Abstract

Admission Control has been proven essential to avoid overloading of resources and for meeting user service demands in utility driven grid computing. Recent emergence of Cloud based services and the popularity of MapReduce paradigm in Cloud Computing environments make the problem of admission control intriguing. We propose a model that allows one to offer MapReduce jobs in the form of on-demand services. We present a learning based opportunistic algorithm that admits MapReduce jobs only if they are unlikely to cross the overload threshold set by the service provider. The algorithm meets deadlines negotiated by users in more than 80% of cases. We employ an automatically supervised Naive Bayes Classifier to label incoming jobs as admissible and non-admissible. From the list of jobs classified as admissible, we then pick a job that is expected to maximize service provider utility. An external supervision rule automatically evaluates decisions made by the algorithm in retrospect, and trains the classifier. We evaluate our algorithm by simulating a MapReduce cluster hosted in the Cloud that offers a set of MapReduce jobs as services to its users. Our results show that admission control is useful in minimizing losses due to overloading of resources, and by choosing jobs that maximize revenue of the service provider.

Abstract

Social media has become highly popular in recent years that people are expressing their views, thoughts about any product, movie through reviews. Reviews are having a great influence on people and decisions made by them. This has led researchers and market analyzers to analyze the opinions of users in reviews and model their preferences. Sometimes reviews are also scored in terms of satisfaction score on any product or movie by customer (ratings). These ratings usually vary on a scale from one to five (stars) or very bad to excellent. In this paper we address the problem of attributing a numerical score (one to five stars) to a review. We view it as a multi-label classification (supervised learning) problem and present two approaches, using Naïve Bayes (NB) and Support Vector Machines (SVM’s). We focus more on feature representations of reviews widely used; problems associated with them and present solutions which address them.

Abstract

There is a growing trend of using cloud environments for ever growing storage and data processing needs. However, adopting a cloud computing paradigm may have positive as well as negative effects on the data security of service consumers. This paper primarily aims to highlight the major security issues existing in current cloud computing environments. We carry out a survey to investigate the security mechanisms that are enforced by major cloud service providers. We also propose a risk analysis approach that can be used by a prospective cloud service for analyzing the data security risks before putting his confidential data into a cloud computing environment.

Abstract

We propose SAGE (Service Oriented Architecture Governance Evaluation) as a quantitative approach, to evaluate the impact of SOA governance policies on prominent quality attributes. Our approach facilitates an exhaustive quantitative impact analysis to evaluate the effectiveness of SOA governance policies. This can be further used to estimate the current maturity level of any SOA based enterprise. The use of SAGE at an initial stage will help to make SOA management more structured and effective.

Abstract

In this paper, we present a two-step language-independent spelling suggestion system. In the first step, candidate suggestions are generated using an Information Retrieval (IR) approach. In step two, candidate suggestions are re-ranked using a new string similarity measure that uses the length of the longest common substrings occurring at the beginning and end of the words. We obtained very impressive results by reranking candidate suggestions using the new similarity measure. The accuracy of first suggestion is 92.3%, 90.0% and 83.5% for Dutch, Danish and Bulgarian language datasets respectively.

Abstract

Ads on the search engine (SE) are generally ranked based on their Click-through rates (CTR). Hence, accurately predicting the CTR of an ad is of paramount importance for maximizing the SE's revenue. We present a model that inherits the click information of rare/new ads from other semantically related ads. The semantic features are derived from the query ad click-through graphs and advertisers account information. We show that the model learned using these features give a very good prediction for the CTR values.

Abstract

Implicit relevance feedback has proved to be a important resource in improving search accuracy and personalization. However, researchers who rely on feedback data for testing their algorithms or other personalization related problems are loomed with problems like unavailability of data, staling up of data and so on. Given these problems, we are motivated towards creating a synthetic user relevance feedback data, based on insights from query log analysis. We call this simulated feedback. We believe that simulated feedback can be immensely beneficial to web search engine and personalization research communities by greatly reducing efforts involved in collecting user feedback. The benefits from” Simulated feedback” are-it is easy to obtain and also the process of obtaining the feedback data is repeatable, customizable and does not need the interactions of the user. In this paper, we describe a simple yet effective approach for creating simulated feedback. We have evaluated our system using the clickthrough data of the users and achieved 77% accuracy in generating click-through data.

Abstract

Contextual Advertising (CA) refers to the placement of ads that are contextually related to the web page content. The science of CA deals with the task of finding advertising keywords from web pages. We present a different candidate selection method to extract advertising keywords from a web page. This method makes use of Part-of-Speech (POS) patterns that restrict the number of potential candidates a classifier has to handle. It fetches words/phrases that belong to the selected set of POS patterns. We design four systems based on chunking method and the features they use. These systems are trained on a naive Bayes classifier with a set of web pages annotated with 'advertising' keywords. The systems can then find advertising keywords from previously unseen web pages. Empirical evaluation shows that systems using the proposed chunking method perform better than the systems using N-Gram based chunking. All improvements in the systems are found statistically significant at a 99% confidence interval.

Abstract

The problem of measuring semantic relatedness between social tags remains largely open. Given the structure of social bookmarking systems, similarity measures need to be addressed from a social bookmarking systems perspective. We address the fundamental problem of weight model for tags over which every similarity measure is based. We propose a weight model for tagging systems that considers the user dimension unlike existing measures based on tag frequency. Visual analysis of tag clouds depicts that the proposed model provides intuitively better scores for weights than tag frequency. We also propose weighted similarity model that is conceptually different from the contemporary frequency based similarity measures. Based on the weighted similarity model, we present weighted variations of several existing measures like Dice and Cosine similarity measures. We evaluate the proposed similarity model using Spearman's correlation coefficient, with WordNet as the gold standard. Our method achieves 20% improvement over the traditional similarity measures like dice and cosine similarity and also over the most recent tag similarity measures like mutual information with distributional aggregation. Finally, we show the practical effectiveness of the proposed weighted similarity measures by performing search over tagged documents using Social SimRank over a large real world dataset.

Abstract

Analysis of human reference summaries of broadcast news showed that humans give preference to anchor speaker segments while constructing a summary. Therefore, we exploit the role of anchor speaker in a news show by tracking his/her speech to construct indicative/informative extractive audio summaries. Speaker tracking is done by Bayesian information criterion (BIC) technique. The proposed technique does not require Automatic Speech Recognition (ASR) transcripts or human reference summaries for training. The objective evaluation by ROUGE showed that summaries generated by the proposed technique are as good as summaries generated by a baseline text summarization system taking manual transcripts as input and summaries generated by a supervised speech summarization system trained using human summaries. The subjective evaluation of audio summaries by humans showed that they prefer summaries generated by proposed technique to summaries generated by supervised speech summarization system.