Data and information management最新文献

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.

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.

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.

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.

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.