A Survey of Parallel Indexing Techniques for Large-Scale Moving Object Databases

2019 ◽  
pp. 72-105
Author(s):  
Eleazar Leal ◽  
Le Gruenwald ◽  
Jianting Zhang
Keyword(s):  
Business Intelligence ◽  
Graphics Processing Units ◽  
Large Scale ◽  
Moving Object ◽  
Future Research ◽  
Large Sets ◽  
Indexing Techniques ◽  
Object Databases ◽  
Graphics Processing ◽  
Moving Object Databases

A moving object database is a database that tracks the movements of objects. As such, these databases have business intelligence applications in areas like trajectory-based advertising, disease control and prediction, hurricane path prediction, and drunk-driver detection. However, in order to extract knowledge from these objects, it is necessary to efficiently query these databases. To this end, databases incorporate special data structures called indexes. Multiple indexing techniques for moving object databases have been proposed. Nonetheless, indexing large sets of objects poses significant computational challenges. To cope with these challenges, some moving object indexes are designed to work with parallel architectures, such as multicore CPUs and GPUs (graphics processing units), which can execute multiple instructions simultaneously. This chapter discusses business intelligence applications of parallel moving object indexes, identifies issues and features of these techniques, surveys existing parallel indexes, and concludes with possible future research directions.

scholarly journals A lightweight approach to performance portability with targetDP

2016 ◽  
Vol 32 (2) ◽  
pp. 288-301
Author(s):  
Alan Gray ◽  
Kevin Stratford
Keyword(s):  
Particle Physics ◽  
Message Passing ◽  
Graphics Processing Units ◽  
High Performance ◽  
Large Scale ◽  
Message Passing Interface ◽  
Graphics Processing Unit ◽  
Processing Unit ◽  
Performance Portability ◽  
Graphics Processing

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

A Survey of Spatio-Temporal Data Warehousing

2010 ◽  
pp. 949-977
Author(s):  
Leticia Gómez ◽  
Bart Kuijpers ◽  
Bart Moelans ◽  
Alejandro Vaisman
Keyword(s):  
Moving Objects ◽  
Route Planning ◽  
Geographic Information ◽  
Moving Object ◽  
Attribute Data ◽  
Object Databases ◽  
Spatio Temporal ◽  
Eu Project ◽  
The University ◽  
Moving Object Databases

Geographic Information Systems (GIS) have been extensively used in various application domains, ranging from economical, ecological and demographic analysis, to city and route planning. Nowadays, organizations need sophisticated GIS-based Decision Support System (DSS) to analyze their data with respect to geographic information, represented not only as attribute data, but also in maps. Thus, vendors are increasingly integrating their products, leading to the concept of SOLAP (Spatial OLAP). Also, in the last years, and motivated by the explosive growth in the use of PDA devices, the field of moving object data has been receiving attention from the GIS community. However, not much has been done in providing moving object databases with OLAP functionality. In the first part of this article we survey the SOLAP literature. We then move to Spatio-Temporal OLAP, in particular addressing the problem of trajectory analysis. We finally provide an in-depth comparative analysis between two proposals introduced in the context of the GeoPKDD EU project: the Hermes-MDC system, and Piet, a proposal for SOLAP and moving objects, developed at the University of Buenos Aires, Argentina.

ASSIGNMENT QUERY AND ITS IMPLEMENTATION IN MOVING OBJECT DATABASES

2010 ◽  
Vol 09 (03) ◽  
pp. 349-372 ◽  
Author(s):  
ALİ R. KONAN ◽  
TAFLAN İ. GÜNDEM ◽  
MURAT E. KAYA
Keyword(s):  
Nearest Neighbor ◽  
Perfect Match ◽  
Moving Object ◽  
Location Based Services ◽  
Matching Problem ◽  
Total Cost ◽  
Wide Range ◽  
Weighted Bipartite Graph ◽  
Object Databases ◽  
Moving Object Databases

Moving object databases (MOD) are being used in a wide range of location-based services that are of growing interest in many application areas. In the literature, several query types such as nearest neighbor, reverse nearest neighbor, k-nearest neighbor, and proximity queries have been considered in MOD. In this paper, we propose a novel operator called the assignment operator as a query type for MOD. The assignment operator is an operator used in a query to solve the assignment problem (also known as the weighted bipartite graph-matching problem). Assignment operator finds a perfect match between two sets of objects in a manner that minimizes a total cost. For instance, a set of moving objects such as taxi cabs are assigned to a set of customers in a manner that minimizes the total cost of traveling for the taxis. A possible implementation of the assignment operator in MOD and its performance evaluation are given.

scholarly journals Discovery of Frequent Sequence Pattern in Moving Object Databases

2008 ◽  
Vol 15D (2) ◽  
pp. 179-186
Author(s):  
Thi Hong Nhan Vu ◽  
Bum-Ju Lee ◽  
Keun-Ho Ryu
Keyword(s):  
Moving Object ◽  
Sequence Pattern ◽  
Frequent Sequence ◽  
Object Databases ◽  
Moving Object Databases

Data-Parallel Techniques for Agent-Based Tissue Modeling on Graphics Processing Units

2008 ◽  
Author(s):  
Ryan S. Richards ◽  
Mikola Lysenko ◽  
Roshan M. D’Souza ◽  
Gary An
Keyword(s):  
Real Time ◽  
Graphics Processing Units ◽  
Large Scale ◽  
Experimental Studies ◽  
Test Case ◽  
Cell Systems ◽  
Von Neumann ◽  
Agent Based ◽  
Cell Behaviors ◽  
Graphics Processing

Agent-Based Modeling has been recently recognized as a method for in-silico multi-scale modeling of biological cell systems. Agent-Based Models (ABMs) allow results from experimental studies of individual cell behaviors to be scaled into the macro-behavior of interacting cells in complex cell systems or tissues. Current generation ABM simulation toolkits are designed to work on serial von-Neumann architectures, which have poor scalability. The best systems can barely handle tens of thousands of agents in real-time. Considering that there are models for which mega-scale populations have significantly different emergent behaviors than smaller population sizes, it is important to have the ability to model such large scale models in real-time. In this paper we present a new framework for simulating ABMs on programmable graphics processing units (GPUs). Novel algorithms and data-structures have been developed for agent-state representation, agent motion, and replication. As a test case, we have implemented an abstracted version of the Systematic Inflammatory Response System (SIRS) ABM. Compared to the original implementation on the NetLogo system, our implementation can handle an agent population that is over three orders of magnitude larger with close to 40 updates/sec. We believe that our system is the only one of its kind that is capable of efficiently handling realistic problem sizes in biological simulations.

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