Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Huaming Liao"'
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783319686981
CCIR
CCIR
In this paper, we investigate the recommendation task in the most common scenario with implicit feedback (e.g., clicks, purchases). State-of-the-art methods in this direction usually cast the problem as to learn a personalized ranking on a set of ite
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b7f6456fb130b687bba2eead83d52091
https://doi.org/10.1007/978-3-319-68699-8_2
https://doi.org/10.1007/978-3-319-68699-8_2
Autor:
Huaming Liao, Guo-Shun Pei
Publikováno v:
Journal of Computer Science and Technology. 23:905-915
Our study introduces a novel distributed query plan refinement phase in an enhanced architecture of distributed query processing engine (DQPE). Query plan refinement generates potentially efficient distributed query plan by reusable aggregate query s
Publikováno v:
ICPADS
MapReduce scheduling is becoming a hot topic as MapReduce attracts more and more attention from both industry and academia. In this paper, we focus on the scheduling of mixed real-time and non-real-time applications in MapReduce environment, which is
Publikováno v:
SKG
Hadoop has shown great power in processing vast data in parallel. Hive, the database on Hadoop, enables more experts to process relational data by providing sql-like interface. However, Hive does not provide an efficient approach for join, a common b
Publikováno v:
SKG
MapReduce programming model has been used in various kinds of intensive data processing and analysis projects for its ease of use and good scalability. In this paper, we discuss about the execution mechanism of cyclic workflow on top of MapReduce fra
Autor:
Huaming Liao, Jie Zhang, Xiaowei Liu, Chao Tian, Yuezhuo Zhang, Hongwei Qi, Li Zha, Weisong Hu
Publikováno v:
SKG
Map Reduce cluster is emerging as a solution of data-intensive scalable computing system. The open source implementation Hadoop has already been adopted for building clusters containing thousands of nodes. Such cloud infrastructure was used to proces
Publikováno v:
GCC
With MapReduce’s restricted structure, multi-datasets merging problem, commonly in many data mining applications, cannot be efficiently resolved with MapReduce. This paper proposes a novel hybrid datasets merging algorithm on top of Map Reduce, HDM
Autor:
Zhen Qin, Li Shen, Hongli Ji, Yunjia Xiang, Huaming Liao, Yunliang Peng, Zhenyu Zhang, Linming Luo
Publikováno v:
Advances in Genetics, Genomics and Control of Rice Blast Disease ISBN: 9781402094996
Chinese national differential varieties have failed to provide clues in virulence changes for the two re-surges of rice blast disease causing severe losses in Sichuan of China since 1986, where hybrid rice has been the major crop since 1980. So Lijia
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4397742cb6a6dd6ce828e63d85c0994
https://doi.org/10.1007/978-1-4020-9500-9_23
https://doi.org/10.1007/978-1-4020-9500-9_23
Publikováno v:
Computer Supported Cooperative Work in Design I ISBN: 9783540294009
CSCWD (Selected papers)
CSCWD (Selected papers)
This paper looks at computer supported cooperative work (CSCW) from an information grid viewpoint. We illustrate two collaborative instances in information grid and point out new CSCW requirements. We discuss key pieces of two models in the VEGA-IG (
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::175ced126c4ff616252ea0dc5c3c829a
https://doi.org/10.1007/11568421_1
https://doi.org/10.1007/11568421_1
Publikováno v:
Grid and Cooperative Computing-GCC 2005 ISBN: 9783540305101
GCC
GCC
In information grid, data sources not only change their data, but also change their schema. On schema changing, the mapping may be left inconsistency. Thus, it is an essential issue to detect and update the inconsistency. We present a novel approach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::30f798ea222b647710a7c9ca9d486d6d
https://doi.org/10.1007/11590354_64
https://doi.org/10.1007/11590354_64