Creating value from Social Big Data: Implications for Smart Tourism Destinations     

Pasquale Del Vecchio  Gioconda Mele  Valentina Ndou  Giustina Secundo 《Information processing & management》2018,54(5):847-860

This paper aims to demonstrate how the huge amount of Social Big Data available from tourists can nurture the value creation process for a Smart Tourism Destination. Applying a multiple-case study analysis, the paper explores a set of regional tourist experiences related to a Southern European region and destination, to derive patterns and opportunities of value creation generated by Big Data in tourism. Findings present and discuss evidence in terms of improving decision-making, creating marketing strategies with more personalized offerings, transparency and trust in dialogue with customers and stakeholders, and emergence of new business models. Finally, implications are presented for researchers and practitioners interested in the managerial exploitation of Big Data in the context of information-intensive industries and mainly in Tourism.  相似文献   

Remaining in Control with an Illusion of Interactivity: The Paternalistic Side of Data Journalism*     

Ester Appelgren 《Journalism Practice》2019,13(8):956-960

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MARC to BIBFRAME: Converting the PCC to Linked Data     

Jacquie Samples  Ian Bigelow 《Cataloging & classification quarterly》2020,58(3-4):403-417

Abstract

The Program for Cooperative Cataloging (PCC) has formal relationships with the Library of Congress (LC), Share-VDE, and Linked Data for Production Phase 2 (LD4P2) for work on Bibliographic Framework (BIBFRAME), and PCC institutions have been very active in the exploration of MARC to BIBFRAME conversion processes. This article will review the involvement of PCC in the development of BIBFRAME and examine the work of LC, Share-VDE, and LD4P2 on MARC to BIBFRAME conversion. It will conclude with a discussion of areas for further exploration by the PCC leading up to the creation of PCC conversion specifications and PCC BIBFRAME data.  相似文献   

Cross-company customer churn prediction in telecommunication: A comparison of data transformation methods     

《International Journal of Information Management》2019

Cross-Company Churn Prediction (CCCP) is a domain of research where one company (target) is lacking enough data and can use data from another company (source) to predict customer churn successfully. To support CCCP, the cross-company data is usually transformed to a set of similar normal distribution of target company data prior to building a CCCP model. However, it is still unclear which data transformation method is most effective in CCCP. Also, the impact of data transformation methods on CCCP model performance using different classifiers have not been comprehensively explored in the telecommunication sector. In this study, we devised a model for CCCP using data transformation methods (i.e., log, z-score, rank and box-cox) and presented not only an extensive comparison to validate the impact of these transformation methods in CCCP, but also evaluated the performance of underlying baseline classifiers (i.e., Naive Bayes (NB), K-Nearest Neighbour (KNN), Gradient Boosted Tree (GBT), Single Rule Induction (SRI) and Deep learner Neural net (DP)) for customer churn prediction in telecommunication sector using the above mentioned data transformation methods. We performed experiments on publicly available datasets related to the telecommunication sector. The results demonstrated that most of the data transformation methods (e.g., log, rank, and box-cox) improve the performance of CCCP significantly. However, the Z-Score data transformation method could not achieve better results as compared to the rest of the data transformation methods in this study. Moreover, it is also investigated that the CCCP model based on NB outperform on transformed data and DP, KNN and GBT performed on the average, while SRI classifier did not show significant results in term of the commonly used evaluation measures (i.e., probability of detection, probability of false alarm, area under the curve and g-mean).  相似文献   

COVID-19, digital privacy,and the social limits on data-focused public health responses     

《International Journal of Information Management》2020

The implementation of digital contact tracing applications around the world to help reduce the spread of the COVID-19 pandemic represents one of the most ambitious uses of massive-scale citizen data ever attempted. There is major divergence among nations, however, between a “privacy-first” approach which protects citizens’ data at the cost of extremely limited access for public health authorities and researchers, and a “data-first” approach which stores large amounts of data which, while of immeasurable value to epidemiologists and other researchers, may significantly intrude upon citizens’ privacy. The lack of a consensus on privacy protection in the contact tracing process creates risks of non-compliance or deliberate obfuscation from citizens who fear revealing private aspects of their lives – a factor greatly exacerbated by recent major scandals over online privacy and the illicit use of citizens’ digital information, which have heightened public consciousness of these issues and created significant new challenges for any collection of large-scale public data. While digital contact tracing for COVID-19 remains in its infancy, the lack of consensus around best practices for its implementation and for reassuring citizens of the protection of their privacy may already have impeded its capacity to contribute to the pandemic response.  相似文献   

Assessing library topics using sentiment analysis in R: a discussion and code sample     

Brady D. Lund 《Public Services Quarterly》2020,16(2):112-123

Abstract

This article discusses the use of R programing language for executing a sentiment analysis of tweets pertaining to library topics. This discussion is situated within the literature of marketing and management sciences, which is employing methods of machine learning and business intelligence to make informed decision-making, and library administration, which has expressed great interest in social media engagement within its literature but has yet to adopt these types of analysis. Presented in this article is a sample code with instructions on how users may execute it within R to retrieve and analyze tweets relevant to library services. Two examples created using the code (analysis of top librarians’ tweets and analysis of posts about major book publishers) are used to demonstrate the functionality of the code. The code presented in this article may be used by libraries to analyze tweets about their library and library-related topics, which, in turn, may inform management and marketing design.  相似文献   

大数据视域下的档案安全管理体系构建及可引入技术分析     

孙宁 《档案管理》2020,(3):12-13

在大数据视域下,以档案管理理论和信息系统安全理论为基础,参考国家相关法规及标准,结合档案管理工作实务,研究当下档案管理工作中的风险,在此基础上初步构建起档案安全管理体系,并对可引入该体系的实用技术进行分析。  相似文献   

基于Oracle Spatial的空间查询技术及其优化     

熊雷 《教育技术导刊》2008,7(8):7-9

从Oracle Spatial的空间查询分析出发,结合实例分析了它的查询模型、空间算子以及常用的空间函数,最后给出了Oracle Spatial中空间查询的优化建议。  相似文献   

Affordances of narrative and numerical data: A social-semiotic approach to data use     

《Studies in Educational Evaluation》2021

Data use in education is a sensemaking process in which practitioners and researchers interact with different systems of meaning such as anecdotes or spreadsheets. The representational qualities of data and their influence on practice are critical but less well-discussed aspects of data use. Drawing on social semiotics, this theoretical article proposes that data should be discussed in terms of narrative and numerical modes of representation. Narrative data typically consist of protagonists and actions organized in a temporal structure, while numerical data typically consist of mathematical notations and visual representations such as graphs and figures. We argue that the representational properties of these two modes affect how data are interpreted and acted upon. We then present two contrasting cases from New Zealand and Norway of how affordances affect teachers’ data use processes. Finally, we discuss five challenges arising from our theorization about the affordances of data.  相似文献   

Capacity building to bridge data use and instructional improvement through evidence on student thinking     

《Studies in Educational Evaluation》2021

While schools and systems across the globe promote data-driven decision making, teachers often struggle to use data, especially from external assessments, to inform daily instruction. In this paper, we examine teacher capacity building for a less typical form of data use - evidence on student thinking. We draw on data from a longitudinal, in-depth qualitative study involving middle school math teachers who were engaged in an instructional improvement project. Findings show that data use occurred when evidence from student thinking was introduced as part of the instructional planning process. This shift was facilitated by an instructional coach whose capacity building efforts with teachers focused on coherence, specifically planning high quality instruction and using data effectively, while also meeting district pacing and unit planning goals. When teachers put new strategies into practice, feedback from formative assessment data allowed teachers to identify and address misconceptions in student thinking. Learning how to use data as part of instruction helped teachers build capacity to reflect on their own practice. Implications for theory, practice, and research are discussed.  相似文献