G
George Kollios
Researcher at Boston University
Publications - 116
Citations - 9364
George Kollios is an academic researcher from Boston University. The author has contributed to research in topics: Range query (data structures) & Scalability. The author has an hindex of 45, co-authored 115 publications receiving 8819 citations. Previous affiliations of George Kollios include AT&T & University of California, Riverside.
Papers
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Proceedings ArticleDOI
Discovering similar multidimensional trajectories
TL;DR: This work formalizes non-metric similarity functions based on the longest common subsequence (LCSS), which are very robust to noise and furthermore provide an intuitive notion of similarity between trajectories by giving more weight to similar portions of the sequences.
Proceedings ArticleDOI
Approximate aggregation techniques for sensor databases
TL;DR: This work generalizes well known duplicate-insensitive sketches for approximating COUNT to handle SUM and presents and analyze methods for using sketches to produce accurate results with low communication and computation overhead, and presents an extensive experimental validation of the methods.
Proceedings ArticleDOI
Dynamic authenticated index structures for outsourced databases
TL;DR: This work defines a variety of essential and practical cost metrics associated with ODB systems and looks at solutions that can handle dynamic scenarios, where owners periodically update the data residing at the servers, both for static and dynamic environments.
Proceedings ArticleDOI
On indexing mobile objects
TL;DR: A lower bound on the number of I/O’s needed to answer the d-dimensional problem is given and a practical approximation algorithm also in the dynamic, external memory setting, which has linear space and expected logarithmic query time is given.
Book ChapterDOI
On trip planning queries in spatial databases
TL;DR: This paper provides a number of approximation algorithms with approximation ratios that depend on either the number of categories, the maximum number of points per category or both, and gives an experimental evaluation of the proposed algorithms using both synthetic and real datasets.