Understanding human-data interaction: Literature review and recommendations for design
درک تعامل انسان با داده ها: بررسی ادبیات و توصیه هایی برای طراحی-2020
The trend of collecting information about human activities to inform and influence actions and decisions poses a series of challenges to analyze this data deluge. The lack of ability to understand and interact with this amount of data prevents people and organizations from taking the best of this information. To investigate how people interact with data, a new area of study called “Human-Data Interaction” (HDI) is emerging. In this article, we conduct a thorough literature review to create the big picture about the subject. We carry out a variety of analyses and visual examinations to understand the characteristics of existing publications, detecting the most frequently addressed research topics and consolidating the research challenges. Based on the needs of HDI we found in the analyzed publications, we organize a set of recommendations and evaluate online systems that demand intensive human-data interaction. The obtained results indicate there are still many open questions for this interesting area, which is maturing with an increase number of publications in the last years, and that systems with large amount of data openly available poorly meet the proposed recommendations
Keywords: Human-Data Interaction | Literature review | Research challenges | Data deluge
سیستم پشتیبانی از تصمیم برای خطرات و اقدامات متقابل ایمنی جاده ای اروپا
سال انتشار: 2019 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 32
سیستم پشتیبانی از تصمیم درباره ایمنی جاده ای اروپا (roadsafety-dss.eu) یک سیستم نوآورانه است که شواهد و مدارک دسترس پذیری را درباره گستره وسیعی از خطرات جاده ای و اقدامات متقابل امکانپذیر فراهم می کند. این مقاله پایه و اساس علمی سیستم پشتیبانی از تصمیم را توصیف می کند. ساختار موجود در سیستم پشتیبانی از تصمیم شامل (1) یک طبقه بندی که به شناسایی عوامل خطر و اقدامات متقابل آن می پردازد و آنها را به همدیگر مرتبط می کند، (2) یک مجموعه ای از مطالعات، و (3) خلاصه هایی که تاثیرات تخمین زده شده در منابع علمی را برای هر عامل و سنجه خطر خلاصه بندی می کنند و (4) یک ابزار ارزیابی کارآمدی اقتصادی (محاسبه گر E3) می شود. سیستم پشتیبانی از تصمیم در یک ابزار نوین مبتنی بر وب با فصل مشترک بسیار انسانی اجرا می شود که به کاربران اجازه می دهد تا مرور اجمالی سریعی داشته باشند یا نتایج هر مطالعه را برطبق نیازهای مخصوص آنها عمیق تر بررسی کنند.
کلیدواژه ها: اقدامات متقابل ایمنی جاده | خطرات جاده ای | سودمندی | سیستم آنلاین | مرور | هزینه – سود
|مقاله ترجمه شده|
Enhancing the long-term performance of recommender system
افزایش عملکرد بلند مدت سیستم توصیه گر-2019
The recommender system is a critically important tool in the online commercial system and provides users with personalized recommendations on items. So far, numerous recommendation algorithms have been made to further improve the recommendation performance in a single-step recommendation, while the long-term recommendation performance is neglected. In this paper, we proposed an approach called Adjustment of Recommendation List (ARL) to enhance the long-term recommendation accuracy. In order to observe the long-term accuracy, we developed an evolution model of network to simulate the interaction between the recommender system and user’s behavior. The result shows that not only long-term recommendation accuracy can be enhanced significantly but the diversity of items in an online system maintains healthy. Notably, an optimal parameter n∗ of ARL existed in the long-term recommendation, indicating that there is a trade-off between keeping the diversity of item and user’s preference to maximize the long-term recommendation accuracy. Finally, we confirmed that the optimal parameter n∗ is stable during the evolving network, which reveals the robustness of the ARL method.
Keywords:Long-term | Recommender system | Evolution model | Robustness
Predictability of diffusion-based recommender systems
پیش بینی پذیری سیستم های توصیه گر مبتنی بر انتشار-2019
Numerous diffusion-based recommendation algorithms (DBA) have been extended to improve the performance of such methods further. However, it is still not clear to what extent recommendation accuracy can be improved if we continue to extend existing algorithms. In this paper, we propose an ideal method to quantify the possible maximum recommendation accuracy of DBA, which is regarded as predictability of algorithms. Accordingly, the ideal method is applied to the extensively analyzed datasets. The result illustrates that the accuracy of DBA can still be improved by optimizing the resource allocation matrix on a dense network. Nevertheless, improving accuracy on sparse networks is difficult, mainly because the current accuracy of DBA is very close to its predictability. We find that the predictability can be enhanced effectively by multi-step resource diffusion, especially for inactive users (with less historical data). In contrast to common belief, there are plausible circumstances where the higher predictability of DBA does not correspond to active users. Additionally, we demonstrate that the recommendation accuracy is overestimated in the real online systems by random partition used in the literature, suggesting the recommendation in the real online systems may be a tough task
Keywords: Predictability | Diffusion-based algorithms | Recommender systems
A mathematical optimization model for efficent efficient management of Nurses’ Quarters in a teaching and referral hospital in Hong Kong
یک مدل بهینه سازی ریاضی برای مدیریت کارآمد ربع ها پرستاران در یک بیمارستان آموزشی و ارجاعی در هنگ کنگ-2016
Article history:Received 13 August 2014Accepted 22 September 2015Available online 9 October 2015Keywords:Nurses’ QuartersMixed-integer programming Resource allocationBed assignmentObjectiveEffective use of available resources is critical in the healthcare industry. Space in the Nurses’ Quarters in an acute regional and teaching hospital in Hong Kong is under pressure and effective use of such scarce resources is warranted. We present an application of mathematical optimization to ensure rooms are optimally assigned for users, in which the least number of rooms need to be opened while entertaining the most number of user requests, while at the same time satisfying various business rules of quarter management.MethodsExtensive consultation was embarked at the start of the project to gather requirements from quarter management and frontline users. Utilization statistics were also gathered and analyzed. A web-based booking system was designed and implemented to streamline the booking workflow for users, while a mixed integer programming model was implemented to automate the room assignment process for quarter management.FindingsMore than 70% of bookings are now booked through the automated system. Unlike the old workflow where users need to travel to the quarter for reservation of rooms, they can now access the online system using any computer workstation that is connected to the hospital intranet. This greatly simplifies the booking workflow for users and decreased the amount of no-shows. In addition, workflow wastage in the then-manual room assignment process was minimized through the use of the room assignment model. Instead of manually assigning requests to rooms each day, an optimized room assignment that takes into account new and complex business rules on room allocation is now automatically generated with a click of a button. Defects and rework that arise from the manual process were eliminated through the implementation of the mathematical model. The implementation of such a fair and transparent assignment methodology also minimized the amount of disputes and complaints from users.ConclusionThe use of operations research methodologies is useful in enhancing workflow efficiency and resource utilization in healthcare. Through the employment of a data-driven and evidence-based methodology, buy-in from stakeholders could be obtained so that a new and enhanced workflow could be successfully implemented.© 2015 Elsevier Ltd. All rights reserved.
Keywords: Nurses’ Quarters | Mixed-integer programming | Resource allocation | Bed assignment