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parallel AI and systems for the Edge (PAISE)

Proceedings - 2019 IEEE 33rd International Parallel and Dist...

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proceedings - 2019 IEEE 33rd international parallel and distributed Processing symposium Workshops, IPDPSW 2019 2019年 874-875页

作者： Beckman, Pete Sankaran, Rajesh Mathematics and Computer Science Division Argonne National Laboratory United States

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30th international Conference on Database and Expert systems Applications, DEXA 2019

30th International Conference on Database and Expert Systems...

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30th international Conference on Database and Expert systems Applications, DEXA 2019

ISBN: (纸本)9783030276171

The proceedings contain 66 papers. The special focus in this conference is on Database and Expert systems Applications. The topics include: Novel Node Importance Measures to Improve Keyword Search over RDF Graphs;Querying in a Workload-Aware Triplestore Based on NoSQL databases;Reverse Partitioning for SPARQL Queries: Principles and Performance Analysis;PFed: Recommending Plausible Federated SPARQL Queries;representing and Reasoning About Precise and Imprecise Time Points and Intervals in Semantic Web: Dealing with Dates and Time Clocks;context-Aware Multi-criteria Recommendation Based on Spectral Graph Partitioning;silverChunk: An Efficient In-Memory parallel Graph Processing System;a Modular Approach for Efficient Simple Question Answering Over Knowledge Base;scalable Machine Learning in the R Language Using a Summarization Matrix;energy Efficient Data Placement and Buffer Management for Multiple Replication;ML-PipeDebugger: A Debugging Tool for Data Processing Pipelines;correlation Set Discovery on Time-Series Data;anomaly Subsequence Detection with Dynamic Local Density for Time Series;trajectory Similarity Join for Spatial Temporal Database;multiviewpoint-Based Agglomerative Hierarchical Clustering;Triplet-CSSVM: Integrating Triplet-Sampling CNN and Cost-Sensitive Classification for Imbalanced Image Detection;discovering Partial Periodic High Utility Itemsets in Temporal databases;using Mandatory Concepts for Knowledge Discovery and Data Structuring;topological Data Analysis with ϵ -net Induced Lazy Witness Complex;analyzing Sequence Pattern Variants in Sequential Pattern Mining and Its Application to Electronic Medical Record systems;querying Knowledge Graphs with Natural Languages;composing distributed Data-Intensive Web Services Using Distance-Guided Memetic Algorithm;keyword Search Based Mashup Construction with Guaranteed Diversity;adaptive Caching for Data-Intensive Scientific Workflows in the Cloud;Succinct BWT-Based Sequence Prediction.

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The Case of a Novel Operational distributed Storage Service for Big Data in a Semiconductor Wafer Fabrication Foundry 24

The Case of a Novel Operational Distributed Storage Service ...

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24th IEEE international Conference on parallel and distributed systems (ICPADS)

作者： Chang, Andy R. K. Chen, Yu-Ling Huang, Yen-Zhou Hsiao, Hung-Chang Hsu, Michael Lee, Chia-Chee Lee, Hsin-Yin Shih, Wei-An Su, Huan-Ping Tsai, Chia-Ping Tseng, Kuan-Po United Microelect Corp Div Informat Technol Hsinchu Taiwan Natl Cheng Kung Univ Comp Sci & Informat Engn Tainan Taiwan Apache HBase Committer & Project Management Comm Tainan Taiwan

ISBN: (纸本)9781538673089

We present in this paper a novel infrastructural service based on Hadoop for big data storage and computing in a Taiwan's semiconductor wafer fabrication foundry. The service is named Hadoop data service (HDS), which has been built and operated in production systems for 3.5 years. It evolves over time by incrementally accommodating users' requirements. HDS is a web-based distributed big data storage facility. Users simply rely on HDS to access data objects stored in Hadoop with the HTTP protocol. In addition, HDS is scalable and reliable. Moreover, HDS is efficient and effective by intelligently selecting either Hadoop distributed file system (HDFS) or database (HBase) for publishing data objects. Specifically, HDS is transparent to existing analytics and data inquiry applications, such as Spark and Hive. This paper discusses the design and implementation features for HDS. The performance metrics of HDS are also demonstrated.

关键词： Hadoop Big data storage distributed file systems distributed databases Web service computing Middleware

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An Efficient Segment Grouping Approach for Active Disk-based Storage systems 24

An Efficient Segment Grouping Approach for Active Disk-based...

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24th IEEE international Conference on parallel and distributed systems (ICPADS)

作者： Xu, Quanqing Xi, Weiya Yong, Khai Leong Jin, Chao ASTAR Inst High Performance Comp Singapore Singapore ASTAR Data Storage Inst Singapore Singapore ASTAR Inst Infocomm Res Singapore Singapore

ISBN: (纸本)9781538673089

Active storage is an excellent big data storage and analysis platform by exploring storage nodes' computing resources since it especially fits read-intensive operations. Active disks have a similar idea with active storage except utilizing the computing resources embedded in disk drives. Active disk-based storage systems have to evenly distribute data and workload among many storage devices so that we could efficiently use available resources to maximize system performance. We propose an efficient Segment Grouping (SG) approach in this paper. SG is a pseudo-random data distribution approach that is designed for active disk-based storage architecture. It is excellent at load balancing because it utilizes hash functions. Any party can independently compute the location of any data segment. SG efficiently maps segment objects to storage devices using the hash functions, and it only needs a compact and hierarchical description of storage devices in the active disk-based storage system. It only needs knowledge of the segment placement policy, and all the required nietadata is little and mostly static. Our experimental results demonstrate that it is efficient and scalable by conducting performance evaluation.

关键词： Disk drives Hash functions Computer architecture Metadata distributed databases Reed-Solomon codes Load management

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A hybrid information extraction approach exploiting structured data within a text mining process 18

A hybrid information extraction approach exploiting structur...

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Datenbanksysteme fur Business, Technologie und Web, BTW 2019 and 18. Fachtagung des GI-Fachbereichs "Datenbanken und Informationssysteme", DBIS 2019 - Database systems for Business, Technology and Web, BTW 2019 and 18th symposium of the GI Department "databases and Information systems", DBIS 2019

作者： Kiefer, Cornelia Reimann, Peter Mitschang, Bernhard University of Stuttgart Graduate School of Excellence Advanced Manufacturing Engineering Nobelstr. 12 Germany University of Stuttgart Institute for Parallel and Distributed Systems Universitätsstraße 38 Germany

ISBN: (纸本)9783885796831

Many data sets encompass structured data fields with embedded free text fields. The text fields allow customers and workers to input information which cannot be encoded in structured fields. Several approaches use structured and unstructured data in isolated analyses. The result of isolated mining of structured data fields misses crucial information encoded in free text. The result of isolated text mining often mainly repeats information already available from structured data. The actual information gain of isolated text mining is thus limited. The main drawback of both isolated approaches is that they may miss crucial information. The hybrid information extraction approach suggested in this paper adresses this issue. Instead of extracting information that in large parts was already available beforehand, it extracts new, valuable information from free texts. Our solution exploits results of analyzing structured data within the text mining process, i.e., structured information guides and improves the information extraction process on textual data. Our main contributions comprise the description of the concept of hybrid information extraction as well as a prototypical implementation and an evaluation with two real-world data sets from aftersales and production with English and German free text fields. © 2019 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Data mining

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Session details: High Performance distributed systems (Best Paper Nominees) 19

Session details: High Performance Distributed Systems (Best ...

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proceedings of the 28th international symposium on High-Performance parallel and distributed Computing

作者： Evgenia Smirni Ali R. Butt College of William and Mary USA Virginia Tech USA

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ISBN: (纸本)9781450366700

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Understanding trolls with efficient analytics of large graphs in Neo4j 18

Understanding trolls with efficient analytics of large graph...

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作者： Allen, David Hodler, Amy E. Hunger, Michael Knobloch, Martin Lyon, William Needham, Mark Voigt, Hannes Neo4j San Mateo United States Neo4j Dresden Germany Avantgarde Labs Dresden Germany Neo4j London United Kingdom Neo4j Leipzig Germany

ISBN: (纸本)9783885796831

Analytics of large graph data set has become an important means of understanding and influencing the world. The use of graph database technology in the international Consortium of Investigative Journalists' (ICIJ) investigation of the Panama Papers and Paradise Papers or in cancer research illustrates how analysing graph-structured data helps to uncover important but hidden relationships. A very current example in that regards shows how graph analytics can help shed light on the operations of social media troll-networks, e.g. on Twitter. In similar fashion, graph analytics can help enterprises to unearth hidden patterns and structures within connected data, to make more accurate predictions and faster decisions. All this requires efficient graph analytics well-integrated with management of graph data. The Neo4j Graph Platform provides such an environment. It provides transactional processing and analytical processing of graph data including data management and analytics tooling. A central element for graph analytics in the Graph Platform are the Neo4j graph algorithms. Neo4j graph algorithms provide efficiently implemented, parallel versions of common graph algorithms, integrated and optimized for the Neo4j transactional database. In this paper, we will describe the design and integration Neo4j Graph Algorithms, demonstrate its utility of our approach with a Twitter Troll analysis, and show case its performance with a few experiments on large graphs. © 2019 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Graph databases

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Chameleon: An Adaptive Wear Balancer for Flash Clusters 32

Chameleon: An Adaptive Wear Balancer for Flash Clusters

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32nd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Zhao, Nannan Anwar, Ali Cheng, Yue Salman, Mohammed Li, Daping Wan, Jiguang Xie, Changsheng He, Xubin Wang, Feiyi Butt, Ali R. Virginia Tech Blacksburg VA 24061 USA George Mason Univ Fairfax VA 22030 USA Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Temple Univ Philadelphia PA 19122 USA Oak Ridge Natl Lab Oak Ridge TN USA

ISBN: (纸本)9781538643686

NAND flash-based Solid State Devices (SSDs) offer the desirable features of high performance, energy efficiency, and fast growing capacity. Thus, the use of SSDs is increasing in distributed storage systems. A key obstacle in this context is that the natural unbalance in distributed I/O workloads can result in wear imbalance across the SSDs in a distributed setting. This, in turn can have significant impact on the reliability, performance, and lifetime of the storage deployment. Extant load balancers for storage systems do not consider SSD wear imbalance when placing data, as the main design goal of such balancers is to extract higher performance. Consequently, data migration is the only common technique for tackling wear imbalance, where existing data is moved from highly loaded servers to the least loaded ones. In this paper, we explore an innovative holistic approach, Chameleon, that employs data redundancy techniques such as replication and erasure-coding, coupled with endurance-aware write offloading, to mitigate wear level imbalance in distributed SSD-based storage. Chameleon aims to balance the wear among different flash servers while meeting desirable objectives of: extending life of flash servers;improving I/O performance;and avoiding bottlenecks. Evaluation with a 50 node SSD cluster shows that Chameleon reduces the wear distribution deviation by 81% while improving the write performance by up to 33%.

关键词： Servers Performance evaluation Redundancy Monitoring distributed databases Arrays Encoding"

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A Robust Fault-Tolerant and Scalable Cluster-wide Deduplication for Shared-Nothing Storage systems 26

A Robust Fault-Tolerant and Scalable Cluster-wide Deduplicat...

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26th IEEE international symposium on Modeling, Analysis and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Khan, Awais Lee, Chang-Gyu Hamandawana, Prince Park, Sungyong Kim, Youngjae Sogang Univ Seoul South Korea Ajou Univ Suwon South Korea

ISBN: (纸本)9781538668863

Deduplication has been largely employed in distributed storage systems to improve space efficiency. Traditional deduplication research ignores the design specifications of shared-nothing distributed storage systems such as no central metadata bottleneck, scalability, and storage rebalancing. Further, deduplication introduces transactional changes, which are prone to errors in the event of a system failure, resulting in inconsistencies in data and deduplication metadata. In this paper, we propose a robust, fault-tolerant and scalable cluster-wide deduplication that can eliminate duplicate copies across the cluster. We design a distributed deduplication metadata shard which guarantees performance scalability while preserving the design constraints of shared-nothing storage systems. The placement of chunks and deduplication metadata is made cluster-wide based on the content fingerprint of chunks. To ensure transactional consistency and garbage identification, we employ a flag-based asynchronous consistency mechanism. We implement the proposed deduplication on Ceph. The evaluation shows high disk-space savings with minimal performance degradation as well as high robustness in the event of sudden server failure.

关键词： Servers Metadata Data structures databases Scalability Fault tolerance Fault tolerant systems

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HPDC 2018 - proceedings of the 2018 international symposium on high-performance parallel and distributed computing

HPDC 2018 - Proceedings of the 2018 international symposium ...

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27th ACM international symposium on High-Performance parallel and distributed Computing, HPDC 2018

ISBN: (纸本)9781450357852

The proceedings contain 22 papers. The topics discussed include: hard real-time scheduling for parallel run-time systems;ABFR: convenient management of latent error resilience using application knowledge;improving performance of iterative methods by lossy checkponting;CommAnalyzer: automated estimation of communication cost and scalability on HPC clusters from sequential code;a high-performance connected components implementation for GPUs;process-in-process: techniques for practical address-space sharing;thread-local concurrency: a technique to handle data race detection at programming model abstraction;and profiling distributed systems in lightweight virtualized environments with logs and resource metrics.

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