The Architectural Pattern of a Highly Extensible System for the Asynchronous Processing of a Large Amount of Data

Hwang, Ro Man; Kim, Soo Kyun; An, Syungog; Park, Dong-Won

doi:10.3745/jips.2013.9.4.567

Cited by 3 publications

(3 citation statements)

References 2 publications

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“…However, as more and more sensors get connected to the Internet, they generate enormous amounts of data. Thus, widespread deployment of IoT requires development of solutions for analyzing the potentially huge amounts of data they generate [2][3][4]. A top-query processing can be applied to facilitate this task.…”

Section: Introductionmentioning

confidence: 99%

Grid-PPPS: A Skyline Method for Efficiently Handling Top-kQueries in Internet of Things

Ihm

Nasridinov

2014

Journal of Applied Mathematics

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A rapid development in wireless communication and radio frequency technology has enabled the Internet of Things (IoT) to enter every aspect of our life. However, as more and more sensors get connected to the Internet, they generate huge amounts of data. Thus, widespread deployment of IoT requires development of solutions for analyzing the potentially huge amounts of data they generate. A top-kquery processing can be applied to facilitate this task. The top-kqueries retrievektuples with the lowest or the highest scores among all of the tuples in the database. There are many methods to answer top-kqueries, where skyline methods are efficient when considering all attribute values of tuples. The representative skyline methods are soft-filter-skyline (SFS) algorithm, angle-based space partitioning (ABSP), and plane-project-parallel-skyline (PPPS). Among them, PPPS improves ABSP by partitioning data space into a number of spaces using hyperplane projection. However, PPPS has a high index building time in high-dimensional databases. In this paper, we propose a new skyline method (called Grid-PPPS) for efficiently handling top-kqueries in IoT applications. The proposed method first performs grid-based partitioning on data space and then partitions it once again using hyperplane projection. Experimental results show that our method improves the index building time compared to the existing state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Grid-PPPS: A Skyline Method for Efficiently Handling Top-kQueries in Internet of Things

Ihm

Nasridinov

2014

Journal of Applied Mathematics

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“…For spring force calculation, two connected node numbers and the rest length of spring should be grouped as one unit and be sent to GPU. GPU takes this unit and calculates spring force using position and velocity which is delivered by node indexes [20]. Figure 6 shows the proposed data structure of GPU parallel computing for spring force calculation.…”

Section: Mass-spring Simulationmentioning

confidence: 99%

Plausible mass-spring system using parallel computing on mobile devices

Hong

Jeon²,

Yum³

et al. 2016

Hum. Cent. Comput. Inf. Sci.

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Recently the hardware performance of mobile devices have been extremely increased and advanced mobile devices provide multi-cores and high clock speed. In addition, mobile devices have advantages in mobility and portability compared with PC and Console, so many games and simulation programs have been developed under mobile environments. Physically-based simulation is a one of the key issues for deformable object modeling which is widely used to represent the realistic expression of 3D soft objects with tetrahedrons for game and 3D simulation. However, it requires high computation power to plausibly and realistically represent the physical behaviors and interactions of deformable objects. In this paper, we implemented parallel cloth and mass-spring simulation using graphics processing unit (GPU) with OpenCL and multi-threaded central processing unit (CPU) on a mobile device. We applied CPU and GPU parallel computing technique into spring force computation and integration methods such as Euler, Midpoint, 4th-order Runge-Kutta to optimize the computational burden of dynamic simulation. The integration methods compute the next step of positions and velocities in each node. In this paper, we tested the performance analysis for the spring force calculation and integration method process using CPU only, multi-threaded CPU, and GPU on mobile device respectively. Our experimental results concluded that the calculation using proposed multi-threaded CPU and GPU multi-threaded CPU are much faster than using just the CPU only.

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“…They are used for verifying the adequacy of the surveillance system designed and the visualization and modification of large amounts of data before the costly system is physically installed [13]. Some researchers [14,15] present a virtual environment to test and compare developed algorithms, but they usually focus on the visual surveillance system only.…”

Section: Introductionmentioning

confidence: 99%

Execution Behavior Modeling Methodology for Large Scale Surveillance System Design and Evaluation

Kim

Hong

et al. 2014

Advances in Multimedia

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This paper presents a performance and evaluation environment for complex surveillance system design. The system consists of environment model, execution model, and application and evaluation model. The environment model interprets the script and creates objects in a surveillance environment so that various situations can be evaluated. The execution model modifies generated data with the perspective of each sensor and reflects algorithm execution behavior. The application model allows building large scale collaborative operations. The system behavior is parameterized for simple representations. The feasibility of the proposed method is illustrated through the case studies for improving the prototype surveillance system.

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The Architectural Pattern of a Highly Extensible System for the Asynchronous Processing of a Large Amount of Data

Cited by 3 publications

References 2 publications

Grid-PPPS: A Skyline Method for Efficiently Handling Top-kQueries in Internet of Things

Grid-PPPS: A Skyline Method for Efficiently Handling Top-kQueries in Internet of Things

Plausible mass-spring system using parallel computing on mobile devices

Execution Behavior Modeling Methodology for Large Scale Surveillance System Design and Evaluation

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