to represent the underlying data pattern. In this paper, the effectiveness co-clustering is explored to create meaningful summary of social network data such as Twitter. Experimental results show that, using co-clustering for creating summary provides significant benefit over the existing techniques.
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  • Software Defined Network-based Scalable Resource Discovery for Internet of Things

    Research Article in EAI Endorsed Transactions on Scalable Information Systems 17(14): e4

    Mahbuba Afrin, Redowan Mahmud

    Abstract
    Geo-distributed and heterog eneous Internet of Things (IoT) devices can gener ate hug e amoun t of data. Ineÿcien t manag emen t of IoT-da ta promotes netw ork cong estion and increases computa tiona…Geo-distributed and heterog eneous Internet of Things (IoT) devices can gener ate hug e amoun t of data. Ineÿcien t manag emen t of IoT-da ta promotes netw ork cong estion and increases computa tional overhead on the data-processing entities. Traditional netw orking architecture, tha t is lack of functional abstr action and monitoring capabilities, often fails to meet the dynamics of IoT. Softw are Define Netw ork (SDN) can be a viable alterna tiv e of the traditional netw orking architecture while dealing with IoT. In SDN, manag emen t, monitoring and context sensing of the connected componen ts are sim plifie and can be customized. In this paper , SDN-sensed contextual inf orma tion of di eren t componen ts (computa tional entities, netw ork, IoT devices) are combined together to facilita te scalable resource discov ery in IoT. The proposed policy targ ets balanced processing and cong estion-less forw arding of IoT-da ta. Through sim ula tion studies, it has been demonstr ated tha t the SDN-based resource discov ery in IoT outperf orms the traditional netw orking based approaches in terms of resource discov ery time and Quality of Service (QoS) satisf action rate.
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  • Software-Defined Network Testbed Using ZodiacFX a Hardware Switch for OpenFlow

    Research Article in EAI Endorsed Transactions on Scalable Information Systems 17(14): e5

    Fahad Nazir, Qazi Humayun, R Badlishah Ahmad, Shamsul Jamel Elias

    Abstract
    Software-defined network (SDN) is a new programmable networking designed to perform tasks easier by enabling network administrators to add network control via a centralized control platform and open …Software-defined network (SDN) is a new programmable networking designed to perform tasks easier by enabling network administrators to add network control via a centralized control platform and open interfaces. Common network use procedures such as traffic shifts, troubleshooting and various types of device configuration. Thus devices are needed to reconfigure with the network, in order to create a reaction with events. In this paper, we have developed an SDN testbed using Zodiac FX a hardware switch for OpenFlow experiment. This research is utilized Zodiac FX a hardware switch to test the usage and discuss about the SDN controllers. Ryu controller is configured for testbed development. The main contribution of this paper in threefold as follows: first it provides the configuration of Ryu controller, second, it provides the configuration of Zodiac FX switch and lastly it develops a simple SDN testbed for OpenFlow.
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  • Intricacies of Unstructured Data

    Research Article in EAI Endorsed Transactions on Scalable Information Systems 17(14): e6

    Rajeshwari K Rai

    Abstract
    This research paper is an attempt to explore unstructured data that has taken the world of data science by storm. Handling the unprecedented growth of unstructured data is a big challenge faced by m…This research paper is an attempt to explore unstructured data that has taken the world of data science by storm. Handling the unprecedented growth of unstructured data is a big challenge faced by most data scientists. This paper discusses about the types and sources of unstructured data and effective ways of processing and analysing this data.
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