Huge Chunk Of Data Research Articles

An Integrated Approach for Identifying Topical Experts in Microblogs

In the recent times, usage of Social Networking and Micro-blogging sites by the people has increased exponentially. Identifying the right topical experts in a particular domain is a tedious task as huge amount of data being processed. The existing approaches limited in accuracy, which leads to providing irrelevant results. Furthermore, few deal with syntactic similarity and not the semantic analysis. In this paper, we introduce an integrated approach which combines focus crawling, semantic analysis and personalized page ranking algorithm to provide top-k ranked results for a topic. Twitter data is taken as input and the twitter lists are extracted to construct the endorsement graph. Links in the graph are weighted using personalized PageRank. Since social network itself has a huge chunk of data, it is obvious for the endorsement graph to have quite large number of nodes. To increase the scalability and performance, focused crawling is applied before the PageRank calculation. The semantic similarity is calculated between the given input query and the Twitter list name/description to yield better results. We compare the results with Twitter’s WTF (Who To Follow) algorithm to showcase that the proposed system provides more obvious and serendipitous topical experts.

Sentiment Analysis Tool using Cosine and Jaccard Implementation

With the evolution of web technology, huge chunk of data is available over the web for users. The users not only explore the resources present on web, but also provide feedback, thus generating additional useful information. Sentiment analysis also known as Opinion mining deals with automating the task of classifying a textual review expressed in natural language as either positive or negative. In general, supervised methods consist of two stages. Firstly is extraction of information followed by its classification. The proposed approach consists of two major algorithms jaccard and cosine to implement a tool that will carry out the process of extraction of sentiments from a textual review and rate it accordingly. In this paper, the shortcomings of the existing approach will be discussed, along with the future & directions for research. General Terms Cosine Similarity, Jaccard Implementation, Sentiment Analyzing tool

New method for scheduling heterogeneous multi-installment systems

Since the past decade, the use of parallel and distributed systems has become more common. In these systems, a huge chunk of data or computations is distributed among many systems in order to obtain better performance. Dividing data is one of the challenges in this type of systems. Divisible Load Theory (DLT) is a proposed method for scheduling data distribution in parallel or distributed systems. Many studies have been done in this field but only a few articles about distributing data in a heterogeneous multi-installment system can be found. In this paper, we present some closed-form formulas for the different steps of scheduling jobs in a heterogeneous multi-installment system (finding the proper number of processors, the proper number of installments, closed-form formula for scheduling internal installments and closed-form formula for scheduling last installment). Two different systems are studied: Computation-Based Systems and Communication-Based Systems. The results of our experiments show that both methods gave better performances than the previous methods (Hsu et al.’s method and Beaumont et al.’s method) and the Communication-Based method has a smaller response time than the Computation-Based method.

A New Method for Scheduling Divisible Data on a Heterogeneous Two-Levels Hierarchical System

Abstract During the last decade, the use of parallel and distributed systems has become more common. In these systems, a huge chunk of data or computation is distributed among many systems in order to obtain better performance. Dividing data is one of the challenges in this type of systems. Divisible Load Theory (DLT) is proposed method for scheduling data distribution in parallel or distributed systems. In many researches carried out in this field, it was assumed that the used network topology is linear or one-level tree but that is not always true in real systems. In the large scale networks, hierarchical topology is usually used. In this article, we propose a new method which includes a closedform formula to schedule jobs in a heterogeneous distributed computing system in which its network topology is two-levels hierarchical system. The experiments show the calculated size for each processor is the best.