Existential active integrity constraints

Caroprese, Luciano; Greco, Sergio; Molinaro, Cristian; Trubitsyna, Irina; Zumpano, Ester

doi:10.1016/j.eswa.2020.114297

Cited by 6 publications

(3 citation statements)

References 53 publications

(64 reference statements)

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“…It remains to study further our founded ECA repairs of Section 4.2 and their grounded versions. We also plan to check whether the more expressive existential AICs of [9] transfer. Finally, we would like to generalise the history component of h-databases from update actions to event algebra expressions as studied e.g.…”

Section: Discussionmentioning

confidence: 99%

Database Repair via Event-Condition-Action Rules in Dynamic Logic

Feuillade

Herzig

Rantsoudis

2022

Lecture Notes in Computer Science

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Event-condition-action (ECA) rules equip a database with information about preferred ways to restore integrity. They face problems of non-terminating executions and only procedural semantics had been given to them up to now. Declarative semantics however exist for a particular class of ECA rules lacking the event argument, called active integrity constraints. We generalise one of these semantics to ECA rules and couch it in a simple dynamic logic with deterministic past.

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Section: Discussionmentioning

confidence: 99%

Database Repair via Event-Condition-Action Rules in Dynamic Logic

Feuillade

Herzig

Rantsoudis

2022

Lecture Notes in Computer Science

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“…Since explaining query answering has recently drawn considerably attention under existential rule languages (e.g., see [10,11,12,13,14,15]), and knowledge representation in general (e.g., in the context of argumentation [16]) an interesting direction for future work is to address such issue in our setting. Also, it would be interesting to account for user preferences when answering queries, as recently done in [17,18], possibly considering other ways of expressing preferences, e.g. by means of CP-nets [19,20].…”

Section: Next Stepsmentioning

confidence: 99%

Querying Data Exchange Settings Beyond Positive Queries

Greco¹,

Molinaro²,

Trubitsyna³

2023

Theory and Practice of Logic Programming

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Data exchange, the problem of transferring data from a source schema to a target schema, has been studied for several years. The semantics of answering positive queries over the target schema has been defined in early work, but little attention has been paid to more general queries. A few proposals of semantics for more general queries exist but they either do not properly extend the standard semantics under positive queries, giving rise to counterintuitive answers, or they make query answering undecidable even for the most important data exchange settings, for example, with weakly-acyclic dependencies. The goal of this paper is to provide a new semantics for data exchange that is able to deal with general queries. At the same time, we want our semantics to coincide with the classical one when focusing on positive queries, and to not trade-off too much in terms of complexity of query answering. We show that query answering is undecidable in general under the new semantics, but it is $\text{co}\text{NP}\text{-complete}$ when the dependencies are weakly-acyclic. Moreover, in the latter case, we show that exact answers under our semantics can be computed by means of logic programs with choice, thus exploiting existing efficient systems. For more efficient computations, we also show that our semantics allows for the construction of a representative target instance, similar in spirit to a universal solution, that can be exploited for computing approximate answers in polynomial time.

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“…The use of null values is the commonly accepted approach for handling incomplete data, and the databases containing null values are usually called incomplete database. Some recent proposals in this direction can be found in [11][12][13][14][15][16].…”

Section: Incomplete Informationmentioning

confidence: 99%

DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation

Shahbazian

Trubitsyna

2022

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Insights and analysis are only as good as the available data. Data cleaning is one of the most important steps to create quality data decision making. Machine learning (ML) helps deal with data quickly, and to create error-free or limited-error datasets. One of the quality standards for cleaning the data includes handling the missing data, also known as data imputation. This research focuses on the use of machine learning methods to deal with missing data. In particular, we propose a generative adversarial network (GAN) based model called DEGAIN to estimate the missing values in the dataset. We evaluate the performance of the presented method and compare the results with some of the existing methods on publicly available Letter Recognition and SPAM datasets. The Letter dataset consists of 20,000 samples and 16 input features and the SPAM dataset consists of 4601 samples and 57 input features. The results show that the proposed DEGAIN outperforms the existing ones in terms of root mean square error and Frechet inception distance metrics.

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Existential active integrity constraints

Cited by 6 publications

References 53 publications

Database Repair via Event-Condition-Action Rules in Dynamic Logic

Database Repair via Event-Condition-Action Rules in Dynamic Logic

Querying Data Exchange Settings Beyond Positive Queries

DEGAIN: Generative-Adversarial-Network-Based Missing Data Imputation

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