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检索条件"任意字段=14th International Symposium on Algorithms and Data Structures, WADS 2015"

共 66 条记录，以下是41-50 订阅

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Scheduling MapReduce Jobs and data Shuffle on Unrelated Processors 14th

引用

14th international symposium on Experimental algorithms (SEA)

作者： Fotakis, Dimitris Milis, Ioannis Papadigenopoulos, Orestis Zampetakis, Emmanouil Zois, Georgios Natl Tech Univ Athens Sch Elect & Comp Engn Athens Greece Athens Univ Econ & Business Dept Informat Athens Greece MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9783319200866;9783319200859

We propose a constant approximation algorithm for generalizations of the Flexible Flow Shop (FFS) problem which form a realistic model for non-preemptive scheduling in MapReduce systems. Our results concern the minimization of the total weighted completion time of a set of MapReduce jobs on unrelated processors and improve substantially on the model proposed by Moseley et al. (SPAA 2011) in two directions: (i) we consider jobs consisting of multiple Map and Reduce tasks, which is the key idea behind MapReduce computations, and (ii) we introduce into our model the crucial cost of the data shuffle phase, i.e., the cost for the transmission of intermediate data from Map to Reduce tasks. Moreover, we experimentally evaluate our algorithm compared with a lower bound on the optimal cost of our problem as well as with a fast algorithm, which combines a simple online assignment of tasks to processors with a standard scheduling policy. As we observe, for random instances that capture data locality issues, our algorithm achieves a better performance.

关键词： MapReduce

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Optimally Weighted Cluster Kriging for Big data Regression 14th

Optimally Weighted Cluster Kriging for Big Data Regression

引用

14th international symposium on Intelligent data Analysis (IDA)

作者： van Stein, Bas Wang, Hao Kowalczyk, Wojtek Back, thomas Emmerich, Michael Leiden Univ Leiden Inst Adv Comp Sci Niels Bohrweg 1 Leiden Netherlands

ISBN: (纸本)9783319244655

In business and academia we are continuously trying to model and analyze complex processes in order to gain insight and optimize. One of the most popular modeling algorithms is Kriging, or Gaussian Processes. A major bottleneck with Kriging is the amount of processing time of at least O(n3) and memory required O(n2) when applying this algorithm on medium to big data sets. With big data sets, that are more and more available these days, Kriging is not computationally feasible. As a solution to this problem we introduce a hybrid approach in which a number of Kriging models built on disjoint subsets of the data are properly weighted for the predictions. the proposed model is both in processing time and memory much more efficient than standard Global Kriging and performs equally well in terms of accuracy. the proposed algorithm is better scalable, and well suited for parallelization.

关键词： Kriging Gaussian Processes K-means Clustering Big-data Regression

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Fleye on the Car: Big data meets the Internet Of things 14

Fleye on the Car: Big Data meets the Internet Of Things

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14th IEEE/ACM international Conference on Information Processing in Sensor Networks (IPSN)

作者： Nasser, Soliman Barry, Andew Doniec, Marek Peled, Guy Rosman, Guy Rus, Daniela Volkov, Mikhail Feldman, Dan Univ Haifa Haifa Israel MIT Cambridge MA 02139 USA

ISBN: (纸本)9781450334754

Vehicle-based vision algorithms, such as the collision alert systems [4], are able to interpret a scene in real-time and provide drivers with immediate feedback. However, such technologies are based on cameras on the car, limited to the vicinity of the car, severely limiting their potential. they cannot find empty parking slots, bypass traffic jams, or warn about dangers outside the car's immediate surrounding. An intelligent driving system augmented with additional sensors and network inputs may significantly reduce the number of accidents, improve traffic congestion, and care for the safety and quality of people's lives. We propose an open-code system, called Fleye, that consists of an autonomous drone (nano quadrotor) that carries a radio camera and flies few meters in front and above the car. the streaming video is transmitted in real time from the quadcopter to Amazon's EC2 cloud together with information about the driver, the drone, and the car's state. the output is then transmitted to the "smart glasses" of the driver. the control of the drone, as well as the sensor data collection from the driver, is done by low cost (<30$) minicomputer. Most computation is done in the cloud, allowing straightforward integration of multiple vehicle behaviour and additional sensors, as well as greater computational capability.

关键词： Quadrotors collision alert system internet of things video streaming

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A Framework for Collecting and Classifying Objects in Satellite Imagery 14th

A Framework for Collecting and Classifying Objects in Satell...

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14th international symposium on Visual Computing (ISVC)

作者： Radhakrishnan, Aswathnarayan Cunningham, Jamie Davis, Jim Ilin, Roman Ohio State Univ Columbus OH 43210 USA AFRL RYAP Wright Patterson AFB OH 45433 USA

ISBN: (纸本)9783030337230;9783030337223

A major issue with data-hungry deep learning algorithms is the lack of annotated ground truth for specific applications. the high volume of satellite imagery available today, coupled with crowd-sourced map data can enable a new means for training and classifying objects in wide-area imagery. In this work, we present an automated pipeline for collecting and labeling satellite imagery to facilitate building custom deep learning models. We demonstrate this approach by automatically collecting labeled imagery of solar power plants and building a classifier to detect the structures. this framework can be used to collect labeled satellite imagery of any object mapped by spatial databases.

关键词： Satellite imagery data generation Automated ground truthing OpenStreetMap Deep learning Solar power plants

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ACM international Conference Proceeding Series

ACM International Conference Proceeding Series

引用

14th Brazilian symposium on Information Systems, SBSI 2018

ISBN: (纸本)9781450365598

the proceedings contain 71 papers. the topics discussed include: REA-AED: open educational resource for the teaching of algorithms and data structures;an analysis of the collaborative aspects of pair programming support tools: a systematic literature mapping;building educational games from a feature model;recommender systems with social elements: a systematic mapping;social perceptions about adopting smartphone applications in the context of collaborative security;alternative title: a method to support the identification of interests in startup ecosystems;promoting users engagement in the resolution of social development problems using the Fisher-Fogg framework;alternative title: MCL: metrics-based constraint language;and alternative title: test automation in a test factory: an experience report.

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Online Multi-threshold Learning with Imbalanced data Stream 14th

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14th international symposium on Neural Networks (ISNN)

作者： Cai, Xufen Yang, Min Zhu, Rong Li, Xiaoyan Ye, Long Zhang, Qin Commun Univ China Dept Informat Engn Beijing Peoples R China Wuhan Univ Comp Sch State Key Lab Software Engn Wuhan Hubei Peoples R China Chinese Acad Sci Inst Comp Technol Beijing Peoples R China

ISBN: (纸本)9783319590721;9783319590714

this paper addresses the imbalanced data problem in an online fashion based on multi-threshold learning. the majority of existing works on processing large-scale imbalanced data stream assume a prior distribution of data based on a training dataset, while we consider a more challenging setting without any assumption of the prior, and propose an online multi-threshold learning (OMTL) method by simultaneously learning multiple classifiers with different threshold based on F-measure incremental updating. the proposed approach shows its potentials on recent benchmark datasets compared to previous cost-sensitive and threshold fine-tuning based online learning algorithms.

关键词： Online learning Imbalanced data Multi-threshold F-measure

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Transactions on Computational Science XIV 1

引用

丛书名： Lecture Notes in Computer Science

1000年

作者： Marina L. Gavrilova C. J. Kenneth Tan Mir Abolfazl Mostafavi

ISBN: (数字)9783642252495

ISBN: (纸本)9783642252488

the 14th issue of the Transactions on Computational Science journal contains nine papers, all revised and extended versions of papers presented at the international symposium on Voronoi Diagrams 2010, held in Quebec City, Canada, in June 2010. the topics covered include: the development of new generalized Voronoi diagrams and algorithms including round-trip Voronoi diagrams, maximal zone diagrams, Jensen-Bregman Voronoi diagrams, hyperbolic Voronoi diagrams, and moving network Voronoi diagrams; new algorithms based on Voronoi diagrams for applications in science and engineering, including geosensor networks deployment and optimization and homotopic object reconstruction; and the application of Delaunay triangulation for modeling and representation of Cosmic Web and rain fall distribution.

关键词： Computer Graphics Algorithm Analysis and Problem Complexity Discrete Mathematics in Computer Science Artificial Intelligence data structures Numeric Computing

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Issues in Handling Complex data structures with NoSQL databases

Issues in Handling Complex Data Structures with NoSQL Databa...

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international symposium on Symbolic and Numeric algorithms for Scientific Computing (SYNASC)

作者： Santo Lombardo Elisabetta Di Nitto Danilo Ardagna Dipartimento di Elettronica e Informazione-DEI Politecnico di Milano Milano Italy

the emerging storage systems for Cloud Computing are so-called NoSQL databases. these systems lack the join mechanism and declarative queries approach but show high scalability and performance. While most famous ICT companies have no doubts about the usage of NoSQL, there is still a lack of methodologies and mechanisms to support the design and management of a NoSQL database starting from a complex high level data schema. the final goal of our work is to equip NoSQL databases with programming abstractions that make them close to the expressiveness of RDBMS, still maintaining their significant advantages in terms of scalability. In this paper we discuss the main shortcomings of using a NoSQL approach and we show how we plan to overcome them.

关键词： data models databases XML Scalability Synchronization Companies data mining

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Efficient Level-Based Top-Down data Cube Computation Using MapReduce 14th

Efficient Level-Based Top-Down Data Cube Computation Using M...

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14th international Conference on data Warehousing and Knowledge Discovery (DaWaK)

作者： Lee, Suan Kim, Jinho Moon, Yang-Sae Lee, Wookey Kangwon Natl Univ Dept Comp Sci Chunchon Kangwon South Korea Inha Univ Dept Ind Engn Inchon South Korea

ISBN: (纸本)9783662478042;9783662478035

data cube is an essential part of OLAP(On-Line Analytical Processing) to support efficiently multidimensional analysis for a large size of data. the computation of data cube takes much time, because a data cube with d dimensions consists of 2(d) (i.e., exponential order of d) cuboids. To build ROLAP (Relational OLAP) data cubes efficiently, many algorithms (e.g., GBLP, PipeSort, PipeHash, BUC, etc.) have been developed, which share sort cost and input data scan and/or reduce data computation time. Several parallel processing algorithms have been also proposed. On the other hand, MapReduce is recently emerging for the framework processing huge volume of data like web-scale data in a distributed/parallel manner by using a large number of computers (e.g., several hundred or thousands). In the MapReduce framework, the degree of parallel processing is more important to reduce total execution time than elaborate strategies like short-share and computation-reduction which existing ROLAP algorithms use. In this paper, we propose two distributed parallel processing algorithms. the first algorithm called MRLevel, which takes advantages of the MapReduce framework. the second algorithm called MRPipeLevel, which is based on the existing PipeSort algorithm which is one of the most efficient ones for top-down cube computation. (Top-down approach is more effective to handle big data, compared to others such as bottom-up and special data structures which are dependent on main-memory size.) the proposed MRLevel algorithm tries to parallelize cube computation and to reduce the number of data scan by level at the same time. the MRPipeLevel algorithm is based on the advantages of the MRLevel and to reduce the number of data scan by pipelining at the same time. We implemented and evaluated the performance of this algorithm under the MapReduce framework. through the experiments, we also identify the factors for performance enhancement in MapReduce to process very huge data.

关键词： data cube ROLAP MapReduce Hadoop Distributed parallel computing

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Using Metalearning for Prediction of Taxi Trip Duration Using Different Granularity Levels 1

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14th international symposium on Intelligent data Analysis (IDA)

作者： Zarmehri, Mohammad Nozari Soares, Carlos Univ Porto Fac Engn INESC TEC Rua Dr Roberto Frias 378 Oporto Portugal

ISBN: (数字)9783319244655

ISBN: (纸本)9783319244655

Trip duration is an important metric for the management of taxi companies, as it affects operational efficiency, driver satisfaction and, above all, customer satisfaction. In particular, the ability to predict trip duration in advance can be very useful for allocating taxis to stands and finding the best route for trips. A data mining approach can be used to generate models for trip time prediction. In fact, given the amount of data available, different models can be generated for different taxis. Given the difference between the data collected by different taxis, the best model for each one can be obtained with different algorithms and/or parameter settings. However, finding the configuration that generates the best model for each taxi is computationally very expensive. In this paper, we propose the use of metalearning to address the problem of selecting the algorithm that generates the model with the most accurate predictions for each taxi. the approach is tested on data collected in the Drive-In project. Our results show that metalearning can help to select the algorithm with the best accuracy.

关键词： Metalearning data mining Machine learning Trip duration prediction

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