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ردیف | عنوان | نوع |
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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 |