Faculty Publications
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Item Feature pattern based representation of multimedia documents for efficient knowledge discovery(Springer New York LLC barbara.b.bertram@gsk.com, 2016) Pushpalatha, K.; Ananthanarayana, V.S.The rapid growth of multimedia documents has raised huge demand for sophisticated multimedia knowledge discovery systems. The knowledge extraction of the documents mainly relies on the data representation model and the document representation model. As the multimedia document comprised of multimodal multimedia objects, the data representation depends on modality of the objects. The multimodal objects require distinct processing and feature extraction methods resulting in different features with different dimensionalities. Managing multiple types of features is challenging for knowledge extraction tasks. The unified representation of multimedia document benefits the knowledge extraction process, as they are represented by same type of features. The appropriate document representation will benefit the overall decision making process by reducing the search time and memory requirements. In this paper, we propose a domain converting method known as Multimedia to Signal converter (MSC) to represent the multimodal multimedia document in an unified representation by converting multimodal objects as signal objects. A tree based approach known as Multimedia Feature Pattern (MFP) tree is proposed for the compact representation of multimedia documents in terms of features of multimedia objects. The effectiveness of the proposed framework is evaluated by performing the experiments on four multimodal datasets. Experimental results show that the unified representation of multimedia documents helped in improving the classification accuracy for the documents. The MFP tree based representation of multimedia documents not only reduces the search time and memory requirements, also outperforms the competitive approaches for search and retrieval of multimedia documents. © 2016, Springer Science+Business Media New York.Item A Hybrid Trust Management Scheme for Wireless Sensor Networks(Springer New York LLC barbara.b.bertram@gsk.com, 2017) Karthik, N.; Ananthanarayana, V.S.Wireless sensor network (WSN) consists of wireless small sensor nodes deployed in the terrain for continuous observation of physical or environmental conditions. The data collected from the WSN is used for making decisions. The condition for making critical decision is to assure the trustworthiness of the data generated from sensor nodes. However, the approaches for scoring the sensed data alone is not enough in WSN since there is an interdependency between node and data item. If the overall trust score of the network is based on one trust component, then the network might be misguided. In this work, we propose the hybrid approach to address the issue by assigning the trust score to data items and sensor nodes based on data quality and communication trust respectively. The proposed hybrid trust management scheme (HTMS) detects the data fault with the help of temporal and spatial correlations. The correlation metric and provenance data are used to score the sensed data. The data trust score is utilized for making decision. The communication trust and provenance data are used to evaluate the trust score of intermediate nodes and source node. If the data item is reliable enough to make critical decisions, a reward is given by means of adding trust score to the intermediate nodes and source node. A punishment is given by reducing the trust score of the source and intermediate nodes, if the data item is not reliable enough to make critical decisions. Result shows that the proposed HTMS detects the malicious, faulty, selfish node and untrustworthy data. © 2017, Springer Science+Business Media, LLC.Item Trust Based Data Gathering in Wireless Sensor Network(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Karthik, N.; Ananthanarayana, V.S.Wireless sensor nodes have been successfully employed in various pervasive applications. In all pervasive applications, a gathering of sensor data from the environment is the main operation held in a sensor network, where sink node or base station gathers all generated data to do data analysis and decision making. The data generated by the sensor node in the pervasive environment should be transmitted to the sink node for data analysis and decision making. We strongly conceive that each process from perceiving the environment to decision making, demands trust based process to ease and ensure the trustworthy data exchange among trustworthy nodes such as trust-based data collection, trust-based data aggregation, trust-based data reconstruction and trust-based data analysis for decision making. In this work, we propose a Trust-based Data Gathering which focus on trust-based data collection, data aggregation, and data reconstruction to show that the absence of trust in a sensor-driven pervasive environment could affect the normal functionality of an application. Experimental results show that the proposed method achieves better performance in detecting data faults, malicious nodes and demonstrates that the absence of trust based process in data collection, data aggregation, and data reconstruction in harsh environment consumes more energy and delay for handling untrustworthy data, untrustworthy node and affects the normal functionality of the application. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item Fine-grained data-locality aware MapReduce job scheduler in a virtualized environment(Springer Science and Business Media Deutschland GmbH info@springer-sbm.com, 2020) Jeyaraj, R.; Ananthanarayana, V.S.; Paul, A.Big data overwhelmed industries and research sectors. Reliable decision making is always a challenging task, which requires cost-effective big data processing tools. Hadoop MapReduce is being used to store and process huge volume of data in a distributed environment. However, due to huge capital investment and lack of expertise to set up an on-premise Hadoop cluster, big data users seek cloud-based MapReduce service over the Internet. Mostly, MapReduce on a cluster of virtual machines is offered as a service for a pay-per-use basis. Virtual machines in MapReduce virtual cluster reside in different physical machines and co-locate with other non-MapReduce VMs. This causes to share IO resources such as disk and network bandwidth, leading to congestion as most of the MapReduce jobs are disk and network intensive. Especially, the shuffle phase in MapReduce execution sequence consumes huge network bandwidth in a multi-tenant environment. This results in increased job latency and bandwidth consumption cost. Therefore, it is essential to minimize the amount of intermediate data in the shuffle phase rather than supplying more network bandwidth that results in increased service cost. Considering this objective, we extended multi-level per node combiner for a batch of MapReduce jobs to improve makespan. We observed that makespan is improved up to 32.4% by minimizing the number of intermediate data in shuffle phase when compared to classical schedulers with default combiners. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.