2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)最新文献_第2页

NMFDIV: A Nonnegative Matrix Factorization Approach for Search Result Diversification on Attributed Networks NMFDIV:一种属性网络搜索结果多样化的非负矩阵分解方法

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00023

Zaiqiao Meng, Hong Shen

{"title":"NMFDIV: A Nonnegative Matrix Factorization Approach for Search Result Diversification on Attributed Networks","authors":"Zaiqiao Meng, Hong Shen","doi":"10.1109/PDCAT.2017.00023","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00023","url":null,"abstract":"Search result diversification is effective way to tackle query ambiguity and enhance result novelty. In the context of large information networks, diversifying search result is also critical for further design of applications such as link prediction and citation recommendation. In previous work, this problem has mainly been tackled in a way of implicit query intent. To further enhance the performance, we propose an explicit search result diversification method that explicitly encode query intent and represent nodes as representation vectors by a novel nonnegative matrix factorization approach, and the diversity of the results node account for the query relevance and the novelty w.r.t. these vectors. To learn representation vectors for networks, we derive the multiplicative update rules to train the nonnegative matrix factorization model. Finally, we perform a comprehensive evaluation on our proposals with various baselines. Experimental results show the effectiveness of our proposed solution, and verify that attributes do help improve diversification performance.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114436530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Privacy-Preserving Cloud-Based Data Management System with Efficient Revocation Scheme 具有有效撤销机制的保护隐私云端数据管理系统

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00011

S. Chang, Ja-Ling Wu

{"title":"A Privacy-Preserving Cloud-Based Data Management System with Efficient Revocation Scheme","authors":"S. Chang, Ja-Ling Wu","doi":"10.1109/PDCAT.2017.00011","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00011","url":null,"abstract":"There are lots of data management systems, according to various reasons, designating their high computational work-loads to public cloud service providers. It is well-known that once we entrust our tasks to a cloud server, we may face several threats, such as privacy-infringement with regard to users attribute information; therefore, an appropriate privacy preserving mechanism is a must for constructing a secure cloud-based data management system (SCBDMS). To design a reliable SCBDMS with server-enforced revocation ability is a very challenging task even if the server is working under the honest-but-curious mode. In existing data management systems, there seldom provide privacy-preserving revocation service, especially when it is outsourced to a third party. In this work, with the aids of oblivious transfer and the newly proposed stateless lazy re-encryption (SLREN) mechanism, a SCBDMS, with secure, reliable and efficient server-enforced attribute revocation ability is built. Comparing with related works, our experimental results show that, in the newly constructed SCBDMS, the storage-requirement of the cloud server and the communication overheads between cloud server and systems users are largely reduced, due to the nature of late involvement of SLREN.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115101448","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Parallel Implementation of Dynamic Programming Problems Using Wavefront and Rank Convergence with Full Resource Utilization 基于波前和秩收敛的动态规划问题并行实现

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00033

Vivek Sourabh, Parth Pahariya, Isha Agarwal, Ankit Gautam, C. R. Chowdary

引用次数: 0

Strike the Balance between System Utilization and Data Locality under Deadline Constraint for MapReduce Clusters MapReduce集群在Deadline约束下如何平衡系统利用率和数据局部性

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00061

Yeh-Cheng Chen, J. Chou

{"title":"Strike the Balance between System Utilization and Data Locality under Deadline Constraint for MapReduce Clusters","authors":"Yeh-Cheng Chen, J. Chou","doi":"10.1109/PDCAT.2017.00061","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00061","url":null,"abstract":"MapReduce paradigm has become a popular platform for massive data processing and Big Data applications. Although MapReduce was initially designed for high throughput and batch processing, it has also been used for handling many other types of applications and workloads due to its scalable and reliable system architecture. One of the emerging requirements for enterprise data-process computing is completion time guar- antee. However, there are only a few research works have been done for MapReduce jobs with deadline constraint. Therefore, in this paper, we aim to prevent jobs from missing deadline while maximizing the resource utilization and data locality of a MapReduce cluster. Our approach is to introduce a two-phase job scheduling mechanism which combines a job admission controller policy and a priority-based scheduling algorithm. We use a series of simulations over diverted workload to evaluate our system. The results show that our approach can guarantee job completion time in a heavy-loaded system, and achieve comparable data locality to the delay schedule algorithm in a light-loaded system. Furthermore, our approach can maximize system throughput by preventing system resources from being wasted by the jobs missing their deadlines.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116347472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Handling Churn in Similarity Based Clustering Overlays Using Weighted Benefit 使用加权收益处理基于相似性的聚类重叠中的波动

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00069

I. Bukhari, A. Harwood, S. Karunasekera

引用次数: 2

Parallel Implementation of Local Similarity Search for Unstructured Text Using Prefix Filtering 基于前缀过滤的非结构化文本局部相似度搜索并行实现

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00025

Manu Agrawal, Kartik Manchanda, Ribhav Soni, A. Lal, C. R. Chowdary

引用次数: 1

SMiPE: Estimating the Progress of Recurring Iterative Distributed Dataflows SMiPE:估计循环迭代分布式数据流的进展

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00034

Jannis Koch, L. Thamsen, Florian Schmidt, O. Kao

{"title":"SMiPE: Estimating the Progress of Recurring Iterative Distributed Dataflows","authors":"Jannis Koch, L. Thamsen, Florian Schmidt, O. Kao","doi":"10.1109/PDCAT.2017.00034","DOIUrl":"https://doi.org/10.1109/PDCAT.2017.00034","url":null,"abstract":"Distributed dataflow systems such as Apache Spark allow the execution of iterative programs at large scale on clusters. In production use, programs are often recurring and have strict latency requirements. Yet, choosing appropriate resource allocations is difficult as runtimes are dependent on hard-to-predict factors, including failures, cluster utilization and dataset characteristics. Offline runtime prediction helps to estimate resource requirements, but cannot take into account inherent variance due to, for example, changing cluster states. We present SMiPE, a system estimating the progress of iterative dataflows by matching a running job to previous executions based on similarity, capturing properties such as convergence, hardware utilization and runtime. SMiPE is not limited to a specific framework due to its black-box approach and is able to adapt to changing cluster states reflected in the current job’s statistics. SMiPE automatically adapts its similarity matching to algorithm-specific profiles by training parameters on the job history. We evaluated SMiPE with three iterative Spark jobs and nine datasets. The results show that SMiPE is effective in choosing useful historic runs and predicts runtimes with a mean relative error of 9.1% to 13.1%.","PeriodicalId":119197,"journal":{"name":"2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130800643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

A Survey of User Preferences Oriented Service Selection and Deployment in Multi-Cloud Environment 多云环境下面向用户偏好的服务选择与部署研究

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00065

Letian Yang, Li Liu, Qi Fan

引用次数: 3

The Computing of Optimized Clustering Threshold Values Based on Quasi-Classes Space for the Merchandise Recommendation 基于准类空间的商品推荐优化聚类阈值计算

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00043

Mingshan Xie, Yanfang Deng, Yong Bai, Mengxing Huang, Wenbo Jiang, Zhuhua Hu

引用次数: 0

Computation Capability Deduction Architecture for MapReduce on Cloud Computing 基于云计算的MapReduce计算能力演绎体系

2017 18th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT) Pub Date : 2017-12-01 DOI: 10.1109/PDCAT.2017.00067

Tzu-Chi Huang, Kuo-Chih Chu, Guo-Hao Huang, Yan-Chen Shen, C. Shieh

引用次数: 0