Showing 49–60 of 74 results
JouleMR: Towards Cost-Effective and Green-Aware Data Processing Frameworks Abstract-Interests have been growing in energy management of the cluster effectively in order to reduce the energy consumption as well as the electricity cost. Renewable energy and dynamic pricing schemes in smart grids are two major emerging trends in energy markets. However, current data processing frameworks are…
Learning-Based Super resolution Land Cover Mapping Abstract?Abstract Superresolution mapping (SRM) is a technique for generating a fine-spatial-resolution land cover map from coarsespatial-resolution fraction images estimated by soft classification.The prior model used to describe the fine-spatial-resolution land cover pattern is a key issue in SRM. Here, a novel learning-based SRM algorithm, whose priormodel is learned fromother…
Machine Learning with Big Data: Challenges and Approaches Abstract– The Big Data revolution promises to transform how we live, work, and think by enabling process optimization, empowering insight discovery and improving decision-making. The realization of this grand potential relies on the ability to extract value from such massive data through data analytics; machine learning is…
Measuring Scale-Up and Scale-Out Hadoop with Remote and Local File Systems and Selecting the Best Platform Abstract-MapReduce is a popular computing model for parallel data processing on large-scale datasets, which can vary from gigabytes to terabytes and petabytes. Though Hadoop MapReduce normally uses Hadoop Distributed File System (HDFS) local file system, it can be configured…
Microblog Dimensionality Reduction?A Deep Learning Approach Abstract? Exploring potentially useful information from huge amount of textual data produced by microblogging services has attracted much attention in recent years. An important preprocessing step of microblog text mining is to convert natural language texts into proper numerical representations. Due to the short-length characteristics of microblog texts, using…
Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications Abstract– There is an ever-increasing migration of people to urban areas. Health care service is one of the most challenging aspects that is greatly affected by the vast influx of people to city centers. Consequently, cities around the world are investing heavily…
Mining Suspicious Tax Evasion Groups in Big Data Abstract?There is evidence that an increasing number of enterprises plot together to evade tax in an unperceived way. At the same time, the taxation infor mation related data is a classic kind of big data. < final year projects > [numbers_sections number=”1″ title=”Including =Packages=” last=”no” ] Complete…
Mining Human Activity Patterns From Smart Home Big Data for Health Care Applications Abstract– Data generated on location-based social networks provide rich information on the whereabouts of urban dwellers. Specifically, such data reveal who spends time where, when, and on what type of activity (e.g., shopping at a mall, or dining at a restaurant). That…
Mutual Privacy Preserving k-Means Clustering in Social Participatory Sensing Abstract-In the frist place, consider the problem of mutual privacy-protection in social participatory sensing in which individuals contribute their private information to build a (virtual) community. Particularly, we propose a mutual privacy preserving k-means clustering scheme that neither discloses individual?s private information nor leaks the community?s…
New Techniques for Mining Frequent Patterns in Unordered Trees Abstract? New Techniques for Mining Frequent Patterns in Unordered Trees. A new tree mining problem that aims to discover restrictedly embedded subtree patterns from a set of rooted labeled unordered trees. We study the properties of a canonical form of unordered trees, and develop new Apriori-based…
On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications Abstract? On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications. The MapReduce programming model simplifies large-scale data processing on commodity cluster by exploiting parallel map tasks and reduce tasks. Although many efforts have been made to improve the performance of MapReduce jobs,…
Parallel Frequent Item set Mining with Spark RDD Framework for Disease Prediction Abstract?The aim behind frequent itemset mining is to find all common sets of items defined as those item sets that have at least a minimum support. There are many well known algorithms for frequent itemset mining. Some of which are A priori, ?clat,…
End of content
End of content
