Multidimensional analysis model for highly pathogenic avian influenza using data cube and data mining techniques

Xu, Zhenshun; Lee, Jong-Uk; Park, Jooyoung; Chung, Yongwha

doi:10.1016/j.biosystemseng.2017.03.004

Cited by 16 publications

(15 citation statements)

References 10 publications

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“…Note that the goal is not to hide information to the user. On the contrary, the goal is to organize and highlight the data relevant to that user so to put him/her in the best position for data comparison and understanding as it happens using the support of data warehouse in the decision making process [ 63 – 65 ].…”

Section: Main Textmentioning

confidence: 99%

Data distribution in public veterinary service: health and safety challenges push for context-aware systems

et al. 2017

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BackgroundToday’s globalised and interconnected world is characterized by intertwined and quickly evolving relationships between animals, humans and their environment and by an escalating number of accessible data for public health. The public veterinary services must exploit new modeling and decision strategies to face these changes. The organization and control of data flows have become crucial to effectively evaluate the evolution and safety concerns of a given situation in the territory. This paper discusses what is needed to develop modern strategies to optimize data distribution to the stakeholders.Main textIf traditionally the system manager and knowledge engineer have been concerned with the increase of speed of data flow and the improvement of data quality, nowadays they need to worry about data overflow as well. To avoid this risk an information system should be capable of selecting the data which need to be shown to the human operator. In this perspective, two aspects need to be distinguished: data classification vs data distribution. Data classification is the problem of organizing data depending on what they refer to and on the way they are obtained; data distribution is the problem of selecting which data is accessible to which stakeholder. Data classification can be established and implemented via ontological analysis and formal logic but we claim that a context-based selection of data should be integrated in the data distribution application. Data distribution should provide these new features: (a) the organization of situation types distinguishing at least ordinary vs extraordinary scenarios (contextualization of scenarios); (b) the possibility to focus on the data that are really important in a given scenario (data contextualization by scenarios); and (c) the classification of which data is relevant to which stakeholder (data contextualization by users).Short conclusionPublic veterinary services, to efficaciously and efficiently manage the information needed for today’s health and safety challenges, should contextualize and filter the continuous and growing flow of data by setting suitable frameworks to classify data, users’ roles and possible situations.

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Section: Main Textmentioning

confidence: 99%

Data distribution in public veterinary service: health and safety challenges push for context-aware systems

et al. 2017

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“…The application of rule-based prediction models has been limited to a few studies for Dengue [ 12 ], Depression [ 34 ] and Diabetes [ 35 , 36 ]. We are aware of only one study which used rule-based models aimed at analyzing AI outbreaks [ 37 ]. Xu et al .…”

Section: Introductionmentioning

confidence: 99%

“…Xu et al . [ 37 ] constructed a data cube model with OLAP (Online Analytical Processing) actions. Then, geographical and temporal insights into disease spread with various abstraction levels were extracted.…”

Section: Introductionmentioning

confidence: 99%

A framework for the risk prediction of avian influenza occurrence: An Indonesian case study

et al. 2021

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Avian influenza viruses can cause economically devastating diseases in poultry and have the potential for zoonotic transmission. To mitigate the consequences of avian influenza, disease prediction systems have become increasingly important. In this study, we have proposed a framework for the prediction of the occurrence and spread of avian influenza events in a geographical area. The application of the proposed framework was examined in an Indonesian case study. An extensive list of historical data sources containing disease predictors and target variables was used to build spatiotemporal and transactional datasets. To combine disparate sources, data rows were scaled to a temporal scale of 1-week and a spatial scale of 1-degree × 1-degree cells. Given the constructed datasets, underlying patterns in the form of rules explaining the risk of occurrence and spread of avian influenza were discovered. The created rules were combined and ordered based on their importance and then stored in a knowledge base. The results suggested that the proposed framework could act as a tool to gain a broad understanding of the drivers of avian influenza epidemics and may facilitate the prediction of future disease events.

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“…Association rule mining, as one of the most widely used data mining techniques, aims at uncovering interesting and useful patterns [29,30]. Through association rule mining, the rules are generated in the form of X → Y that satisfies the predefined minimum support and confidence threshold from any given data (X and Y are a set of items).…”

Section: Association Rule Miningmentioning

confidence: 99%

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

et al. 2017

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Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

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Multidimensional analysis model for highly pathogenic avian influenza using data cube and data mining techniques

Cited by 16 publications

References 10 publications

Data distribution in public veterinary service: health and safety challenges push for context-aware systems

Data distribution in public veterinary service: health and safety challenges push for context-aware systems

A framework for the risk prediction of avian influenza occurrence: An Indonesian case study

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

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