Optimal Energy Management Strategy for Parallel Scheduling

2014 ◽  
Vol 568-570 ◽  
pp. 1539-1546
Author(s):  
Xin Li Li
Keyword(s):  
Data Streams ◽  
Power Efficiency ◽  
Data Stream ◽  
Large Scale ◽  
Massive Data ◽  
Energy Management Strategy ◽  
Parallel Scheduling ◽  
Large Scale Data ◽  
Scheduling Strategies ◽  
Scale Data

Large-scale data streams processing is now fundamental to many data processing applications. There is growing focus on manipulating Large-scale data streams on GPUs in order to improve the data throughput. Hence, there is a need to investigate the parallel scheduling strategy at the task level for the Large-scale data streamsprocessing, and to support them efficiently. We propose two different parallel scheduling strategies to handle massive data streamsin real time. Additionally, massive data streamsprocessing on GPUs is energy-consumed computation task. So we consider the power efficiency as an important factor to the parallel strategies. We present an approximation method to quantify the power efficiency for massive data streams during the computing phase. Finally, we test and compare the two parallel scheduling strategies on a large quantity of synthetic and real stream datas. The simulation experiments and compuatation results in practice both prove the accuracy of analysis on performance and power efficiency.

Scalable KDE-based top-n local outlier detection over large-scale data streams

2020 ◽  
Vol 204 ◽  
pp. 106186 ◽  
Author(s):  
Fang Liu ◽  
Yanwei Yu ◽  
Peng Song ◽  
Yangyang Fan ◽  
Xiangrong Tong
Keyword(s):  
Outlier Detection ◽  
Data Streams ◽  
Large Scale ◽  
Large Scale Data ◽  
Scale Data ◽  
Local Outlier

scholarly journals CVS: Fast cardinality estimation for large-scale data streams over sliding windows

2016 ◽  
Vol 194 ◽  
pp. 107-116 ◽  
Author(s):  
Jingsong Shan ◽  
Jianxin Luo ◽  
Guiqiang Ni ◽  
Zhaofeng Wu ◽  
Weiwei Duan
Keyword(s):  
Data Streams ◽  
Large Scale ◽  
Sliding Windows ◽  
Cardinality Estimation ◽  
Large Scale Data ◽  
Scale Data

scholarly journals An online classification algorithm for large scale data streams: iGNGSVM

2017 ◽  
Vol 262 ◽  
pp. 67-76 ◽  
Author(s):  
Andrés L. Suárez-Cetrulo ◽  
Alejandro Cervantes
Keyword(s):  
Data Streams ◽  
Large Scale ◽  
Classification Algorithm ◽  
Large Scale Data ◽  
Online Classification ◽  
Scale Data

scholarly journals A Data Dictionary Approach To Meeting User Requests For Accounting Information

1999 ◽  
Vol 3 (1) ◽  
pp. 53-60
Author(s):  
Kristi Yuthas ◽  
Dennis F. Togo
Keyword(s):  
Large Scale ◽  
Accounting Information ◽  
User Involvement ◽  
Massive Data ◽  
Data Dictionary ◽  
Large Scale Data ◽  
User Access ◽  
Increasing Demand ◽  
User Friendly ◽  
Scale Data

In this era of massive data accumulation, dynamic development of large-scale data-bases and interfaces intended to be user-friendly, there is still an increasing demand on analysts as actual user access to databases is still not a common practice. A data dictionary approach, that includes providing users with a list of relevant data items within the database, can expedite the analysis of information requirements and the development of user-requested information systems. Furthermore, this approach enhances user involvement and reduces the demands on the analysts for systems devel-opment projects.

Management of Data Streams for Large-Scale Data Mining

2011 ◽  
pp. 136-153
Author(s):  
Jon R. Wright ◽  
Gregg T. Vesonder ◽  
Tamraparni Dasu
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Information Acquisition ◽  
Large Scale ◽  
Knowledge Engineering ◽  
Mining Operation ◽  
Large Scale Data ◽  
Technical Issues ◽  
Automate Information ◽  
Scale Data

In an enterprise setting, a major challenge for any data mining operation is managing data streams or feeds, both data and metadata, to ensure a stable and certifiably accurate flow of data. Data feeds in this environment can be complex, numerous and opaque. The management of frequently changing data and metadata presents a considerable challenge. In this paper, we articulate the technical issues involved in the task of managing enterprise data and propose a multi-disciplinary solution, derived from fields such as knowledge engineering and statistics, to understand, standardize, and automate information acquisition and quality management in preparation for enterprise mining.

Data Streaming Processing Window Joined With Graphics Processing Units (GPUs)

2021 ◽  
pp. 602-623
Author(s):  
Shen Lu ◽  
Richard S. Segall
Keyword(s):  
Big Data ◽  
Data Streams ◽  
Graphics Processing Units ◽  
Data Stream ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Data Streaming ◽  
Large Scale Data ◽  
Graphics Processing

Big data is large-scale data and can be either discrete or continuous. This article entails research that discusses the continuous case of big data often called “data streaming.” More and more businesses will depend on being able to process and make decisions on streams of data. This article utilizes the algorithmic side of data stream processing often called “stream analytics” or “stream mining.” Data streaming Windows Join can be improved by using graphics processing unit (GPU) for higher performance computing. Data streams are generated by two independent threads: one thread can be used to generate Data Stream A, and the other thread can be used to generate Data Stream B. One would use a Windows Join thread to merge the two data streams, which is also the process of “Data Stream Window Join.” The Window Join process can be implemented in parallel that can efficiently improve the computing speed. Experiments are provided for Data Stream Window Joins using both static and dynamic data.

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