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1 |
MagLoc : A magnetic induction based localization scheme for fresh food logistics
MagLoc: یک طرح محلی سازی مبتنی بر القای مغناطیسی برای تدارکات مواد غذایی تازه-2022 An IoT infrastructure to continuously monitor the fresh food supply chain can quickly detect
food quality and contamination issues and thereby reduce costs and food wastage. This, in turn,
involves several challenges including the development of inexpensive quality/contamination
sensors to be deployed in a fine grain manner in the food boxes, technologies for sensor
level communications, online data management and analytics, and logistics driven by such
analytics. In this paper, we study the issues related to the communication among sensing
modules deployed in the fresh food boxes and thereby an automated localization of the boxes
that may have quality/contamination issues. In this context we study the near-field magnetic
induction (NFMI) based communication and localization, as the ubiquitous RF communications
suffer high attenuation through the water/mineral rich tissue media. An accurate localization
of the sensors inside boxes within the food pallets is very challenging in this environment. In
this paper we propose a novel magnetic induction based localization scheme, and show that
with a small number of anchor nodes, the localization can be done without any errors for boxes
as small as 0.5 meter on the side, and with small errors even for boxes half as big.
Keywords: Smart sensing | Industrial sensors | Food supply chain | Physical Internet | Magnetic communication | Localization |
مقاله انگلیسی |
2 |
X-PHM: Prognostics and health management knowledge-based framework for SME
X-PHM: پیش آگهی و چارچوب دانش مبتنی بر مدیریت سلامت برای SME-2021 Prognostics and Health Management (PHM) is an emerging concept based on industrial data management. The implementation of PHM in
small and medium-sized enterprises (SMEs) is currently limited due to data accessibility difficulties. In order to overcome this pitfall, one could
formalize the operators’ knowledge and integrate it in the SME’s information system. Thus, the implementation of the PHM will be based
on this information system associating data with knowledge. To this end, we propose a collaborative PHM approach (X-PHM) to ensure the
extraction of operators’ knowledge and its integration into the PHM process. The decision resulting from this approach is restituted with a concern
of explainability. This paper details the proposed approach while focusing on the data management process and its integration in explainable
decisions. This new framework is applied in a French SME to understand its production process and facilitate its digital transformation.
Keywords: PHM | Knowledge formalization and integration | Explainable artificial intelligence | SME | Data analysis. |
مقاله انگلیسی |
3 |
Big data management capabilities and librarians innovative performance: The role of value perception using the theory of knowledge-based dynamic capability
قابلیت های مدیریت داده های بزرگ و عملکرد نوآورانه کتابداران: نقش ادراک ارزش با استفاده از تئوری قابلیت پویایی مبتنی بر دانش-2021 This study extended the concept of knowledge-based dynamic capabilities from a firm level to individual level
and investigated the relationship between big data management capabilities and innovative performance of
university librarians in selected Ghanaian universities. The role of big data value perception as a mediator was
also assessed using the PLS-SEM. Data were validated with Cronbach’s alpha above 0.8 and with factor analysis
and further convergent and discriminant validity tests. AVE values were higher than 0.5 and CR above AVE and
discriminant validity test scores below 0.6. Statistical significance was at a P-value of 0.05. Knowledge-based
dynamic capabilities (KDC) were found not to have a direct significant influence on innovative performance
(IP) (r2 = 0.109) of librarians. However, KDC positively influenced the perceived value for big data management
(BDVP) (r2 = 0.674) with the later having a significant effect on the innovative performance of librarians (r2 =
0.777). BDVP among librarians was found to significantly mediate the relationship between KDC and IP such that
KDC indirectly recorded a higher path coefficient (r2 = 0.524) than its initial direct effect of 0.109. Library
managers and librarians are encouraged to develop big data management capability of staff to help create
positive perceptions about the relevance of the field to enhance innovation and improved performance. keywords: توانایی پویا مبتنی بر دانش | عملکرد نوآورانه | مدیریت داده های بزرگ | کتابخانه ها | علم اطلاعات | Knowledge-based dynamic capability | Innovative performance | Big data management | Libraries | Information science |
مقاله انگلیسی |
4 |
Fifty years of information management research: A conceptual structure analysis using structural topic modeling
پنجاه سال تحقیقات مدیریت اطلاعات: تجزیه و تحلیل ساختار مفهومی با استفاده از مدل سازی موضوع ساختاری-2021 Information management is the management of organizational processes, technologies, and people which
collectively create, acquire, integrate, organize, process, store, disseminate, access, and dispose of the infor-
mation. Information management is a vast, multi-disciplinary domain that syndicates various subdomains and
perfectly intermingles with other domains. This study aims to provide a comprehensive overview of the infor-
mation management domain from 1970 to 2019. Drawing upon the methodology from statistical text analysis
research, this study summarizes the evolution of knowledge in this domain by examining the publication trends
as per authors, institutions, countries, etc. Further, this study proposes a probabilistic generative model based on
structural topic modeling to understand and extract the latent themes from the research articles related to in-
formation management. Furthermore, this study graphically visualizes the variations in the topic prevalences
over the period of 1970 to 2019. The results highlight that the most common themes are data management,
knowledge management, environmental management, project management, service management, and mobile
and web management. The findings also identify themes such as knowledge management, environmental
management, project management, and social communication as academic hotspots for future research. keywords: مدیریت اطلاعات | مدل های اصلی ساختاری | مدل سازی موضوع | مدل های مولد | تجزیه و تحلیل متن | Information management | Structural topic models | Topic modeling | Generative models | Text analytics |
مقاله انگلیسی |
5 |
A knowledge-based Digital Shadow for machining industry in a Digital Twin perspective
یک سایه دیجیتال مبتنی بر دانش برای صنعت ماشینکاری در یک چشم انداز دیجیتال دوتایی-2021 This paper addresses the problems of data management and analytics for decision-aid by proposing a new vision
of Digital Shadow (DS) which would be considered as the core component of a future Digital Twin. Knowledge
generated by experts and artificial intelligence, is transformed into formal business rules and integrated into the
DS to enable the characterization of the real behavior of the physical system throughout its operation stage. This
behavior model is continuously enriched by direct or derived learning, in order to improve the digital twin. The
proposed DS relies on data analytics (based on unsupervised learning) and on a knowledge inference engine. It
enables the incidents to be detected and it is also able to decipher its operational context. An example of this
application in the aeronautic machining industry is provided to stress both the feasibility of the proposition and
its potential impact on shop floor performance. keywords: سایه دیجیتال | دوقلو | داده ها و مدیریت دانش | ماشینکاری | Digital shadow | Digital twin | Data and knowledge management | Machining |
مقاله انگلیسی |
6 |
A data management framework for strategic urban planning using blue-green infrastructure
یک چارچوب مدیریت داده برای برنامه ریزی شهری استراتژیک با استفاده از زیرساخت های آبی سبز-2021 Spatial planning of Blue-Green Infrastructure (BGI) should ideally be based on well-evaluated and context
specific solutions. One important obstacle to reach this goal relates to adequate provisioning of data to ensure
good governance of BGI, i.e., appropriate planning, design, construction, and maintenance. This study explores
the gap between data availability and implementation of BGI in urban planning authorities in Sweden. A multi
method approach including brainstorming, semi-structured interviews with urban planners and experts on BGI
and Geographical Information System (GIS), and validating workshops were performed to develop a framework
for structured and user-friendly data collection and use. Identified challenges concern data availability, data
management, and GIS knowledge. There is a need to improve the organisation of data management and the skills
of trans-disciplinary cooperation to better understand and interpret different types of data. Moreover, different
strategic goals require different data to ensure efficient planning of BGI. This calls for closer interactions between
development of strategic political goals and data collection. The data management framework consists of three
parts: A) Ideal structure of data management in relation to planning process, data infrastructure and organisa-
tional structure, and B) A generic list of data needed, and C) The development of structures for data gathering
and access. We conclude that it is essential to develop pan-municipal data management systems that bridge
sectors and disciplines to ensure efficient management of the urban environment, and which is able to support
the involvement of citizens to collect and access relevant data. The framework can assist in such development. keywords: زیرساخت آبی سبز | مدیریت اطلاعات | برنامه ریزی فضایی | برنامه ریزی استراتژیک | مدیریت طوفان | انطباق تغییرات اقلیمی | فضاهای سبز شهری | Blue-green infrastructure | Data management | Spatial planning | Strategic planning | Stormwater management | Climate change adaptation | Urban green spaces |
مقاله انگلیسی |
7 |
Big data management capabilities in the hospitality sector: Service innovation and customer generated online quality ratings
قابلیتهای مدیریت دادههای بزرگ در بخش مهماننوازی: نوآوری خدمات و رتبهبندی کیفیت آنلاین توسط مشتری-2021 Despite the wide usage of big data in tourism and the hospitality sector, little research has been done to un-
derstand the role of organizations’ capability of managing big data in value creation. This study bridges this gap
by investigating how big data management capabilities lead to service innovation and high online quality rat-
ings. Instead of treating big data management as a whole, we access big data management capabilities at the
strategic and operational level. Using a sample of 202 hotels in Pakistan, we collected the primary data for big
data capabilities, knowledge creation and service innovation; the secondary data about quality rating were
collected from Booking.com. Structural equation modelling through SmartPLS was used for data analysis. The
results indicated that big data management capabilities lead to high online quality ratings through the mediation
of knowledge creation and service innovation. We contribute to the current literature by empirically testing how
strategic level big data capabilities enable the firm to add value in innovativeness and positive online quality
ratings through acquiring, contextualizing, experimenting and applying big data.
Author contribution keywords: مدیریت داده های بزرگ | قابلیت های پویا | نوآوری خدمات | ایجاد دانش | رتبه بندی کیفیت آنلاین ایجاد شده توسط مشتری | مهمان نوازی | Big data management | Dynamic capabilities | Service innovation | Knowledge creation | Customer generated online quality rating | Hospitality |
مقاله انگلیسی |
8 |
TITAN: A knowledge-based platform for Big Data workflow management
TITAN: یک پلت فرم مبتنی بر دانش برای مدیریت گردش کار داده های بزرگ-2021 Modern applications of Big Data are transcending from being scalable solutions of data processing
and analysis, to now provide advanced functionalities with the ability to exploit and understand the
underpinning knowledge. This change is promoting the development of tools in the intersection of data
processing, data analysis, knowledge extraction and management. In this paper, we propose TITAN, a
software platform for managing all the life cycle of science workflows from deployment to execution
in the context of Big Data applications. This platform is characterised by a design and operation mode
driven by semantics at different levels: data sources, problem domain and workflow components. The
proposed platform is developed upon an ontological framework of meta-data consistently managing
processes and models and taking advantage of domain knowledge. TITAN comprises a well-grounded
stack of Big Data technologies including Apache Kafka for inter-component communication, Apache
Avro for data serialisation and Apache Spark for data analytics. A series of use cases are conducted for
validation, which comprises workflow composition and semantic meta-data management in academic
and real-world fields of human activity recognition and land use monitoring from satellite images./
keywords: تجزیه و تحلیل داده های بزرگ | مفاهیم | استخراج دانش | Big Data analytics | Semantics | Knowledge extraction |
مقاله انگلیسی |
9 |
چارچوب حاکمیتی هوش تجاری در دانشگاه: مطالعه موردی دانشگاه دو لا کاستا
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 25 دانشگاه ها و شرکت ها دارای فرآیندهای تصمیم گیری هستند که به آنها اجازه می دهد تا به اهداف سازمانی دست پیدا کنند. در حال حاضر، تحلیل داده ها نقش مهمی در ایجاد دانش، بدست آوردن الگوهای مهم و پیش بینی استراتژی ها ایفا می کنند.این مقاله طراحی چارچوب نظارت هوش تجاری را برای دانشگاه دو لا کاستا ارائه کرده است که به آسانی برای سازمان های دیگر هم قابل استفاده است. برای این منظور، تشخیص انجام شده به منظور شناسایی میزان بلوغ تحلیلی انجام شده است. با استفاده از این چشم انداز، مدلی برای تقویت فرهنگ سازمانی ، زیر ساختارها، مدیریت داده، تحلیل داده و نظارت ارائه شده است.این مدل در بر گیرنده تعریف چارچوب نظارتی، اصول هدایت کننده، استراتژی ها، نهادهای تصمیم گیرنده و نقش ها می باشد. بنابراین، این چارچوب برای استفاده از کنترل های موثر جهت اطمینان از موفقیت پروژه های هوش تجاری و دست یابی به اهداف برنامه توسعه همراه با چسم انداز تحلیلی سازمان ارائه شده است.
کلمات کلیدی: هوش تجاری | نظارت | دانشگاه | تحلیل | تصمیم گیری |
مقاله ترجمه شده |
10 |
Biomedical and Clinical Research Data Management
مدیریت داده های تحقیقات زیست پزشکی و بالینی-2020 Systems medicine describes an interdisciplinary approach in medicine with the aim of improving disease prevention, diagnosis,
targeted treatment, and prognosis (Apweiler et al., 2018). Often, statistical, mathematical, and computational concepts of systems
biology are translated to systems medicine for clinical use (Bauer et al., 2017). This approach typically requires large amounts of
structured clinical and biomedical data covering the respective disease and patients (Gietzelt et al., 2016a). Thus, it is important to
have efficient procedures and policies in place to prepare the data from different data sources. For research projects in systems
medicine, some of the data are generated as part of the project, while others were generated beforehand, often for other purposes, e.g.
in clinical routine and in different legal context. Therefore, one of the first steps of a project is to ensure the availability of the data
needed. This task not only includes the process of assembling data files from various sources. After retrieval of the data, they typically
have to be checked and filtered for their quality (Huebner et al., 2016), converted into an appropriate format, pre-processed, and
harmonized into common formats that reflect standardized data definitions and ontologies (Krishnankutty et al., 2012).
In addition, biomedical research often relies on patient-related data, requiring additional steps like checking the permission to
use the data for the intended purpose or de-identifying the data—usually based on informed consent or special legislation.
While these steps seem to be straightforward and common to most research projects, many projects re-implement customized
solutions to build up their own infrastructure and cope with the data management challenges. Since projects usually focus on a biomedical research question, the effort of data preparation, harmonization, and management is often underestimated and
scientists with a data or computer science background are often invited to the project only at a later stage. |
مقاله انگلیسی |