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نتیجه جستجو - Data issues

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 On the use of simulation as a Big Data semantic validator for supply chain management
استفاده از شبیه سازی به عنوان یک اعتبار سنج معنایی داده های بزرگ برای مدیریت زنجیره تامین-2020
Simulation stands out as an appropriate method for the Supply Chain Management (SCM) field. Nevertheless, to produce accurate simulations of Supply Chains (SCs), several business processes must be considered. Thus, when using real data in these simulation models, Big Data concepts and technologies become necessary, as the involved data sources generate data at increasing volume, velocity and variety, in what is known as a Big Data context. While developing such solution, several data issues were found, with simulation proving to be more efficient than traditional data profiling techniques in identifying them. Thus, this paper proposes the use of simulation as a semantic validator of the data, proposed a classification for such issues and quantified their impact in the volume of data used in the final achieved solution. This paper concluded that, while SC simulations using Big Data concepts and technologies are within the grasp of organizations, their data models still require considerable improvements, in order to produce perfect mimics of their SCs. In fact, it was also found that simulation can help in identifying and bypassing some of these issues.
Keywords: Simulation | Big Data | Data issues | Semantic validation | Supply chain management | Industry 4.0
مقاله انگلیسی
2 Generating Big Data Repositories for Research in Medical Imaging
تولید داده های بزرگ مخازن برای پژوهش در تصویربرداری پزشکی-2016
The production of medical imaging data has grown tremendously in the last decades. The proliferation of digital acquisition equipment has enabled even small institutions to produce considerable amounts of studies. Furthermore, the general trend for new imaging modalities is to produce more data per examinations. As a result, the design and implementation of tomorrow’s storage and communication systems must deal with Big Data issues. Moreover, new realities such as the outsourcing of medical imaging infrastructures to the Cloud impose additional pressure on these systems. The research on technologies for coping with Big Data issues on large scale medical imaging environments is still in its early stages. This is mostly due to the difficulty of implementing and validating new technological approaches in real environments, without interfering with clinical practice. Therefore, it is crucial to create test bed environments for research purposes. This article proposes a methodology for creating simulated Big Data repositories. The system is able to use a real world repository to collect data from a representative time window and expand it according to research needs. In addition, the solution provides anonymization tools and allows exporting two types of simulated data: a structured file repository containing DICOM objects and a statistical summary of the archive content based on its characteristics, such as the number of produced studies per day, their modality and their associated data volumes.
Keywords : medical imaging | pacs, dicom | big data | repositories.
مقاله انگلیسی
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