Informatics Publishing Limited

O. Obulesu ¹, A. Rama Mohan Reddy ²

Affiliations
1 Department of IT, SVEC, JNTUA Anantapuramu, Tirupati, Chittoor (Dt), A.P., IN
2 Dept. of CSE, S.V. University College of Engineering, Tirupati, Chittoor (Dt), A.P., IN

Abstract

Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining is useful in Weather Forecasting, Fraud Detection and GIS Applications. In General, spatio-temporal pattern discovery process finds the partially ordered subsets of the event-types whose instances are located together and occur serially for a given collection of Boolean spatio-temporal event-types.  Big Data concerns large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data is now rapidly expanding in all science and engineering domains, including physical, biological and bio-medical sciences.  In this paper, a new framework is proposed to find spatiotemporal patterns from Big Data. Existing algorithms are well in computation of necessary patterns, but more problematic when they are applied to Big Data. Big Data is a new trend used to analyse the datasets that due to their large size and complexity, Developers cannot manage them with traditional current algorithms or data mining software tools. Big Data mining is the capability of extracting useful information from these large datasets or streams of data, that due to its volume, variety, and velocity, it was not possible before to do it. The Big Data challenge is becoming one of the most exciting opportunities for the next years. This Paper focuses on a broad overview of pattern mining algorithms and significance in Spatiotemporal Databases, its current status, trade-offs, and forecast to the big data pattern mining future.

Keywords

Periodicity Detection, Spatial Patterns, Big Data, Cascading Spatiotemporal Pattern Discovery, MapR.

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O. Obulesu ¹, A. Rama Mohan Reddy ²

Affiliations
1 Department of IT, SVEC, Tirupati – 517102, Andhra Pradesh, IN
2 SVUCE, Tirupati - 517502, Andhra Pradesh, IN

Abstract

Objectives: To find effective periodic patterns throughthe symbolic database representation of spatiotemporal data by using an efficient algorithm, ETMA (Enhanced Tree-based Mining Algorithm for large Databases). Methods/Statistical Analysis: There are distinct types of notions used to store and manage transactions data horizontally such as segment, sequence and individual symbols. The proposed algorithm can mine periodic patterns in full-series and subsection series also.The proposed algorithm based on prefix and suffix tree concurrently discover signal, series and section periodicity furthermore recognize and report only the efficient and non-redundant periods through the pruning techniques to abolish redundant (repetitive) periods. Findings: This algorithm finds interesting results with the help of a mixture of testing have been performed to validate the peroformance, strength, scalability, and correctness of the produced results in comparison with traditional algorithms. It is used to identify the three distinctcategories of maximal patterns effectively on various synthetic and reallife datasets. All tests are completedon distinct types of noisy such as insertion, deletion and replacement. ETMA reduces the running time and buildsservice of the proficient symbolic process. Moreover, ETMA simply report time-series instances dynamically, interms of symbol, sequence and segment approaches respectively. The length of the pattern, and proving efficiency of the pruning and searching strategies from synthetic and real datasets is a really open & challenging mining problem. Application/Improvements: This algorithm is 50% better than MAFIA and 20% better than STNR algorithms on Accident dataset and 50% better than STNR and 74% better than ECLAT algorithms on Mushroom dataset.

Keywords

Periodic Patterns, Spatioitemporal Databases, Symbol Periodicity, Segement Periodicity, Sequence Periodicity.

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Chukka Santhaiah ¹, A. Rama Mohan Reddy ¹

Affiliations
1 C. S. E Department, S. V. University College of Engineering, S. V. University, Tirupati - 517502, Andhra Pradesh, IN

Abstract

Background/Objectives: Multiple Sequence alignment based on Log Expectation (MUSCLE) is a new approach for multiple sequence alignments of DNA or Protein sequences. The MUSCLE shows the high accuracy than the Clustal W and MAFFT and T-Coffee approaches. Based on MUSCLE approach, we aim to construct the Phylogenetic tree for Cyclooxygenase sequences. Method/Statistical Analysis: In MUSCLE there is a tree steps of process to find the accuracy of alignment i.e. are draft progressive, improved progressive and refinement. In each step it has the another sub steps to complete the alignment. Findings: MUSCLE is the faster algorithm than the ClustalW by adding the additional options for input sequences. It executes more sequences than the ClustalW and MAFFT. The MUSCLE displays the results with accuracy values of every input sequence. Application/Improvement: MUSCLE gives the Phylogenetic tree with very closeness of the sequences of input sequences alignment.

Keywords

DNA, Hippocampus, MUSCLE, Phylogenetic Tree.

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K. Munivara Prasad ¹, A. Rama Mohan Reddy ², M. Ganesh Karthik ³

Affiliations
1 Department of Computer Science and Engineering, Rayalaseema University, Kurnool, IN
2 Departmentof Computer Science and Engineering, SVUCE, SV University, Tirupati, IN
3 Department of Computer Science and Engineering, Sree vidyanikethan Engg. College, Tirupati, IN

Abstract

The Internet Threat Monitoring (ITM),is a globally scoped Internet monitoring system whose goal is to measure, detect, characterize, and track threats such as distribute denial of service(DDoS) attacks and worms. To block the monitoring system in the internet the attackers are targeted the ITM system. In this paper we address flooding attack against ITM system in which the attacker attempt to exhaust the network and ITM's resources, such as network bandwidth, computing power, or operating system data structures by sending the malicious traffic. We propose an information-theoretic frame work that models the flooding attacks using Botnet on ITM. Based on this model we generalize the flooding attacks and propose an effective attack detection using Honeypots.

Keywords

Internet Threat Monitors (ITM), DDoS, Flooding Attack, Botnet and Honeypot.

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M. Abhilash Kumar Reddy ¹, A. Rama Mohan Reddy ¹

Affiliations
1 Department of Computer Science and Engineering, Sri Venkateswara University College of Engineering, Tirupati, Andhra Pradesh, IN

Abstract

In Vehicular ad hoc networks road safety and traffic management applications required stable communication channel with minimum disturbances. In vanets vehicles used bandwidth to forward packets towards destination with the help of relay nodes. While exchanging information there are chances of collisions due to improper bandwidth allocation in a network. Some applications such as safety and traffic management required consistent channel conditions. However the design of an efficient medium access control (MAC) is a challenging task due to dynamic topology changes. The existing cluster based TDMA MAC protocols used traffic loads for bandwidth allocation in such a manner position of vehicle in the cluster is not considered. As part of that, vehicles located at end of cluster region can request for channel, as consequence the cluster head (CH) allocates bandwidth based on traffic loads. In this case vehicles are not in a stage to use total reserved slot allocated for usage, because in short span vehicles move to next cluster region, it shows inefficient allocation of channel. In this paper we proposed an efficient approach for allocation of bandwidth which depends on vehicle position in cluster. Here vehicles at starting position can get more bandwidth when compare to end position vehicles. The simulation shows position based bandwidth allocation where the channel is allocated based on effective and reduces collisions as result improves overall network performance.

Keywords

Vanets, Collision Free, Cluster and Position Based MAC, QoS, and Queue Theory.

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