Ontology-Based Data Quality Management for Data Streams

Geisler, Sandra; Quix, Christoph; Weber, Sven; Jarke, Matthias

doi:10.1145/2968332

Cited by 34 publications

(28 citation statements)

References 31 publications

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“…Furthermore, the adoption of ontology in data quality management reduces extensive involvement of domain expert and data users during data quality assessment and improvement process. Additionally, previous researchers adopted ontology in data quality assessment because its ability to infer and to represent data from heterogeneous data source or data schema [12,14], [31][32][33][34][35]. The adoption of ontology also allowed data quality assessment of large data to be conducted without expert involvement [12,30], [32][33][34].…”

Section: Ontology Adoption In Data Qualitymentioning

confidence: 99%

Replacing missing values using trustworthy data values from web data sources

Jaya

Sidi

Yusof

et al. 2017

J. Phys.: Conf. Ser.

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Section: Ontology Adoption In Data Qualitymentioning

confidence: 99%

Replacing missing values using trustworthy data values from web data sources

Jaya

Sidi

Yusof

et al. 2017

J. Phys.: Conf. Ser.

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“…The data quality model is based on our previous works for data warehouses [7] and data streams [6]. We define quality factors based on metrics which perform data quality measurements by executing queries (which return some aggregate value) or by invoking some specific functions (implemented as Java methods).…”

Section: Data Quality Controlmentioning

confidence: 99%

Constance

Hai

Geisler

Quix

2016

Proceedings of the 2016 International Conference on Management of Data

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155

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As the challenge of our time, Big Data still has many research hassles, especially the variety of data. The high diversity of data sources often results in information silos, a collection of non-integrated data management systems with heterogeneous schemas, query languages, and APIs. Data Lake systems have been proposed as a solution to this problem, by providing a schema-less repository for raw data with a common access interface. However, just dumping all data into a data lake without any metadata management, would only lead to a 'data swamp'. To avoid this, we propose Constance 1 , a Data Lake system with sophisticated metadata management over raw data extracted from heterogeneous data sources. Constance discovers, extracts, and summarizes the structural metadata from the data sources, and annotates data and metadata with semantic information to avoid ambiguities. With embedded query rewriting engines supporting structured data and semi-structured data, Constance provides users a unified interface for query processing and data exploration. During the demo, we will walk through each functional component of Constance. Constance will be applied to two real-life use cases in order to show attendees the importance and usefulness of our generic and extensible data lake system.

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“…Data quality (DQ) is important in DSMS, but it is not extensive. Their group also generalized the DQ of a data stream using ontology [23].…”

Section: Related Workmentioning

confidence: 99%

Vehicle Embedded Data Stream Processing Platform for Android Devices

Akiyama¹,

Nakamoto²,

Yamaguchi³

et al. 2015

ijacsa

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Abstract-Automotive information services utilizing vehicle data are rapidly expanding. However, there is currently no data centric software architecture that takes into account the scale and complexity of data involving numerous sensors. To address this issue, the authors have developed an in-vehicle datastream management system for automotive embedded systems (eDSMS) as data centric software architecture. Providing the data stream functionalities to drivers and passengers are highly beneficial. This paper describes a vehicle embedded data stream processing platform for Android devices. The platform enables flexible query processing with a dataflow query language and extensible operator functions in the query language on the platform. The platform employs architecture independent of data stream schema in in-vehicle eDSMS to facilitate smoother Android application program development. This paper presents specifications and design of the query language and APIs of the platform, evaluate it, and discuss the results.

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Ontology-Based Data Quality Management for Data Streams

Cited by 34 publications

References 31 publications

Replacing missing values using trustworthy data values from web data sources

Replacing missing values using trustworthy data values from web data sources

Constance

Vehicle Embedded Data Stream Processing Platform for Android Devices

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