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检索条件"主题词=distributed databases"

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Differentiating data collection for cloud environment monitoring

Differentiating data collection for cloud environment monito...

引用

IFIP/IEEE International Symposium on Integrated Network Management

作者： You Meng Zhongzhi Luan Zhendong Cheng Depei Qian Sino-German Joint Software Institute (JSI) Beihang University Beijing China

ISBN: (纸本)9781467352291

In a growing number of information processing applications, data takes the form of continuous data streams rather than traditional stored databases. Monitoring systems that seek to provide monitoring services in cloud environment must be prepared to deal gracefully with huge data collection without compromising system performance. In this paper, we show that by using a concept of urgent data, system can shorten the response time for most `urgent' queries while guarantee lower bandwidth consumption. We argue that monitoring data can be treated differently. Some data capture critical system events, the arrival of these data will significantly influence the monitoring reaction speed, we call them urgent data. High speed urgent data collection would help system to act in real time when facing fatal error. On the other hand, slowing down the collection speed of others may render more bandwidth. Then several urgent data collection strategies that focus on reducing the urgent data volume are also proposed and evaluated to guarantee the efficiency.

关键词： Monitoring Data collection Bandwidth distributed databases Filtering algorithms Real-time systems Engines

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An Objective Function for Dividing Class Family in NoSQL Database

An Objective Function for Dividing Class Family in NoSQL Dat...

引用

International Conference on Computer Science and Service System (CSSS)

作者： Zhikun Chen Shuqiang Yang Hui Zhao Hong Yin Computer Science School National University of Defense Technology Changsha China

ISBN: (纸本)9781467307215

Column Family has been used in NoSQL Database, and we have to think over how to divide the Column Family in the designing of NoSQL Database. But the dividing of column family is similar to the vertical partitioning in the distributed database. And there are some object functions of vertical partitioning in the database system, but they do not fit to the NoSQL Database. In this paper we will propose an objective function for dividing class family in NoSQL database. The objective function derived generalizes and subsumes earlier work on vertical partitioning in distributed database and the scenarios of NoSQL database. Furthermore, the approach proposed in this paper is shown to be useful to evaluate the cost of dividing column family.

关键词： Partitioning algorithms Linear programming distributed databases Internet Algorithm design and analysis Active appearance model

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Experimenting with artificial neural networks-artificial intelligence mini-tutorial. 3

Experimenting with artificial neural networks-artificial int...

引用

Annual IEEE Symposium on Computer-Based Medical Systems

作者： E.A. Irani J.M. Long J.R. Slagle University of Minnesota Minneapolis MN USA

For pt.1 see ibid., p.33-42; for pt.2 see ibid., p.43-4. To show how neural nets work, experiences in an experiment using them are described. The experiment involves using AI techniques to assist in the discovery of causal relationships between the variables existing in a large clinical trial database. A peripheral vascular disease database was used to acquire a feeling for the complexities involved in developing a distributed encoding scheme and to determine the computational resources required to train a neural net for the type of data used. By testing several models the effects of changes in the encoding scheme and the number of training iterations the system needed to predict the appropriate change needed could be determined. These results were compared to the information available from other analyses of the same data. The generative capabilities of the system were then tested by training it over one sample of cases and applying it to cases it had not encountered before. Some idea of the computational resources needed in terms of time and memory capacity was developed.< >

关键词： Tutorial Artificial neural networks Encoding System testing Artificial intelligence Clinical trials Atherosclerosis distributed databases Computer peripherals distributed computing

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Proposed distributed System Architecture and Preliminary Measurements for the Detection of Trapped Individuals Inside Motorway Tunnels

Proposed Distributed System Architecture and Preliminary Mea...

引用

International Conference on Information, Communication and Networks (ICICN)

作者： Sotirios Kontogiannis Anestis Kastellos George Kokkonis Theodosios Gkamas Christos Pikridas School of Rural and Surveying Eng. Aristotle University of Thessaloniki Thessaloniki Greece University of Ioannina Ioannina Greece University of Western Macedonia Grevena Greece

ISBN: (纸本)9781665411578

This paper presents the architecture of a distributed system based on Bluetooth detectors for locating vehicles and people trapped in a motorway tunnel accident. The distributed architecture of the proposed system includes an automated dynamic way of adding or removing detectors from the system, a non-relational database for storing sensory measurements and an-easy-to-use graphical interface for displaying real-time or close to real-time detection information. The installation of the proposed system detectors is installed on the escape exits inside tunnels. The proposed system has been experimentally placed to both directions of a tunnel at the EGNATIA motorway in Greece and its functionality has been validated. Then, with the use of existing tunnel TMS inductive loop detectors, the accuracy of the detector measurements has been evaluated.

关键词： Bluetooth Conferences Systems architecture Estimation distributed databases Detectors Receivers

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CliZ: Optimizing Lossy Compression for Climate Datasets with Adaptive Fine-tuned Data Prediction

CliZ: Optimizing Lossy Compression for Climate Datasets with...

引用

International Symposium on Parallel and distributed Processing (IPDPS)

作者： Zizhe Jian Sheng Di Jinyang Liu Kai Zhao Xin Liang Haiying Xu Robert Underwood Shixun Wu Jiajun Huang Zizhong Chen Franck Cappello University of California Riverside CA USA Argonne National Laboratory Lemont IL USA Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA Florida State University Tallahassee FL USA University of Kentucky Lexington KY USA National Center for Atmospheric Research Boulder CO USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Benefiting from the cutting-edge supercomputers that support extremely large-scale scientific simulations, climate research has advanced significantly over the past decades. However, new critical challenges have arisen regarding efficiently storing and transferring large-scale climate data among distributed repositories and databases for post hoc analysis. In this paper, we develop CliZ, an efficient online error-controlled lossy compression method with optimized data prediction and encoding methods for climate datasets across various climate models. On the one hand, we explored how to take advantage of particular properties of the climate datasets (such as mask-map information, dimension permutation/fusion, and data periodicity pattern) to improve the data prediction accuracy. On the other hand, CliZ features a novel multi-Huffman encoding method, which can significantly improve the encoding efficiency. Therefore significantly improving compression ratios. We evaluated CliZ versus many other state-of-the-art error-controlled lossy compressors (including SZ3, ZFP, SPERR, and QoZ) based on multiple real-world climate datasets with different models. Experiments show that CliZ outperforms the second-best compressor (SZ3, SPERR, or QoZ1.1) on climate datasets by 20%-200% in compression ratio. CliZ can significantly reduce the data transfer cost between the two remote Globus endpoints by 32%-38%.

关键词： distributed processing Costs distributed databases Predictive models Data transfer Encoding Compressors

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Application-Assisted Writeback for Hadoop Clusters

Application-Assisted Writeback for Hadoop Clusters

引用

IEEE International Conference on Cluster Computing

作者： Jungi Jeong Daewoo Lee Seungryoul Maeng School of Computing KAIST Daejeon Korea National Security Research Institute Daejeon Korea

ISBN: (纸本)9781509036547

Achieving low and predictable execution time of short jobs in Hadoop clusters has gained a great attention due to their importance on system productivity and user experience. However, one major contributor that makes it challenging is diskI/O interference. We observed that disk writes unintentionally block latency-sensitive short jobs and cause unexpected high latency. Unfortunately, previous research including a disk read bandwidth throttling do not suffice to mitigate such interference. This paper proposes the application-assisted writeback that allows the Hadoop framework to control asynchronous writebacks. We applied the application-assisted writeback to optimize short jobs by preventing asynchronous writebacks when they are expected to interfere with short jobs. Our evaluation resultedin reduction on the average and 99-th percentile execution time of short jobs by 22% and 40%, respectively, without imposing non-acceptable overheads on co-running throughput-oriented batch jobs. In addition, combining the application-assisted writeback with the user-level disk bandwidth throttling can further accelerate short jobs.

关键词： Interference Kernel Bandwidth Throughput distributed databases Productivity

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CDRM: A Cost-Effective Dynamic Replication Management Scheme for Cloud Storage Cluster

CDRM: A Cost-Effective Dynamic Replication Management Scheme...

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IEEE International Conference on Cluster Computing

作者： Qingsong Wei Bharadwaj Veeravalli Bozhao Gong Lingfang Zeng Dan Feng Data Storage Institute Agency for Science Technology and Research (ASTAR) Singapore National University of Singapore Singapore Huazhong University of Science and Technology China

Data replication has been widely used as a mean of increasing the data availability of large-scale cloud storage systems where failures are normal. Aiming to provide cost-effective availability, and improve performance and load-balancing of cloud storage, this paper presents a cost-effective dynamic replication management scheme referred to as CDRM. A novel model is proposed to capture the relationship between availability and replica number. CDRM leverages this model to calculate and maintain minimal replica number for a given availability requirement. Replica placement is based on capacity and blocking probability of data nodes. By adjusting replica number and location according to workload changing and node capacity, CDRM can dynamically redistribute workloads among data nodes in the heterogeneous cloud. We implemented CDRM in Hadoop distributed File System (HDFS) and experiment results conclusively demonstrate that our CDRM is cost effective and outperforms default replication management of HDFS in terms of performance and load balancing for large-scale cloud storage.

关键词： Availability Clouds Silicon distributed databases Bandwidth File systems Parallel processing

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Web Mining from Interpretable Compressed Representation of Sparse Web

Web Mining from Interpretable Compressed Representation of S...

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2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Connor C.J. Hryhoruk Carson K. Leung Department of Computer Science University of Manitoba Winnipeg MB Canada

Large datasets often contain computational constraints when under the non-trivial extraction of implicit, previously unknown, and potentially useful information. These datasets are everywhere, with a popular example being the World Wide Web. It acts as a mass data producer and consumer across multiple devices in a distributed fashion worldwide, containing massive amounts of data. The discovery of knowledge on the Web requires web intelligence solutions, which take advantages of data mining and data science. In the case of web mining, the mining of web structures provides commonly recommended web pages to web surfers by examining incoming and outgoing links on web pages. The overall size of the web is however sparse. Sparsity of the web comes from a high number of vertex nodes (i.e., web pages), with a small number of directed edges (i.e., incoming and outgoing hyperlinks between web pages). In this paper, we present a solution to the mining of frequent patterns from the sparse web. From the sparsity of the web, web pages are captured in compressed bitmaps that are then mined for discovery of these patterns. Our bitmap model ensures readability, flexibility, and allows for the capturing of important information across multiple 31-bit groups. The mining process is demonstrated on real-life web data to present its capacity of mining for interesting patterns from interpretable compressed representation of sparse data.

关键词： Knowledge engineering Memory management Web pages distributed databases Web mining Data science Data models

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Meta-level control of rule execution in a parallel and distributed expert database system

Meta-level control of rule execution in a parallel and distr...

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International Workshop on Research Issues in Data Engineering

作者： H.M. Dewan S.J. Stolfo Dept. of Comput. Sci. Columbia Univ. New York NY USA Department of Computer Science Columbia University New York NY USA

The PARADISER system is a parallel and distributed programming environment for expert databases. It extends the memory model of expert systems to disk-resident databases, and adopts parallel and distributed processing to address expert system scaling issues. The kernel rule language, PARULEL, has parallel execution semantics and a metarule facility that allows for user-specified "programmable" conflict resolution. Under a parallel and distributed processing model of a rule language that has inherently parallel execution semantics, issues of distributed control assume paramount importance, since database consistency and synchronization must be guaranteed. We discuss various alternative strategies for implementing metarules within the PARADISER architecture.< >

关键词： Control systems Database systems distributed databases Expert systems distributed processing Load management Protocols Computer science Kernel distributed control

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A Mobility Forecasting Framework with Vertical Federated Learning

A Mobility Forecasting Framework with Vertical Federated Lea...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Fatima Zahra Errounda Yan Liu Department of Electrical and Computer Engineering Concordia University Montreal Canada

ISBN: (数字)9781665488105

ISBN: (纸本)9781665488112

With the prevalence of mobile devices and location-based services, forecasting human mobility has become a critical topic in ubiquitous computing. Existing forecasting approaches usually adopt frameworks with a centralized mobility data holder. However, mobility data typically pertains to independent organizations, introducing two learning challenges. First, since each organization only holds a location domain subset, none can tackle a forecasting model that covers the whole location domain. Second, distributed mobility data compromises the spatio-temporal correlation between locations hindering learning. Hence, reducing the forecasting accuracy. This work proposes a mobility vertical federated forecasting (MVFF) framework that allows the learning process to be jointly conducted over vertically partitioned data belonging to multiple organizations. MVFF enables the forecasting of mobility predictions covering a joint location domain. We evaluate MVFF's performance over two real-world datasets using different spatial and temporal neural network algorithms. Experimental results demonstrate that the two datasets' mean percentage error performance gains are up to 12% and 4% compared to the state-of-the-art, respectively.

关键词： Correlation Computational modeling distributed databases Organizations Collaborative work Ubiquitous computing Spatial databases

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