Efficient data management on 3D stacked memory for big data applications

2015 ◽  
Author(s):  
Cheng Qian ◽  
Libo Huang ◽  
Peng Xie ◽  
Nong Xiao ◽  
Zhiying Wang
Keyword(s):  
Big Data ◽  
Data Management ◽  
Big Data Applications ◽  
Efficient Data ◽  
Stacked Memory

scholarly journals Sports Big Data: Management, Analysis, Applications, and Challenges

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhongbo Bai ◽  
Xiaomei Bai
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Data Management ◽  
Rapid Growth ◽  
Growth Trend ◽  
Research Directions ◽  
Research Issues ◽  
Big Data Applications ◽  
Rich Information ◽  
Sports Data

With the rapid growth of information technology and sports, analyzing sports information has become an increasingly challenging issue. Sports big data come from the Internet and show a rapid growth trend. Sports big data contain rich information such as athletes, coaches, athletics, and swimming. Nowadays, various sports data can be easily accessed, and amazing data analysis technologies have been developed, which enable us to further explore the value behind these data. In this paper, we first introduce the background of sports big data. Secondly, we review sports big data management such as sports big data acquisition, sports big data labeling, and improvement of existing data. Thirdly, we show sports data analysis methods, including statistical analysis, sports social network analysis, and sports big data analysis service platform. Furthermore, we describe the sports big data applications such as evaluation and prediction. Finally, we investigate representative research issues in sports big data areas, including predicting the athletes’ performance in the knowledge graph, finding a rising star of sports, unified sports big data platform, open sports big data, and privacy protections. This paper should help the researchers obtaining a broader understanding of sports big data and provide some potential research directions.

Are They Ready for the Big Thing?

2021 ◽  
pp. 200-210
Author(s):  
Matthias Lederer ◽  
Juluis Lederer
Keyword(s):  
Big Data ◽  
Data Management ◽  
Process Management ◽  
Business Processes ◽  
Future Trend ◽  
Data Driven ◽  
Big Data Applications ◽  
All Solutions ◽  
Management Approaches ◽  
And Control

Data-driven business processes management (BPM) is regarded as a central future trend because automation often makes huge amounts of data (big data) available for the optimisation and control of workflows. Software manufacturers also use this trend and call their solutions big data applications, even if some features are reminiscent of traditional data management approaches. This chapter derives from the basic definitions of big data including 13 central requirements that a big data BPM solution must meet in order to be described as such. One hundred twenty-one process management solutions are evaluated on the basis of these to determine whether they are real big data applications. As a result, less than 5% of all solutions analysed meet all requirements.

scholarly journals Big Data Management Canvas: A Reference Model for Value Creation from Data

2019 ◽  
Vol 3 (1) ◽  
pp. 19 ◽  
Author(s):  
Michael Kaufmann
Keyword(s):  
Big Data ◽  
Data Management ◽  
Value Creation ◽  
Reference Model ◽  
Solution Space ◽  
Use Cases ◽  
Data Systems ◽  
Big Data Applications ◽  
Big Data Systems ◽  
Map Data

Many big data projects are technology-driven and thus, expensive and inefficient. It is often unclear how to exploit existing data resources and map data, systems and analytics results to actual use cases. Existing big data reference models are mostly either technological or business-oriented in nature, but do not consequently align both aspects. To address this issue, a reference model for big data management is proposed that operationalizes value creation from big data by linking business targets with technical implementation. The purpose of this model is to provide a goal- and value-oriented framework to effectively map and plan purposeful big data systems aligned with a clear value proposition. Based on an epistemic model that conceptualizes big data management as a cognitive system, the solution space of data value creation is divided into five layers: preparation, analysis, interaction, effectuation, and intelligence. To operationalize the model, each of these layers is subdivided into corresponding business and IT aspects to create a link from use cases to technological implementation. The resulting reference model, the big data management canvas, can be applied to classify and extend existing big data applications and to derive and plan new big data solutions, visions, and strategies for future projects. To validate the model in the context of existing information systems, the paper describes three cases of big data management in existing companies.

Highly efficient data migration and backup for big data applications in elastic optical inter-data-center networks

IEEE Network ◽  
2015 ◽  
Vol 29 (5) ◽  
pp. 36-42 ◽  
Author(s):  
Ping Lu ◽  
Liang Zhang ◽  
Xiahe Liu ◽  
Jingjing Yao ◽  
Zuqing Zhu
Keyword(s):  
Big Data ◽  
Data Center ◽  
Data Migration ◽  
Data Center Networks ◽  
Highly Efficient ◽  
Big Data Applications ◽  
Efficient Data

Efficient data management tools for the heterogeneous big data warehouse

2016 ◽  
Vol 13 (5) ◽  
pp. 689-692 ◽  
Author(s):  
A. A. Alekseev ◽  
V. V. Osipova ◽  
M. A. Ivanov ◽  
A. Klimentov ◽  
N. V. Grigorieva ◽  
...  
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Warehouse ◽  
Management Tools ◽  
Efficient Data ◽  
Big Data Warehouse

Modeling and Evaluating the Effects of Big Data Storage Resource Allocation in Global Scale Cloud Architectures

2016 ◽  
Vol 12 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Enrico Barbierato ◽  
Marco Gribaudo ◽  
Mauro Iacono
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Storage ◽  
Global Scale ◽  
Big Data Applications ◽  
Storage Resource ◽  
Management Techniques ◽  
Cloud Elasticity ◽  
Cloud Infrastructures ◽  
Cloud Applications

The availability of powerful, worldwide span computing facilities offering application scalability by means of cloud infrastructures perfectly matches the needs for resources that characterize Big Data applications. Elasticity of resources in the cloud enables application providers to achieve results in terms of complexity, performance and availability that were considered beyond affordability, by means of proper resource management techniques and a savvy design of the underlying architecture and of communication facilities. This paper presents an evaluation technique for the combined effects of cloud elasticity and Big Data oriented data management layer on global scale cloud applications, by modeling the behavior of both typical in memory and in storage data management.

scholarly journals A Study on Big Data Techniques and Applications

2016 ◽  
Vol 5 (2) ◽  
pp. 101
Author(s):  
K. Radha ◽  
B. Thirumala Rao
Keyword(s):  
Big Data ◽  
Financial Services ◽  
High Rate ◽  
Map Reduce ◽  
Future Research ◽  
Unique Challenge ◽  
Data Intensive ◽  
Supply Chain Logistics ◽  
Big Data Applications ◽  
Efficient Data

<p>We are living in on-Demand Digital Universe with data spread by users and organizations at a very high rate. This data is categorized as Big Data because of its Variety, Velocity, Veracity and Volume. This data is again classified into unstructured, semi-structured and structured. Large datasets require special processing systems; it is a unique challenge for academicians and researchers. Map Reduce jobs use efficient data processing techniques which are applied in every phases of Map Reduce such as Mapping, Combining, Shuffling, Indexing, Grouping and Reducing. Big Data has essential characteristics as follows Variety, Volume and Velocity, Viscosity, Virality. Big Data is one of the current and future research frontiers. In many areas Big Data is changed such as public administration, scientific research, business, The Financial Services Industry, Automotive Industry, Supply Chain, Logistics, and Industrial Engineering, Retail, Entertainment, etc. Other Big Data applications are exist in atmospheric science, astronomy, medicine, biologic, biogeochemistry, genomics and interdisciplinary and complex researches.  This paper is presents the Essential Characteristics of Big Data Applications and State of-the-art tools and techniques to handle data-intensive applications and also building index for web pages available online and see how Map and Reduce functions can be executed by considering input as a set of documents.</p><p> </p>

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