ProSA—Using the CHASE for Provenance Management

Auge, Tanja; Heuer, Andreas

doi:10.1007/978-3-030-28730-6_22

Cited by 7 publications

(4 citation statements)

References 21 publications

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“…For reasons of transparency, however, the backward direction must also be provided in order to be able to disclose later on which base data a result was obtained [55]. Why-and how-provenance can be used for this purpose [56]. In addition to human-in-the-loop approaches, however, there is also the countertrend, namely AI-assisted fully automated data preparation [57].…”

Section: Data Administrationmentioning

confidence: 99%

Data Is the New Oil–Sort of: A View on Why This Comparison Is Misleading and Its Implications for Modern Data Administration

Stach

2023

Future Internet

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Currently, data are often referred to as the oil of the 21st century. This comparison is not only used to express that the resource data are just as important for the fourth industrial revolution as oil was for the technological revolution in the late 19th century. There are also further similarities between these two valuable resources in terms of their handling. Both must first be discovered and extracted from their sources. Then, the raw materials must be cleaned, preprocessed, and stored before they can finally be delivered to consumers. Despite these undeniable similarities, however, there are significant differences between oil and data in all of these processing steps, making data a resource that is considerably more challenging to handle. For instance, data sources, as well as the data themselves, are heterogeneous, which means there is no one-size-fits-all data acquisition solution. Furthermore, data can be distorted by the source or by third parties without being noticed, which affects both quality and usability. Unlike oil, there is also no uniform refinement process for data, as data preparation should be tailored to the subsequent consumers and their intended use cases. With regard to storage, it has to be taken into account that data are not consumed when they are processed or delivered to consumers, which means that the data volume that has to be managed is constantly growing. Finally, data may be subject to special constraints in terms of distribution, which may entail individual delivery plans depending on the customer and their intended purposes. Overall, it can be concluded that innovative approaches are needed for handling the resource data that address these inherent challenges. In this paper, we therefore study and discuss the relevant characteristics of data making them such a challenging resource to handle. In order to enable appropriate data provisioning, we introduce a holistic research concept from data source to data sink that respects the processing requirements of data producers as well as the quality requirements of data consumers and, moreover, ensures a trustworthy data administration.

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Section: Data Administrationmentioning

confidence: 99%

Data Is the New Oil–Sort of: A View on Why This Comparison Is Misleading and Its Implications for Modern Data Administration

Stach

2023

Future Internet

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“…In the relation database, when evaluating queries over the annotated data, the primary question is how the annotation data should be managed when processing each query operation [31]. Specifically, for the Select, Project, Join, and Union (SPJU) queries, we need to acquire how to combine the annotations of the joined tuples and how to combine the annotations when there are different copies of the same tuple.…”

Section: Query Processing Over Annotated Datamentioning

confidence: 99%

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Zou¹,

Zou²,

Wang³

2023

Journal of Computers

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<p>Data annotation is the categorization and labelling of data for applications, such as machine learning, artificial intelligence, and data integration. The categorization and labelling are done to achieve a specific use case in relation to solving problems. Existing data annotation systems and modules face imperfections such as knowledge and annotation not being formally integrated, narrow application range, and difficulty to apply on existing database management applications. To analyze and process annotated data, obtain the relationship between different annotations, and capture metainformation in data provenance and probabilistic databases, in this paper, we design a back-end query processing framework as a supplementary interface for the database management system to extend operation to datasets and boost efficiency. The framework utilizes Java language and the MVC model for development to achieve lightweight, cross-platform, and high adaptability identities. The contribution of this paper is mainly reflected in two aspects. The first contribution is to implement query processing, provenance semiring, and semiring homomorphism over annotated data. The second contribution is to combine query processing and provenance with SQL statements in order to enable the database manager to invoke operations to annotation.</p> <p> </p>

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“…Since queries that occur in the context of projects can become arbitrarily complex -simple selections and projections, even joins and aggregations -the inversion of these queries is often not 100% possible or necessary. However, using provenance enables us to perform this inversion as accurately as possible [1]. This implies a natural conflict of interest between publishing original data (provenance) and protecting these…”

Section: Privacy Vs Provenancementioning

confidence: 99%

“…These data can be reconstructed by where-, whyand howprovenance with the techniques described in [1]. We will now consider the privacy aspects of these results of the provenance queries: (1) Using where, there is generally not enough data worth protecting and reproducibility of the data is not guaranteed.…”

Section: Querymentioning

confidence: 99%

Privacy Aspects of Provenance Queries

Auge¹,

Scharlau²,

Heuer³

2021

Preprint

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Given a query result of a big database, why-provenance can be used to calculate the necessary part of this database, consisting of so-called witnesses. If this database consists of personal data, privacy protection has to prevent the publication of these witnesses. This implies a natural conflict of interest between publishing original data (provenance) and protecting these data (privacy).In this paper, privacy goes beyond the concept of personal data protection. The paper gives an extended definition of privacy as intellectual property protection. If the provenance information is not sufficient to reconstruct a query result, additional data such as witnesses or provenance polynomials have to be published to guarantee traceability. Nevertheless, publishing this provenance information might be a problem if (significantly) more tuples than necessary can be derived from the original database. At this point, it is already possible to violate privacy policies, provided that quasi identifiers are included in this provenance information. With this poster, we point out fundamental problems and discuss first proposals for solutions.

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ProSA—Using the CHASE for Provenance Management

Cited by 7 publications

References 21 publications

Data Is the New Oil–Sort of: A View on Why This Comparison Is Misleading and Its Implications for Modern Data Administration

Data Is the New Oil–Sort of: A View on Why This Comparison Is Misleading and Its Implications for Modern Data Administration

Annolog: A Query Processing Framework for Modelling and Reasoning with Annotated Data

Privacy Aspects of Provenance Queries

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