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GISQF: An Efficient Spatial Query processing System 7

GISQF: An Efficient Spatial Query Processing System

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7th IEEE international conference on Cloud Computing (CLOUD)

作者： Al-Naami, Khaled Mohammed Seker, Sadi Khan, Latifur Univ Texas Dallas Dept Comp Sci Dallas TX 75083 USA Istanbul Medeniyet Univ Dept Business Istanbul Turkey

ISBN: (纸本)9781479950638

Collecting observations from all international news coverage and using TABARI software to code events, the Global Database of Event, Language, and Tone (GDELT) is the only global political georeferenced event dataset with 250+ million observations covering all countries in the world from January 1, 1979 to the present with daily updates. The purpose of this widely used dataset is to help understand and uncover spatial, temporal and perceptual trends and behaviors of the social and international system. To query such big geospatial data, traditional RDBMS can no longer be used and the need for parallel distributed solutions has become a necessity. MapReduce paradigm has proved to be a scalable platform to process and analyze Big Data in the cloud. Hadoop as an implementation of MapReduce is an open source application that has been widely used and accepted in academia and industry. However, when dealing with Spatial Data, Hadoop is not equipped well and falls short as it doesnt perform efficiently in terms of running time. SpatialHadoop is an extension of Hadoop with the support of spatial data. In this paper, we present Geographic Information System Querying Framework (GISQF) to process Massive Spatial Data. This framework has been built on top of the open source SpatialHadoop system which exploits two-layer spatial indexing techniques to speed up query processing. We show how this solution outperforms Hadoop query processing by orders of magnitude when applying queries on GDELT dataset with a size of 60 GB. We show the results for three types of queries, Longitude Latitude Point queries, Circle-Area queries, and Aggregation queries.

关键词： Query processing

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A Novel Framework for JPEG Image Compression Using Baseline Coding with parallel Process 5

A Novel Framework for JPEG Image Compression Using Baseline ...

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5th IEEE international conference on Computational Intelligence and Computing Research (IEEE ICCIC)

作者： Naidu, Balaka Ramesh Babu, M. S. Prasad AITAM Engn Coll Autonomous Dept IT Tekkali India Andhra Univ Dept Comp Sci & Syst Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9781479939756

Digital image communication is being used in many areas like Remote sensing, Weather forecast, Medical image Satellite applications. Data transformation of images in network is a difficult task because of their size and requirement of image quality. To fulfill this requirement several researchers proposed different Compression and Decompression techniques, still they required further exploration. In this paper, we proposed parallel processing Technique (PPT) in Joint Photographic Experts Group (JPEG) compression using Discrete Cosine Transform (DCT). The main goal of this proposed method is to reduce the storage space, execution time and effective utilization of bandwidth. Experiments are performed on benchmark Red-Green-Blue (RGB) images. We performed time calculations for Encoder, Decoder and also evaluated Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR) and Compression Ratio (CR) values.

关键词： Discrete Cosine Transform Peak Signal-to-Noise Ratio Compression Ratio Root Mean Square Error Joint Photographic Experts Group

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GPUs for real-time processing in HEP trigger systems

GPUs for real-time processing in HEP trigger systems

引用

15th international Workshop on Advanced Computing and Analysis techniques in Physics Research (ACAT)

作者： Ammendola, R. Biagioni, A. Deri, L. Fiorini, M. Frezza, O. Lamanna, G. Lo Cicero, F. Lonardo, A. Messina, A. Sozzi, M. Pantaleo, F. Paolucci, P. S. Rossetti, D. Simula, F. Tosoratto, L. Vicini, P. Ist Nazl Fis Nucl Ple A Moro 2 I-00185 Rome Italy Univ Pisa Pisa Italy Univ Ferrara I-44122 Ferrara Italy INFN Pisa I-56127 Pisa Italy Univ Roma 1 I-00185 Rome Italy CERN Geneva Switzerland

We describe a pilot project (GAP - GPU Application Project) for the use of GPUs (Graphics processing units) for online triggering applications in High Energy Physics experiments. Two major trends can be identified in the development of trigger and DAQ systems for particle physics experiments: the massive use of general-purpose commodity systems such as commercial multicore PC farms for data acquisition, and the reduction of trigger levels implemented in hardware, towards a fully software data selection system ("trigger-less"). The innovative approach presented here aims at exploiting the parallel computing power of commercial GPUs to perform fast computations in software not only in high level trigger levels but also in early trigger stages. General-purpose computing on GPUs is emerging as a new paradigm in several fields of science, although so far applications have been tailored to the specific strengths of such devices as accelerators in offline computation. With the steady reduction of GPU latencies, and the increase in link and memory throughputs, the use of such devices for real-time applications in high energy physics data acquisition and trigger systems is becoming relevant. We discuss in detail the use of online parallel computing on GPUs for synchronous low-level triggers with fixed latency. In particular we show preliminary results on a first test in the CERN NA62 experiment. The use of GPUs in high level triggers is also considered, the CERN ATLAS experiment being taken as a case study of possible applications.

关键词： Graphics processing unit

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Hybrid Multicore Algorithms for Some Semi-Numerical applications and Graphs

Hybrid Multicore Algorithms for Some Semi-Numerical Applicat...

引用

作者： Dip Sankar Banerjee International Institute of Information Technology Hyderabad

学位级别：博士

The computing industry has undergone several paradigm shifts in the last few decades. Fueled by the need of faster computing, larger data and real time processing needs parallel computing has emerged as one of the dominant paradigms. Motivated by the success achieved in distributed computing mod- els and the limitations faced by single core processors, parallel computing is the only alternative for building faster computers. parallel computing is one of the most challenging areas computer science in the present and developing algorithms and optimization techniques for utilizing the processing power present in a current generation parallel computer is still a very exciting area for research. The parallel computing industry underwent a massive shift with the conventional sequential com- puters hitting the power wall. It led to the development of multicore and many-core computing chips that had multiple sequential computing cores packed into a single chip. The immediate impact was the need for (re)designing sequential algorithms in order to utilize the computing power of such chips. Combined with the intricate memory and cache structures, parallel algorithms require a higher degree of engineering for the most optimal performance. The many-core revolution started with the release of Graphics processing Units (GPU) which had a large number of compute cores and offered massive parallelism. With the evolution of the many-core chips, the GPUs found application in graphics, gaming as well as general purpose computation. In the same time frame, the Central processing Units (CPU) too under went a sea of innovation and emerged as more powerful and mature computing machines. However, the multicore CPUs were mostly ignored in its initial days. With the advancementof accelerator platforms, the CPUs and GPUs are now able to communicate in a more efficient manner. In the recent times there has been quite a few works such as the ones in [79, 91, 43] that shows that hybrid algorithms

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From Graphs to Tables the Design of Scalable Systems for Graph Analytics 23

From Graphs to Tables the Design of Scalable Systems for Gra...

引用

23rd international conference on World Wide Web (WWW)

作者： Gonzalez, Joseph E. Univ Calif Berkeley AMPLab Berkeley CA 94720 USA

ISBN: (纸本)9781450327459

From social networks to language modeling, wing scale and importance of graph data has driven thedevelopwenl of numerous new graph-parallel systems (e.g Giraph and GraphLab). By restricting the types of computation that can be expressed and by introducing new techniques to partition and distribute graphs, these systems can efficiently execute sophisticated graph algorithms orders of magnitude faster than more general data-parallel systems. However, the same restrictions that enable graph-parallel systems to achieve substantial performance gains also limit their ability to express many of the important stages in a typical grapil-analytics pipeline. Moreover, while graph parallel systems are optimized for iterative diffusion algorithms like PageRank they are not well suited for more basic tasks like constructing the graph, modifying its structure, or expressing computation that, spans multiple graphs. While existing systems address specific stages of a typical graphanalytics pipeline, they do not address the entire pipeline, forcing the user to deal with multiple systems, complex and brittle file interfaces, and inefficient data-movement, and duplication. To fill the need for a holistic approach to graph-analytics we introduce GraphX, which unifies graph and data parallel computation under a single API and system. GraphX recasts advances in graph-processing in the context of relational algebra and distributed join optimization enabling more general data-parallel systems to process graphs efficiently. We evaluate the GraphX system on several real world tasks and show that its end-to-end performance can exceed that of specialized systems. This talk describes recent work at the PC Berkeley AM-PLab in collaboration with Reynold S. Xin, Ankur Dave, Daniel Crankshaw, Michael J. Franklin, and Ton Stolen.

关键词： distributed computation graph-parallel

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A faster and intelligent Steganography Detection using Graphics processing Unit in cloud

A faster and intelligent Steganography Detection using Graph...

引用

international conference on High Performance Computing and applications (ICHPCA)

作者： Tiwary, Mayank Priyadarshini, Rojalina Misra, Rachita CV Raman Coll Engn Dept Informat Technol Bhubaneswar Orissa India

ISBN: (纸本)9781479959587

Now a day's security in big data is a major concern and there is always a threat to the integrity of data. Contents of the data are often challenged by several external attacks. We cannot deny the fact of presence of unnecessary data in form of secret messages. Through steganographic techniques, a text messages can be hidden inside the images, video files, mp3's, etc. So, there is always a need to store disinfected data in cloud database. But researchers find it very difficult to differentiate between infected and disinfected data, when the size of data is huge;it takes lot of time to process the infected data and remove the noises in conventional uniprocessing system is used. This paper introduces faster and artificially intelligent steganalysis techniques based on parallel algorithms. The technique involves image estimation based on order statistic filters, feature generation from the estimated image and finally classifying the generated images into stego and non-stego type. An artificial neural network is used as a machine classifier on the generated images to detect either stego or clean images. The whole process in a single processor environment is computationally intensive. Therefore the technique offloads the calculations to the high performance Graphics processing Units for faster operations. This piece of work uses the Compute Unified Device Architecture (CUDA) for the integration process.

关键词： CUDA GPU JPEG Steganalysis Image Estimation Feature Generation

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A Collaboration Model for Community-Based Software Development with Social Machines 10

A Collaboration Model for Community-Based Software Developme...

引用

10th IEEE international conference on Collaborative Computing Networking applications and Worksharing

作者： Murray-Rust, Dave Scekic, Ognjen Truong, Hong-Linh Robertson, Dave Dustdar, Schahram Univ Edinburgh Sch Informat Ctr Intelligent Syst & Applicat Edinburgh EH8 9YL Midlothian Scotland Vienna Univ Technol Distributed Syst Grp Vienna Austria

ISBN: (纸本)9781631900433

Today's crowdsourcing systems are predominantly used for processing independent tasks with simplistic coordination. As such, they offer limited support for handling complex, intellectually and organizationally challenging labour types, such as software development. In order to support crowdsourcing of the software development processes, the system needs to enact coordination mechanisms which integrate human creativity with machine support. While workflows can be used to handle highly structured and predictable labour processes, they are less suitable for software development methodologies where unpredictability is an unavoidable part the process. This is especially true in phases of requirement elicitation and feature development, when both the client and development communities change with time. In this paper we present models and techniques for coordination of human workers in crowdsourced software development environments. The techniques augment the existing Social Compute Unit (SCU) concept a general framework for management of ad-hoc human worker teams with versatile coordination protocols expressed in the Lightweight Social Calculus (LSC). This approach allows us to combine coordination and quality constraints with dynamic assessments of software-user's desires, while dynamically choosing appropriate software development coordination models.

关键词： Crowdsourcing

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ASPLOS 2014 - 19th international conference on Architectural Support for Programming Languages and Operating Systems

ASPLOS 2014 - 19th International Conference on Architectural...

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19th international conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2014

ISBN: (纸本)9781450323055

The proceedings contain 49 papers. The topics discussed include: Rhythm: harnessing data parallel hardware for server workloads;Paraprox: pattern-based approximation for data parallel applications;virtual ghost: protecting applications from hostile operating systems;finding Trojan message vulnerabilities in distributed systems;REF: resource elasticity fairness with sharing incentives for multiprocessors;underprovisioning backup power infrastructure for datacenters;comprehending performance from real-world execution traces: a device-driver case;leveraging the short-term memory of hardware to diagnose production-run software failures;prototyping symbolic execution engines for interpreted languages;Q100: the architecture and design of a database processing unit;DianNao: a small-footprint high-throughput accelerator for ubiquitous machine-learning;disengaged scheduling for fair, protected access to fast computational accelerators;and KVM/ARM: the design and implementation of the Linux ARM hypervisor.

关键词： Computer operating systems

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distributed K-Distance Indexing Approach for Efficient Shortest Path Discovery on Large Graphs

Distributed K-Distance Indexing Approach for Efficient Short...

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19th international conference on Database Systems for Advanced applications (DASFAA)

作者： Hong, Jihye Kim, Hyunwook Nawaz, Waqas Park, Kisung Jeong, Byeong-Soo Lee, Young-Koo Kyung Hee Univ Dept Comp Engn Yongin 449701 South Korea

ISBN: (纸本)9783662439845;9783662439838

The emergence of large real life networks such as social networks, web page links, and traffic networks exhibits complex graph structures with millions of vertices and edges. Among many operations for exploiting these graphs, the shortest path discovery is a major and expensive one. Besides the in-memory approaches, many efficient shortest path computation methods have been developed on top of distributed and parallel platforms. Pregel, a bulk synchronous parallel framework, is one of them for processing large graphs. The known shortest path computation approach with Pregel is computation intensive and unable to target real-time services. In this paper, we propose a Pregel based efficient k-distance index technique that allows efficient single pair shortest path discovery. We reduce the network cost and unnecessary operations by transmitting more information in a single superstep. The extensive experiments on both real and synthetic datasets reveal the superiority of the proposed approach.

关键词： Shortest path discovery Large graph Graph indexing Pregel

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An efficient processing of k-dominant skyline query in MapReduce 14

An efficient processing of k-dominant skyline query in MapRe...

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1st international Workshop on Bringing the Value of "Big Data" to Users, Data4U 2014 - In Conjunction with the 40th international conference on Very Large Data Bases

作者： Tian, Hao Siddique, Md. Anisuzzaman Morimoto, Yasuhiko Graduate School of Engineering Hiroshima University 1-7-1 Kagamiyama Higashi-Hiroshima 739-8521 Japan

ISBN: (纸本)9781450331869

Filtering uninteresting data is important to utilize "big data". Skyline query is one of popular techniques to filter uninteresting data, in which it selects a set of points that are not dominated by another from a given large database. However, a skyline query often retrieves too many points to analyze intensively especially for high-dimensional dataset. In order to solve the problem, k-dominant skyline queries have been introduced, which can control the number of retrieved points. However, conventional algorithms for computing k-dominant skyline queries are not well suited for parallel and distributed environments, such as the MapReduce framework. In this paper we considered an efficient parallel algorithm to process k-dominant skyline query in the MapReduce framework. Extensive experiments are conducted to evaluate the algorithm under different settings of data distribution, dimensionality, and cardinality. © 2014 ACM.

关键词： MapReduce

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