Execution Time Efficiency Research Articles

Large-scale video copy retrieval with temporal-concentration SIFT

The scale-invariant feature transform (SIFT) feature plays a very important role in multimedia content analysis, such as near-duplicate image and video retrieval. However, the storage and query costs of SIFT become unbearable for large-scale databases. In this paper, SIFT features are robustly encoded with temporal information by tracking the SIFT to generate temporal-concentration SIFT (TCSIFT), which highly compresses the quantity of local features to reduce visual redundancy, and keeps the advantages of SIFT as much as possible at the same time. On the basis of TCSIFT, a novel framework for large-scale video copy retrieval is proposed in which the processes of retrieval and validation are implemented at the feature and frame level. Experimental results for two different datasets, i.e., CC_WEB_VIDEO and TRECVID, demonstrate that our method can yield comparable accuracy, compact storage size, and more efficient execution time, as well as adapt to various video transformations.

Performance Evaluation and Comparison of Sahi Pro and Selenium Webdriver

Software testing is a crucial phase in software development life cycle that reduce errors, cut maintenance and overall software costs. Manual testing takes a lot of time and effort, so automation tools are used by testers. There are number of automation testing tools available, but to choose the appropriate tool for the task requires various parameters to be considered. In this paper an evaluation on web-based software testing tools; Sahi Pro and Selenium Webdriver, based on execution time, recording and playback efficiency, browser and platform compatibility, result reporting, ease of learning and cost has been presented. It will help testers to take informed decision to choose a tool according to their requirements and resources.

Timestamp Embedding Query Stream Processing Engine

Background/Objectives: To improve the performance of the streaming data through RDF vocabulary and multi query optimization. Methods/Statistical Analysis: The scalability and performance of the query processor is improved with the use of native timestamp embedding query processor with the help of semantic approach. This offers vibrant, scalable services for continuous query processing. Continuous data is controlled through dynamic sliding window for incremental evaluation of streaming data. Static queries are processed continuously and all processing is done within main memory. Semantic sensor web is used to processor sensor data semantically by query processing engine and metadata will be stored as triple stores. Findings: The continuous stream of queries is properly handled by the dynamic sliding window and sent to the query optimizer for optimization. The triple formatted outcomes are warehoused into OWL, which stores and adopt the relational database format. The data conversion will be done by the data transformer and finally results will be realized as stream of data. In this proposed model, the RDF vocabulary has been redesigned by adding timestamps along with incoming triples. The classes and properties are depicted in terms of the triples (Subject S, Object O and Predicate P) and are designed as OWL. Data sets are split into smaller parts for continuous updates and collect them into massive data sets so as to obtain faster insertion rates. It takes less time to process temporal, complex and multi queries in proposed system. Since timestamp is attached as one of the object in the triples, it is easy to retrieve and execute timestamp embedding queries. The proposed system is appropriate for all types of queries and proves efficiency in execution time. Application/Improvement: Temporal, complex and multi join queries are executed and evaluated for weather monitoring system.

Performance Comparison of Roulette WheelSelection and Steady state Selection in GeneticNucleotide Sequence

In this paper the attempt has been made analyse the performance comparison of nucleotide sequence in DNA cells. The basic idea behind this proposed method is estimate the efficiency of roulette wheel selection and steady state selection with respect to memory and running time. Here this algorithm is applied to find the genetic operator such as mutation, crossover and selection in large dataset. In order to evaluate the proposed methodology, Comparisons are made based on the Execution time and memory efficiency in finding best fitness value in each generations and their distance estimation between each generations.. The extracted rules and analysed results are graphically demonstrated. The performance is analysed based on the different no of instances and confidence in DNA sequence data set. In genetic algorithms, the roulette wheel selection operator has spirit of utilization while steady state selection is influenced by exploration. The proposed solution is implemented in MATLAB using DNA Nucleotide Sequence of Cancer cells and the results were compared with roulette wheel selection and steady state selection with different problem sizes.

GRAPPE : a system for determining optimal connecting route to target person based on mutual intimacy index

Recently, the growth of social network service (SNS, Facebook) has required the search technique to utilize its distinctive characteristic which links people to people. This paper discusses the design and implementation of GRAPPE which suggests the ranked list of optimal connecting routes between two people by interaction like SNS, phone calls, texts, mails. It is based on mutual intimacy index (MII) which indicate how closely two people are related. MII is calculated periodically when a user interact with other person by smartphone. In this study, we propose a simple two-ended search method for finding optimal routes between a source person and a destination person. For the real-time service it searches optimal path within 3 step-away relationship in human network that is effective in real life while existing services in SNS usually provide one-ended search on entire paths. Furthermore, we also use the pruning technique for efficient execution time.

Novel Conflict Resolution Model for Multi‐Uav Based on Cpn and 4d Trajectories

AbstractThis paper presents a conflict resolution (CR) model in colored Petri net (CPN) formalism for 4D (three‐dimensional + time) planned trajectories of cooperating unmanned aerial vehicles (UAVs). The CR model is integrated with the conflict detection algorithm using spatial data structure to detect time‐based separation infringements among UAVs and to generate an intuitionistic representation of 4D conflict information. The main contribution of this paper is the proposed CR model, which is based on the logic of cause‐and‐effect analysis. This model not only chooses a preferred trajectory according to the priority for solving the current conflict but also considers the follow‐up influence (domino effect) to update segments and conflicts. The novel causal model exploits the state space to achieve the solution using CPN. The model is validated with the experimental results of a scenario involving multiple UAVs (composed of clusters) cruising in a bounded region and exhibits the main advantages of scalability, efficiency, and short execution time.

An optimal many-core model-based supercomputing for accelerating video-equipped fire detection

Automatic fire detection has become more and more appealing because of the increasing use of video capabilities in surveillance systems used for early detection of fire. However, its high computational complexities limit its use in real-time applications. To meet the real-time processing of today's fire detection techniques, this study proposes a single instruction, multiple data many-core model. To design an efficient many-core model for image processing applications such as fire detection, a key design parameter is the image data-per-processing-element (IDPE) variation of the many-core system, which is the amount of image data directly mapped to each processing element PE. This study quantitatively evaluates the impact of the IDPE variation on system performance and energy efficiency for the multi-stage fire detection approach that consists of movement-containing region detection, color segmentation, fire feature extraction of fires, and decision making if there is a fire or non-fire in a processing video frame. In this study, we use six IDPE ratios to determine an optimal many-core model that provides the most efficient operation for fire detection using architectural and workload simulation. Experimental results indicate that the most efficient many-core model is achieved at the 64 IDPE value in terms of the worst-case execution time and energy efficiency. In addition, this study compares the performance of the most efficient many-core configuration with that of a commercial graphics processing unit (Nvidia GeForce GTX 480) to show the improved performance of the proposed many-core model for the fire detection algorithm. This many-core configuration outperforms the commercial graphic processing unit in the worst-case execution time and energy efficiency.

Influencer Events in Episode Rules: A Way to Impact the Occurrence of Events

Episode rules are event patterns mined from a single event sequence. They are mainly used to predict the occurrence of events (the consequent of the rule), once the antecedent has occurred. The occurrence of the consequent of a rule may however be disturbed by the occurrence of another event in the sequence (that does not belong to the antecedent). We refer such an event to as an influencer event. To the best of our knowledge, the identification of such events in the context of episode rules has never been studied. However, identifying influencer events is of the highest importance as these events can be viewed as a way to act to impact the occurrence of events, here the consequent of rules. We propose to identify three types of influencer events: distance influencer events, confidence influencer events and disappearance events. To identify these influencer events, we propose to rely on the set of episode rules discovered by mining algorithms. The proposed approach for discovering influencer events is evaluated on an event sequence of social networks messages. Experiments measure the execution time efficiency according to the adopted episode rules mining algorithm. In addition, they show that some events do actually highly influence the consequent of some rules, that influencer events may not only influence several consequents, but also influence several characteristics of rules.

Implementation of Parallelization Contract Mechanism Extension of Map Reduce Framework for the Efficient Execution Time over Geo-Distributed Dataset

The world is surrounded by technology and Internet with extreme dynamic changes day by day in it.Due to that quintillion bytes of data is created. Source of this data is in the form of petabytes and zettabytes,which is known as Big data.Examples of such data are climate information, trajectory information, transaction records, web site usage data etc .As this data is in the abundant form so that not easy to process and require more time to execute.Hadoop is only scalable that is it can reliably store and process petabytes. Hadoop plays an important role in processing and handling

고속의 클러스터 추정을 위한 매니코어 프로세서의 디자인 공간 탐색

This paper implements and improves the performance of high computational subtractive clustering algorithm using a single instruction, multiple data (SIMD) based many-core processor. In addition, this paper implements five different processing element (PE) architectures (PEs=16, 64, 256, 1,024, 4,096) to select an optimal PE architecture for the subtractive clustering algorithm by estimating execution time and energy efficiency. Experimental results using two different medical images and three different resolutions (128x128, 256x256, 512x512) show that PEs=4,096 achieves the highest performance and energy efficiency for all the cases. ▸

A GaBP-GPU Algorithm of Solving Large-Scale Sparse Linear Systems

According to GaBP (Gaussian Belief Propagation) algorithm, this article presents a GaBP-GPU algorithm of solving large-scale symmetric diagonally dominant sparse linear systems based on GPU. Combined with GaBP-GPU algorithm, a storage format (MCSC) is presented. We extract some diagonally dominant matrices from the University of Florida Sparse Matrix Collection as test examples. The experimental results show that our algorithm has high efficiency in execution time under the same accuracy.

Thermal Modeling and Analysis of Cloud Data Storage Systems

An explosive increment of data and a variety of data analysis make it indispensable to lower power and cooling costs of cloud datacenters. To address this issue, we investigate the thermal impact of I/O access patterns on data storage systems. Firstly, we conduct some preliminary experiments to study the thermal behavior of a data storage node. The experimental results show that disks have ignorable thermal impacts as processors to outlet temperatures of storage nodes. We raise an approach to model the outlet temperature of a storage node. The thermal models generated by our approach gains a precision error less than 6%. Next, we investigate the thermal impact of data placement strategies on storage systems. We compare the cooling cost of storage systems governed by different data placement schemes. Our study shows that evenly distributing the data leads to highest outlet temperature for the sake of shortest execution time and energy efficiency. According to the energy consumption of various data placement schemes, we propose a thermal-ware energy-efficient data placement strategy. We further show that this work can be extended to analyze the cooling cost of data centers with massive storage capacity. Big data, which is composed of a collection of huge and complex data sets, has been positioned as must have commodity and resource in industry, government, and academia. Processing big data requires a large-scale storage system, which increases both power and cooling costs. In this study, we investigate the thermal behavior of real storage systems and their I/O access patterns, which offer a guideline of building energy-efficient cloud storage systems. The cooling consumption of data centers can be considerably reduced by using an efficient thermal management for storage systems. However, disk is not considered in traditional thermal models for data centers. In this paper, we investigate the thermal impact of hard disks and propose a thermal modeling approach for storage systems. In addition, we estimate the outlet temperature of a storage server by applying the proposed

FUNCTIONAL PRINCIPAL COMPONENT ANALYSIS FOR RECOGNITION OF ARM GESTURES AND HUMANOID IMITATION

This paper investigates the use of functional principal component analysis (FPCA) for automatic recognition of dynamic human arm gestures and robot imitation. FPCA is a statistical technique of functional data analysis that generalizes standard multivariate principal component analysis. Functional data analysis signals (e.g., gestures) are functions that are considered as observations of a random variable on a functional space. In particular, FPCA reduces the dimensionality of the input data by projecting them onto a finite-dimensional space spanned by a few prominent eigenfunctions. The main contribution of this work is the proposal of a novel technique for unsupervised clustering of training data and dynamic gesture recognition based on FPCA. FPCA has not been considered in previous studies on humanoid learning. The proposed approach has been evaluated in two experimental settings for motion capture. In the first setup single arm gestures are recognized from inertial sensors attached to the arm of the user. In the second setup the method is extended to two-arm gestures acquired from a range sensor. Recognized gestures are reproduced by a small humanoid robot. The FPCA method has also been compared to a high performance algorithm for gesture classification based on dynamic time warping (DTW). The FPCA algorithm achieves comparable results in both recognition rate and robustness to missing data, while it outperforms DTW in terms of efficiency in execution time.

Efficient Video Stream Monitoring for Near-Duplicate Detection and Localization in a Large-Scale Repository

In this article, we study the efficiency problem of video stream near-duplicate monitoring in a large-scale repository. Existing stream monitoring methods are mainly designed for a short video to scan over a query stream; they have difficulty being scalable for a large number of long videos. We present a simple but effective algorithm called incremental similarity update to address the problem. That is, a similarity upper bound between two videos can be calculated incrementally by leveraging the prior knowledge of the previous calculation. The similarity upper bound takes a lightweight computation to filter out unnecessary time-consuming computation for the actual similarity between two videos, making the search process more efficient. We integrate the algorithm with inverted indexing to obtain a candidate list from the repository for the given query stream. Meanwhile, the algorithm is applied to scan each candidate for locating exact near-duplicate subsequences. We implement several state-of-the-art methods for comparison in terms of accuracy, execution time, and memory consumption. Experimental results demonstrate the proposed algorithm yields comparable accuracy, compact memory size, and more efficient execution time.

A Pipeline Model to Discover Frequent Itemsets in an Hierarchical Systems

Like all the other fields of data processing, the modern information systems have integrated the results of the advanced technologies of the last decades. These systems contain implicit data which it will be necessary to extract and exploit, by using data Mining techniques. Mining association rules which trends to find interesting association or correlation relationships among large amounts of data is one of these techniques. It is a twosteps process, the first step finds all frequent itemsets and the second step constructs association rules from these frequent sets. The overall performance of mining association rules is determined by the first step which becomes the focus problem. This step is expensive with high demands for computation and data access. Parallel computing seems to have a natural role to play since parallel computers provides scalability. In this paper, we examine the issue of mining association rules among items in large databases transactions using the algorithm Apriori proposed by Agrawal. In this context, we propose a new parallel version of the Apriori algorithm of Agrawal, that is the main algorithm of each data mining technique. Parallel computing seems to have a natural role to play since parallel computers provides scalability. In fact, our objective of our work is to have an efficient parallel execution time that requires a delicate balance between program granularity and communication latency (synchronization overhead) between the different granules. Unlike previous work on parallelization of specific data mining algorithms, our approaches consist to discover the different granularity levels of parallelism and their impact on the performance. In this paper we focus on task and data parallelism (hybrid approach) under distributed memory. In particular, if communication latency is minimal then fine grain partitioning will yield the best performance. This is the case when data parallelism is used. If communication latency is large (as in a loosely coupled system), then coarse grain partitioning is more appropriate. For the target architecture used in this work (distributed-shared memory).), the problem of load balancing among the nodes becomes a more critical issue in attempts to yield high performance. We have carried out a detailed evaluation of the parallelization techniques and the impact of combining different types of parallelism (task, data and pipeline) on the effectiveness of the system.

SQL Model in Language Encapsulation and Compression Technique for Association Rules Mining

Discovery of association rules is an important for Data mining. One of the most famous association rule learning algorithms is Apriori rule. Apriori algorithm is one of algorithms for generation of association rules. The drawback of Apriori Rule algorithm is the number of time to read data in the database equal number of each candidate is generate. Many research papers have been published trying to reduce the amount of time needed to read data from the database. In this paper, we propose a new algorithm that will work rapidly and without frequency tree or temporary candidate itemsets in RAM or Hard disk. SQL Model in Language Encapsulation and Compression Technique for Association Rules Mining (SMILE-ARM). This algorithm will generate candidates are greater than minimum support on-the-fly by SQL. This algorithm is based on two major ideas. Firstly, compress data. Secondly, generation of candidate itemsets on-the-fly by SQL. Based on the experimental results, an increase in the number of transactions or the number of items did not affect the speed at which candidates were generated by this algorithm. The construction method of SQL Model in Language Encapsulation and Compression Technique for Association Rules Mining (SMILE-ARM) technique has twenty times higher mining efficiency in execution time than Apriori Rule.

Hierarchical Resampling Algorithm and Architecture for Distributed Particle Filters

In this paper, we introduce a hierarchical resampling (HR) algorithm and architecture for distributed particle filters (PFs). While maintaining the same accuracy as centralized resampling in statistics, the proposed HR algorithm decomposes the resampling step into two hierarchies including intermediate resampling (IR) and unitary resampling (UR), which suits PFs for distributed hardware implementation. Also presented includes a residual cumulative resampling (RCR) method that pipelines and accelerates the UR step. The corresponding architecture, when compared with traditional distributed architectures, eliminates the particle redistribution step, and has such advantages as short execution time and high memory efficiency. The prototype containing 8 PEs has been developed in Xilinx Virtex IV FPGA (XC4VFX100-12FF1152) for the bearings-only tracking (BOT) problem, and the result shows that the input observations can be processed at 37.21 KHz with 8 K particles and a clock speed of 80 MHz.

Iterative Model and Trajectory Refinement for Launch Optimization of Automotive Powertrains

A recently proposed iterative model and trajectory refinement (IMTR) approach is applied to launch control of an automotive powertrain with a turbocharged gasoline engine and a dual-clutch transmission. The optimal clutch torque trajectory is determined by a piecewise linear in time function that minimizes a cost function while meeting multiple constraints on vehicle acceleration and engine speed. A high-fidelity model of the powertrain and a simplified physics-based model are used iteratively to refine solutions. The iterative method converges on a solution that meets target performance metrics with efficient execution time.

The Algorithm for Graphic Method of Rain-Flow Counting in Programming

In this article, a new algorithm for rain-flow counting method in computer program is introduced. In the whole counting process, this algorithm entirely follows the principle of graphic method of rain-flow counting method, and so it can embodies the mechanical basis of extracting load cycle in rain-flow counting method; during the processing of counting, the information of every load cycle can be accurately recorded, such as starting and ending time, the time of peak or valley, the value of starting point and peak (or valley), mean value and amplitude. Besides, the counting result is in full accordance with the result from graphic method. Compared to four peak-valley method, this algorithm has shorter execution time and higher efficiency.

The Comparative of Boolean Algebra Compress and Apriori Rule Techniques for New Theoretic Association Rule Mining Model

The Data Mining refers to extracting or “mining” knowledge from large amounts of data. The Association Rule the one of technique to knowledge discovery. The Association Rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. One of the most famous association rule learning algorithms is Apriori rule. Apriori algorithm is one of algorithms for generation of association rules. The drawback of Apriori Rule algorithm is the number of time to read data in the database equal number of each candidate is generated. Many research papers have been published trying to reduce the amount of time needed to read data from the database. In this paper, we propose a new algorithm that will work rapidly. Boolean Algebra Compress technique for Association Rule Mining (B-Compress). This algorithm adopts three major ideas. Firstly, compress data. Secondly, reduce the amount of times to scan database tremendously. Thirdly, reduce file size. The construction method of Boolean Algebra Compress technique for association rule mining has ten times higher mining efficiency in execution time than Apriori Rule.