scholarly journals An Experimental Performance Comparison for Indexing Mobile Objects on the Plane

Author(s):  
Spyros Sioutas ◽  
G. Papaloukopoulos ◽  
K. Tsichlas ◽  
Y. Manolopoulos

In this paper, the authors present a time-efficient approach to index objects moving on the plane in order to answer range queries about their future positions. Each object is moving with non small velocity u, meaning that the velocity value distribution is skewed (Zipf) towards in some range , where is a positive lower threshold. This algorithm enhances a previously described solution (Sioutas, Tsakalidis, Tsichlas, Makris, & Manolopoulos, 2007) by accommodating the ISB-tree access method as presented in Kaporis et al. (2005). Experimental evaluation shows the improved performance, scalability, and efficiency of the new algorithm.

Author(s):  
S.Sioutas ◽  
G. Papaloukopoulos ◽  
K. Tsichlas ◽  
Y. Manolopoulos

In this paper, the authors present a time-efficient approach to index objects moving on the plane in order to answer range queries about their future positions. Each object is moving with non small velocity u, meaning that the velocity value distribution is skewed (Zipf) towards in some range , where is a positive lower threshold. This algorithm enhances a previously described solution (Sioutas, Tsakalidis, Tsichlas, Makris, & Manolopoulos, 2007) by accommodating the ISB-tree access method as presented in Kaporis et al. (2005). Experimental evaluation shows the improved performance, scalability, and efficiency of the new algorithm.


2021 ◽  
Author(s):  
Danila Piatov ◽  
Sven Helmer ◽  
Anton Dignös ◽  
Fabio Persia

AbstractWe develop a family of efficient plane-sweeping interval join algorithms for evaluating a wide range of interval predicates such as Allen’s relationships and parameterized relationships. Our technique is based on a framework, components of which can be flexibly combined in different manners to support the required interval relation. In temporal databases, our algorithms can exploit a well-known and flexible access method, the Timeline Index, thus expanding the set of operations it supports even further. Additionally, employing a compact data structure, the gapless hash map, we utilize the CPU cache efficiently. In an experimental evaluation, we show that our approach is several times faster and scales better than state-of-the-art techniques, while being much better suited for real-time event processing.


Author(s):  
Lucas R. Prando ◽  
Eduardo R. de Lima ◽  
Leonardo S. de Moraes ◽  
Marcio Biehl Hamerschmidt ◽  
Gustavo Fraindenraich

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4272 ◽  
Author(s):  
Muhammad Shafiq ◽  
Ivar Kiitam ◽  
Kimmo Kauhaniemi ◽  
Paul Taklaja ◽  
Lauri Kütt ◽  
...  

Already installed cables are aging and the cable network is growing rapidly. Improved condition monitoring methods are required for greater visibility of insulation defects in the cable networks. One of the critical challenges for continuous monitoring is the large amount of partial discharge (PD) data that poses constraints on the diagnostic capabilities. This paper presents the performance comparison of two data acquisition techniques based on phase resolved partial discharge (PRPD) and pulse acquisition (PA). The major contribution of this work is to provide an in-depth understanding of these techniques considering the perspective of randomness of the PD mechanism and improvements in the reliability of diagnostics. Experimental study is performed on the medium voltage (MV) cables in the laboratory environment. It has been observed that PRPD based acquisition not only requires a significantly larger amount of data but is also susceptible to losing the important information especially when multiple PD sources are being investigated. On the other hand, the PA technique presents improved performance for PD diagnosis. Furthermore, the use of the PA technique enables the efficient practical implementation of the continuous PD monitoring by reducing the amount of data that is acquired by extracting useful signals and discarding the silent data intervals.


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