摘要 :
In recent years companies and public institutions have long been accumulating and analyzing log data, and various types of data. It is building big data analytics platform for a variety of data analysis. Big data analysis platform...
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In recent years companies and public institutions have long been accumulating and analyzing log data, and various types of data. It is building big data analytics platform for a variety of data analysis. Big data analysis platform may consist of distributed processing type quickly analysis large amounts of data. Recently, build a big data systems for companies and public institutions in the analysis of large amounts of data. Companies are providing a variety of services to users through data analysis. The public institutions are used to analysis a specific period of time and is useful in transportation, urban design, and commercial area analysis. In addition, national authorities to share that holds the data and the public and various analysis. However, it is a big data analytics projects that emerged as an issue in recent years this trend. Big Data Analytics project is to derive business results through data collection, data analysis and then build a big data platform. Currently, many companies have problems with the case of big data analysis projects using a methodology developed its own methodology and the CBD doing business. In this paper defines big data Analysis Methodology, and suggests construct standards and construct procedures.
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摘要 :
In recent years companies and public institutions have long been accumulating and analyzing log data, and various types of data. It is building big data analytics platform for a variety of data analysis. Big data analysis platform...
展开
In recent years companies and public institutions have long been accumulating and analyzing log data, and various types of data. It is building big data analytics platform for a variety of data analysis. Big data analysis platform may consist of distributed processing type quickly analysis large amounts of data. Recently, build a big data systems for companies and public institutions in the analysis of large amounts of data. Companies are providing a variety of services to users through data analysis. The public institutions are used to analysis a specific period of time and is useful in transportation, urban design, and commercial area analysis. In addition, national authorities to share that holds the data and the public and various analysis. However, it is a big data analytics projects that emerged as an issue in recent years this trend. Big Data Analytics project is to derive business results through data collection, data analysis and then build a big data platform. Currently, many companies have problems with the case of big data analysis projects using a methodology developed its own methodology and the CBD doing business. In this paper defines big data Analysis Methodology, and suggests construct standards and construct procedures.
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摘要 :
Nowadays, many industries and government can exploit Big Data to extract valuable insight. Such insight can help decision makers to enhance their strategies and optimize their plans. It helps the organization to gain a competitive...
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Nowadays, many industries and government can exploit Big Data to extract valuable insight. Such insight can help decision makers to enhance their strategies and optimize their plans. It helps the organization to gain a competitive advantage and provides added value for many economic and social sectors. In fact, several governments have launched programs, with important funds, in order to enhance research and development in the field of Big Data. Private sector has also made many investments to maximize profits and optimize resources. This article presents several Big Data projects, opportunities, examples and models in many sectors such as healthcare, commerce, tourism and politics. It gives also examples of technologies and solutions developed to face Big Data challenges.
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摘要 :
Nowadays, many industries and government can exploit Big Data to extract valuable insight. Such insight can help decision makers to enhance their strategies and optimize their plans. It helps the organization to gain a competitive...
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Nowadays, many industries and government can exploit Big Data to extract valuable insight. Such insight can help decision makers to enhance their strategies and optimize their plans. It helps the organization to gain a competitive advantage and provides added value for many economic and social sectors. In fact, several governments have launched programs, with important funds, in order to enhance research and development in the field of Big Data. Private sector has also made many investments to maximize profits and optimize resources. This article presents several Big Data projects, opportunities, examples and models in many sectors such as healthcare, commerce, tourism and politics. It gives also examples of technologies and solutions developed to face Big Data challenges.
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摘要 :
Big Data encompasses huge amounts of raw material which influence multitude of research fields as well as different industries performance such as business, marketing, social network analysis, educational systems, healthcare, IoT,...
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Big Data encompasses huge amounts of raw material which influence multitude of research fields as well as different industries performance such as business, marketing, social network analysis, educational systems, healthcare, IoT, meteorology, fraud detection. It aimed to uncover hidden trends and has prompted a development from a model-driven perspective to a data-driven approach. Among numerous properties of Big Data, datasets of Big Data are identified primary as 3Vs attributes which have high variety, velocity and volume. These provide an invaluable insight and assist in making precise decisions. Analyzing this information and outlining the outcome into helpful data is the method for extricating an incentive from these enormous volumes of datasets. Nevertheless, Big Data containing unique features that cannot be handled and processed using the conventional methods. This has presented a significant challenge to the industry. This research paper presents a general outline of the characteristics of Big Data as well as expounds on the present challenges and limitations in this area. It further discusses the future scope in particular the future direction for Big Data research.
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摘要 :
Big Data encompasses huge amounts of raw material which influence multitude of research fields as well as different industries performance such as business, marketing, social network analysis, educational systems, healthcare, IoT,...
展开
Big Data encompasses huge amounts of raw material which influence multitude of research fields as well as different industries performance such as business, marketing, social network analysis, educational systems, healthcare, IoT, meteorology, fraud detection. It aimed to uncover hidden trends and has prompted a development from a model-driven perspective to a data-driven approach. Among numerous properties of Big Data, datasets of Big Data are identified primary as 3Vs attributes which have high variety, velocity and volume. These provide an invaluable insight and assist in making precise decisions. Analyzing this information and outlining the outcome into helpful data is the method for extricating an incentive from these enormous volumes of datasets. Nevertheless, Big Data containing unique features that cannot be handled and processed using the conventional methods. This has presented a significant challenge to the industry. This research paper presents a general outline of the characteristics of Big Data as well as expounds on the present challenges and limitations in this area. It further discusses the future scope in particular the future direction for Big Data research.
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Today, big data is considered an important area in IT and business fields. Big data is the driving force behind the effective running and competitiveness of several companies and organizations. Based on a research literature surve...
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Today, big data is considered an important area in IT and business fields. Big data is the driving force behind the effective running and competitiveness of several companies and organizations. Based on a research literature survey, there is a lack of standard definition and agreed upon characteristics for big data from scientific and business perspectives. This paper provides a review of big data definitions by various researchers and attempts to standardize a definition. In doing so, the paper identifies some issues related to big data definition and characteristics as well as raise some research questions. The paper categorizes these characteristics in terms of those relevant to big data versus those that related to processing and tools of big data. It is expected that the work presented in this paper will contribute to better alignment of big data terminology with its characteristics by various researchers, and it will provide a standard set of characteristics for the big data.
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In the era of big data, the development of the railway industry faces new challenges, and brings new opportunities. Big Data processing technology is a powerful tool to deal with Electric Multiple Unit (EMU) big data analysis. App...
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In the era of big data, the development of the railway industry faces new challenges, and brings new opportunities. Big Data processing technology is a powerful tool to deal with Electric Multiple Unit (EMU) big data analysis. Application of Big Data processing technology is a critical development stage of intelligent EMU. In the process of EMU's operation, repair and maintenance, it generate a large number of heterogeneous, multi-state data. Only the data are effectively dealt with before it can be more quickly to access and manipulate. Therefore, the method of processing data become more and more important. combining the current development situation of EMU, This paper explores and analyses Big Data processing technology based on EMU.
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Network big data refer to the massive data generated by interaction and fusion of the Ternary human-machine-thing universe in the Cyberspace and available on the Internet. The Increase of their scale and complexity provided that o...
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Network big data refer to the massive data generated by interaction and fusion of the Ternary human-machine-thing universe in the Cyberspace and available on the Internet. The Increase of their scale and complexity provided that of the capacity of hardware characterized by the Moore law, which brings the grand challenges to the architecture and the processing and computing Capacity of the contemporary IT systems, meanwhile set presents live opportunities on Deep mining and taking full advantage of the big value of network big data. Therefore, it is Pressing to research the disciplinary issues and discover the common laws of network big data, And further study the fundamental theory and basic approach to qualitatively or quantitatively Dealing with network big data. This paper analysis thezens caused by the complexity, Uncertainty and emergence of network big data, and summarizes major issues and research status of The awareness, representation, storage, management, mining, and social computing of network Big data, as well as network data platforms and applications. It is looking ahead to the development Trends of big data science, new modes and paradigm of data computing, new IT infrastructures, And data security and privacy, etc.
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Controversially, more data is not necessary better than less data. The explosion of the data lead to a number of interesting practical and theoretical problems. Among those problems are the need to filter, process, verify, index, ...
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Controversially, more data is not necessary better than less data. The explosion of the data lead to a number of interesting practical and theoretical problems. Among those problems are the need to filter, process, verify, index, distribute, protect and make redundant copies of the data. This data "massaging" usually take a lot of time and processing power. However, the quantity of the collected data does not necessary mean quality, as a lot of data is repetitive or does not contain any new information. Nevertheless, it still has to be processed, filtered, consumes high communication volume, has to be protected from breaches and from storage failures. In this position paper we propose to perform data reduction techniques on the collected (big) data prior to gathering of the data in a single location. In many cases (exemplified by two use-cases), especially in Internet-of-Things (IoT), those techniques might save tremendous amounts of power, processing time and network traffic.
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