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A virtue ethical approach to the use of artificial intelligence 使用人工智能的美德伦理方法
Pub Date : 2023-03-01 DOI: 10.1016/j.dim.2023.100037
Michael J. Cuellar
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引用次数: 2
The past, the present, and the future of information and data sciences: A pragmatic view 信息和数据科学的过去、现在和未来:一种务实的观点
Pub Date : 2023-03-01 DOI: 10.1016/j.dim.2023.100028
Chirag Shah

While data science and information science emerged as two separate disciplines with different roots, in the recent past, they have been getting integrated and intertwined in interesting and impactful ways. The traditional distinction between data and information does not easily explain the differences and overlaps between the two sciences named after them. If one claims, for instance, that information is ‘meaningful data’ then it is important to note that a main objective of data science is indeed to derive meaningful information out of data. Information science is not necessarily a superset or a higher level of data science. Both of these disciplines have earned their place in sciences through different pasts, paths, and possibilities. Keeping that in mind, they are discussed here while tracing their origins and understanding their positionalities in the current context. More than the past and the present, what becomes then important is where they are heading next. Several suggestions are provided to keep data science a meaningful offering within information science – as a uniqueness for the former with the strengths of the latter.

虽然数据科学和信息科学是两个有着不同根源的独立学科,但在最近的一段时间里,它们以有趣而有影响力的方式融合在一起。传统的数据和信息之间的区别很难解释以它们命名的两门科学之间的差异和重叠。例如,如果有人声称信息是“有意义的数据”,那么需要注意的是,数据科学的主要目标确实是从数据中获得有意义的信息。信息科学不一定是数据科学的超集或更高层次。这两个学科都通过不同的过去、道路和可能性在科学中赢得了一席之地。考虑到这一点,我们在这里讨论它们,同时追溯它们的起源,了解它们在当前背景下的地位。比过去和现在更重要的是,他们下一步要去哪里。提供了一些建议,以保持数据科学在信息科学中的有意义的提供——作为前者的独特性和后者的优势。
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引用次数: 0
Seven ways to make a data science project fail 让数据科学项目失败的七种方法
Pub Date : 2023-03-01 DOI: 10.1016/j.dim.2023.100029
Robert J. Glushko

The rapid emergence of data science as a field has made it a rival or replacement for information science from an industry perspective. In particular, the “big data” meme in data science and a heavy reliance on “black box” technology emphasize the quantity of data used in a project and asks, “what data do we have” rather than “what data do we need to solve our business problems.” This perspective also undermines the perceived importance of domain expertise, user research, data semantics and provenance, and other considerations valued in information science. This article uses a composite (and somewhat caricatured) case study of a data science project and discusses seven ways in which it is destined to fail, and then explains how “good information science” would have prevented or ameliorated them. Data science and information science need to recognize that together they can accomplish more than they can accomplish separately.

数据科学作为一个领域的迅速出现,使其从行业角度成为信息科学的竞争对手或替代品。特别是,数据科学中的“大数据”模因和对“黑匣子”技术的严重依赖强调了项目中使用的数据量,并询问“我们有什么数据”,而不是“我们需要什么数据来解决我们的业务问题”。这种观点也削弱了领域专业知识、用户研究、数据语义和来源的重要性,以及信息科学中有价值的其他考虑因素。本文使用了一个数据科学项目的综合(有点讽刺)案例研究,讨论了它注定会失败的七种方式,然后解释了“好的信息科学”是如何预防或改善它们的。数据科学和信息科学需要认识到,它们一起可以完成比单独完成更多的任务。
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引用次数: 0
Introduction to the special issue on data science and information science 数据科学与信息科学特刊导论
Pub Date : 2023-03-01 DOI: 10.1016/j.dim.2023.100034
Feicheng Ma , Gary Marchionini
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引用次数: 0
Research on linkage of science and technology in the library and information science field 图书馆情报学领域科技联动研究
Pub Date : 2023-02-01 DOI: 10.1016/j.dim.2023.100033
X. Yang, Lingzi Feng, Junpeng Yuan
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引用次数: 0
Information science: Why it is not data science 信息科学:为什么它不是数据科学
Pub Date : 2023-02-01 DOI: 10.1016/j.dim.2023.100027
Michael Seadle , Stefanie Havelka
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引用次数: 0
Investigating the knowledge structure of research on massively multiplayer online role-playing games: A bibliometric analysis 大型多人在线角色扮演游戏研究的知识结构调查——文献计量分析
Pub Date : 2022-11-24 DOI: 10.1016/j.dim.2022.100024
Seungjong Sun , Dongyan Nan , ShaoPeng Che , Jang Hyun Kim

As the concept of the Metaverse rapidly spread, massively multiplayer online role-playing game (MMORPG), one of the Metaverse games, received public's attention again. Additionally, MMORPGs have garnered significant academic interest in multidisciplinary fields, including human-computer interaction, computer science, and psychology. This study provides a general overview of the knowledge structures on MMORPGs in various academic fields using a bibliometric approach. To this end, we collected articles on MMORPGs published between 2001 and 2021 from the Web of Science. Then, main research forces in the field of MMORPGs are identified by examining productive authors, institutions, and countries/regions. Additionally, we applied VOSviewer to conduct co-citation analyses for identifying highly cited authors, journals, and literatures on MMORPGs. The results revealed that the USA and South Korea are the most productive countries. In addition, players' motivation, players' demographics, in-game social interaction, and pathological usage are the main research topics in the field. We expect that our results will help researchers and stakeholders to gain a general understanding of the development of the MMORPG academic field.

随着 "元宇宙 "概念的迅速传播,作为 "元宇宙 "游戏之一的大型多人在线角色扮演游戏(MMORPG)再次受到公众的关注。此外,MMORPG 在人机交互、计算机科学和心理学等多学科领域也引起了学术界的极大兴趣。本研究采用文献计量学方法对各学术领域中有关 MMORPG 的知识结构进行了概括。为此,我们从 Web of Science 收集了 2001 年至 2021 年间发表的有关 MMORPG 的文章。然后,通过研究有成果的作者、机构和国家/地区,确定了 MMORPG 领域的主要研究力量。此外,我们还应用 VOSviewer 进行了共引分析,以确定 MMORPG 的高被引作者、期刊和文献。结果显示,美国和韩国是高产国家。此外,玩家的动机、玩家的人口统计、游戏中的社交互动和病态使用也是该领域的主要研究课题。我们希望我们的研究结果能帮助研究人员和相关人士对 MMORPG 学术领域的发展有一个总体的了解。
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引用次数: 0
A review on method framework construction of Chinese Information Science 中国情报学方法框架构建述评
Pub Date : 2022-10-01 DOI: 10.1016/j.dim.2022.100023
Bowen Li , Liang Tian , Yingyi Zhang , Heng Zhang , Chengzhi Zhang

As the unique academic culture in Chinese philosophy and social sciences, researches on method framework provide an opportunity for understanding the thinking model and value orientation of the ancient eastern civilization. The field of information science has achieved fruitful results in the method framework research closely related to the unique discipline history and academic mission. The paper reviews information science method frameworks in China and presents their academic features from three aspects: 1. levels of the framework, 2. research strategies, and 3. essential techniques. At the same time, we summarize the value of this Chinese academic wisdom and the practical experience of information science in China to promote this research.

方法框架研究作为中国哲学社会科学独特的学术文化,为理解东方古代文明的思维模式和价值取向提供了契机。信息科学领域在方法框架研究方面取得了丰硕的成果,这与它独特的学科历史和学术使命密切相关。本文综述了国内情报学方法框架,并从三个方面阐述了它们的学术特点:1.情报学方法框架;框架的层次,2。3.研究策略;基本技术。同时,总结这一中国学术智慧的价值和中国情报学的实践经验,推动这一研究。
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引用次数: 0
Collaborative Filtering system based on multi-level user clustering and aspect sentiment 基于多层次用户聚类和方面情感的协同过滤系统
Pub Date : 2022-10-01 DOI: 10.1016/j.dim.2022.100021
Samin Poudel, Marwan Bikdash

A Collaborative Filtering (CF) method predicts an unknown overall rating of a target user towards an item based on the known overall ratings of the users that are similar to the target user. The similarity between two users is generally found based on their overall ratings toward items that both have reviewed. Two users may have similar overall ratings towards a given item, but different sentiments towards various aspects of the item. Understanding the effect of user sentiment towards specific aspects on overall ratings will sharpen estimates of user similarity as well as provide an rationale for making specific recommendations. We propose an Aspect-Sentiments based Multi-level Clustering of Users (ASMCU) approach that finds the multiple clusters of users similar to a specific user where similarity between users is based on various aspect sentiments. The proposed ASMCU CF approach can be used to predict both the overall ratings and the aspect-sentiments. The ASMCU based CF approach performed mostly better than and sometimes comparable to the eight well-established CF methods that rely only on the overall ratings or a particular aspect-sentiments. Note however that the ASMCU can also explicitly justify the recommendation in terms of aspect sentiments. We evaluated our approach using three datasets: One Hotel dataset and Two Beer datasets. The Hotel dataset involved six aspects and each Beer dataset has four aspects. Each dataset has one overall rating matrix and one sentiment tensor.

协同过滤(CF)方法基于已知的与目标用户相似的用户的总体评分来预测未知的目标用户对某项的总体评分。两个用户之间的相似性通常是基于他们对两个人都评论过的物品的总体评分来发现的。两个用户可能对给定的物品有相似的总体评分,但对物品的各个方面有不同的看法。理解用户对特定方面的情感对总体评分的影响,将提高对用户相似度的估计,并为提出具体建议提供依据。我们提出了一种基于方面情感的用户多级聚类(ASMCU)方法,该方法可以找到与特定用户相似的多个用户集群,其中用户之间的相似性基于各种方面情感。所提出的ASMCU CF方法可用于预测整体评级和方面情绪。基于ASMCU的CF方法通常比仅依赖于总体评级或特定方面-情绪的八种成熟的CF方法表现得更好,有时甚至可以与之媲美。但是请注意,ASMCU也可以根据方面的情绪明确地证明建议的合理性。我们使用三个数据集来评估我们的方法:一个酒店数据集和两个啤酒数据集。酒店数据集涉及六个方面,每个啤酒数据集有四个方面。每个数据集有一个总体评价矩阵和一个情感张量。
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引用次数: 2
Twenty important theories and applications of empirical research on IS 信息系统实证研究的二十个重要理论与应用
Pub Date : 2022-10-01 DOI: 10.1016/j.dim.2022.100003
Chuanhui Wu , Shijing Huang
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引用次数: 0
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Data and information management
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