Key Methodologies for Designing Big Data Mining Platform Based on Cloud Computing

2016 ◽  
pp. 190-196
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Mining Area ◽  
Mining System ◽  
Big Data Mining ◽  
Key Technologies ◽  
Data Mining System

Cloud Computing plays a big function in the in data mining area of numerous sectors in today's culture. Building the data mining system based upon cloud computing is useful to accomplish effective data mining This paper evaluates the basic architecture of the big data mining platform based on cloud computing and the key technologies for its building on the basis of relevant concepts of cloud computing and also data mining.

Supporting Semantic Concept Retrieval with Negative Correlations in a Multimedia Big Data Mining System

2016 ◽  
Vol 10 (02) ◽  
pp. 247-267 ◽  
Author(s):  
Yilin Yan ◽  
Mei-Ling Shyu ◽  
Qiusha Zhu
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Semantic Gap ◽  
Semantic Concept ◽  
Multimedia Big Data ◽  
Mining System ◽  
Big Data Mining ◽  
Concept Mining ◽  
Data Mining System ◽  
High Level

With the extensive use of smart devices and blooming popularity of social media websites such as Flickr, YouTube, Twitter, and Facebook, we have witnessed an explosion of multimedia data. The amount of data nowadays is formidable without effective big data technologies. It is well-acknowledged that multimedia high-level semantic concept mining and retrieval has become an important research topic; while the semantic gap (i.e., the gap between the low-level features and high-level concepts) makes it even more challenging. To address these challenges, it requires the joint research efforts from both big data mining and multimedia areas. In particular, the correlations among the classes can provide important context cues to help bridge the semantic gap. However, correlation discovery is computationally expensive due to the huge amount of data. In this paper, a novel multimedia big data mining system based on the MapReduce framework is proposed to discover negative correlations for semantic concept mining and retrieval. Furthermore, the proposed multimedia big data mining system consists of a big data processing platform with Mesos for efficient resource management and with Cassandra for handling data across multiple data centers. Experimental results on the TRECVID benchmark datasets demonstrate the feasibility and the effectiveness of the proposed multimedia big data mining system with negative correlation discovery for semantic concept mining and retrieval.

scholarly journals Data Mining and Cloud Computing

2012 ◽  
Vol 4 (2) ◽  
Author(s):  
Robert Vrbić
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Data Warehouses ◽  
Cloud Data ◽  
Big Data Mining ◽  
New Knowledge ◽  
Flexible Infrastructure

Cloud computing provides a powerful, scalable and flexible infrastructure into which one can integrate, previously known, techniques and methods of Data Mining. The result of such integration should be strong and capacitive platform that will be able to deal with the increasing production of data, or that will create the conditions for the efficient mining of massive amounts of data from various data warehouses with the aim of creating (useful) information or the production of new knowledge. This paper discusses such technology - the technology of big data mining, known as Cloud Data Mining (CDM).

scholarly journals A Novel Dbmd Implementation for Big Data Mining and Clustering Via Cloud Computing

2021 ◽  
Vol 23 (06) ◽  
pp. 29-35
Author(s):  
A. Vaitheeswari ◽  
◽  
Dr. N. Krishnaveni ◽  
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Virtual Machines ◽  
Big Data Analysis ◽  
Correspondence Problem ◽  
Weighted Norm ◽  
Matrix Structure ◽  
Method Of Multipliers ◽  
Big Data Mining

Matrix structure was one of the most important devices for finding data from big data. Here you’ll find data produced by current applications using cloud computing. However, moving big data using such a system in a performance computer or through virtual machines is still inefficient or impossible. Furthermore, big data is often gathered data from a variety of data sources and stored on a variety of machines using scheduling algorithms. As a result, such data usually bear solid shifted commotion. Growing circulated matrix deterioration is necessary and beneficial for big data analysis. Such a plan should have a good chance of succeeding. Represent the diverse clamor and deal with the correspondence problem in a disseminated manner. In order to do this, we used a Bayesian matrix decay model (DBMD) for big data mining and grouping. Only three approaches to disseminated computation are considered: 1) accelerate slope drop, 2) alternating path method of multipliers (ADMM), and 3) observable derivation. We look at how these approaches could be mixed together in the future. To deal with the commotion’s heterogeneity, we suggest an ideal module weighted norm that reduces the assessment’s differentiation. Finally, a comparison was made between these approaches in order to understand the differences in their outcomes.

The Development of Data Mining System Based on Cloud Computing

2014 ◽  
Vol 926-930 ◽  
pp. 2280-2283
Author(s):  
Qiong Ren
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Current Data ◽  
Mining System ◽  
Computing Platform ◽  
Data Mining Algorithms ◽  
Cloud Computing Platform ◽  
Data Mining System ◽  
Process Cost ◽  
Mining Algorithms

With the increasing of input data size, process cost will be very long, for the explosive growth of the Internet data even reached the point of single machine can handle. This article mainly introduces the architecture of the concept of cloud computing and, the mainstream of the analysis of the current data mining algorithms, based on cloud computing to develop the data mining system, providing the operation feasibility of data mining in cloud computing platform, having strong guiding significance.

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