A framework for a World Wide Web-based Data Mining system

Wong, Johnny; Nayar, Rishi; Mikler, Armin R.

doi:10.1006/jnca.1998.0071

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…The identification of environmental events and their ecological effects requires a statistical analysis of the acquired datasets. Data mining techniques [9][10] [21] on these datasets are useful to correlate ecological events as captured by the sensors. The deployment of a sensor infrastructure in a watershed may contain hundreds or thousands of sensor nodes over an area of several hundred square kilometers.…”

Section: B Why Sensor Network In a Watershedmentioning

confidence: 99%

IEEE Global Telecommunications Conference

2004

IEEE Global Telecommunications Conference 2004 GLOBECOM 04 GLOCOM-04

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Meteorological and hydrological sensors deployed over several hundred kilometers of geographical area comprise an environmental sensor network. Large amounts of data need to be processed in minimal time and transmitted over the available low speed and low bandwidth links. This paper describes algorithms for optimal data collection and data fusion. An inductive model using exponential back-off policy is used to collect optimal amount of data. The data measurements for temperature, pH and specific conductance collected for a year from the sensors deployed at Lake Lewisville are used to test the inductive model. Energy savings of 90% are achieved even with 1% of degree of tolerance. The problem of data fusion is addressed by the introduction of a novel concept of a super-sensor, based on self-organization and collaboration among sensors. A histogram application is described that uses recursive doubling for global collaboration between sensors. The performance of the networked supersensor in comparison to a centralized polling approach is analyzed for optimality on two different geographical areas. I.

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Section: B Why Sensor Network In a Watershedmentioning

confidence: 99%

IEEE Global Telecommunications Conference

2004

IEEE Global Telecommunications Conference 2004 GLOBECOM 04 GLOCOM-04

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“…Data mining can be considered to be an inter-disciplinary ®eld involving concepts from machine learning, database technology, statistics, mathematics, clustering and visualization, among others Johnny S.K. Wong et al (1998) and Xia Huosong et al (2000).…”

Section: Factorsmentioning

confidence: 99%

Enterprise knowledge tree model and factors of KMS based on E‐C

Xia

Du²,

Cai³

2003

Journal of Knowledge Management

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In the emerging economy, knowledge is now recognized as an important basis for competitive advantage and many ®rms are beginning to establish knowledge management systems (KMS). Within the last few years, although the topic of knowledge management (KM) has been studied, our understanding of how the design of the KMS affects its use and de®nition of KM is still rather limited. This paper presents a model of the enterprise's knowledge trees, which is related to several knowledge management processes. The gray information and knowledge are, in their respective capacities, to provide the enterprise with great effectiveness. The gray dimension of enterprise knowledge is de®ned in line with the model of enterprise knowledge and fractal dimension. Also the key factors of KMS of shared knowledge are discussed in this paper. The results of the study will bene®t not only the design of KMS, but also the business model transformation of competitive advantage.

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An asynchronous and synchronous coupling approach in networked rapid product development

Fei-ya

Juanqi

et al. 2003

The International Journal of Advanced Manufacturing Technology

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Faced with today's competitive market, the development of innovative product needs to be accelerated. But this is limited by heterogeneous systems and a distributed environment. A way of utilising network technology to this end must be found. In this paper, we use a new approach, called asynchronous and synchronous coupling, which is a network-based product development approach. This is then applied to rapidresponse manufacturing by small-and medium-sized enterprises in a distributed environment. It demonstrates integrated thinking, high development speed and a transparent development process. Visual technology and real-time monitoring technology are used to solve asynchronous process and data management problems. An ICE (integrated collaborative environment) is also created to support synchronous collaboration.Keywords distributed environment AE rapid product development AE asynchronous and synchronous coupling AE integrated collaborative environment Int

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A framework for a World Wide Web-based Data Mining system

Cited by 4 publications

References 5 publications

IEEE Global Telecommunications Conference

IEEE Global Telecommunications Conference

Enterprise knowledge tree model and factors of KMS based on E‐C

An asynchronous and synchronous coupling approach in networked rapid product development

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