Research summary for my STAT645 course fall 2016. Paper Scalable Algorithms for Nearest-Neighbor Joins on Big Trajectory Data by Fang, Cheng, Tang, Maniu, Yang. http://ieeexplore.ieee.org/document/7498408/
The document describes an adiabatic quantum algorithm for computing PageRank vectors. PageRank is the principal eigenvector of the Google matrix G, which represents the probability of a random web surfer moving between pages. The algorithm maps the PageRank computation to the ground state of a Hamiltonian whose ground state encodes the PageRank vector. It was found that this algorithm could prepare the PageRank vector in time scaling as poly(log n), providing an exponential speedup over classical algorithms. Additionally, the top ranked pages in the PageRank vector could be read out with a polynomial speedup over classical methods.
Methods have been proposed to transform a single time series to a complex network so that the dynamics of the process can be understood by investigating the topological properties of the network. The recently developed method of Visibility Graphs transforms a time series into a complex network which inherits several properties of the time series in its structure.
Process RTK Data Based On Kalman Filtering AlgorithmIJRES Journal
With the development of satellite positioning technology, there is a strong need for high accuracy
position information. Currently the most widely used high-precision positioning technology is RTK(Real-Time
Kinematic).RTK technology is the key to using carrier phase measurements. It takes advantage of the base
stations and monitor stations observed error of spatial correlation, except monitor stations observed by means of
differential most of the errors in the data, in order to achieve high accuracy positioning.[3]Based on Kalman
filtering algorithm to handle the noise of RTK data and selecting appropriate models to further improve the
accuracy of the data. This paper will explore the use of Kalman filtering method of RTK data processing, which
reduces random noise interference, thus improving the accuracy of GNSS deformation monitoring data.[1]
A Novel Technique in Software Engineering for Building Scalable Large Paralle...Eswar Publications
Parallel processing is the only alternative for meeting computational demand of scientific and technological advancement. Yet first few parallelized versions of a large application code- in the present case-a meteorological Global Circulation Model- are not usually optimal or efficient. Large size and complexity of the code cause making changes for efficient parallelization and further validation difficult. The paper presents some novel techniques to enable change of parallelization strategy keeping the correctness of the code under control throughout the modification.
A Novel Route Optimized Cluster Based Routing Protocol for Pollution Controll...IRJET Journal
This document summarizes a research paper that proposes a novel cluster-based routing protocol for sensor networks aimed at pollution monitoring. The protocol aims to optimize routes while maintaining moderate energy efficiency. It first discusses challenges in sensor network routing related to dynamic topology and limited device power. It then outlines two lemmas: 1) reducing cluster head detection time can decrease routing time; and 2) changing cluster heads randomly over time can improve network lifetime. The proposed protocol selects cluster heads randomly based on energy levels and predicts cluster heads to reduce overhead. It aims to optimize routing time and energy efficiency through these techniques.
This document describes a MapReduce algorithm for determining the optimal k value in k-means clustering. It presents the G-means algorithm, which uses recursive k-means clustering and normality testing to split clusters until all points are normally distributed around cluster centers. The document outlines challenges in implementing G-means in MapReduce, and describes solutions to reduce I/O, jobs, maximize parallelism and limit memory usage. It compares the proposed MapReduce G-means approach to existing multi-k-means methods, finding it has better quality and comparable speed on synthetic datasets.
This document presents a new algorithm for flexible route planning that allows customizing a linear combination of two metrics like travel time and cost. The algorithm precomputes shortcuts for a graph based on contracting nodes in a customized order. It develops the concept of "gradual parameter interval splitting" to improve the node ordering for different parameter values. The algorithm combines node contraction with a goal-directed technique to further improve performance of flexible queries.
Performance Assessment of Polyphase Sequences Using Cyclic Algorithmrahulmonikasharma
Polyphase Sequences (known as P1, P2, Px, Frank) exist for a square integer length with good auto correlation properties are helpful in the several applications. Unlike the Barker and Binary Sequences which exist for certain length and exhibits a maximum of two digit merit factor. The Integrated Sidelobe level (ISL) is often used to define excellence of the autocorrelation properties of given Polyphase sequence. In this paper, we present the application of Cyclic Algorithm named CA which minimizes the ISL (Integrated Sidelobe Level) related metric which in turn improve the Merit factor to a greater extent is main thing in applications like RADAR, SONAR and communications. To illustrate the performance of the P1, P2, Px, Frank sequences when cyclic Algorithm is applied. we presented a number of examples for integer lengths. CA(Px) sequence exhibits the good Merit Factor among all the Polyphase sequences that are considered.
The document describes an adiabatic quantum algorithm for computing PageRank vectors. PageRank is the principal eigenvector of the Google matrix G, which represents the probability of a random web surfer moving between pages. The algorithm maps the PageRank computation to the ground state of a Hamiltonian whose ground state encodes the PageRank vector. It was found that this algorithm could prepare the PageRank vector in time scaling as poly(log n), providing an exponential speedup over classical algorithms. Additionally, the top ranked pages in the PageRank vector could be read out with a polynomial speedup over classical methods.
Methods have been proposed to transform a single time series to a complex network so that the dynamics of the process can be understood by investigating the topological properties of the network. The recently developed method of Visibility Graphs transforms a time series into a complex network which inherits several properties of the time series in its structure.
Process RTK Data Based On Kalman Filtering AlgorithmIJRES Journal
With the development of satellite positioning technology, there is a strong need for high accuracy
position information. Currently the most widely used high-precision positioning technology is RTK(Real-Time
Kinematic).RTK technology is the key to using carrier phase measurements. It takes advantage of the base
stations and monitor stations observed error of spatial correlation, except monitor stations observed by means of
differential most of the errors in the data, in order to achieve high accuracy positioning.[3]Based on Kalman
filtering algorithm to handle the noise of RTK data and selecting appropriate models to further improve the
accuracy of the data. This paper will explore the use of Kalman filtering method of RTK data processing, which
reduces random noise interference, thus improving the accuracy of GNSS deformation monitoring data.[1]
A Novel Technique in Software Engineering for Building Scalable Large Paralle...Eswar Publications
Parallel processing is the only alternative for meeting computational demand of scientific and technological advancement. Yet first few parallelized versions of a large application code- in the present case-a meteorological Global Circulation Model- are not usually optimal or efficient. Large size and complexity of the code cause making changes for efficient parallelization and further validation difficult. The paper presents some novel techniques to enable change of parallelization strategy keeping the correctness of the code under control throughout the modification.
A Novel Route Optimized Cluster Based Routing Protocol for Pollution Controll...IRJET Journal
This document summarizes a research paper that proposes a novel cluster-based routing protocol for sensor networks aimed at pollution monitoring. The protocol aims to optimize routes while maintaining moderate energy efficiency. It first discusses challenges in sensor network routing related to dynamic topology and limited device power. It then outlines two lemmas: 1) reducing cluster head detection time can decrease routing time; and 2) changing cluster heads randomly over time can improve network lifetime. The proposed protocol selects cluster heads randomly based on energy levels and predicts cluster heads to reduce overhead. It aims to optimize routing time and energy efficiency through these techniques.
This document describes a MapReduce algorithm for determining the optimal k value in k-means clustering. It presents the G-means algorithm, which uses recursive k-means clustering and normality testing to split clusters until all points are normally distributed around cluster centers. The document outlines challenges in implementing G-means in MapReduce, and describes solutions to reduce I/O, jobs, maximize parallelism and limit memory usage. It compares the proposed MapReduce G-means approach to existing multi-k-means methods, finding it has better quality and comparable speed on synthetic datasets.
This document presents a new algorithm for flexible route planning that allows customizing a linear combination of two metrics like travel time and cost. The algorithm precomputes shortcuts for a graph based on contracting nodes in a customized order. It develops the concept of "gradual parameter interval splitting" to improve the node ordering for different parameter values. The algorithm combines node contraction with a goal-directed technique to further improve performance of flexible queries.
Performance Assessment of Polyphase Sequences Using Cyclic Algorithmrahulmonikasharma
Polyphase Sequences (known as P1, P2, Px, Frank) exist for a square integer length with good auto correlation properties are helpful in the several applications. Unlike the Barker and Binary Sequences which exist for certain length and exhibits a maximum of two digit merit factor. The Integrated Sidelobe level (ISL) is often used to define excellence of the autocorrelation properties of given Polyphase sequence. In this paper, we present the application of Cyclic Algorithm named CA which minimizes the ISL (Integrated Sidelobe Level) related metric which in turn improve the Merit factor to a greater extent is main thing in applications like RADAR, SONAR and communications. To illustrate the performance of the P1, P2, Px, Frank sequences when cyclic Algorithm is applied. we presented a number of examples for integer lengths. CA(Px) sequence exhibits the good Merit Factor among all the Polyphase sequences that are considered.
The document discusses parallel k-means clustering algorithms implemented using MapReduce and Spark. It first describes the standard k-means algorithm, which assigns data points to clusters based on distance to centroids. It then presents a MapReduce-based parallel k-means approach where the distance calculations between data points and centroids are distributed across nodes. The map tasks calculate distances and assign points to clusters, combine tasks aggregate results, and reduce tasks calculate new centroids. Experimental results show sub-linear speedup and good scaling to larger datasets. Finally, it briefly mentions k-means implementations on Spark.
This document describes a parallel k-means clustering algorithm implemented with MPI. The algorithm partitions data points into k clusters based on their distances from centroid points. It was tested with various configurations of centroids, data points, processors, and tasks. Testing showed that processing time increases dramatically with more centroids and data, but only slowly increases or decreases with more processors and tasks, up to a point where adding more processors results in too little data per processor. The algorithm iterates until the difference between new and old centroid positions is minimal, converging on the cluster solutions.
This document describes research on modeling and simulating the sound generated by airflow around a circular cylinder. It begins with the inspiration from an IYPT problem and reviews relevant literature. Background theory is provided on computational aeroacoustics methods like Lighthill's acoustic analogy. The project approach uses 2D and 3D large eddy simulation CFD to model the unsteady flow field, then applies the Ffowcs Williams-Hawkins equation to propagate sound from the source to receivers. Results show good agreement with experiments in drag coefficient, lift coefficient, Strouhal number, and acoustic spectra. Future work is proposed on flexible bodies, rotating cylinders, and propulsion.
The best known deterministic polynomial-time algorithm for primality testing right now is due to
Agrawal, Kayal, and Saxena. This algorithm has a time complexity O(log15=2(n)). Although this algorithm is
polynomial, its reliance on the congruence of large polynomials results in enormous computational requirement.
In this paper, we propose a parallelization technique for this algorithm based on message-passing
parallelism together with four workload-distribution strategies. We perform a series of experiments on an
implementation of this algorithm in a high-performance computing system consisting of 15 nodes, each with
4 CPU cores. The experiments indicate that our proposed parallelization technique introduce a significant
speedup on existing implementations. Furthermore, the dynamic workload-distribution strategy performs
better than the others. Overall, the experiments show that the parallelization obtains up to 36 times speedup.
This document discusses Kinect fusion-based simultaneous localization and mapping (SLAM) systems for mobile robotics. It presents an example Kinect-based SLAM system that uses Kinect fusion to provide dense, real-time 3D mapping of a volume. The system aims to map larger volumes while also tracking human targets. It compares several variants of the iterative closest point (ICP) algorithm used for point cloud alignment, including ones using constant velocity and non-linear least squares estimation models. It also explores using RGB and depth data with features to initialize ICP transformations. Results show the frame-wise errors of different ICP variants on benchmark datasets.
This document summarizes an implementation of k-opt moves for the Lin-Kernighan traveling salesman problem heuristic. It describes LKH-2, which allows k-changes for any k from 2 to n. This generalizes a previous version, LKH-1, which uses 5-changes. The effectiveness of LKH-2 is demonstrated on instances with 10,000 to 10 million cities, finding high-quality solutions in polynomial time like the original Lin-Kernighan heuristic.
This document discusses using variational inference methods to perform online change point detection when the underlying model is not in the exponential family. Specifically:
- It develops variational approximations to the posterior on change point times for efficient inference when the underlying model, like a Rice distribution, does not have tractable posterior predictive distributions.
- It applies this methodology to radar tracking, using a Rice distribution to model signal-to-noise ratio features, and develops a variational method for inferring the parameters of the non-exponential Rice distribution online.
- It provides improvements to online variational inference that allow updating approximations efficiently as new data arrives, enabling the use of variational inference within the Bayesian online change point detection framework.
4A_ 3_Parallel k-means clustering using gp_us for the geocomputation of real-...GISRUK conference
This document proposes using parallel k-means clustering on GPUs to quickly generate customized geodemographic classifications in real-time. Standard k-means clustering is slow and unstable. The parallel k-means approach runs multiple k-means instances simultaneously on different GPUs, significantly speeding up the process. Compared to standard k-means, parallel k-means achieved up to 94% increased efficiency and produced geodemographic classifications within seconds rather than hours. This enables on-the-fly generation of bespoke classifications based on live data sources.
CFD is a critical tool for scramjet engine design and analysis because it is not possible through ground testing to exactly reproduce hypersonic flight conditions or measure all relevant properties. CFD is used to extrapolate ground test results to flight conditions, examine the effects of modeled conditions, and identify configurations through sensitivity studies. Current CFD for scramjet design uses 3D steady-state RAS with eddy viscosity turbulence models and reduced finite rate chemical kinetics. Limitations include uncertainty in turbulence modeling and inability to capture important unsteady effects. Advanced techniques like hybrid RAS/LES and PDF methods show promise but require significantly more computational resources.
The document provides an introduction to the Kalman filter. It discusses that the Kalman filter is used to estimate the state of a system from a series of incomplete and noisy measurements. It does this optimally by using a linear feedback control. The Kalman filter works recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state. It is effective in applications involving tracking or navigation such as object tracking. The Kalman filter assumes the true state is modeled as a linear dynamical system subject to Gaussian noise. It estimates the state of the system based on a series of measurements observed over time, with external disturbances and measurement noise.
This document summarizes techniques for mapping application topologies to interconnect network topologies. It discusses how improving data locality through topology mapping can reduce communication costs, execution time, and energy consumption. Several common mapping techniques are described, including linear programming formulations, greedy approaches, partitioning approaches, transformative approaches, and those based on graph similarity. The document notes that finding an optimal mapping is NP-complete and different techniques may work better depending on the topology.
Distributed approximate spectral clustering for large scale datasetsBita Kazemi
The document proposes a distributed approximate spectral clustering (DASC) algorithm to process large datasets in a scalable way. DASC uses locality sensitive hashing to group similar data points and then approximates the kernel matrix on each group to reduce computation. It implements DASC using MapReduce and evaluates it on real and synthetic datasets, showing it can achieve similar clustering accuracy to standard spectral clustering but with an order of magnitude better runtime by distributing the computation across clusters.
GRAPH MATCHING ALGORITHM FOR TASK ASSIGNMENT PROBLEMIJCSEA Journal
Task assignment is one of the most challenging problems in distributed computing environment. An optimal task assignment guarantees minimum turnaround time for a given architecture. Several approaches of optimal task assignment have been proposed by various researchers ranging from graph partitioning based tools to heuristic graph matching. Using heuristic graph matching, it is often impossible to get optimal task assignment for practical test cases within an acceptable time limit. In this paper, we have parallelized the basic heuristic graph-matching algorithm of task assignment which is suitable only for cases where processors and inter processor links are homogeneous. This proposal is a derivative of the basic task assignment methodology using heuristic graph matching. The results show that near optimal assignments are obtained much faster than the sequential program in all the cases with reasonable speed-up.
This document summarizes research on analyzing the aero-elastic behavior of composite wing structures. The researchers used a velocity-damping method to estimate flutter speed and frequencies. They developed a finite element model of a composite wing and analyzed its normal modes to obtain natural frequencies. These frequencies were input into an analytical code to compute the wing's flutter speed. The analysis showed the composite wing had a higher flutter speed of 283.4 m/s compared to 264.6 m/s for an existing metallic wing, demonstrating improved aero-elastic performance from the composite material.
This document summarizes the application of Marchenko imaging to a 2D ocean-bottom cable dataset from the North Sea Volve field. Marchenko redatuming estimates the full wavefield from virtual sources inside the medium using only surface reflection measurements and a smooth velocity model. The authors processed the field data to obtain an estimate of the reflection response required by Marchenko and used it to iteratively estimate focusing functions and retrieve up-going and down-going Green's functions. They performed target-oriented imaging at different depth levels using the redatumed reflection responses, revealing structures not visible in standard reverse-time migration. Marchenko imaging provides a way to obtain high-resolution images of target zones without needing detailed overburden models.
This document summarizes a thesis on implementing Kalman filter-based state estimation for lithium-ion battery models in MATLAB and VHDL. It discusses battery modeling techniques, state estimation methods including coulomb counting and Kalman filtering. It presents a proposed battery model with two RC elements and discretizes it for use in a Kalman filter. The Kalman filter is evaluated on the battery model in MATLAB, and alternative forms like sequential and information filters are compared. Finally, the Kalman filter implementation is discussed for VHDL with comparisons between MATLAB and VHDL estimations.
Comparative Study of Object Detection AlgorithmsIRJET Journal
This document compares different object detection algorithms that use convolutional neural networks: Single Shot Detector (SSD), Faster R-CNN, and R-FCN. These algorithms are evaluated based on their speed and accuracy when combined with different feature extractors like VGG-16, ResNet-101, Inception ResNet, and MobileNet. The algorithms are trained on the COCO dataset and their performance is measured using mean average precision (mAP). SSD is found to be the fastest since it performs all computations in one network without needing region proposals. However, Faster R-CNN and R-FCN achieve higher accuracy. The best combinations are found to be Faster R-CNN with ResNet-101 and R-FCN with ResNet
An improved fading Kalman filter in the application of BDS dynamic positioningIJRES Journal
Aiming at the poor dynamic performance and low navigation precision of traditional fading
Kalman filter in BDS dynamic positioning, an improved fading Kalman filter based on fading factor vector is
proposed. The fading factor is extended to a fading factor vector, and each element of the vector corresponds to
each state component. Based on the difference between the actual observed quantity and the predicted one, the
value of the vector is changed automatically. The memory length of different channel is changed in real time
according to the dynamic property of the corresponding state component. The actual observation data of BDS is
used to test the algorithm. The experimental results show that compared with the traditional fading Kalman filter
and the method of the third references, the positioning precision of the algorithm is improved by 46.3% and
23.6% respectively.
Mathematical Calculation toFindtheBest Chamber andDetector Radii Used for Mea...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Investigating the Performance of Distanced-Based Weighted-Voting approaches i...Dario Panada
This document investigates the performance of different distance-based weighted voting approaches in k-nearest neighbor (kNN) classifiers. It compares the results of implementing 7 weighted voting algorithms on 11 datasets using 10-fold cross validation. The weighted voting algorithms assign higher weights to nearer neighbors of the sample being classified compared to farther neighbors. The results found no statistically significant difference in performance between the weighted voting implementations based on the Friedman test.
Capacitated Kinetic Clustering in Mobile Networks by Optimal Transportation T...Chien-Chun Ni
Presented in INFOCOM 2016
http://www3.cs.stonybrook.edu/~chni/publication/optran/
--
We consider the problem of capacitated kinetic clustering in which
n
n
mobile terminals and
k
k
base stations with respective operating capacities are given. The task is to assign the mobile terminals to the base stations such that the total squared distance from each terminal to its assigned base station is minimized and the capacity constraints are satisfied. This paper focuses on the development of distributed and computationally efficient algorithms that adapt to the motion of both terminals and base stations. Suggested by the optimal transportation theory, we exploit the structural property of the optimal solution, which can be represented by a power diagram on the base stations such that the total usage of nodes within each power cell equals the capacity of the corresponding base station. We show by using the kinetic data structure framework the first analytical upper bound on the number of changes in the optimal solution, i.e., its stability. On the algorithm side, using the power diagram formulation we show that the solution can be represented in size proportional to the number of base stations and can be solved by an iterative, local algorithm. In particular, this algorithm can naturally exploit the continuity of motion and has orders of magnitude faster than existing solutions using min-cost matching and linear programming, and thus is able to handle large scale data under mobility.
The document discusses parallel k-means clustering algorithms implemented using MapReduce and Spark. It first describes the standard k-means algorithm, which assigns data points to clusters based on distance to centroids. It then presents a MapReduce-based parallel k-means approach where the distance calculations between data points and centroids are distributed across nodes. The map tasks calculate distances and assign points to clusters, combine tasks aggregate results, and reduce tasks calculate new centroids. Experimental results show sub-linear speedup and good scaling to larger datasets. Finally, it briefly mentions k-means implementations on Spark.
This document describes a parallel k-means clustering algorithm implemented with MPI. The algorithm partitions data points into k clusters based on their distances from centroid points. It was tested with various configurations of centroids, data points, processors, and tasks. Testing showed that processing time increases dramatically with more centroids and data, but only slowly increases or decreases with more processors and tasks, up to a point where adding more processors results in too little data per processor. The algorithm iterates until the difference between new and old centroid positions is minimal, converging on the cluster solutions.
This document describes research on modeling and simulating the sound generated by airflow around a circular cylinder. It begins with the inspiration from an IYPT problem and reviews relevant literature. Background theory is provided on computational aeroacoustics methods like Lighthill's acoustic analogy. The project approach uses 2D and 3D large eddy simulation CFD to model the unsteady flow field, then applies the Ffowcs Williams-Hawkins equation to propagate sound from the source to receivers. Results show good agreement with experiments in drag coefficient, lift coefficient, Strouhal number, and acoustic spectra. Future work is proposed on flexible bodies, rotating cylinders, and propulsion.
The best known deterministic polynomial-time algorithm for primality testing right now is due to
Agrawal, Kayal, and Saxena. This algorithm has a time complexity O(log15=2(n)). Although this algorithm is
polynomial, its reliance on the congruence of large polynomials results in enormous computational requirement.
In this paper, we propose a parallelization technique for this algorithm based on message-passing
parallelism together with four workload-distribution strategies. We perform a series of experiments on an
implementation of this algorithm in a high-performance computing system consisting of 15 nodes, each with
4 CPU cores. The experiments indicate that our proposed parallelization technique introduce a significant
speedup on existing implementations. Furthermore, the dynamic workload-distribution strategy performs
better than the others. Overall, the experiments show that the parallelization obtains up to 36 times speedup.
This document discusses Kinect fusion-based simultaneous localization and mapping (SLAM) systems for mobile robotics. It presents an example Kinect-based SLAM system that uses Kinect fusion to provide dense, real-time 3D mapping of a volume. The system aims to map larger volumes while also tracking human targets. It compares several variants of the iterative closest point (ICP) algorithm used for point cloud alignment, including ones using constant velocity and non-linear least squares estimation models. It also explores using RGB and depth data with features to initialize ICP transformations. Results show the frame-wise errors of different ICP variants on benchmark datasets.
This document summarizes an implementation of k-opt moves for the Lin-Kernighan traveling salesman problem heuristic. It describes LKH-2, which allows k-changes for any k from 2 to n. This generalizes a previous version, LKH-1, which uses 5-changes. The effectiveness of LKH-2 is demonstrated on instances with 10,000 to 10 million cities, finding high-quality solutions in polynomial time like the original Lin-Kernighan heuristic.
This document discusses using variational inference methods to perform online change point detection when the underlying model is not in the exponential family. Specifically:
- It develops variational approximations to the posterior on change point times for efficient inference when the underlying model, like a Rice distribution, does not have tractable posterior predictive distributions.
- It applies this methodology to radar tracking, using a Rice distribution to model signal-to-noise ratio features, and develops a variational method for inferring the parameters of the non-exponential Rice distribution online.
- It provides improvements to online variational inference that allow updating approximations efficiently as new data arrives, enabling the use of variational inference within the Bayesian online change point detection framework.
4A_ 3_Parallel k-means clustering using gp_us for the geocomputation of real-...GISRUK conference
This document proposes using parallel k-means clustering on GPUs to quickly generate customized geodemographic classifications in real-time. Standard k-means clustering is slow and unstable. The parallel k-means approach runs multiple k-means instances simultaneously on different GPUs, significantly speeding up the process. Compared to standard k-means, parallel k-means achieved up to 94% increased efficiency and produced geodemographic classifications within seconds rather than hours. This enables on-the-fly generation of bespoke classifications based on live data sources.
CFD is a critical tool for scramjet engine design and analysis because it is not possible through ground testing to exactly reproduce hypersonic flight conditions or measure all relevant properties. CFD is used to extrapolate ground test results to flight conditions, examine the effects of modeled conditions, and identify configurations through sensitivity studies. Current CFD for scramjet design uses 3D steady-state RAS with eddy viscosity turbulence models and reduced finite rate chemical kinetics. Limitations include uncertainty in turbulence modeling and inability to capture important unsteady effects. Advanced techniques like hybrid RAS/LES and PDF methods show promise but require significantly more computational resources.
The document provides an introduction to the Kalman filter. It discusses that the Kalman filter is used to estimate the state of a system from a series of incomplete and noisy measurements. It does this optimally by using a linear feedback control. The Kalman filter works recursively on streams of noisy input data to produce a statistically optimal estimate of the underlying system state. It is effective in applications involving tracking or navigation such as object tracking. The Kalman filter assumes the true state is modeled as a linear dynamical system subject to Gaussian noise. It estimates the state of the system based on a series of measurements observed over time, with external disturbances and measurement noise.
This document summarizes techniques for mapping application topologies to interconnect network topologies. It discusses how improving data locality through topology mapping can reduce communication costs, execution time, and energy consumption. Several common mapping techniques are described, including linear programming formulations, greedy approaches, partitioning approaches, transformative approaches, and those based on graph similarity. The document notes that finding an optimal mapping is NP-complete and different techniques may work better depending on the topology.
Distributed approximate spectral clustering for large scale datasetsBita Kazemi
The document proposes a distributed approximate spectral clustering (DASC) algorithm to process large datasets in a scalable way. DASC uses locality sensitive hashing to group similar data points and then approximates the kernel matrix on each group to reduce computation. It implements DASC using MapReduce and evaluates it on real and synthetic datasets, showing it can achieve similar clustering accuracy to standard spectral clustering but with an order of magnitude better runtime by distributing the computation across clusters.
GRAPH MATCHING ALGORITHM FOR TASK ASSIGNMENT PROBLEMIJCSEA Journal
Task assignment is one of the most challenging problems in distributed computing environment. An optimal task assignment guarantees minimum turnaround time for a given architecture. Several approaches of optimal task assignment have been proposed by various researchers ranging from graph partitioning based tools to heuristic graph matching. Using heuristic graph matching, it is often impossible to get optimal task assignment for practical test cases within an acceptable time limit. In this paper, we have parallelized the basic heuristic graph-matching algorithm of task assignment which is suitable only for cases where processors and inter processor links are homogeneous. This proposal is a derivative of the basic task assignment methodology using heuristic graph matching. The results show that near optimal assignments are obtained much faster than the sequential program in all the cases with reasonable speed-up.
This document summarizes research on analyzing the aero-elastic behavior of composite wing structures. The researchers used a velocity-damping method to estimate flutter speed and frequencies. They developed a finite element model of a composite wing and analyzed its normal modes to obtain natural frequencies. These frequencies were input into an analytical code to compute the wing's flutter speed. The analysis showed the composite wing had a higher flutter speed of 283.4 m/s compared to 264.6 m/s for an existing metallic wing, demonstrating improved aero-elastic performance from the composite material.
This document summarizes the application of Marchenko imaging to a 2D ocean-bottom cable dataset from the North Sea Volve field. Marchenko redatuming estimates the full wavefield from virtual sources inside the medium using only surface reflection measurements and a smooth velocity model. The authors processed the field data to obtain an estimate of the reflection response required by Marchenko and used it to iteratively estimate focusing functions and retrieve up-going and down-going Green's functions. They performed target-oriented imaging at different depth levels using the redatumed reflection responses, revealing structures not visible in standard reverse-time migration. Marchenko imaging provides a way to obtain high-resolution images of target zones without needing detailed overburden models.
This document summarizes a thesis on implementing Kalman filter-based state estimation for lithium-ion battery models in MATLAB and VHDL. It discusses battery modeling techniques, state estimation methods including coulomb counting and Kalman filtering. It presents a proposed battery model with two RC elements and discretizes it for use in a Kalman filter. The Kalman filter is evaluated on the battery model in MATLAB, and alternative forms like sequential and information filters are compared. Finally, the Kalman filter implementation is discussed for VHDL with comparisons between MATLAB and VHDL estimations.
Comparative Study of Object Detection AlgorithmsIRJET Journal
This document compares different object detection algorithms that use convolutional neural networks: Single Shot Detector (SSD), Faster R-CNN, and R-FCN. These algorithms are evaluated based on their speed and accuracy when combined with different feature extractors like VGG-16, ResNet-101, Inception ResNet, and MobileNet. The algorithms are trained on the COCO dataset and their performance is measured using mean average precision (mAP). SSD is found to be the fastest since it performs all computations in one network without needing region proposals. However, Faster R-CNN and R-FCN achieve higher accuracy. The best combinations are found to be Faster R-CNN with ResNet-101 and R-FCN with ResNet
An improved fading Kalman filter in the application of BDS dynamic positioningIJRES Journal
Aiming at the poor dynamic performance and low navigation precision of traditional fading
Kalman filter in BDS dynamic positioning, an improved fading Kalman filter based on fading factor vector is
proposed. The fading factor is extended to a fading factor vector, and each element of the vector corresponds to
each state component. Based on the difference between the actual observed quantity and the predicted one, the
value of the vector is changed automatically. The memory length of different channel is changed in real time
according to the dynamic property of the corresponding state component. The actual observation data of BDS is
used to test the algorithm. The experimental results show that compared with the traditional fading Kalman filter
and the method of the third references, the positioning precision of the algorithm is improved by 46.3% and
23.6% respectively.
Mathematical Calculation toFindtheBest Chamber andDetector Radii Used for Mea...theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Investigating the Performance of Distanced-Based Weighted-Voting approaches i...Dario Panada
This document investigates the performance of different distance-based weighted voting approaches in k-nearest neighbor (kNN) classifiers. It compares the results of implementing 7 weighted voting algorithms on 11 datasets using 10-fold cross validation. The weighted voting algorithms assign higher weights to nearer neighbors of the sample being classified compared to farther neighbors. The results found no statistically significant difference in performance between the weighted voting implementations based on the Friedman test.
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Presented in INFOCOM 2016
http://www3.cs.stonybrook.edu/~chni/publication/optran/
--
We consider the problem of capacitated kinetic clustering in which
n
n
mobile terminals and
k
k
base stations with respective operating capacities are given. The task is to assign the mobile terminals to the base stations such that the total squared distance from each terminal to its assigned base station is minimized and the capacity constraints are satisfied. This paper focuses on the development of distributed and computationally efficient algorithms that adapt to the motion of both terminals and base stations. Suggested by the optimal transportation theory, we exploit the structural property of the optimal solution, which can be represented by a power diagram on the base stations such that the total usage of nodes within each power cell equals the capacity of the corresponding base station. We show by using the kinetic data structure framework the first analytical upper bound on the number of changes in the optimal solution, i.e., its stability. On the algorithm side, using the power diagram formulation we show that the solution can be represented in size proportional to the number of base stations and can be solved by an iterative, local algorithm. In particular, this algorithm can naturally exploit the continuity of motion and has orders of magnitude faster than existing solutions using min-cost matching and linear programming, and thus is able to handle large scale data under mobility.
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Hanjun Dai, PhD Student, School of Computational Science and Engineering, Geo...MLconf
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1. The document presents an algorithm to match strokes between hand-drawn doodle inputs and extract a low-dimensional latent doodle space from the inputs.
2. The algorithm uses k-means clustering to find stroke correspondences between inputs and assigns costs based on translation, orientation, and topological differences.
3. The algorithm then builds a latent doodle space using principal component analysis or Gaussian processes on resampled, aligned strokes to enable synthesis and interpolation of new drawings.
Massively Parallel K-Nearest Neighbor Computation on Distributed Architectures Intel® Software
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This document describes a program to numerically solve Poisson's equation for the electrostatic potential of a T-shaped structure. The program uses the method of moments with basis functions to expand Poisson's equation. Point matching is used to approximate matrix integration. The structure is divided into subsections and the charge distribution is calculated for each subsection and summed. The potential function is then calculated using the total charge. The results are demonstrated in MatLab. Theoretical proofs are shown by varying the angle of the horizontal plate and tracking the capacitance to validate the results.
This document discusses various techniques for load forecasting, including extrapolation, estimation of periodic components, autoregressive models, and econometric models. It describes forecasting for different time horizons from very short term to long term. Methodologies include extrapolation, correlation, or a combination. Techniques can be deterministic or stochastic/probabilistic. Reactive load forecasting is also discussed as more challenging than active load forecasting due to network configuration changes.
This document discusses various techniques for load forecasting, including extrapolation, estimation of periodic components, autoregressive models, and econometric models. It describes forecasting for different time horizons from very short term to long term. Methodologies include extrapolation, correlation, or a combination. Techniques can be deterministic or stochastic/probabilistic. Reactive load forecasting is also discussed as more challenging than active load forecasting due to network configuration changes.
A comparison-of-first-and-second-order-training-algorithms-for-artificial-neu...Cemal Ardil
This document compares first and second order training algorithms for artificial neural networks. It summarizes that feedforward network training is a special case of functional minimization where no explicit model of the data is assumed. Gradient descent, conjugate gradient, and quasi-Newton methods are discussed as first and second order training methods. Conjugate gradient and quasi-Newton methods are shown to outperform gradient descent methods experimentally using share rate data. The backpropagation algorithm and its variations are described for finding the gradient of the error function with respect to the network weights. Conjugate gradient techniques are discussed as a way to find the search direction without explicitly computing the Hessian matrix.
Manifold Blurring Mean Shift algorithms for manifold denoising, presentation,...Florent Renucci
(General) To retrieve a clean dataset by deleting outliers.
(Computer Vision) the recovery of a digital image that has been contaminated by additive white Gaussian noise.
The document discusses clustering techniques and provides details about the k-means clustering algorithm. It begins with an introduction to clustering and lists different clustering techniques. It then describes the k-means algorithm in detail, including how it works, the steps involved, and provides an example illustration. Finally, it discusses comments on the k-means algorithm, focusing on aspects like choosing the value of k, initializing cluster centroids, and different distance measurement methods.
This document discusses the design of machine tool gearboxes to provide multiple spindle speeds. It begins with an introduction and overview of how multi-speeds are provided on the spindle shaft through gearing arrangements. The key considerations in gearbox design like selecting optimum speed steps and progressions are explained. Arithmatic and geometric progressions for speed steps are described, with geometric progression being preferred. Methods to determine speed ratios, range ratios, transmission ratios and structural formulae for multi-stage gearboxes are provided. The document aims to provide fundamental concepts and approaches for machine tool gearbox design to achieve multiple spindle speeds.
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3. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Trajectory Joins Vocabulary
• Trajectory: series of locations that depicts movement of
an entity over time.
• Trajectory Object: snapshot of time and location; many
trajectory objects in a single trajectory.
• Trajectory Join: given two sets M and R of trajectories,
join(M, R) returns trajectory objects from M and R within
some proximity of space and time.
• Joining Criterion: criteria by which objects in M and R are
joined. This paper uses the k-nearest-neighbors algorithm
to join objects.
4. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Example Use Case
• Hubble space telescope generates 140GB/week about
movements of stars and asteroids. Analysis of proximity
among trajectory objects helps to uncover behavior of
outer-space objects, discover meteors, etc. We can use
trajectory joins to find objects in some proximity to one
another.
• Given two groups A and B of asteroids, return the
identities of asteroids from B that have been close to
those in A.
5. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
MapReduce Basics
• Divide-and-conquer ”big data” on share-nothing clusters.
• Master node partitions data and assigns it to map nodes.
• Map performs analysis on local data.
• Shuffle step redistributes data after the map step.
• Reduce performs a summary operation over data from the
the Map step.
• MapReduce software handles the data partitioning,
execution over distributed nodes, error recovery.
1
1
https://goo.gl/0nbYhp
6. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Problem Statement
kNN Join
Find the K nearest neighbors from set R for objects in M over
time interval [ts, te] ⊆ [Ts, Te].
(h,k)NN Join
Find a list of h objects from M over time interval
[ts, te] ⊆ [Ts, Te] that minimize function f . Then return the k
nearest neighbors for each of the h objects.
7. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
kNN Example
Figure illustrates a kNN Join. An (h,k)NN join with h = 1, k = 2
might use f (m1) = max{d1, d2} = d2 to return the k nearest
neighbors of d2 = {r1, r2}.
8. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Some Fundamental Operations
• Min/max distance from point to line-segment.
• Min/max distance from point to trajectory.
• Min/max distance from trajectory to trajectory.
• kNN from trajectory object to trajectory objects.
2
2
Formulas omitted for brevity, available in section 3.
9. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Sub-optimal Solutions
Single Machine Brute Force (BF)
Nested loop to compute euclidean distance between every pair
of points in M and R. Worst-case O(|M||N|l) for l points in
trajectory of interest tr.
Single Machine Sweep Line (SL)
Pre-sort the data based on time and compute only distances for
overlapping trajectories. Also worst-case O(|M||N|l).
Naive MapReduce
Map divides objects in M and R randomly into disjoint subsets.
Reduce joins all pairs of subsets to compute distance. A second
MapReduce job selects the k nearest neighbors.
10. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Overview of kNN Join
Each of the steps is composed of its own MapReduce algorithm for a
total of 6 algorithms.
12. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Pre-processing Phase
Algorithm 1
1 Input: non-partitioned trajectories.
2 Map splits trajectories in sets M and R into T temporal
partitions. O(l + T) where l is the size of a trajectory.
3 Reduce splits each temporal partition into N spatial
partitions. O((|M| + |R|)(l + N))
4 Output: trajectories partitioned by time and space.
13. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Sub-Trajectory Extraction
• An anchor trajectory must span an entire time partition.
• TrL
i is object i in trajectory r in set L in time partiton T.
Algorithm 2
1 Input: trajectories partitioned by time and space.
2 Map retrieves all sub-trajectories in [ts, te]3. Ot(log(l)),
Os(l)
3 Reduce finds anchor trajectories that will be used in next
step. Ot(|TrL
i |2l), Os(|TrL
i |l).
4 Output: anchor trajectories
3
the queried time window
14. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Anchor Trajectories
• An anchor trajectory must span an entire time partition ts
to te.
15. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Computing Time-dependent Bound (TDB)
• The TDB is a circle c(t) that bounds the k nearest
neighbors of a set S of objects at time t.
• The TDB for a set S of objects can change over time.
Algorithm 4, containing Algorithm 3
1 Input: anchor trajectories
2 Map computes the maximum distance from each anchor
trajectory to each central point pi in each temporal
partition T. Ot(N · l), Os(l)
3 Reduce computes the TDB of TrM
i based on the maximum
distances. Ot(|R|log|R|), Os(|R|) for the set of objects R.
4 Output: Time-dependent Bounds
16. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Time-dependent Bounds
• The TDB is a circle c(t) that bounds the k nearest
neighbors of a set S of objects at time t.
• The TDB for a set S of objects can change over time.
White dots are objects from M. Black dots are objects from R. c(t)
needs a small circle to encompass k = 2 points. c(t ) needs a bigger
circle to encompass k = 2 points.
17. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Finding Candidate Trajectories
Algorithm 5
1 Input: partition of trajectories TrR
j .
2 Map classifies each partition of trajectories TrR
j as having
no candidates, all candidates, or some candidates.
Ot(|Tr|Nl), Os(|Tr|l).
3 Reduce gathers the candidates for a join into CR
i . Ot(1),
Os(|CR
i |l).
4 Output: a set of candidate trajectories CR
i .
18. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Candidate Trajectories
Finding candidates for TrR
j (red). Case 1 have no overlap. Case 2
have complete overlap. Case 3 have partial overlap.
19. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Trajectory Join
Algorithm 6
1 Input: candidate trajectories
2 Map joins each partition TrM
i with corresponding
candidates CR
i using a single machine. O(|Tr||CR
i |l).
3 Reduce sorts each object’s neighbors and leaves only the k
nearest. O(kN).
4 Output: each queried object with its k nearest neighbors.
20. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Extension: kNN Load Balancing
1 Hash the trajectory objects by an ID to distribute them
more uniformly among compute nodes.
2 Requires modification in the sub-trajectory extraction,
finding candidates, and trajectory join.
21. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Extension: hkNN Join
1 Review: finds the h objects from M that minimize some
function f and returns each of their k nearest neighbors.
2 Forced to compute a smaller TDB.
3 Smaller query result hxk size. kNN query was |M|xk.
4 Time and space complexities remain the same.
22. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Evaluation Setup
• 2 Synthetic and 2 real datasets.
• Non-trivial size, up to 1.2B observations and 17.2GB.
• Hadoop cluster with 60 slave nodes, multi-core 3.40GHz
and 16GB memory per node.
• Using Sweep Line (SL) for single-node parts.
• Measuring query execution time and MapReduce shuffling
cost.4
• k = 10, N = 400 constant for all datasets. T and tq
varied.
4
The amount of data sent from mappers to reducers.
23. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Effect of T (number of temporal partitions)
As T grows the time decreases until it hits an inflection point. This
happens to be similar for both datasets. We are still spending the
most time on single-node SL.
24. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
kNN Results Summary
• Increasing N (number of temporal patitions) improves
performance to a point of inflection. This point is different
for the two datasets. Fig. 15.
• Balanced Sweep-Line (BL-SL) is the more efficient
single-node algorithm. Fig. 16.5
• Adding slave nodes improves performance. Rate of change
is slow, likely due to I/O overhead. Fig. 17.
• As k increases the running time and shuffle cost increase.
TDB makes a difference. Fig. 18.
• Increases in tq show a near-linear increase in running time
and shuffling cost. TDB and load balancing make a
difference. Fig. 19.
• Time increases linearly with dataset size. Sharper increase
in shuffling cost than time. Fig. 20.
5
I think they mixed up the figure labels.
25. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
hkNN Results Summary
• Time is constant as h grows (probably because k is
constant).
• (h,k)NN is 2x faster than kNN methods.
• Load-balanced is faster than non-load-balanced.
26. Scalable kNN
Joins, Fang
presented by
Alex Klibisz
Introduction
Trajectory Joins
Introduction
Motivation
MapReduce
Introduction
Problem
Statement
Trajectory
Operations
Sub-optimal
Solutions
Solution: kNN
Join
Pre-processing
Phase
Querying Phase
Extension: kNN
Load Balancing
Extension:
hkNN Join
Results
Evaluation Setup
kNN Results
hkNN Results
Summary
Conclusion
Conclusion
Contributions
1 Leverage share-nothing MapReduce structure for kNN
joins, which typically rely on shared indices.
2 Introduce the TDB and load-balancing methods, which
yield tangible improvements.
Questions
1 Most of the time is still spent on the single-node
computation. What is the theoretical bound for
improvement via parallelization?
2 How much time does the partitioning step take?
3 The partitioning step probably has to be re-run when new
data arrives. Does this prevent a real-time
implementation?
4 Any benefit to localize data instead of using HDFS?