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Xiang Lian
Researcher at Kent State University
Publications - 116
Citations - 3161
Xiang Lian is an academic researcher from Kent State University. The author has contributed to research in topics: Probabilistic logic & Uncertain data. The author has an hindex of 28, co-authored 110 publications receiving 2799 citations. Previous affiliations of Xiang Lian include University of Texas at Austin & Northeastern University.
Papers
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Book ChapterDOI
Reverse kNN search in arbitrary dimensionality
TL;DR: The proposed algorithms for exact processing of RkNN with arbitrary values of k on dynamic multidimensional datasets utilize a conventional data-partitioning index on the dataset and do not require any pre-computation.
Proceedings ArticleDOI
Monochromatic and bichromatic reverse skyline search over uncertain databases
Xiang Lian,Lei Chen +1 more
TL;DR: This paper model the probabilistic reverse skyline query on uncertain data, in both monochromatic and bichromatic cases, and propose effective pruning methods to reduce the search space of query processing.
Journal ArticleDOI
Reliable diversity-based spatial crowdsourcing by moving workers
TL;DR: Wang et al. as discussed by the authors proposed a reliable diversity-based spatial crowdsourcing (RDB-SC) problem to assign workers to spatial tasks such that the completion reliability and the spatial/temporal diversities of spatial tasks are maximized.
Proceedings Article
Indexable PLA for efficient similarity search
TL;DR: A novel distance function in the reduced PLA-space is proposed, and it is proved that this function indeed results in a lower bound of the Euclidean distance between the original time series, which can lead to no false dismissals during the similarity search.
Journal ArticleDOI
Task Assignment on Multi-Skill Oriented Spatial Crowdsourcing
TL;DR: Wang et al. as discussed by the authors proposed three heuristic approaches, including greedy, g-divide-and-conquer and cost-model-based adaptive algorithms, to find an optimal worker and task assignment strategy, such that skills between workers and tasks match with each other.