Mónica Caniupán

The consistency extractor system: Answer set programs for consistent query answering in databases

We describe the Consistency Extractor System (Cons Ex) that computes consistent answers to Datalog queries with negation posed to relational databases that may be inconsistent with respect to certain integrity constraints. In order to solve this task, Cons Ex uses answers set programming. More precisely, Cons Ex uses disjunctive logic programs with stable models semantics to specify and reason with the repairs, i.e. with the consistent virtual instances that minimally depart from the original database. The consistent information is invariant under all repairs. Cons Ex achieves efficient query evaluation by implementing magic sets techniques. We describe the general methodology, its optimizations for query answering, and the architecture of the system. We also present encouraging experimental results.

The Consistency Extractor System: Querying Inconsistent Databases Using Answer Set Programs

We present the Consistency Extractor System(ConsEx) that uses answer set programmingto compute consistent answers to first-order queries posed to relational databases that may be inconsistent wrt their integrity constraints. Among other features, ConsEximplements a magic setstechnique to evaluate queries via disjunctive logic programs with stable model semantics that specify the repair of the original database. We describe the methodology and the system; and also present some experimental results.

Optimizing repair programs for consistent query answering

Databases may not satisfy integrity constraints (ICs) for several reasons. Nevertheless, in most of the cases an important part of the data is still consistent wrt certain desired ICs, and the database can still give some correct answers to queries wrt those ICs. Consistent query answers are characterized as ordinary answers obtained from every minimally repaired and consistent version of the database. Database repairs can be specified as stable models of disjunctive logic programs with program constraints. In this paper, we optimize repair programs, model computation, and query evaluation from them. We make repair programs more compact by eliminating redundant rules and unnecessary programs denial constraints. These results facilitate the application of magic sets techniques to query evaluation in general, and in DLV, a logic programming system that implements the stable models semantics, in particular. We also analyze the implementation in DLV of queries with aggregate functions.

Consistent query answering under spatial semantic constraints

Consistent query answering (CQA) is an inconsistency tolerant approach to obtaining semantically correct answers from a database that may be inconsistent with respect to a set of integrity constraints. In this work, we formalize the notion of consistent query answer for spatial databases with respect to a special but relevant class of spatial semantic integrity constraints (SICs). In order to do this, we first characterize conflicting spatial data, and next, define admissible instances that restore consistency while staying close to the original instance. In this way we obtain a repair semantics, which is used as an instrumental concept to define consistent answers as a set-theoretic and geometric aggregation of answers from all admissible repairs. After establishing the intractability of consistent query answering, we identify and investigate a class of denial SICs (IDSICs) and spatial queries for which it is possible to efficiently compute consistent query answers via core computation.

Repairing inconsistent dimensions in data warehouses

A dimension in a data warehouse (DW) is a set of elements connected by a hierarchical relationship. The elements are used to view summaries of data at different levels of abstraction. In order to support an efficient processing of such summaries, a dimension is usually required to satisfy different classes of integrity constraints. In scenarios where the constraints properly capture the semantics of the DW data, but they are not satisfied by the dimension, the problem of repairing (correcting) the dimension arises. In this paper, we study the problem of repairing a dimension in the context of two main classes of integrity constraints: strictness and covering constraints. We introduce the notion of minimal repair of a dimension: a new dimension that is consistent with respect to the set of integrity constraints, which is obtained by applying a minimal number of updates to the original dimension. We study the complexity of obtaining minimal repairs, and show how they can be characterized using Datalog programs with weak constraints under the stable model semantics.

An inconsistency tolerant approach to querying spatial databases

In order to deal with inconsistent databases, a repair semantics defines a set of admissible database instances that restore consistency, while staying close to the original instance. This set can be used to characterize consistent data and consistent query answers in inconsistent databases. In this work we present a repair semantics for spatial databases and spatial integrity constraints, i.e. constraints that combine semantic and topological aspects of spatial data. We also propose the notion of consistent answer to a spatial conjunctive query. This introduces the idea of inconsistency tolerance in the spatial domain, shifting the goal from the consistency of a spatial database to the consistency of query answers.

Repairing dimension hierarchies under inconsistent reclassification

On-Line Analytical Processing (OLAP) dimensions are usually modelled as a hierarchical set of categories (the dimension schema), and dimension instances. The latter consist in a set of elements for each category, and relations between these elements (denoted rollup). To guarantee summarizability, a dimension is required to be strict, that is, every element of the dimension instance must have a unique ancestor in each of its ancestor categories. In practice, elements in a dimension instance are often reclassified, meaning that their rollups are changed (e.g., if the current available information is proved to be wrong). After this operation the dimension may become non-strict. To fix this problem, we propose to compute a set of minimal r-repairs for the new non-strict dimension. Each minimal r-repair is a strict dimension that keeps the result of the reclassification, and is obtained by performing a minimum number of insertions and deletions to the instance graph. We show that, although in the general case finding an r-repair is NP-complete, for real-world dimension schemas, computing such repairs can be done in polynomial time. We present algorithms for this, and discuss their computational complexity.

Handling inconsistencies in data warehouses

Data warehouses (DWs) can become inconsistent when some dimensional constraints are not satisfied by the dimension instances. In this paper, we present preliminary results about the effects of the violation of partitioning constraints in homogeneous dimension instances over aggregation queries, and in particular over the summarizability property (SUMM) of the DWs. We are interested in finding ways to retrieve consistent answers even when the DW is inconsistent. We give a notion of repair for inconsistent instances based on a notion of prioritized minimization. We also describe a notion of consistent answer in DWs.Data warehouses (DWs) can become inconsistent when some dimensional constraints are not satisfied by the dimension instances In this paper, we present preliminary results about the effects of the violation of partitioning constraints in homogeneous dimension instances over aggregation queries, and in particular over the summarizability property (SUMM) of the DWs We are interested in finding ways to retrieve consistent answers even when the DW is inconsistent We give a notion of repair for inconsistent instances based on a notion of prioritized minimization We also describe a notion of consistent answer in DWs.

Data Warehouse Fixer: Fixing Inconsistencies in Data Warehouses

Dimensions in Data Warehouses (DWs) are set of elements connected by a hierarchical relationship. Usually, dimensions are required to be strict and covering to support summarizations at different levels of granularity. A dimension is strict if all they rollup relations are functions, and is covering if every element in a category is connected with an element in its ancestor categories. We present the Data Warehouse Fixer (DWF), a system that restores consistency of dimensions that fail to satisfy their strictness constraints. The system checks consistency, computes minimal repairs for inconsistent dimensions by implementing Datalog programs with negation and weak constraints, and also fixes inconsistent dimensions.

Extended dimensions for cleaning and querying inconsistent data warehouses

A dimension in a data warehouse (DW) is an abstract concept that groups data that share a common semantic meaning. The dimensions are modeled using a hierarchical schema of categories. A dimension is called strict if every element of each category has exactly one ancestor in each parent category, and covering if each element of a category has an ancestor in each parent category. If a dimension is strict and covering we can use pre-computed results at lower levels to answer queries at higher levels. This capability of computing summaries is vital for efficiency purposes. Nevertheless, when dimensions are not strict/covering it is important to know their strictness and covering constraints to keep the capability of obtaining correct summarizations. Real world dimensions might fail to satisfy these constraints, and, in these cases, it is important to find ways to fix the dimensions (correct them) or find ways to get correct answers to queries posed on inconsistent dimensions. A minimal repair is a new dimension that satisfies the strictness and covering constraints, and that is obtained from the original dimension through a minimum number of changes. The set of minimal repairs can be used as a tool to compute answers to aggregate queries in the presence of inconsistencies. However, computing all of them is NP-hard. In this paper, instead of trying to find all possible minimal repairs, we define a single compatible repair that is consistent with respect to both strictness and covering constraints, is close to the inconsistent dimension, can be computed efficiently and can be used to compute approximate answers to aggregate queries. In order to define the compatible repair we defined the notion of extended dimension that supports sets of elements in categories.