Violating Integrity Constraints Research Articles

Semantic data management in P2P systems driven by self-esteem

Abstract This paper is a contribution to semantic data management in P2P systems. It is based on the previous works of the same authors in which a declarative semantics for P2P systems is defined: under this semantics, only facts not making the local databases inconsistent are imported (Weak Models) and the Maximal Weak Models are those in which peers import maximal sets of facts not violating integrity constraints. The proposal, presented in this paper, stems from the following two observations: (i) the Maximal Weak Model Semantics ensures that locally consistent P2P systems always admit a maximal weak model, but fails for locally inconsistent P2P systems; (ii) the self-esteem degree of a peer should impact on the interaction with other peers in the system so that driving the integration process. From the above-mentioned observations a more general framework is presented. Our proposal is able to manage local inconsistencies and allows to model a peer integration process driven by the self-esteem degree of each peer. Different self-esteem degrees could be considered; in this paper we focus our attention on three different scenarios in which a generic peer can declare an high, low or medium self-esteem degree, stating respectively that it trusts its own knowledge more, less or equally with respect to the knowledge that can be provided from the rest of the system. Three different basic semantics are proposed, High, Low, Medium Self-Esteem Semantics and results about the computational complexity of P2P logic queries are investigated by considering brave and cautions reasoning. The paper also presents an extension of the basic framework of the Self-Esteem Semantics that models a finer-grained self-esteem concept and allows each peer to exhibit different levels of self-esteem each of them defined with respect to a subset of its mapping rules.

Declarative Semantics for P2P Data Management System

The paper presents a logic-based framework for modeling the interaction among peers. It is assumed that each peer consists of a database, a set of standard logic rules, a set of mapping rules allowing to import a maximal set of atoms not leading to inconsistency and a set of integrity constraints. The proposal relies on previous works in Calvanese et al. (in: PODS, 2004) and Caroprese et al. (in: FLAIRS, 2006) where a (declarative) semantics for P2P systems is defined. Under this semantics, only facts not making the local databases inconsistent can be imported—Weak Models. This mechanism leads to the concept of Maximal Weak Models that are weak models in which peers import maximal sets of facts not violating integrity constraints. Different extensions to the basic framework, that aim at introducing significant mechanisms of preferences among different scenarios in the case of conflicting information, can be provided. This paper presents the basic framework of Maximal Weak Models and two extensions: the Trusted Weak Model Semantics and the Dynamic Weak Model Semantics. The Trusted Weak Model Semantics stems from the observation that the framework proposed in Calvanese et al. (in: PODS, 2004) and Caroprese et al. (in: FLAIRS, 2006) does not provide any mechanism to set priorities among mapping rules, rules that “integrate” data of a source peer into the database of a target peer. Anyhow, while collecting data, it is quite natural for a source peer to associate different degrees of reliability to the portion of data provided by its neighbor peers. Therefore, this paper enhances the basic semantics by using priority levels among mapping rules in order to select the weak models containing a maximum number of mapping atoms according to their importance. We will call these weak models, Trusted Weak Models, and we will show they can be computed as stable models of a logic program with weak constraints. The Dynamic Weak Model Semantics further enhances the basic framework by introducing aggregates and different levels of preference criteria that are not rigid, i.e., fixed a priori at design time, but depends on the database instance. The extended framework allows to model concepts like “in the case of conflicting information, it is preferable to import data from the neighbor peer that can provide the maximum number of tuples” or “in the case of conflicting information, it is preferable to import data from the neighbor peer such that the sum of the values of an attribute is minimum” without selecting a priori preferred peers. We will call these weak models, Dynamic Weak Models, and we will show they can be computed as stable models of a logic program with a list of sets of priorities.

On the complexity of sampling query feedback restricted database repair of functional dependency violations

An inconsistent database is a database instance violating integrity constraints. A repair of an inconsistent database is a maximal consistent subset. Sampling from the repair space is an alternative approach meeting the needs of many applications. In this paper, we introduce a new class of repair, query feedback restricted repair, based on the feedback on user's witness query. We first map out a picture of both data and combined complexities of repair existence problems under different cases to identify the intractable cases. Especially, we show that if the query is a projection or a union query, then the decision problem is NP-complete; even worse, if the query is a conjunctive query, the decision problem becomes Σ2P-complete. However, we prove that the combined complexity of the repair existence problem is in LOGSPACE when the witness query is a selection-join query, and this conclusion also implies that the combined complexity of side-effect free deletion propagation problem under group-deletion is in LOGSPACE which is not considered in previous works. Additionally, we provide a polynomial random repair sampling algorithm under combined complexity. At last, we revisit the key preserving condition [1] and show that it will simplify the problem, i.e., some cases become tractable for certain key preserving views, as opposed to their counterparts that are not key preserving.

Consistent query answering over inconsistent databases

This paper addresses the problem of managing inconsistent databases, that is, databases violating integrity constraints. Most of the works dealing with the problem of extracting reliable informatio...

Consistent query answering over inconsistent databases

This paper addresses the problem of managing inconsistent databases, that is, databases violating integrity constraints. Most of the works dealing with the problem of extracting reliable information from inconsistent databases are based on the notion of repair, a minimal set of update operations which leads the database to a consistent state (called repaired database), and the notion of consistent query answer, a query answer that can be obtained from every repaired database. The problem of computing consistent query answers in the presence of different classes of constraints and under different repair strategies is analyzed.

Querying and repairing inconsistent numerical databases

The problem of extracting consistent information from relational databases violating integrity constraints on numerical data is addressed. In particular, aggregate constraints defined as linear inequalities on aggregate-sum queries on input data are considered. The notion of repair as consistent set of updates at attribute-value level is exploited, and the characterization of several data-complexity issues related to repairing data and computing consistent query answers is provided. Moreover, a method for computing “reasonable” repairs of inconsistent numerical databases is provided, for a restricted but expressive class of aggregate constraints. Several experiments are presented which assess the effectiveness of the proposed approach in real-life application scenarios.

A logical framework for querying and repairing inconsistent databases

In this paper, we address the problem of managing inconsistent databases, i.e., databases violating integrity constraints. We propose a general logic framework for computing repairs and consistent answers over inconsistent databases. A repair for a possibly inconsistent database is a minimal set of insert and delete operations which makes the database consistent, whereas a consistent answer is a set of tuples derived from the database, satisfying all integrity constraints. In our framework, different types of rules defining general integrity constraints, repair constraints (i.e., rules defining conditions on the insertion or deletion of atoms), and prioritized constraints (i.e., rules defining priorities among updates and repairs) are considered. We propose a technique based on the rewriting of constraints into (prioritized) extended disjunctive rules with two different forms of negation (negation as failure and classical negation). The disjunctive program can be used for two different purposes: to compute "repairs" for the database and produce consistent answers, i.e., a maximal set of atoms which do not violate the constraints. We show that our technique is sound, complete (each preferred stable model defines a repair and each repair is derived from a preferred stable model), and more general than techniques previously proposed.

Extending a relational database with deferred referential integrity checking and intelligent joins

Interactive use of relational database management systems (DBMS) requires a user to be knowledgeable about the semantics of the application represented in the database. In many cases, however, users are not trained in the application field and are not DBMS experts. Two categories of functionality are problematic for such users: (1) updating a database without violating integrity constraints imposed by the domain and (2) using join operations to retrieve data from more than one relation. We have been conducting research to help an uninformed or casual user interact with a relational DBMS. This paper describes two capabilities to aid an interactive database user who is neither an application specialist nor a DBMS expert. We have developed deferred Referential Integrity Checking (RIC) and Intelligent Join (IJ) which extend the operations of a relational DBMS. These facilities are made possible by explicit representation of database semantics combined with a relational schema. Deferred RIC is a static validation procedure that checks uniqueness of tuples, non-null keys, uniqueness of keys, and inclusion dependencies. IJ allows a user to identify only the “target” data which is to be retrieved without the need to additionally specify “join clauses”. In this paper we present the motivation for these facilities, describe the features of each, and present examples of their use.