Faculty Publications
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Publications by NITK Faculty
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Item A bottom-up approach towards achieving semantic web services(IEEE Computer Society, 2013) Kamath S․, S.S.; Ananthanarayana, V.S.With the advent of Service-oriented Architecture, Web Services have already become the preferred way of integrating multi-platform, cross-vendor business applications. However, due to the steadily increasing number of published services on the Web, discovery of the most relevant Web services as per the stated requirements is very challenging. Due to this explosive growth of Web data and services, there is a significant need for streamlining the process of service discovery to provide better matching, composition and integration capabilities without human intervention. The proposed Semantic Web approach aims to address these issues by the inclusion of explicit semantics in service descriptions. However, it is still far from realization and the fact remains that it is complex to develop a semantic Web service from the ground-up. In this paper, we propose a framework that uses a bottom-up approach for building semantic Web services from existing service descriptions on the Web, thus reducing the time and cost involved in large scale manual annotation. © 2013 IEEE.Item A modified Ant Colony optimization algorithm with load balancing for job shop scheduling(IEEE Computer Society help@computer.org, 2013) Chaukwale, R.; Kamath S․, S.S.The problem of efficiently scheduling jobs on several machines is an important consideration when using Job Shop scheduling production system (JSP). JSP is known to be a NP-hard problem and hence methods that focus on producing an exact solution can prove insufficient in finding an optimal resolution to JSP. Hence, in such cases, heuristic methods can be employed to find a good solution within reasonable time. In this paper, we study the conventional ACO algorithm and propose a Load Balancing ACO algorithm for JSP. We also present the observed results, and discuss them with reference to the conventional ACO. It is observed that the proposed algorithm gives better results when compared to conventional ACO. © 2013 IEEE.Item Sentiment analysis based approaches for understanding user context in Web content(2013) Kamath S․, S.S.; Bagalkotkar, A.; Khandelwal, A.; Pandey, S.; Poornima, K.In our day to day lives, we highly value the opinions of friends in making decisions about issues like which brand to buy or which movie to watch. With the increasing popularity of blogs, online reviews and social networking sites, the current trend is to look up reviews, expert opinions and discussions on the Web, so that one can make an informed decision. Sentiment analysis, also known as opinion mining is the computational study of opinions, sentiments and emotions expressed in natural language for the purpose of decision making. Sentiment analysis applies natural language processing techniques and computational linguistics to extract information about sentiments expressed by authors and readers about a particular subject, thus helping users in making sense of huge volume of unstructured Web data. Applications like review classification, product review mining and trend prediction benefit from sentiment analysis based techniques. This paper presents a study of different approaches in this field, the state of the art techniques and current research in Sentiment Analysis based approaches for understanding user's context. © 2013 IEEE.Item Comparative evaluation of algorithms for effective data leakage detection(2013) Kumar, A.; Goyal, A.; Kumar, A.; Chaudhary, N.K.; Kamath S․, S.S.Researchers have proposed several mechanisms to secure data from unauthorized use but there is very less work in the field of detecting and managing an authorized or trustworthy agent that has caused a data leak to some third party advertently or unknowingly. In this paper, we implement methods aimed at improving the odds of detecting such leakages when a distributer's sensitive data has been leaked by trustworthy agents and also to possibly identify the agent(s) that leaked the data. We also implement some data allocation strategies that can improve the probability of identifying leakages and can also be used to assess the likelihood of a leak at a particular agent assuming the fact that the data was not simply guessed by the third party where the leaked data set has been found. We also propose new allocation strategies that work on the basis of No-Wait model, i.e. agent does not need to wait for other agents' allocation and it is different from already proposed model that makes an agent wait for others. These methods do not rely on the alterations of the distributed data, but rather focus on minimizing the overlapping of the allocated data items to various agents, thus facilitating an exact determination of the guilty agent in a particular data leakage scenario. © 2013 IEEE.Item A semantic search engine for answering domain specific user queries(2013) Kamath S․, S.S.; Piraviperumal, D.; Meena, G.; Karkidholi, S.; Kumar, K.With the exponential growth in web content and due to its sheer volume, the answers provided by traditional search engines by query specific keywords to content has resulted in markedly high recall and low precision. In order to alleviate this problem, the notion of incorporating semantics in content and in Search Engines, i.e., a Semantic Search Engine is increasingly crucial. Several Semantic Search Engines (SSEs) have been proposed and deployed till date and each is inherently different from the other. As such, the objective of this paper is to present a discussion on semantically enhanced search engines for intelligent web content discovery. We also present the architecture of a new SSE based on a bottom up approach that focuses on building a semantic base for Web content first and then carry out the process of querying it for attaining high precision and lower recall. © 2013 IEEE.Item A novel technique for efficient text document summarization as a service(2013) Bagalkotkar, A.; Khandelwal, A.; Pandey, S.; Kamath S․, S.S.Due to an exponential growth in the generation of web data, the need for tools and mechanisms for automatic summarization of Web documents has become very critical. Web data can be accessed from multiple sources, for e.g. on different Web pages, which makes searching for relevant pieces of information a difficult task. Therefore, an automatic summarizer is vital towards reducing human effort. Text summarization is an important activity in the analysis of a high volume text documents and is currently a major research topic in Natural Language Processing. It is the process of generation of the summary of an input document by extracting the representative sentences from it. In this paper, we present a novel technique for generating the summarization of domain-specific text from a single Web document by using statistical NLP techniques on the text in a reference corpus and on the web document. The summarizer proposed generates a summary based on the calculated Sentence Weight (SW), the rank of a sentence in the document's content, the number of terms and the number of words in a sentence, and using term frequency in the input corpus. © 2013 IEEE.Item Resource aware scheduling in Hadoop for heterogeneous workloads based on load estimation(2013) Kapil, B.S.; Kamath S․, S.S.Currently, most cloud based applications require large scale data processing capability. Data to be processed is growing at a rate much faster than available computing power. Hadoop is used to enable distributed processing on large clusters of commodity hardware. In large clusters, the workloads may be heterogeneous in nature, that is, I/O bound, CPU bound or network intensive jobs that demand different types of resources requirement so as to run simultaneously on large cluster. Hadoops job scheduling is based on FIFO where, parallelization based on types of job has not been taken into account for scheduling. In this paper, we propose a new scheduling algorithm for Hadoop based distributed system, based on the classification of workloads to assign a specific category to a particular cluster according to current load of the cluster. The proposed scheduler increases the performance of both CPU and I/O resources in a cluster under heterogeneous workloads, by approximately 12% when compared to Hadoops FIFO scheduler. © 2013 IEEE.Item Automated stock price prediction and trading framework for Nifty intraday trading(2013) Bhat, A.A.; Kamath S․, S.S.Research on automated systems for Stock price prediction has gained much momentum in recent years owing to its potential to yield profits. In this paper, we present an automatic trading system for Nifty for deciding the buying and selling calls for intra-day trading that combines various methods to improve the quality and precision of the prediction. Historical data has been used to implement the various technical indicators and also to train the Neural Network that predicts movement for intra-day Nifty. Further, Sentiment Analysis techniques are applied to popular blog articles written by domain experts and to user comments to find sentiment orientation, so that analysis can be further improved and better prediction accuracy can be achieved. The system makes a prediction for every trading day with these methods to forecast if next day will be a positive day or negative. Further, buy and sell calls for intra-day trading are also decided by the system thus achieving full automation in stock trading. © 2013 IEEE.Item A spatial clustering approach for efficient landmark discovery using geo-tagged photos(Institute of Electrical and Electronics Engineers Inc., 2016) Deeksha, S.D.; Ashrith, H.C.; Bansode, R.; Kamath S․, S.S.Geo-tagged photos enable people to share their personal experiences while visiting various vacation spots through image sharing social networks like Flickr. The geo-tag information offers a wealth of information for capturing additional information on traveler behavior, trends, opinions and interests. In this paper, we propose a landmark discovery system that aims to discover popular tourist attractions in a city by assuming that the popularity of a tourist attraction is positively dependent on the visitor statistics and also the amount of tourist uploaded photos clicked on site. It is a known fact that places with lots of geo-tagged photos uploaded to Flickr are visited more often by social-media savvy tourists, who plan their trip based on others' experiences. We propose to build a system that identifies the most popular tourist places in a particular city by using geo-tagged photos collected from Flickr and recommend the same to the user. In this paper, we present the methodology of spatially clustering the geo-tagged images and present an analysis of algorithm performance in identifying landmarks and their popularity. © 2015 IEEE.Item Towards Semantic Web Services: An empirical evaluation of service ontology generation tools(Institute of Electrical and Electronics Engineers Inc., 2016) Kamath S․, S.S.; Ananthanarayana, V.S.Web services are currently the most popular way of achieving service orientation for enterprise systems over the Web. Ontologies like OWL-S and WSMO have been defined as part of the semantic Web initiative to add semantics to service descriptions. The shift from mainly using the syntactic description of a web service, WSDL, to also providing a semantic description is fundamental to the effort of incorporating semantics in various service lifecycle tasks like service discovery, matchmaking and composition. With a few tools available for manual and semi-automated mapping of WSDL to OWL-S, choosing the best tool becomes a difficult choice to make. In this paper, a comprehensive comparative evaluation of some mapping tools currently available is presented and their performance is analyzed based on different parameters in order to assist service developers in choosing the right tool. The tools considered for the evaluation presented here are WSDL2OWL-S, Mindswap OWL-S API and OWL-S Editor. © 2015 IEEE.
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