Many data analysis tasks can be viewed as search or mining in a multidimensional space (MDS). In such MDSs, dimensions capture potentially important factors for given applications, and cells represent combinations of values for the factors. To systematically analyze data in MDS, an interesting notion, called "cubegrade" was recently introduced by Imielinski, et al. [IKA02], which focuses on the notable changes in measures in MDS by comparing a cell (which we refer as probe cell) with its gradient cells, namely its ancestors, descendants and siblings. We call such queries gradient analysis queries (GQs). Since an MDS can contain billions of cells, it is important to answer GQs efficiently. In this study, we focus on developing efficient methods for mining GQs constrained by certain (weakly) anti-monotone constraints. Instead of conducting an independent gradient-cell search once per probe cell, which is inefficient due to much repeated work, we propose an efficient algorithm, LiveSet-Driven. This algorithm finds all good gradient-probe cell pairs in one search pass. It utilizes measure-value analysis and dimensionmatch analysis in a set-oriented manner, to achieve bi-directional pruning between the sets of hopeful probe cells and of hopeful gradient cells. Moreover, it adopts a hyper-tree structure and an H-cubing method to compress data and to maximize sharing of computation. Our performance study shows that this algorithm is efficient and scalable. In addition to data cubes, we extend our study to another important scenario: mining constrained gradients in transactional databases where each item is associated with some measures such as price. Such transactional databases can be viewed as sparse MDSs where items represent dimensions, although they have significantly different characteristics than data cubes. We outline efficient mining methods for this problem in this paper.

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Those who gamble compulsively, and those who shop or buy in a compulsive manner share a number of common characteristics, stemming from similar impulse-control issues. As such, it is predicted that a lexical analysis of personal narratives of compulsion would share similarities. Using secondary data from an online mental health forum, Psychforums, the research analyzed narratives of compulsive gambling ( n = 199) and compulsive buying ( n = 196) using the automated text analysis tool, LIWC. The results indicated that compulsive buying narratives rated significantly higher in clout and emotional tone and significantly lower in authenticity, with no significant differences noted in analytical thinking between the two compulsion narratives. Recommendations for future research include that demographic variables be incorporated and that narratives sourced from different online platforms should be contrasted.

The Journal of Wine Research (JWR) has a near 30-year history of publishing varied articles examining aspects of viticulture, oenology and the international wine trade. The primary objective of this research is to examine the characteristics of literature published in the JWR through bibliographic analysis. The research utilises the Scopus database to identify the most productive authors, countries and papers published in the JWR since it was established in 1990. The total number of publications and citations are used to assess productivity, in terms of the number of publications, influence, and the number of citations of both authors and countries. Network maps were created using the VOSViewer software examining patterns of co-authorship, cocitation and keyword co-occurrence. The results indicate that the most prolific authors, in terms of the number of publications, are not necessarily producing the most profound research that gains traction through citations in the field. This study is helpful for prospective JWR authors, readers and editors who wish to understand the intellectual landscape of the journal in terms of the productivity and impact of different stakeholder groups and the noted trends of publications. The paper concludes by acknowledging limitations of the study and discussing implications for future authors.