Figure 1 - uploaded by K. R. Singh
Content may be subject to copyright.
Download
View publication
Architecture for distributed data mining  

Architecture for distributed data mining  

Source publication
Figure 1: Architecture for distributed data mining  
Figure 2: Flow diagram of the system  
Figure 3: Time required in milliseconds along with the nodes and...
Figure 5: Time required for mining on 2-Nodes If we further add one...
Figure 6: Time required for mining on 3-Nodes  
Frequent Itemset Mining for Distributed Systems using Hadoop
Article
Full-text available
  • Apr 2016
Frequent Itemset Mining is one of the most popular techniques to extract knowledge from data. But, these mining methods become more problematic when they are applied to Big Data. Fortunately, recent improvements in the field of parallel programming provide many tools to tackle this problem. However, these tools come with their own technical challen...
Cite
Download full-text

Context in source publication

Context 1
... distributed data mining, data is located at distributed locations and mining is performed on every local database to find globally mined data. Figure 1 depicts the architecture for distributed data mining. ...

Similar publications

Figure 1: BDEv design overview 
Table 1 : Comparison of batch, stream and hybrid frameworks
Figure 2: Overview of the Intel RAPL architecture for a dual-socket system 
Table 3 : Frameworks supported in BDEv
+3 Table 4 : Benchmarks supported in BDEv 3.0
BDEv 3.0: Energy efficiency and microarchitectural characterization of Big Data processing frameworks
Article
Full-text available
  • Apr 2018
As the size of Big Data workloads keeps increasing, the evaluation of distributed frameworks becomes a crucial task in order to identify potential performance bottlenecks that may delay the processing of large datasets. While most of the existing works generally focus only on execution time and resource utilization, analyzing other important metric...
View
Table 1 . Adjustable operating parameters.
Table 2 . Reduction in dependence of parameters.
Table 4 . Optimization of reference values under different load conditions.
Figure 5. Clustering of the main steam temperature water supply...
+1 Table 5 . Parameter sensitivity coefficient of 400 MW load condition.
Big-Data-Mining-Based Improved K-Means Algorithm for Energy Use Analysis of Coal-Fired Power Plant Units: A Case Study
Article
Full-text available
  • Sep 2018
The energy use analysis of coal-fired power plant units is of significance for energy conservation and consumption reduction. One of the most serious problems attributed to Chinese coal-fired power plants is coal waste. Several units in one plant may experience a practical rated output situation at the same time, which may increase the coal consump...
View
Fig. 1. Some Vs'of Big Data. 
Fig. 2. Illustration of the layers for the Service-Oriented Computing 
Fig. 3. Big Data framework [14]. 
Big Data and Cloud Computing: Trends and Challenges
Article
Full-text available
  • Apr 2017
Big data is currently one of the most critical emerging technologies. Big Data are used as a concept that refers to the inability of traditional data architectures to efficiently handle the new data sets. The 4V’s of big data – volume, velocity, variety and veracity makes the data management and analytics challenging for the traditional data wareho...
View
Fig. 1. Big data intellectual challenges [27].
Spatiotemporal Aspects of Big Data
Article
Full-text available
  • Dec 2018
Data has evolved into a large-scale data as big data in the recent era. The analysis of big data involves determined attempts on previous data. As new era of data has spatiotemporal facts that involve the time and space factors, which make them distinct from traditional data. The big data with spatiotemporal aspects helps achieve more efficient res...
View
View