Enabling Real-Time Business Intelligence by Stream Data Mining

Fong, Simon; Yang, Hang

doi:10.5772/13123

Cited by 4 publications

(2 citation statements)

References 28 publications

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“…As it was already pointed out in [6] the latency would increase probably exponentially as the training data size grows to certain amount; it means the classifier will become increasingly slow as fresh data continue to stream in, because of the continually training. In order to tackle with the drawback of batch-mode learning, a new breed of data mining algorithms called data stream mining has been recently invented [7] whose algorithms are founded on incremental learning.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

Fong¹,

Zhang²,

Fiaidhi

et al. 2013

BioMed Research International

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Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

Fong¹,

Zhang²,

Fiaidhi

et al. 2013

BioMed Research International

Self Cite

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“…A recent jargon called Real-time BI [7] has been used popularly in the industry. Real-time BI certainly has its merits in many aspects of a business; as shown in Figure 2a, the value of the action taken based on the information declines in time.…”

Section: Introductionmentioning

confidence: 99%

The Six Technical Gaps between Intelligent Applications and Real-Time Data Mining: A Critical Review

Fong

Yang²

2011

JETWI

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Intelligent application is characterized by its ability to make decision autonomously. Some examples are distributed agents that collaboratively complete a complex task, yet each one of them is able to work and reason independently given the dynamic situation where they are in. The underlying think tank is often a collection of core mechanisms that include environment sensing, data capturing, data mining, ETL, knowledge processing, and decision making etc. All these techniques when placed and function together as a whole intelligent system, they will have to fulfill stringent deadlines imposed by the requirements of a real-time system. In the literature many research papers can be found on a wide variety of data mining techniques that enable intelligent applications operating in real-time. This report offers a critical review of the relevant literature, and contributes to the knowledge of identifying their shortcomings, so-called the technical gaps between the real-time requirements of a general intelligent system and the supporting components. A discussion follows on the possibilities of future intelligent applications that are empowered by data stream mining

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Business Intelligence and Analytics in Dairy Goat Livestock: Current and Future Challenge

Primawati

Sitanggang

Annisa

et al. 2021

2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS

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Enabling Real-Time Business Intelligence by Stream Data Mining

Cited by 4 publications

References 28 publications

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

The Six Technical Gaps between Intelligent Applications and Real-Time Data Mining: A Critical Review

Business Intelligence and Analytics in Dairy Goat Livestock: Current and Future Challenge

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