Data Quality Observation in Pervasive Environments

Li, Fēi; Nastić, Stefan; Dustdar, Schahram

doi:10.1109/iccse.2012.88

Cited by 32 publications

(39 citation statements)

References 17 publications

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“…Decision situations usually rely on the data quality of (large) sets of data values. However, many data quality metrics in the literature do not provide (consistent) aggregation rules for different data view levels [cf., e.g., Hipp et al 2001;Hipp et al 2007;Li et al 2012;Alpar and Winkelsträter 2014]. As the above justification reveals, this may lead to wrong decisions when evaluating different decision alternatives (cf.…”

Section: Requirement 4 (R4): Sound Aggregation Of the Metric Valuesmentioning

confidence: 99%

Requirements for Data Quality Metrics

Heinrich

Hristova

Klier

et al. 2017

J. Data and Information Quality

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Data quality and especially the assessment of data quality have been intensively discussed in research and practice alike. To support an economically oriented management of data quality and decision-making under uncertainty, it is essential to assess the data quality level by means of well-founded metrics. However, if not adequately defined, these metrics can lead to wrong decisions and economic losses. Therefore, based on a decision-oriented framework, we present a set of five requirements for data quality metrics. These requirements are relevant for a metric that aims to support an economically oriented management of data quality and decision-making under uncertainty. We further demonstrate the applicability and efficacy of these requirements by evaluating five data quality metrics for different data quality dimensions. Moreover, we discuss practical implications when applying the presented requirements.

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Section: Requirement 4 (R4): Sound Aggregation Of the Metric Valuesmentioning

confidence: 99%

Requirements for Data Quality Metrics

Heinrich

Hristova

Klier

et al. 2017

J. Data and Information Quality

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show abstract

“…In previous work, we have developed a set of feasible metrics and applied it to real-world datasets (Li et al, 2012). Based on these metrics, formal semantic annotations is being developed for annotating data streams with quality information.…”

Section: The Eupaas Architecturementioning

confidence: 99%

EUPaaS - Elastic Ubiquitous Platform as a Service for Large-scale Ubiquitous Applications

Li¹,

Dustdar²,

Bardram³

et al. 2013

Proceedings of the 3rd International Conference on Cloud Computing and Services Science

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Abstract:Large-scale ubiquitous computing applications are rapidly emerging in the fields of pervasive healthcare, smart cities and so on. They present unprecedented challenges to state-of-the-art ubiquitous systems in the respects of accommodating fluctuating user demands, handling volatile data quality and adaptation to complex system and user contexts. Driven by a motivating scenario in future mega-hospital environment, we propose to exploit the potential of cloud computing in supporting large-scale ubiquitous computing applications. This position paper will present the novel concept of EUPaaS (Elastic Ubiquitous Platform as a Service), outline the key research topics, and propose a cloud-based ubiquitous application platform.

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“…A flexible model that presents data quality dissemination and processing is used to capture, process, and deliver quality features and provide corresponding business tasks. Li et al [6] define the metrics and observe real-world data by the use of three commonly used indicators: timeliness, availability, and effectiveness. The definition of these indicators ensures that their parameters are interpretable and are obtained by analyzing historical data.…”

Section: Related Workmentioning

confidence: 99%

“…There are dozens of metrics currently used to assess the quality of sensory data, but the search for a common and valid data quality assessment framework is still ongoing. Data cleaning aims at how to detect and eliminate data errors originated from the initial data [6]. The current data cleaning strategies generally deal with repeated object detection, outlier value detection, and missing data processing.…”

Section: Introductionmentioning

confidence: 99%

Data quality analysis and cleaning strategy for wireless sensor networks

Cheng

Feng

Shi

et al. 2018

J Wireless Com Network

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The quality of data in wireless sensor networks has a significant impact on decision support, and data cleaning is an effective way to improve data quality. However, if the data cleaning strategies are not correctly designed, it might result in an unsatisfactory cleaning effect with increased system cleaning costs. Initially, data quality evaluation indicators and their measurement methods in wireless sensor networks were introduced. We then explored the impact of relationship between different indicators which are used in the quality assessment. Finally, data cleaning strategy for wireless sensor networks based on the relationship between data quality indicators was proposed by comparing and analyzing data cleaning schemes with different orders. The experimental results showed that the proposed data cleaning strategy can effectively improve data availability and have a better cleaning effect in wireless sensor networks for the same cleaning cost.

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Data Quality Observation in Pervasive Environments

Cited by 32 publications

References 17 publications

Requirements for Data Quality Metrics

Requirements for Data Quality Metrics

EUPaaS - Elastic Ubiquitous Platform as a Service for Large-scale Ubiquitous Applications

Data quality analysis and cleaning strategy for wireless sensor networks

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