PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

The rise and widespread use of Linked Data has encouraged data providers to publish and link their content in order to classify and organize information in a useful fashion. Interlinking between datasets enhances data navigation and facilitates searching. As a result, the use of interlinking tools as a way of connecting data items to the Linked Open Data cloud has become more prevalent. In this paper, we examine the results obtained by three interlinking tools used to link a large educational collection to the Linked Open Data datasets. The generated output by the interlinking tools, which was later assessed by human experts, illustrates that data publishers can rely on current interlinking approaches and thus adopt them to connect their resources to the Web of Data. Our findings also provide evidence that two of these tools, namely Silk and LIMES, can be considered as the most promising.

Linked open data allow interlinking and integrating any kind of data on the web. Links between various data sources play a key role insofar as they allow software applications (e.g., browsers, search engines) to operate over the aggregated data space as if it was a unique local database. In this new data space, where DBpedia, a data set including structured information from Wikipedia, seems to be the central hub, we analyzed and highlighted outgoing links from this hub in an effort to discover broken links. The paper reports on an experiment to examine the causes of broken links and proposes some treatments for solving this problem.

The COVID-19 pandemic is having a significant impact on tourism, and emotion projection is one way to understand the extent of destination image resiliency during the crisis. Therefore, this research captured emotions expressed in social media during a peak pandemic month to compare to the prior year period. Toronto and New York were selected due to their tourism importance within their countries but to also compare the effects of different policy approaches used during the pandemic. This study found resiliency of the destination images although there was a significant increase in projections of fear for both cities. Additionally, there was a significant divergence observed for the two cities with a decrease in joy and an increase in sadness projections for New York versus Toronto. This implies that tourism destination marketers have a stable basis of emotions to use in communications, but there are weaknesses to address.

The emergence of Web of Data enables new opportunities for relating resources identified by URIs combined with the usage of RDF as a lingua franca for describing them. There have been to date some efforts in the direction of exposing learning object metadata following the conventions of Linked Data. However, they have not addressed an analysis on the different strategies to expose Linked Data that could be used as a basis for leveraging the metadata currently curated in repositories following common conventions and established standards. This paper describes an approach for exposing IEEE LOM metadata as Linked Data and discusses alternative strategies and their tradeoffs. The recommended approach applies common principles for Linked Data to the specificities of LOM data types and elements, identifying opportunities for interlinking exhaustively. A case study and a reference implementation along with an evaluation are also presented as a proof of concept of this mapping.