8,871 research outputs found
Streaming algorithms for line simplification under the Fréchet distance
We study the following variant of the well-known linesimplification problem: we are getting a possibly infinite sequence of points p0, p1, p2, . . . defining a polygonal path, and as we receive the points we wish to maintain a simplification of the path seen so far. We study this problem in a streaming setting, where we only have a limited amount of storage so that we cannot store all the points. We analyze the competitive ratio of our algorithm, allowing resource augmentation: we let our algorithm maintain a simplification with 2k (internal) points, and compare the error of our simplification to the error of the optimal simplification with k points
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
Towards Streaming Evaluation of Queries with Correlation in Complex Event Processing
Complex event processing (CEP) has gained a lot of attention for evaluating complex patterns over high-throughput data streams. Recently, new algorithms for the evaluation of CEP patterns have emerged with strong guarantees of efficiency, i.e. constant update-time per tuple and constant-delay enumeration. Unfortunately, these techniques are restricted for patterns with local filters, limiting the possibility of using joins for correlating the data of events that are far apart.
In this paper, we embark on the search for efficient evaluation algorithms of CEP patterns with joins. We start by formalizing the so-called partition-by operator, a standard operator in data stream management systems to correlate contiguous events on streams. Although this operator is a restricted version of a join query, we show that partition-by (without iteration) is equally expressive as hierarchical queries, the biggest class of full conjunctive queries that can be evaluated with constant update-time and constant-delay enumeration over streams. To evaluate queries with partition-by we introduce an automata model, called chain complex event automata (chain-CEA), an extension of complex event automata that can compare data values by using equalities and disequalities. We show that this model admits determinization and is expressive enough to capture queries with partition-by. More importantly, we provide an algorithm with constant update time and constant delay enumeration for evaluating any query definable by chain-CEA, showing that all CEP queries with partition-by can be evaluated with these strong guarantees of efficiency
Anticipatory Buffer Control and Quality Selection for Wireless Video Streaming
Video streaming is in high demand by mobile users, as recent studies
indicate. In cellular networks, however, the unreliable wireless channel leads
to two major problems. Poor channel states degrade video quality and interrupt
the playback when a user cannot sufficiently fill its local playout buffer:
buffer underruns occur. In contrast to that, good channel conditions cause
common greedy buffering schemes to pile up very long buffers. Such
over-buffering wastes expensive wireless channel capacity.
To keep buffering in balance, we employ a novel approach. Assuming that we
can predict data rates, we plan the quality and download time of the video
segments ahead. This anticipatory scheduling avoids buffer underruns by
downloading a large number of segments before a channel outage occurs, without
wasting wireless capacity by excessive buffering. We formalize this approach as
an optimization problem and derive practical heuristics for segmented video
streaming protocols (e.g., HLS or MPEG DASH). Simulation results and testbed
measurements show that our solution essentially eliminates playback
interruptions without significantly decreasing video quality
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