The statistical computing environment XploRe and state-of-the-art density and regression smoothing

1993 ◽  
Vol 3 (1) ◽  
pp. 23-26 ◽  
Author(s):  
M. G. Schimek ◽  
K. G. Schmaranz
Keyword(s):  
State Of The Art ◽  
Statistical Computing ◽  
Computing Environment

Efficient Distributed RNN Query Processing with Caching

2014 ◽  
Vol 556-562 ◽  
pp. 5352-5355
Author(s):  
Yin Bo Shao ◽  
Jing Wei Xie ◽  
Yuan Quan Li ◽  
Sun Ying Gao ◽  
Chang Qing Ji
Keyword(s):  
Mobile Cloud Computing ◽  
State Of The Art ◽  
Massive Data ◽  
Nearest Neighbour ◽  
Distributed Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Internet Of Vehicles ◽  
Distributed Caching ◽  
Synthetic Datasets

Reverse Nearest Neighbour (RNN) queries play an important role in applications such as internet of vehicles, decision support systems, profile based marketing and so on. Recently, more attention has been paid to the problem of efficient distributed RNN computation in mobile cloud computing environment. A major downside of the existing RNN is its inherent sequential nature and using in-memory algorithm, which limits its applicability to massive data. In this paper, we propose a novel distributed caching based method to efficiently improve the performance of the RNN calculation in a distributed environment. Extensive experiments using both real and synthetic datasets demonstrated that our proposed methods are the state-of-the-art algorithms in scalable RNN queries.

XploRe: An Interactive Statistical Computing Environment.

1996 ◽  
Vol 45 (3) ◽  
pp. 388
Author(s):  
Robert Bartels ◽  
W. Hardle ◽  
S. Klinke ◽  
B. A. Turlach
Keyword(s):  
Statistical Computing ◽  
Computing Environment

scholarly journals Shp2graph: Tools to Convert a Spatial Network into an Igraph Graph in R

2018 ◽  
Vol 7 (8) ◽  
pp. 293 ◽  
Author(s):  
Binbin Lu ◽  
Huabo Sun ◽  
Paul Harris ◽  
Miaozhong Xu ◽  
Martin Charlton
Keyword(s):  
Network Analysis ◽  
Road Network ◽  
R Package ◽  
Statistical Computing ◽  
Spatial Network ◽  
Computing Environment ◽  
Vast Array ◽  
Urban Road ◽  
Analytical Tools ◽  
Statistical Functions

In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraphR package. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R statistical computing environment and its vast array of statistical functions. Through three urban road network case studies, the calculation of road network distances with shp2graph and with igraph is demonstrated through four key stages: (i) confirming the connectivity of a spatial network; (ii) integrating points/locations with a network; (iii) converting a network into a graph; and (iv) calculating network distances (and travel times). Throughout, the required R commands are given to provide a useful tutorial on the use of shp2graph.

scholarly journals High Performance Energy-Aware Cloud Computing: A Scope of Future Computing

2020 ◽  
Vol 9 (6) ◽  
pp. 667-683
Keyword(s):  
Energy Consumption ◽  
Power Management ◽  
High Performance ◽  
State Of The Art ◽  
Control Measures ◽  
Computing Environment ◽  
Energy Aware ◽  
Control Techniques ◽  
Hybrid Techniques ◽  
Consumption Control

This publication discusses high-performance energyaware cloud (HPEAC) computing state-of-the-art strategies to acknowledgement and categorization of systems and devices, optimization methodologies, and energy / power control techniques in particular. System types involve single machines, clusters, networks, and clouds, while CPUs, GPUs, multiprocessors, and hybrid systems are known to be device types. Objective of Optimization incorporates multiple calculation blends, such as “execution time”, “consumption of energy”& “temperature” with the consideration of limiting power/energy consumption. Control measures usually involve scheduling policies, frequency based policies (DVFS, DFS, DCT), programmatic API’s for limiting the power consumptions (such as” Intel- RAPL”,” NVIDIA- NVML”), standardization of applications, and hybrid techniques. We address energy / power management software and APIs as well as methods and conditions in modern HPEACC systems for forecasting and/or simulating power/energy consumption. Eventually, programming examples are discussed, i.e. programs & tests used in specific works. Based on our study, we point out some areas and there significant issues related to tools & technologies, important for handling energy aware computations in HPEAC computing environment

A New Generation of a Statistical Computing Environment on the Net

COMPSTAT ◽  
1996 ◽  
pp. 135-148
Author(s):  
Swetlana Schmelzer ◽  
Thomas Kötter ◽  
Sigbert Klinke ◽  
Wolfgang Härdle
Keyword(s):  
Statistical Computing ◽  
Computing Environment ◽  
New Generation
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