Discovering Data Quality Problems

To achieve ambitious climate goals, it is necessary to increase the rate of purposeful retrofit measures in the building sector. As a result, Energy Performance Certificates have been designed as important evaluation and rating criterion to increase the retrofit rate in the EU and Germany. Yet, today’s most frequently used and legally required methods to quantify building energy performance show low prediction accuracy, as recent research reveals. To enhance prediction accuracy, the research community introduced data-driven methods which obtained promising results. However, there are no insights in how far Energy Quantification Methods are particularly suited for energy performance prediction. In this research article the data-driven methods Artificial Neural Network, D-vine copula quantile regression, Extreme Gradient Boosting, Random Forest, and Support Vector Regression are compared with and validated by real-world Energy Performance Certificates of German residential buildings issued by qualified auditors using the engineering method required by law. The results, tested for robustness and systematic bias, show that all data-driven methods exceed the engineering method by almost 50% in terms of prediction accuracy. In contrast to existing literature favoring Artificial Neural Networks and Support Vector Regression, all tested methods show similar prediction accuracy with marginal advantages for Extreme Gradient Boosting and Support Vector Regression in terms of prediction accuracy. Given the higher prediction accuracy of data-driven methods, it seems appropriate to revise the current legislation prescribing engineering methods. In addition, data-driven methods could support different organizations, e.g., asset management, in decision-making in order to reduce financial risk and to cut expenses.

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“…For this purpose, we apply the two-stage LANG approach to check for semantic and syntactic data constraints in Sect. 4 (Zhang et al 2019).…”

Section: Methodology and Study Designmentioning

confidence: 99%

Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

Wenninger

Wiethe

2021

Bus Inf Syst Eng

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“…It gives a complete summary that describes the length, data type, length, variance, uniqueness, null ratio, and domain range. It shows the full view of data quality related to all data source attributes [39], [40]. Data profiling is an important step in the building process of DW and data mart.…”

Section: Data Profilingmentioning

confidence: 99%

Implementing data-driven decision support system based on independent educational data mart

Hamoud

Hussein

Alhilfi

et al. 2021

IJECE

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<span>Decision makers in the educational field always seek new technologies and tools, which provide solid, fast answers that can support decision-making process. They need a platform that utilize the students’ academic data and turn them into knowledge to make the right strategic decisions. In this paper, a roadmap for implementing a data driven decision support system (DSS) is presented based on an educational data mart. The independent data mart is implemented on the students’ degrees in 8 subjects in a private school (Al-Iskandaria Primary School in Basrah province, Iraq). The DSS implementation roadmap is started from pre-processing paper-based data source and ended with providing three categories of online analytical processing (OLAP) queries (multidimensional OLAP, desktop OLAP and web OLAP). Key performance indicator (KPI) is implemented as an essential part of educational DSS to measure school performance. The static evaluation method shows that the proposed DSS follows the privacy, security and performance aspects with no errors after inspecting the DSS knowledge base. The evaluation shows that the data driven DSS based on independent data mart with KPI, OLAP is one of the best platforms to support short-to-long term academic decisions.</span>

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“…erefore, we define the quality dimensions according to the quality problems and the use of annotation. Inspired by some existing work [14][15][16][17][18][19], the dimensions of completeness, accuracy, and consistency are selected as the core set of the data quality dimensions. By considering the theory of cognitive perception, we redefine some elements based on annotation characteristics.…”

Section: Annotation Quality Assessment Frameworkmentioning

confidence: 99%

“…Besides, data quality is of multidimensional characteristics. By reviewing the related literature [14][15][16][17][18][19], a core set of data quality dimensions is defined, including the completeness, accuracy, and consistency. Moreover, there are a fair number of researches about annotation quality.…”

Section: Introductionmentioning

confidence: 99%

Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets

Guo

Xie

et al. 2020

Discrete Dynamics in Nature and Society

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In many supervised computer vision tasks such as object detection, manual annotation crowdsourcing platforms are widely used for acquiring large-scale labeled data. However, the annotation quality may suffer low quality that can severely affect the training of models. As a result, the evaluation of the annotations within the dataset is critical, yet it has seldom been addressed in object detection. In this paper, we present a fine-grained annotation quality assessment (FGAQA) framework for evaluating the quality of object detection datasets. First, we formulate a generic annotation quality assessment framework based on the core generalpurpose data quality dimensions, using the bounding box and the label. Second, cognition theory in terms of hierarchy and continuity is utilized to refine the basic framework, including the consistency of the bounding box, completeness of the category, hierarchical accuracy of the label, and the consistency of the label. Comprehensive experiments on the two object detection datasets are used for performance evaluation. It is found that the ground truth annotations of the Urban Traffic Surveillance dataset have more quality issues than the ones of the PASCAL VOC 2007 detection dataset. e proposed FGAQA framework performs an effective fine-grained evaluation of the annotations, which is significant for quality assurance of annotations from crowdsourcing platforms and the subsequent model's training.

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Discovering Data Quality Problems

Cited by 48 publications

References 36 publications

Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany

Implementing data-driven decision support system based on independent educational data mart

Conceptual Cognitive Modeling for Fine-Grained Annotation Quality Assessment of Object Detection Datasets

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