Proceedings of the 5th Asia-Pacific Symposium on Internetware最新文献_第3页

A probability based algorithm for influence maximization in social networks 基于概率的社交网络影响力最大化算法

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532455

Zhen Wang, Zhuzhong Qian, Sanglu Lu

{"title":"A probability based algorithm for influence maximization in social networks","authors":"Zhen Wang, Zhuzhong Qian, Sanglu Lu","doi":"10.1145/2532443.2532455","DOIUrl":"https://doi.org/10.1145/2532443.2532455","url":null,"abstract":"In a social network, information runs from word-of-mouth based on the relationship of the users. The influence maximization is to find a limited number of initial users (nodes) to spread the information, so that the maximum number of other users could accept the information, which is a useful technique for marketing, information monitoring and advertising in a social network. Diffusion model of social networks imitates the process of information spreading in social networks, and Independent Cascade (IC) Model and Linear Threshold (LT) Model, are well-known stochastic information influence models. In this paper, we extend the classical IC model according to the observation of users' behaviors in social networks and propose an effective influence maximization algorithm based on this extended IC model. This novel algorithm calculates the influence probability of each node in sub-graphs that other nodes can engendered to it iteratively. The simulation experiments on real social network datasets show that our algorithm is much faster than the greedy hill-climbing algorithm, while the results are very close to the greedy algorithm and out-perform the other heuristic algorithms.","PeriodicalId":362187,"journal":{"name":"Proceedings of the 5th Asia-Pacific Symposium on Internetware","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132439410","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

MR-runner: a modularized map-reduce job management tool MR-runner:模块化的map-reduce作业管理工具

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532474

Xinsheng Yang, Wei Wang, Lijie Xu, Jie Liu, Jun Wei

{"title":"MR-runner: a modularized map-reduce job management tool","authors":"Xinsheng Yang, Wei Wang, Lijie Xu, Jie Liu, Jun Wei","doi":"10.1145/2532443.2532474","DOIUrl":"https://doi.org/10.1145/2532443.2532474","url":null,"abstract":"Map-Reduce is a powerful solution for processing and analyzing large-scale data. Just as Hadoop and Spark are able to deal with terabyte data and even more. Users only need to complete \"map\" and \"reduce\" function, the Map-Reduce framework can finish variety jobs. But many machine learning and data mining algorithms cannot leverage the Map-Reduce framework or it would take large efforts to modify the algorithm itself. This issue can be explained by the following ways: 1. Map-Reduce is a batch operation so that most of Map-Reduce frameworks do not built-in to support iteration. 2. Map-Reduce is absolutely parallel, each vertex cannot obtain all records, so none of them could get the global optimal model. In this paper, we proposed a job management tool to enable the Map-Reduce framework to support iteration, called \"de-parallel\". This make the Map-Reduce framework like Hadoop so that Map-Reduce could run more algorithms and support more various tasks. In addition, our tool does not modify the Map-Reduce framework itself. In face MR-Runner interacts with Map-Reduce framework like a \"client\", therefore MR-Runner could be deployed in any single PC instead of Map-Reduce cluster. We also abstract the mainly interface related to Map-Reduce frameworks, this makes our tool portable to the representative Map-Reduce frameworks.","PeriodicalId":362187,"journal":{"name":"Proceedings of the 5th Asia-Pacific Symposium on Internetware","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126950185","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: 2

Generating API-usage example for project developers 为项目开发人员生成api使用示例

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532470

Zixiao Zhu, Yanzhen Zou, Yong Jin, Bing Xie

引用次数: 2

b-bit minwise hashing in practice 实践中的b位最小哈希

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532446

Ping Li, Anshumali Shrivastava, A. König

{"title":"b-bit minwise hashing in practice","authors":"Ping Li, Anshumali Shrivastava, A. König","doi":"10.1145/2532443.2532446","DOIUrl":"https://doi.org/10.1145/2532443.2532446","url":null,"abstract":"Minwise hashing is a standard technique in the context of search for approximating set similarities. The recent work [26, 32] demonstrated a potential use of b-bit minwise hashing [23, 24] for efficient search and learning on massive, high-dimensional, binary data (which are typical for many applications in Web search and text mining). In this paper, we focus on a number of critical issues which must be addressed before one can apply b-bit minwise hashing to the volumes of data often used industrial applications. Minwise hashing requires an expensive preprocessing step that computes k (e.g., 500) minimal values after applying the corresponding permutations for each data vector. We developed a parallelization scheme using GPUs and observed that the preprocessing time can be reduced by a factor of 20 ~ 80 and becomes substantially smaller than the data loading time. Reducing the preprocessing time is highly beneficial in practice, e.g., for duplicate Web page detection (where minwise hashing is a major step in the crawling pipeline) or for increasing the testing speed of online classifiers. Another critical issue is that for very large data sets it becomes im- possible to store a (fully) random permutation matrix, due to its space requirements. Our paper is the first study to demonstrate that b-bit minwise hashing implemented using simple hash functions, e.g., the 2-universal (2U) and 4-universal (4U) hash families, can produce very similar learning results as using fully random permutations. Experiments on datasets of up to 200GB are presented.","PeriodicalId":362187,"journal":{"name":"Proceedings of the 5th Asia-Pacific Symposium on Internetware","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126216887","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: 12

Accelerate MapReduce on GPUs with multi-level reduction 在gpu上使用多级缩减加速MapReduce

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532447

Ran Zheng, Kai Liu, Hai Jin, Qin Zhang, Xiaowen Feng

{"title":"Accelerate MapReduce on GPUs with multi-level reduction","authors":"Ran Zheng, Kai Liu, Hai Jin, Qin Zhang, Xiaowen Feng","doi":"10.1145/2532443.2532447","DOIUrl":"https://doi.org/10.1145/2532443.2532447","url":null,"abstract":"With Graphics Processing Units (GPUs) becoming more and more popular in general purpose computing, more attentions have been paid on building a framework to provide convenient interfaces for GPU programming. MapReduce can greatly simplify the programming for data-parallel applications in cloud computing environment, and it is also naturally suitable for GPUs. However, there are some problems in recent reduction-based MapReduce implementation on GPUs. Its performance is dramatically degraded when handling massive distinct keys because the massive data cannot be stored in tiny shared memory entirely. A new MapReduce framework on GPUs, called Jupiter, is proposed with continuous reduction structure. Two improvements are supported in Jupiter, a multi-level reduction scheme tailored for GPU memory hierarchy and a frequency-based cache policy on key-value pairs in shared memory. Shared memories are utilized efficiently for various data-parallel applications whether involving little or abundant distinct keys. Experiments show that Jupiter can achieve up to 3x speedup over the original reduction-based GPU MapReduce framework on the applications with lots of distinct keys.","PeriodicalId":362187,"journal":{"name":"Proceedings of the 5th Asia-Pacific Symposium on Internetware","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133963478","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

Mining user daily behavior patterns from access logs of massive software and websites 从海量软件和网站的访问日志中挖掘用户的日常行为模式

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532462

Wei Zhao, Jie Liu, Dan Ye, Jun Wei

引用次数: 2

COCO 椰子树

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532471

Wenjia Zhang, Wei Song, Xiaoxing Ma, Qiliang Yang, Xuewei Zhang

引用次数: 1

Towards an operating system for the campus 面向校园的操作系统

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532468

Pengfei Yuan, Yao Guo, Xiangqun Chen

引用次数: 5

A distributed rule execution mechanism based on MapReduce in sematic web reasoning 语义web推理中基于MapReduce的分布式规则执行机制

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532457

Haijiang Wu, Jie Liu, Dan Ye, Hua Zhong, Jun Wei

引用次数: 6

A scalable crawler framework for FLOSS data 用于FLOSS数据的可伸缩爬虫框架

Proceedings of the 5th Asia-Pacific Symposium on Internetware Pub Date : 2013-10-23 DOI: 10.1145/2532443.2532454

Lingxiao Zhang, Yanzhen Zou, Bing Xie

引用次数: 3