Data Capturing and Processing
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Holistic energy management in the shipping industry involves reliable data collection, systematic processing and smart analysis. The era of digitisation allows sensor technology to be used on-board vessels, converting different forms of signal into a digital format that can be exported conveniently for further processing. Appropriate sensor selection is important to ensure continuous data collection when vessels sail through harsh conditions. However, without proper processing, this leads to the collection of big data sets but without resulting useful intelligence that benefits the industry. The adoption of digital and computer technology, allows the next phase of fast data processing. This contributes to the growing area of big data analysis, which is now a problem for many technological sectors, including the maritime industry. Enormous databases are often stored without clear goals or suitable uses. Processing of data requires engineering knowledge to ensure suitable filters are applied to raw data. This systematic processing of data leads to transparency in real time data display and contributes to predictive analysis. In addition, the generation of series of raw data when coupled with other external data such as weather information provides a rich database that reflects the true scenario of the vessel. Subsequent processing will then provide improved decision making tools for optimal operations. These advances open the door for different market analyses and the generation of new knowledge. This paper highlights the crucial steps needed and the challenges of sensor installation to obtain accurate data, followed by pre and post processing of data to generate knowledge. With this, big data can now provide information and reveal hidden patterns and trends regarding vessel operations, machinery diagnostics and energy efficient fleet management. A case study was carried out on a tug boat that operates in the North Sea, firstly to demonstrate confidence in the raw data collected and secondly to demonstrate the systematic filtration, aggregation and display of useful information.
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A considerable amount of computing power is needed for Gaia data processing during the mission. A pan European system of six data centres are working together to perform different parts of the processing and combine the results. Data processing estimates suggest around 1020 FLOP total processing is required. Data will be transferred daily around Europe and with a final raw data volume approaching 100 TB. With these needs in mind the centres are already gearing up for Gaia. We present the status and plans of the Gaia Data Processing Centres.
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Data acquisition and pre-processing are important steps in any traffic data collection, analysis or simulation project. In this paper the authors present the results of a case study they have done on traffic simulation for a stretch of the E17 highway near Antwerp, Belgium. They will especially focus on the data collection and processing part. The objective of the work presented here is to illustrate how traffic data can be collected and processed in such a way that it can be used for modeling and prediction purposes. The data collection and processing process consists of three steps: data acquisition, interfacing, and pre-processing. During this process a central database with raw sensor data is created. In general the raw sensor data will contain errors or missing values due to sensor failures, data link errors, biases, and other measurement errors. Therefore, the authors introduce a pre-processing step in order to deal with data that is known to be corrupt or for which the value is temporarily not available (many simulation packages require data records that are and contain no gaps). After the three steps have been carried out, they have a database of clean and complete traffic data that can be used for further analysis or that can be used in other applications such as highway traffic simulation or traffic forecasts. (A) For the covering abstract see ITRD E106484.
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AbstractAbstractData analysis and processing is playing an important role because of the large amount of data generated through various sources of big data. It is an important component in big data-based applications. Data qualities are the main concern in the data acquisition, transformation and data pre-processing under big data applications. Data pre-processing is required because of inconsistent, noisy and incomplete data generation in big data applications. Data analysis basically encompasses different methods and a function applicable to data's to detect characteristics such as data type, size, format, patterns and so on. Based on data format, it's easy to identify data qualities for further use in various applications. Moreover, data analysis and processing includes various steps such as data qualities identification, statistical analysis of data, defining modeling, and hypothetical testing of model and result from analysis. Raw data is unused data and required analysis, filtering, and processing in any system. This paper deals with the analysis and processing aspects of raw data and cleaned data in big data applications. This paper also deals with data cleaning and its implementation concepts.Subject Classification: 68U35Keywords: Data cleaningBig dataData visualizationData acquisitionData filtering
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This paper examined the roles and problems of data collection for student evaluation. They following areas are discussed. Meaning of data collection, roles of data collection for student evaluation, types of data collection viz-a-viz their roles in an evaluation process. Problem of data collection as it affects evaluation during teaching and learning process were also examined.
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Objective data collection on roadside memorial-making reveals values, attitudes, trends and behaviour patterns, but hidden behind the raw data is the experience of grief that can not always be best reached by objective analysis. This paper examines ideas about erecting roadside memorials held by those who built them and at the same time validates a professionally informed but still a more intuitive response to the process of data collection. In taking this approach, the paper aims to tap into the stories embedded behind the data, which may otherwise be lost in the usual process of a statistically based data analysis. Is there more that can be learned when the data collector is permitted to analyse and reflect upon what emerges during the process of data collection, rather than on the results of that collection alone?
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In this paper, based on the characteristics of the fixed data, this paper compares the various mathematical statistics analysis methods and chooses the improved Grabs criterion to analyze the data, and through the analysis of the data processing, the data processing method is not suitable. It is proved that this method can be applied to the processing of fixed raw data. This paper provides a reference for reasonably determining the effective quota analysis data.
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