Forthcoming articles

 


International Journal of Knowledge Engineering and Data Mining

 

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International Journal of Knowledge Engineering and Data Mining (6 papers in press)

 

Regular Issues

 

  • CLUSTERING ANALYSIS ON THE DESIGN OF COMPLEX DNA NANOSTRUCTURES USING DNA HYBRIDIZATION, SWARM INTELLIGENCE AND GENETIC ALGORITHM   Order a copy of this article
    by Ganeshbabu Karmegam 
    Abstract: The main objective of this work 'clustering analysis on the design of complex DNA nanostructures using DNA hybridisation, swarm intelligence and genetic algorithm', is to study the clustering techniques with swarm intelligence and genetic algorithm to create new algorithms and to improve the existing algorithms in order to apply them to the issues of design the nanostructure to computationally simulate the DNA sequence recognition process and to achieve an optimised conformation for the complex structure, the relative orientation between motif pattern and DNA hybridisation such that to bind the complementary DNA sequences to form structure. In the design of nanostructure, DNA molecular recognition capacities make DNA an appealing candidate for the construction of novel nanomaterials. The modified Boyer Moore algorithm and modified K-nearest neighbour is for design the complex nanostructures to improve a candidate solution with regard to a given measure of quality.
    Keywords: Clustering; Swarm Intelligence; Genetic Algorithm; DNA Nanostructures; DNA Hybridization.
    DOI: 10.1504/IJKEDM.2018.10013707
     
  • Performance Evaluation of Bandwidth for Virtual Machine Migration in Cloud Computing   Order a copy of this article
    by ADITYA BHARDWAJ, Rama Krishna Challa 
    Abstract: The proliferation of virtualisation enables cloud computing industries, such as Amazon, IBM, Google, Microsoft, and Rackspace to increase their physical hardware utilisation by multi-tenant model. One important key characteristic of server virtualisation is virtual machine (VM) migration. With the popularity of cloud paradigm, the number of enterprise applications deployed over cloud-systems are increasing. This shift in cloud services has brought new challenges of frequently varying resource requirement by the VM migration operation. Inaccurate allocation of bandwidth may affect end-user experience and reduce the cloud service provider's profit, Therefore, in this work we setup QEMU-KVM cloud VM migration experimental testbed environment and run CPU, memory intensive cloud benchmarks on the migrated VM. From the experimental results, it is found that increase in bandwidth significantly lower the migration time and downtime up-to an extent, however, reserving maximum bandwidth does not help anymore for performance improvement of migration mechanism.
    Keywords: Cloud Computing; Virtualization; Pre-copy VM Migration Technique; Migration time; Downtime; KVM hypervisor.
    DOI: 10.1504/IJKEDM.2018.10014619
     
  • A stabilising optimal k-out-of-ℓ resources allocation algorithm   Order a copy of this article
    by HADID RACHID 
    Abstract: In distributed systems, resource allocation consists in managing fair access to a large number of processes to a typically small number of reusable resources. When the number of available resources is greater than one, the efficiency in concurrent accesses becomes an important issue, as a crucial goal is to minimise the waiting times to utilise these resources. In this paper, we present a k-out-of-ℓ resources allocation solution in tree networks. The k-out-of-ℓ resources allocation problem is a generalisation of the well-known ℓ resources allocation problemthere are ℓ units of the shared resource, any process can request up to k units (1 ≤ k ≤ ℓ) and no resource unit can be allocated to more than one process simultaneously. The proposed solution is optimal in terms of waiting times of processes to enter critical sections (get the requested units of the shared resource), i.e., between two entries of a process to its critical section, no other process can enter its critical section more than once after stabilisation. Furthermore, every process's request is granted in at most O(n×h) rounds. Since our algorithm is stabilising, it does not require initialisation and withstands transient faults. The stabilisation time of the algorithm is 3×h + 6 rounds and the waiting time is (n-1), where h and n are the height and the size (number of processes) of the tree, respectively. In addition, this algorithm satisfies all the requirements of the k-out-of-ℓ resources allocation problem: safety, fairness and (k,ℓ)-liveness.
    Keywords: resources allocation; k-out-of-l resources allocation; k-out-of-l exclusion; l resources allocation; PIF scheme; distributed systems; fault tolerance; self-stabilizing.
    DOI: 10.1504/IJKEDM.2018.10014813
     
  • Optimal Resource Usage in a Multiclass Customer Based Cloud Centers   Order a copy of this article
    by Nishikant Choudhury, Veena Goswami 
    Abstract: Cloud computing is an evolutionary computing paradigm, whereby shared resources are provided to users on-demand over the Internet as services. Although the benefits and opportunities of cloud computing are tremendous but its challenges and problems require a huge amount of effort to be addressed. The ability to deliver guaranteed quality of services is crucial for the commercial success of this computing paradigm. We consider the cloud centers which handle the requests from different classes of customers such as high-priority, low-priority, etc. on the basis of service level agreements. Both the modes have equal chances of receiving service, and one and more virtual machines are used to serve each arrival mode. We analyse the system considering an infinite-buffer multi-server queuing system with two priority classes of impatient customers. We obtain various performance measures to analyse such a system which is too complex due to abandonment phenomenon.
    Keywords: cloud computing; virtual machines; queueing; resource; performance evaluation.
    DOI: 10.1504/IJKEDM.2018.10014797
     
  • An Efficient Algorithm for Mining Closed Frequent Intervals   Order a copy of this article
    by Naba Jyoti Sarmah, Anjana Kakoti Mahanta 
    Abstract: In this paper, we propose a method for mining closed frequent intervals from interval dataset. Already known algorithms for mining closed frequent intervals use either the maximal frequent intervals or a tree structure to generate the closed intervals. The algorithm proposed in this paper mines the closed frequent intervals directly from interval datasets. Mathematical proofs of certain properties of closed intervals that are used in the proposed algorithm have been given. The proposed algorithm has been tested with real life and synthetic datasets. Complexity of the proposed algorithm is O(n2), which is better than any other existing algorithms developed for mining closed frequent intervals.
    Keywords: Data Mining; Interval Data Mining; Closed Frequent Interval; Maximal Frequent Interval.

  • Heuristic-based hybrid privacy-preserving data stream mining approach using SD-Perturbation and Multi-Iterative K-Anonymization   Order a copy of this article
    by Paresh Solanki, Sanjay Garg, Hitesh Chhinkaniwala 
    Abstract: Different e-sources are regularly generating huge volumes of data. Data mining is the technique of gathering knowledge from a dataset, but dataset often contains sensitive information so discharging such data may cause privacy breaches. The problem of privateness desires to be is addressed earlier than streaming facts are launched for mining and evaluation functions. Various algorithms proposed so far have focused mainly on static data and very few are on data streams. Perturbation and k-anonymity have received significant attention over other privacy-preserving techniques because of its easiness and effectiveness in guarding data. The proposed hybrid approach is an extension to heuristic-based data perturbation where privacy is preserved through computed tuple values for each instance and users define sensitive drift (SD) and an extension to k-anonymisation where privacy gain has been worked out for choosy anonymisation for a set of tuples and perturbs the sensitive attribute values on data streams.
    Keywords: Data Mining;Stream Mining;Anonymization;Perturbation;Privacy Preserving.