75,924 research outputs found
Continuous Nearest Neighbor Queries over Sliding Windows
AbstractâThis paper studies continuous monitoring of nearest neighbor (NN) queries over sliding window streams. According to this model, data points continuously stream in the system, and they are considered valid only while they belong to a sliding window that contains 1) the W most recent arrivals (count-based) or 2) the arrivals within a fixed interval W covering the most recent time stamps (time-based). The task of the query processor is to constantly maintain the result of long-running NN queries among the valid data. We present two processing techniques that apply to both count-based and time-based windows. The first one adapts conceptual partitioning, the best existing method for continuous NN monitoring over update streams, to the sliding window model. The second technique reduces the problem to skyline maintenance in the distance-time space and precomputes the future changes in the NN set. We analyze the performance of both algorithms and extend them to variations of NN search. Finally, we compare their efficiency through a comprehensive experimental evaluation. The skyline-based algorithm achieves lower CPU cost, at the expense of slightly larger space overhead. Index TermsâLocation-dependent and sensitive, spatial databases, query processing, nearest neighbors, data streams, sliding windows.
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An agent-based DDM for high level architecture
The Data Distribution Management (DDM) service is one of the six services provided in the Runtime Infrastructure (RTI) of High Level Architecture (HLA). Its purpose is to perform data filtering and reduce irrelevant data communicated between federates. The two DDM schemes proposed for RTI, region based and grid based DDM, are oriented to send as little irrelevant data to subscribers as possible, but only manage to filter part of this information and some irrelevant data is still being communicated. Previously (G. Tan et al., 2000), we employed intelligent agents to perform data filtering in HLA, implemented an agent based DDM in RTI (ARTI) and compared it with the other two filtering mechanisms. The paper reports on additional experiments, results and analysis using two scenarios: the AWACS sensing aircraft simulation and the air traffic control simulation scenario. Experimental results show that compared with other mechanisms, the agent based approach communicates only relevant data and minimizes network communication, and is also comparable in terms of time efficiency. Some guidelines on when the agent based scheme can be used are also give
Real-time Spatial Detection and Tracking of Resources in a Construction Environment
Construction accidents with heavy equipment and bad decision making can be based on poor knowledge of the site environment and in both cases may lead to work interruptions and costly delays. Supporting the construction environment with real-time generated three-dimensional (3D) models can help preventing accidents as well as support management by modeling infrastructure assets in 3D. Such models can be integrated in the path planning of construction equipment operations for obstacle avoidance or in a 4D model that simulates construction processes. Detecting and guiding resources, such as personnel, machines and materials in and to the right place on time requires methods and technologies supplying information in real-time. This paper presents research in real-time 3D laser scanning and modeling using high range frame update rate scanning technology. Existing and emerging sensors and techniques in three-dimensional modeling are explained. The presented research successfully developed computational models and algorithms for the real-time detection, tracking, and three-dimensional modeling of static and dynamic construction resources, such as workforce, machines, equipment, and materials based on a 3D video range camera. In particular, the proposed algorithm for rapidly modeling three-dimensional scenes is explained. Laboratory and outdoor field experiments that were conducted to validate the algorithmâs performance and results are discussed
SURGE: Continuous Detection of Bursty Regions Over a Stream of Spatial Objects
With the proliferation of mobile devices and location-based services,
continuous generation of massive volume of streaming spatial objects (i.e.,
geo-tagged data) opens up new opportunities to address real-world problems by
analyzing them. In this paper, we present a novel continuous bursty region
detection problem that aims to continuously detect a bursty region of a given
size in a specified geographical area from a stream of spatial objects.
Specifically, a bursty region shows maximum spike in the number of spatial
objects in a given time window. The problem is useful in addressing several
real-world challenges such as surge pricing problem in online transportation
and disease outbreak detection. To solve the problem, we propose an exact
solution and two approximate solutions, and the approximation ratio is
in terms of the burst score, where is a parameter
to control the burst score. We further extend these solutions to support
detection of top- bursty regions. Extensive experiments with real-world data
are conducted to demonstrate the efficiency and effectiveness of our solutions
The Pan-STARRS Moving Object Processing System
We describe the Pan-STARRS Moving Object Processing System (MOPS), a modern
software package that produces automatic asteroid discoveries and
identifications from catalogs of transient detections from next-generation
astronomical survey telescopes. MOPS achieves > 99.5% efficiency in producing
orbits from a synthetic but realistic population of asteroids whose
measurements were simulated for a Pan-STARRS4-class telescope. Additionally,
using a non-physical grid population, we demonstrate that MOPS can detect
populations of currently unknown objects such as interstellar asteroids.
MOPS has been adapted successfully to the prototype Pan-STARRS1 telescope
despite differences in expected false detection rates, fill-factor loss and
relatively sparse observing cadence compared to a hypothetical Pan-STARRS4
telescope and survey. MOPS remains >99.5% efficient at detecting objects on a
single night but drops to 80% efficiency at producing orbits for objects
detected on multiple nights. This loss is primarily due to configurable MOPS
processing limits that are not yet tuned for the Pan-STARRS1 mission.
The core MOPS software package is the product of more than 15 person-years of
software development and incorporates countless additional years of effort in
third-party software to perform lower-level functions such as spatial searching
or orbit determination. We describe the high-level design of MOPS and essential
subcomponents, the suitability of MOPS for other survey programs, and suggest a
road map for future MOPS development.Comment: 57 Pages, 26 Figures, 13 Table
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