As growth of the data analytics field continues, more and more academic departments and professional training programs are offering skill training to potential data analysts. However, not all business domains require their data analysts to possess the same sets of technical skills. Based on over 2,500 data-analyst job advertisements posted on LinkedIn and utilizing subject analysis, cross-tabulation analysis, heat map, Kruskal-Wallis test, and Kappa test, this study examined relationships between various business domains and the technical skills required for data analysts within the respective business domains. Among many findings, the study discovered noteworthy disparities among the business domains concerning their requirements of technical-skill sets for data analysts. The findings of this study offer valuable insights to educators and potential data analysts.

As the data-analysts job market grows, many colleges and universities have started offering a data analytics curriculum. However, there are potential gaps between the skills business organizations expect of data analysts and the skills universities and colleges teach their students. This study collected 2500+ data-analyst job ads posted on LinkedIn and analyzed them using distribution analysis, cross-tabulation analysis, and cluster analysis. Among many findings, this study identified five most essential nontechnical skills and five most essentials areas of technical skills. In addition, of 90+ computer programs business organizations expect data analysts to use, this study identified SQL, Microsoft Excel, Tableau, Python, and Microsoft Power BI to be the five most essential computer programs for potential data analysts to master.

Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.

This study investigates the content of questions and responses about the Zika virus on Yahoo! Answers as a recent example of how public concerns regarding an international health issue are reflected in social media. We investigate the contents of posts about the Zika virus on Yahoo! Answers, identify and reveal subject patterns about the Zika virus, and analyze the temporal changes of the revealed subject topics over 4 defined periods of the Zika virus outbreak. Multidimensional scaling analysis, temporal analysis, and inferential statistical analysis approaches were used in the study. A resulting 2‐layer Zika virus schema, and term connections and relationships are presented. The results indicate that consumers’ concerns changed over the 4 defined periods. Consumers paid more attention to the basic information about the Zika virus, and the prevention and protection from the Zika virus at the beginning of the outbreak of the Zika virus. During the later periods, consumers became more interested in the role that the government and health organizations played in the public health emergency.

Relevance judgment plays an extremely significant role in information retrieval. This study investigates the differences between American users and Chinese users in relevance judgment during the information retrieval process. 384 sets of relevance scores with 50 scores in each set were collected from 16 American users and 16 Chinese users as they judged retrieval records from two major search engines based on 24 predefined search tasks from 4 domain categories. Statistical analyses reveal that there are significant differences between American assessors and Chinese assessors in relevance judgments. Significant gender differences also appear within both the American and the Chinese assessor groups. The study also revealed significant interactions among cultures, genders, and subject categories. These findings can enhance the understanding of cultural impact on information retrieval and can assist in the design of effective cross-language information retrieval systems.

This article describes how as internet technology continues to change and improve lives and societies worldwide, effective global information management becomes increasingly critical, and effective Internet information retrieval systems become more and more significant in providing Internet users worldwide with accurate and complete information. Search engine evaluation is an important research field as search engines directly determine the quality of information users' Internet searches. Relevance-decrease pattern/model plays an important role in search engine result evaluation. This research studies effective measurement of search results through investigating relevance-decrease patterns of search results from two popular search engines: Google and Bing. The findings can be applied to relevance-evaluation of search results from other information retrieval systems such as OPAC, can help make search engine evaluations more accurate and sound, and can provide global information management personnel with valuable insights.

We investigated a topic-based navigation guidance system in the World Health Organization portal, compared the link connection network and the semantic connection network derived from the guidance system, analyzed the characteristics of the 2 networks from the perspective of the node centrality (in_closeness, out_closeness, betweenness, in_degree, and out_degree), and provided the suggestions to optimize and enhance the topic-based navigation guidance system. A mixed research method that combines the social network analysis method, clustering analysis method, and inferential analysis methods was used. The clustering analysis results of the link connection network were quite different from those of the semantic connection network. There were significant differences between the link connection network and the semantic network in terms of density and centrality. Inferential analysis results show that there were no strong correlations between the centrality of a node and its topic information characteristics. Suggestions for enhancing the navigation guidance system are discussed in detail. Future research directions, such as application of the same research method presented in this study to other similar public health portals, are also included.

We investigated a subject directory in the US Agriculture Department‐Economic Research Service portal. Parent–child relationships, related connections among the categories, and related connections among the subcategories in the subject directory were optimized using social network analysis. The optimization results were assessed by both density analysis and edge strength analysis methods. In addition, the results were evaluated by domain experts. From this study, it is recommended that four subcategories be switched from their original four categories into two different categories as a result of the parent–child relationship optimization. It is also recommended that 132 subcategories be moved to 40 subcategories and that eight categories be moved to two categories as a result of the related connection optimization. The findings show that optimization boosted the densities of the optimized categories, and the recommended connections of both the related categories and subcategories were stronger than the existing connections of the related categories and subcategories. This paper provides visual displays of the optimization analysis as well as suggestions to enhance the subject directory of this portal.

Abstract Session characteristics taken from large transaction logs of three Web search environments (academic Web site, public search engine, consumer health information portal) were modeled using cluster analysis to determine if coherent session groups emerged for each environment and whether the types of session groups are similar across the three environments. The analysis revealed three distinct clusters of session behaviors common to each environment: “hit and run” sessions on focused topics, relatively brief sessions on popular topics, and sustained sessions using obscure terms with greater query modification. The findings also revealed shifts in session characteristics over time for one of the datasets, away from “hit and run” sessions toward more popular search topics. A better understanding of session characteristics can help system designers to develop more responsive systems to support search features that cater to identifiable groups of searchers based on their search behaviors. For example, the system may identify struggling searchers based on session behaviors that match those identified in the current study to provide context sensitive help.

Abstract Concerns about health issues cover a wide spectrum. Consumer health information, which has become more available on the Internet, plays an extremely important role in addressing these concerns. A subject directory as an information organization and browsing mechanism is widely used in consumer health‐related Websites. In this study we employed the information visualization technique Self‐Organizing Map (SOM) in combination with a new U‐matrix algorithm to analyze health subject clusters through a Web transaction log. An experimental study was conducted to test the proposed methods. The findings show that the clusters identified from the same cells based on path‐length‐1 outperformed both the clusters from the adjacent cells based on path‐length‐1 and the clusters from the same cells based on path‐length‐2 in the visual SOM display. The U‐matrix method successfully distinguished the irrelevant subjects situated in the adjacent cells with different colors in the SOM display. The findings of this study lead to a better understanding of the health‐related subject relationship from the users' traversal perspective.

Abstract The authors investigated 11 sports‐related query keywords extracted from a public search engine query log to better understand sports‐related information seeking on the Internet. After the query log contents were cleaned and query data were parsed, popular sports‐related keywords were identified, along with frequently co‐occurring query terms associated with the identified keywords. Relationships among each sports‐related focus keyword and its related keywords were characterized and grouped using multidimensional scaling (MDS) in combination with traditional hierarchical clustering methods. The two approaches were synthesized in a visual context by highlighting the results of the hierarchical clustering analysis in the visual MDS configuration. Important events, people, subjects, merchandise, and so on related to a sport were illustrated, and relationships among the sports were analyzed. A small‐scale comparative study of sports searches with and without term assistance was conducted. Searches that used search term assistance by relying on previous query term relationships outperformed the searches without the search term assistance. The findings of this study provide insights into sports information seeking behavior on the Internet. The developed method also may be applied to other query log subject areas.

Abstract Index modeling and computer simulation techniques are used to examine the influence of indexing frequency distributions, indexing exhaustivity distributions, and three weighting methods on hypothetical document spaces in a vector‐based information retrieval (IR) system. The way documents are indexed plays an important role in retrieval. The authors demonstrate the influence of different indexing characteristics on document space density (DSD) changes and document space discriminative capacity for IR. Document environments that contain a relatively higher percentage of infrequently occurring terms provide lower density outcomes than do environments where a higher percentage of frequently occurring terms exists. Different indexing exhaustivity levels, however, have little influence on the document space densities. A weighting algorithm that favors higher weights for infrequently occurring terms results in the lowest overall document space densities, which allows documents to be more readily differentiated from one another. This in turn can positively influence IR. The authors also discuss the influence on outcomes using two methods of normalization of term weights (i.e., means and ranges) for the different weighting methods.

Abstract A multidimensional‐scaling approach is used to analyze frequently used medical‐topic terms in queries submitted to a Web‐based consumer health information system. Based on a year‐long transaction log file, five medical focus keywords (stomach, hip, stroke, depression, and cholesterol) and their co‐occurring query terms are analyzed. An overlap‐coefficient similarity measure and a conversion measure are used to calculate the proximity of terms to one another based on their co‐occurrences in queries. The impact of the dimensionality of the visual configuration, the cutoff point of term co‐occurrence for inclusion in the analysis, and the Minkowski metric power k on the stress value are discussed. A visual clustering of groups of terms based on the proximity within each focus‐keyword group is also conducted. Term distributions within each visual configuration are characterized and are compared with formal medical vocabulary. This investigation reveals that there are significant differences between consumer health query‐term usage and more formal medical terminology used by medical professionals when describing the same medical subject. Future directions are discussed.

Abstract The authors investigate the influence of index term distributions, and indexing exhaustivity levels on the document space within a visual information retrieval environment called DARE. Using combinations of three levels of term distributions (shallow, observed, steep) and indexing exhaustivity (low, observed, high), hypothetical document sets were generated and projected onto the DARE environment. The results from the simulated document sets demonstrate the importance of term distribution and exhaustivity characteristics on the density of document spaces and their implications for retrieval, particularly when different term weighting schemes are used. The results also demonstrate how different combinations of exhaustivity and term distributions may result in similar document space density characteristics.

Abstract A visual term discrimination value analysis method is introduced using a document density space within a distance–angle‐based visual information retrieval environment. The term discrimination capacity is analyzed using the comparison of the distance and angle‐based visual representations with and without a specified term, thereby allowing the user to see the impact of the term on individual documents within the density space. Next, the concept of a “term density space” is introduced for term discrimination analysis. Using this concept, a term discrimination capacity for distinguishing one term from others in the space can also be visualized within the visual space. Applications of these methods facilitate more effective assignment of term weights to index terms within documents and may assist searchers in the selection of search terms.

This article presents a distance and angle similarity measure. The integrated similarity measure takes the strengths of both the distance and direction of measured documents into account. This article analyzes the features of the similarity measure by comparing it with the traditional distance-based similarity measure and the cosine measure, providing the iso-similarity contour, investigating the impacts of the parameters and variables on the new similarity measure. It also gives the further research issues on the topic.

This article presents a visualization tool for information retrieval. Some retrieval evaluation models are interpreted in the two-dimensional space comprising direction and distance. The two different similarity measures—angle and distance—are displayed in the visual space. A new retrieval means based on the visual retrieval tool, the controlling bar, is developed for a search.