Multi-Source Data Processing and Fusion Method for Power Distribution Internet of Things Based on Edge Intelligence

Yuan, Quande; Pi, Yuzhen; Kou, Lei; Zhang, Fangfang; Li, Yahui; Zhang, Zhenming

doi:10.3389/fenrg.2022.891867

Cited by 16 publications

(10 citation statements)

References 26 publications

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“…The AI and database communities have taken into consideration several approaches, such as rule-based systems, Dempster-Shafer, and fussy sets, for modeling uncertain infor-mation [21,30,53,54]. For instance, some efforts used NULL values representation to replace all partial information about the imperfect parts of a description, and hence, uncertainty was ignored.…”

Section: Uncertainty Modelingmentioning

confidence: 99%

“…In fact, the fusion of multi-valid or invalid attribute values from probabilistic merged entities represents a major reconciliation challenge [3,8,19]. Therefore, proposing advanced fusion methods with the capabilities of accepting and handling uncertain data from probabilistic entities merging, considering the role of source dependence, and storing versions of data values with an associated likelihood of correctness is a crucial issue in the current age of information integration and data quality fields [3,8,11,17,19,21,26].…”

Section: Data Fusion Related Workmentioning

confidence: 99%

“…Over the last two decades, intensive efforts toward proposing precise and/or probabilistic data fusion approaches have been observed in the data integration community [8]. Related works that address the management of uncertain data values throughout the proposal of probabilistic data fusion solutions can be found in [8,18,21,24,25,[67][68][69][70][72][73][74][75][76]. This intensive effort has been established due to the importance of obtaining quality information for the intended users.…”

Section: Data Fusion Related Workmentioning

confidence: 99%

“…Another major issue, named "data fusion" or "data conflict resolution", has been recognized; this requires further research to achieve consistent, integrated data [8][9][10]20]. Data fusion is about how to find a true value from contradicting attribute values when integrating data from several sources [17,21,22]. Although the resolution of data conflicts has been mostly neglected, several approaches and techniques have evolved [2,17,[23][24][25], many of which attempt to "prevent" data conflicts by focusing solely on the uncertainty of missing values, while others employ a variety of resolution strategies to "resolve" conflicts [3,9,23,24].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the data value domains for the global Phone and Address attributes are presented in Figure 4d. From Figure 4a, we see that Name and Address global attributes are obtained from their corresponding restaurant's Name, and Address, as existed in three participated iDOs, where each iDO originated from a different data source, i.e., (𝑁𝑎𝑚𝑒 1 13 , 𝑁𝑎𝑚𝑒 1 21 , 𝑁𝑎𝑚𝑒 1 31 ) → 𝑁𝑎𝑚𝑒 1.1 𝐺𝑠 . 𝑖𝑑𝑛, 𝑎𝑛𝑑 (𝐴𝑑𝑑𝑟𝑒𝑠𝑠 3 13 , 𝐴𝑑𝑑𝑟𝑒𝑠𝑠 3 21 , 𝐴𝑑𝑑𝑟𝑒𝑠𝑠 2 31 ) → (𝐴𝑑𝑑𝑟𝑒𝑠𝑠 3 𝐺𝑠 .…”

mentioning

confidence: 99%

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A Probabilistic Data Fusion Modeling Approach for Extracting True Values from Uncertain and Conflicting Attributes

Jaradat

Safieddine

Deraman

et al. 2022

BDCC

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Real-world data obtained from integrating heterogeneous data sources are often multi-valued, uncertain, imprecise, error-prone, outdated, and have different degrees of accuracy and correctness. It is critical to resolve data uncertainty and conflicts to present quality data that reflect actual world values. This task is called data fusion. In this paper, we deal with the problem of data fusion based on probabilistic entity linkage and uncertainty management in conflict data. Data fusion has been widely explored in the research community. However, concerns such as explicit uncertainty management and on-demand data fusion, which can cope with dynamic data sources, have not been studied well. This paper proposes a new probabilistic data fusion modeling approach that attempts to find true data values under conditions of uncertain or conflicted multi-valued attributes. These attributes are generated from the probabilistic linkage and merging alternatives of multi-corresponding entities. Consequently, the paper identifies and formulates several data fusion cases and sample spaces that require further conditional computation using our computational fusion method. The identification is established to fit with a real-world data fusion problem. In the real world, there is always the possibility of heterogeneous data sources, the integration of probabilistic entities, single or multiple truth values for certain attributes, and different combinations of attribute values as alternatives for each generated entity. We validate our probabilistic data fusion approach through mathematical representation based on three data sources with different reliability scores. The validity of the approach was assessed via implementation into our probabilistic integration system to show how it can manage and resolve different cases of data conflicts and inconsistencies. The outcome showed improved accuracy in identifying true values due to the association of constructive evidence.

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Section: Uncertainty Modelingmentioning

confidence: 99%