2 research outputs found
A Novel Framework for Online Amnesic Trajectory Compression in Resource-constrained Environments
State-of-the-art trajectory compression methods usually involve high
space-time complexity or yield unsatisfactory compression rates, leading to
rapid exhaustion of memory, computation, storage and energy resources. Their
ability is commonly limited when operating in a resource-constrained
environment especially when the data volume (even when compressed) far exceeds
the storage limit. Hence we propose a novel online framework for error-bounded
trajectory compression and ageing called the Amnesic Bounded Quadrant System
(ABQS), whose core is the Bounded Quadrant System (BQS) algorithm family that
includes a normal version (BQS), Fast version (FBQS), and a Progressive version
(PBQS). ABQS intelligently manages a given storage and compresses the
trajectories with different error tolerances subject to their ages. In the
experiments, we conduct comprehensive evaluations for the BQS algorithm family
and the ABQS framework. Using empirical GPS traces from flying foxes and cars,
and synthetic data from simulation, we demonstrate the effectiveness of the
standalone BQS algorithms in significantly reducing the time and space
complexity of trajectory compression, while greatly improving the compression
rates of the state-of-the-art algorithms (up to 45%). We also show that the
operational time of the target resource-constrained hardware platform can be
prolonged by up to 41%. We then verify that with ABQS, given data volumes that
are far greater than storage space, ABQS is able to achieve 15 to 400 times
smaller errors than the baselines. We also show that the algorithm is robust to
extreme trajectory shapes.Comment: arXiv admin note: substantial text overlap with arXiv:1412.032
PolyFit: Polynomial-based Indexing Approach for Fast Approximate Range Aggregate Queries
Range aggregate queries find frequent application in data analytics. In some
use cases, approximate results are preferred over accurate results if they can
be computed rapidly and satisfy approximation guarantees. Inspired by a recent
indexing approach, we provide means of representing a discrete point data set
by continuous functions that can then serve as compact index structures. More
specifically, we develop a polynomial-based indexing approach, called PolyFit,
for processing approximate range aggregate queries. PolyFit is capable of
supporting multiple types of range aggregate queries, including COUNT, SUM, MIN
and MAX aggregates, with guaranteed absolute and relative error bounds.
Experiment results show that PolyFit is faster and more accurate and compact
than existing learned index structures.Comment: 13 page