A methodology toward manufacturing grid-based virtual enterprise operation platform

Most data mining approaches assume that the data can be provided from a single source. If data was produced from many physically distributed locations like Wal-Mart, these methods require a data center which gathers data from distributed locations. Sometimes, transmitting large amounts of data to a data center is expensive and even impractical. Therefore, distributed and parallel data mining algorithms were developed to solve this problem. In this paper, we survey the-state-of-the-art algorithms and applications in distributed data mining and discuss the future research opportunities.

Section: Internet and Gird Computing Basedmentioning

confidence: 99%

Distributed data mining: a survey

Zeng

Duan

et al. 2012

“…However, several factors hamper large-scale DM applications on a grid. To begin with, Grid computing [50,58,62] itself is relatively new, and relevant standards and technologies are still evolving. A plethora of DM technologies and a staggering number of largely varying DM application scenarios further complicate matters.…”

Section: Ddm Systems Based On Meta-learningmentioning

confidence: 99%

Distributed data mining for e-business

Liu

Cao

2011

In the internet-based e-business environment, most business data are distributed, heterogeneous and private. To achieve true business intelligence, mining large amounts of distributed data is necessary. Through a thorough literature review, this paper identifies four main issues in distributed data mining (DDM) systems for e-business and classifies modern DDM systems into three classes with representative samples. To address these identified issues, this paper proposes a novel DDM model named DRHPDM (Data source Relevance-based Hierarchical Parallel Distributed data mining Model). In addition, to improve the quality of the final result, the data sources are divided into a centralized mining layer and a distributed mining layer, according to their relevance. To improve the openness, cross-platform ability, and intelligence of the DDM system, web service and multi-agent technologies are adopted. The feasibility of DRHPDM was verified by building a prototype system and applying it to a web usage mining scenario.

“…For example, Wang and Rong [1] propose the operational mechanism of the Agile Supply Chain (ASC) and Agile Knowledge Supply Chain (AKSC), which can explore a new approach to knowledge-based quick responses in the emergency decision-making support. Tan et al [2] propose M-Grid-Based VE operation platform (MGVEOP) which is efficient and agile. Moreover, some scholars point out that it is necessary to make decision efficiently according to the serious situation.…”

Section: Introductionmentioning

confidence: 99%

Research on the group decision-making about emergency event based on network technology

Xie

Gang

et al. 2011

In order to improve decision-making efficiency about emergency event, this paper proposes a novel concept, i.e., Agile-Delphi Method, which is an integration of agile decision and Delphi Method implicating that the decision-makers instantly deliver, respond, treat, and utilize information via Delphi process while conducting group decision-making about emergency event. The paper details the mechanism of group decision-making about emergency event based on network technology and Agile-Delphi Method. Finally, the paper conducts an empiric analysis taking the ''111 event'', i.e., the liquid ammonia spill event happened on November 1, 2006 in a phosphorus chemical company in China, as an example.