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Bolin Ding
Researcher at Microsoft
Publications - 105
Citations - 5685
Bolin Ding is an academic researcher from Microsoft. The author has contributed to research in topics: Computer science & Query optimization. The author has an hindex of 33, co-authored 64 publications receiving 4864 citations. Previous affiliations of Bolin Ding include Urbana University & The Chinese University of Hong Kong.
Papers
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Proceedings ArticleDOI
Finding Top-k Min-Cost Connected Trees in Databases
TL;DR: This paper proposes a novel parameterized solution, with l as a parameter, to find the optimal GST-1, in time complexity O(3ln + 2l ((l + logn)n + m), where n and m are the numbers of nodes and edges in graph G, which can handle graphs with a large number of nodes.
Journal ArticleDOI
Swarm: mining relaxed temporal moving object clusters
TL;DR: In ObjectGrowth, two effective pruning strategies are proposed to greatly reduce the search space and a novel closure checking rule is developed to report closed swarms on-the-fly.
Proceedings ArticleDOI
Mining periodic behaviors for moving objects
TL;DR: A two-stage algorithm, Periodica, is proposed to solve the problem of mining periodic behaviors for moving objects, and it is proposed that the observed movement is generated from multiple interleaved periodic behaviors associated with certain reference locations.
Proceedings Article
Collecting telemetry data privately
TL;DR: This paper develops new LDP mechanisms geared towards repeated collection of counter data, with formal privacy guarantees even after being executed for an arbitrarily long period of time, which have been deployed by Microsoft to collect telemetry across millions of devices.
Proceedings ArticleDOI
Online mobile Micro-Task Allocation in spatial crowdsourcing
TL;DR: This paper identifies a more practical micro-task allocation problem, called the Global Online Micro-task Allocation in spatial crowdsourcing (GOMA) problem, and proposes a two-phase-based framework, based on which the TGOA algorithm with 1 over 4 -competitive ratio under the online random order model is presented.