This category consists of Hadoop/Bigdata Projects list, latest IEEE Papers on Hadoop/Bigdata Projects
A System to Filter Unwanted Messages from OSN User Walls
Abstract: One fundamental issue in today On-line Social Networks (OSNs) is to give users the ability to control the messages posted on their own private space to avoid that unwanted content is displayed. Up to now OSNs provide little support to this requirement. To overcome this problem, we propose a system allowing OSN users to have a direct control on the messages posted on their walls. This is achieved through a flexible rule-based system, that allows users to customize the filtering criteria to be applied to their walls, and a Machine Learning based soft classifier automatically labeling messages in content-based filtering.
Anti-Money Laundering Strategy For Financial Institutions using hadoop
Abstract: With the development of economic globalization, economic exchanges between people have become increasingly close and frequent, it not only a symbol of economic prosperity, but also provides more possibility to all kinds of economic crimes. The Money Laundering, among them, refers to activities that disguise money receive through illegal operations and make them become legitimate. This causes the most serious harm to national security, financial system and development of global economic with involving an amount in large, covering a wide range and complicated process. It leaves serious consequence that may lead to economy corruption. So this project focuses on anti money laundering which is topic of highly concerned. The project aims to determine an effective approach to detect and curb the money laundering. In work of anti-money laundering, effective monitoring of suspicious transactions and, intelligence gathering and investigate all those transactions carefully. The data mining of all the transactions will be done
Dynamic MR allocation for map reduce framework
Abstract: MapReduce is one among the famous processing model for huge scale information (Big Data) processing in distributed computing. Since there may be a possibility of slot based MapReduce framework (eg. Hadoop MRv1) displaying some poor execution as a result of its unoptimized resource allocation. To venture on this, this paper finds and further streamlines the data distribution and resource allocation from the following three key perspectives. To begin with, because of the pre-configuration of the map slots and reduce slots which are not replaceable slots can be extremely under used. Since map slots may be completely used while reduce slots are empty and the other way around, considering the slot based model we set forth an option strategy called Dynamic Hadoop Slot Allocation. It unwinds the slot allocation parameters to permit slots to be reallocated to map or reduce task assignments relying upon their needs. Second the speculative execution can handle the straggler issue which sufficiently fit to enhance the execution for a job however to determine the expense of cluster proficiency. In context of this we further show Speculative Execution Performance Balancing so as to adjust the execution exchange between a single job and a batch of jobs. Third, delay scheduling has indicated to enhance the information and data locality at the fair cost. On the other hand we propose a method called Slot Pre Scheduling that can enhance the data locality yet with no effect on cost. At last by melding all the strategies together we make an orderly slot allocation framework called DynMR (Dynamic Map Reduce) which can enhance the execution of MapReduce workloads significantly.
Obd Simulator - Monitoring Inefficient And Unsafe Driving Behavior
Abstract: Many automobile drivers are aware of the driving behaviors and habits that can lead to inefficient and unsafe driving. However, it is often the case that these same drivers unknowingly exhibit these inefficient and unsafe driving behaviors in their everyday driving activity and technical details for evaluation of Real Time Car Monitoring. Above project proposes a practical and economical way to capture / measure / evaluate inefficient, uneconomical and unsafe driving with the details about Performance, Diagnostics (Using Diagnostics Trouble Code - DTC), Fuel Consumption & Autonomy and Emission from a Vehicle. The proposed solution consists of a mobile application, running on an Android Smartphone, paired with a compatible OBD-II (On-board diagnostics II) reader.
Product Aspect Ranking And Its Applications Using Sentiment Analysis
Abstract: In this proposed system, given consumer reviews of a product, we first identify the product aspects by a shallow dependency parser. Then determine consumers' opinions on these aspects via a sentiment classifier. We develop an aspect ranking algorithm to identify the important aspects by simultaneously considering the aspect frequency and the influence of consumers' opinions given to each aspect on their overall opinions. The experimental results on 5 popular products in four domains demonstrate the effectiveness of our approach. We further apply the aspect ranking results to the application of document-level sentiment classification, and improve the performance significantly. In this project, we use product reviews and perform sentiment analysis i.e. to find positive, negative or neutral sense of aspects of the product. Each review may contain number comments and new comments are added every minute, in order to handle so many reviews we are using apache Hadoop framework.
Twitter Sentiment Analysis Using Hadoop
Abstract: In this project, we investigate the utility of linguistic features for detecting the sentiment of Twitter messages using apache storm framework. We evaluate the usefulness of existing lexical resources as well as features that capture information about the informal and creative language used in microblogging. We take a supervised approach to the problem, but leverage existing hashtags in the Twitter data for building training data. In the past few years, there has been a huge growth in the use of microblogging platforms such as Twitter. Influenced by that growth, companies and media organizations are increasingly seeking ways to mine Twitter for information about what people think and feel about their products and services. In this project, we download Twitter messages for a particular hashtag and perform sentiment analysis i.e. to find positive, negative or neutral sense of that tweet using hadoop framework. Each hashtag may have 1000 of comments and new comments are added every minute, in order to handle so many tweets we are using apache hadoop framework.
Video Surveillance Over Camera Network Using Hadoop
Abstract: Object detection and tracking are two fundamental tasks in multicamera surveillance. The most important technique of this multicamera related technique is to track and analyze objects within the images. The core technology of multicamera analysis is used in detecting, analyzing, and tracking the object's motion. In addition, when the light's color or direction changes, it is difficult to trace the object. Firstly use the block based algorithm for detecting the change scene in video if the scene is change is detected then video is stored on the server for further analysis. Once the video was stored on the server. Stored videos are dived in to chunks and send to different nodes for analysis using map reduce technology of Hadoop. for detecting object we apply the object tracking algorithm using a novel Bayesian Kalman filter with simplified Gaussian mixture (BKF-SGM).Using Hadoop we minimize the analysis time Finally draw the graphs in which show the no of objects to be detected and time to be required for analysis and stored analysis result into database for security purpose.
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