Probabilistic query answering over inconsistent databases

This paper presents a framework for querying inconsistent databases in the presence of functional dependencies. 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 tuple insertions and deletions 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. In this work, both the notion of repair and query answer differ from the original ones. In the presence of functional dependencies, tuple deletions are the only operations that are performed in order to restore the consistency of an inconsistent database. However, deleting a tuple to remove an integrity violation potentially eliminates useful information in that tuple. In order to cope with this problem, we adopt a notion of repair, based on tuple updates, which allows us to better preserve information in the source database. A drawback of the notion of consistent query answer is that it does not allow us to discriminate among non-consistent answers, namely answers which can be obtained from a non-empty proper subset of the repaired databases. To obtain more informative query answers, we propose the notion of probabilistic query answer, that is query answers are tuples associated with probabilities. This new semantics of query answering over inconsistent databases allows us to give a measure of uncertainty to query answers. We show that the problem of computing probabilistic query answers is FP ^#P -complete. We also propose a technique for computing probabilistic answers to arbitrary relational algebra queries.     

Study on consistent query answering in inconsistent databases

Consistent query answering is an approach to retrieving consistent answers over databases that might be inconsistent with respect to some given integrity constraints. The approach is based on a concept of repair. This paper surveys several recent researches on obtaining consistent information from inconsistent databases, such as the underlying semantic model, a number of approaches to computing consistent query answers and the computational complexity of this problem. Furthermore, the work outlines potential research directions in this area.     

Taxonomy-based relaxation of query answering in relational databases

Traditional information search in which queries are posed against a known and rigid schema over a structured database is shifting toward a Web scenario in which exposed schemas are vague or absent and data come from heterogeneous sources. In this framework, query answering cannot be precise and needs to be relaxed, with the goal of matching user requests with accessible data. In this paper, we propose a logical model and a class of abstract query languages as a foundation for querying relational data sets with vague schemas. Our approach relies on the availability of taxonomies, that is, simple classifications of terms arranged in a hierarchical structure. The model is a natural extension of the relational model in which data domains are organized in hierarchies, according to different levels of generalization between terms. We first propose a conservative extension of the relational algebra for this model in which special operators allow the specification of relaxed queries over vaguely structured information. We also study equivalence and rewriting properties of the algebra that can be used for query optimization. We then illustrate a logic-based query language that can provide a basis for expressing relaxed queries in a declarative way. We finally investigate the expressive power of the proposed query languages and the independence of the taxonomy in this context.     

Prioritized repairing and consistent query answering in relational databases

A consistent query answer in an inconsistent database is an answer obtained in every (minimal) repair. The repairs are obtained by resolving all conflicts in all possible ways. Often, however, the user is able to provide a preference on how conflicts should be resolved. We investigate here the framework of preferred consistent query answers, in which user preferences are used to narrow down the set of repairs to a set of preferred repairs. We axiomatize desirable properties of preferred repairs. We present three different families of preferred repairs and study their mutual relationships. Finally, we investigate the complexity of preferred repairing and computing preferred consistent query answers.     

Top-k typicality queries and efficient query answering methods on large databases

Finding typical instances is an effective approach to understand and analyze large data sets. In this paper, we apply the idea of typicality analysis from psychology and cognitive science to database query answering, and study the novel problem of answering top-k typicality queries. We model typicality in large data sets systematically. Three types of top-k typicality queries are formulated. To answer questions like “Who are the top-k most typical NBA players?”, the measure of simple typicality is developed. To answer questions like “Who are the top-k most typical guards distinguishing guards from other players?”, the notion of discriminative typicality is proposed. Moreover, to answer questions like “Who are the best k typical guards in whole representing different types of guards?”, the notion of representative typicality is used. Computing the exact answer to a top-k typicality query requires quadratic time which is often too costly for online query answering on large databases. We develop a series of approximation methods for various situations: (1) the randomized tournament algorithm has linear complexity though it does not provide a theoretical guarantee on the quality of the answers; (2) the direct local typicality approximation using VP-trees provides an approximation quality guarantee; (3) a local typicality tree data structure can be exploited to index a large set of objects. Then, typicality queries can be answered efficiently with quality guarantees by a tournament method based on a Local Typicality Tree. An extensive performance study using two real data sets and a series of synthetic data sets clearly shows that top-k typicality queries are meaningful and our methods are practical.     

On the finite controllability of conjunctive query answering in databases under open-world assumption

In this paper we study queries over relational databases with integrity constraints (ICs). The main problem we analyze is OWA query answering, i.e., query answering over a database with ICs under open-world assumption. The kinds of ICs that we consider are inclusion dependencies and functional dependencies, in particular key dependencies; the query languages we consider are conjunctive queries and unions of conjunctive queries. We present results about the decidability of OWA query answering under ICs. In particular, we study OWA query answering both over finite databases and over unrestricted databases, and identify the cases in which such a problem is finitely controllable, i.e., when OWA query answering over finite databases coincides with OWA query answering over unrestricted databases. Moreover, we are able to easily turn the above results into new results about implication of ICs and query containment under ICs, due to the deep relationship between OWA query answering and these two classical problems in database theory. In particular, we close two long-standing open problems in query containment, since we prove finite controllability of containment of conjunctive queries both under arbitrary inclusion dependencies and under key and foreign key dependencies. The results of our investigation are very relevant in many research areas which have recently dealt with databases under an incomplete information assumption: e.g., data integration, data exchange, view-based information access, ontology-based information systems, and peer data management systems.     

Using ontology databases for scalable query answering,inconsistency detection,and data integration

An ontology database is a basic relational database management system that models an ontology plus its instances. To reason over the transitive closure of instances in the subsumption hierarchy, for example, an ontology database can either unfold views at query time or propagate assertions using triggers at load time. In this paper, we use existing benchmarks to evaluate our method—using triggers—and we demonstrate that by forward computing inferences, we not only improve query time, but the improvement appears to cost only more space (not time). However, we go on to show that the true penalties were simply opaque to the benchmark, i.e., the benchmark inadequately captures load-time costs. We have applied our methods to two case studies in biomedicine, using ontologies and data from genetics and neuroscience to illustrate two important applications: first, ontology databases answer ontology-based queries effectively; second, using triggers, ontology databases detect instance-based inconsistencies—something not possible using views. Finally, we demonstrate how to extend our methods to perform data integration across multiple, distributed ontology databases.     

Likelog for flexible query answering

 Starting from unification based on similarity, a logic programming language, called LIKEness in LOGic (Likelog) is derived, thorougly relying on similarity. An operational semantics and a fix-point semantics of the language are defined, using an extension principle for fuzzy operators. The two approaches are proved to be related and a fuzzy extension of the least Herbrand model is given. One of the principal feature of such a logic programming language is to allow flexible query answering to deductive databases, which we show through an example. Moreover, we describe a system for web information retrieval through Likelog. I want to thank Ferrante Formato with whom I started and I continued this research and Prof. Giangiacomo Gerla for his great support and contribution given to this field.     

Neighborhood and associative query answering

Cooperative query answering extends the classical notion of query answering to provide neighborhood and associated information. Neighborhood query answering relaxes the query and its answer via abstract representations. To integrate the abstraction view with the subsumption (is-a) and composition (part-of) views of type hierarchy, the notion of type abstraction hierarchy is introduced. To evaluate and control query relaxation, a nearness measure mechanism is provided. Associative query answering provides information conceptually related to, but not explicitly asked by the query. As object association is context sensitive, a DB-Pattern-KB framework is developed that couples domain-specific knowledge and participating objects in localized problem domains via virtual database patterns. Associative query answering can then be accomplished through tracing the behavior dependencies among cooperating objects in those problem domains. Such a framework allows related databases and knowledge bases to be linked dynamically in various contexts yet be maintained relatively independent of each other. The proposed approach has been implemented in the cooperative database system tested, CoBase, at UCLA. Our experience reveals that the proposed techniques are effective for cooperative query answering.This research is supported by DARPA contract N00174-91-C-0107.     

Efficient query evaluation on probabilistic databases

We describe a framework for supporting arbitrarily complex SQL queries with “uncertain” predicates. The query semantics is based on a probabilistic model and the results are ranked, much like in Information Retrieval. Our main focus is query evaluation. We describe an optimization algorithm that can compute efficiently most queries. We show, however, that the data complexity of some queries is #P-complete, which implies that these queries do not admit any efficient evaluation methods. For these queries we describe both an approximation algorithm and a Monte-Carlo simulation algorithm.     

Intelligent query answering in rule based systems

We propose that in large knowledge bases which are collections of atomic facts and general rules (Horn clauses), the rules should be allowed to occur in the answer for a query. We introduce a new concept of the answer for a query which includes both atomic facts and general rules. We provide a method of transforming rules by relational algebra expressions built from projection, join, and selection and demonstrate how the answers consisting of both facts and general rules can be generated.     

Data exchange: semantics and query answering

Data exchange is the problem of taking data structured under a source schema and creating an instance of a target schema that reflects the source data as accurately as possible. In this paper, we address foundational and algorithmic issues related to the semantics of data exchange and to the query answering problem in the context of data exchange. These issues arise because, given a source instance, there may be many target instances that satisfy the constraints of the data exchange problem.We give an algebraic specification that selects, among all solutions to the data exchange problem, a special class of solutions that we call universal. We show that a universal solution has no more and no less data than required for data exchange and that it represents the entire space of possible solutions. We then identify fairly general, yet practical, conditions that guarantee the existence of a universal solution and yield algorithms to compute a canonical universal solution efficiently. We adopt the notion of the “certain answers” in indefinite databases for the semantics for query answering in data exchange. We investigate the computational complexity of computing the certain answers in this context and also address other algorithmic issues that arise in data exchange. In particular, we study the problem of computing the certain answers of target queries by simply evaluating them on a canonical universal solution, and we explore the boundary of what queries can and cannot be answered this way, in a data exchange setting.     

Intelligent query answering by knowledge discovery techniques

Knowledge discovery facilitates querying database knowledge and intelligent query answering in database systems. We investigate the application of discovered knowledge, concept hierarchies, and knowledge discovery tools for intelligent query answering in database systems. A knowledge-rich data model is constructed to incorporate discovered knowledge and knowledge discovery tools. Queries are classified into data queries and knowledge queries. Both types of queries can be answered directly by simple retrieval or intelligently by analyzing the intent of query and providing generalized, neighborhood or associated information using stored or discovered knowledge. Techniques have been developed for intelligent query answering using discovered knowledge and/or knowledge discovery tools, which includes generalization, data summarization, concept clustering, rule discovery, query rewriting, deduction, lazy evaluation, application of multiple-layered databases, etc. Our study shows that knowledge discovery substantially broadens the spectrum of intelligent query answering and may have deep implications on query answering in data- and knowledge-base systems     

Matching dependencies: semantics and query answering

Matching dependencies (MDs) are used to declaratively specify the identification (or matching) of certain attribute values in pairs of database tuples when some similarity conditions on other values are satisfied. Their enforcement can be seen as a natural generalization of entity resolution. In what we call the pure case of MD enforcement, an arbitrary value from the underlying data domain can be used for the value in common that is used for a matching. However, the overall number of changes of attribute values is expected to be kept to a minimum. We investigate this case in terms of semantics and the properties of data cleaning through the enforcement of MDs. We characterize the intended clean instances, and also the clean answers to queries, as those that are invariant under the cleaning process. The complexity of computing clean instances and clean query answering is investigated. Tractable and intractable cases depending on the MDs are identified and characterized.     

ACRES: efficient query answering on large compressed sequences

With the advances in next generation sequencing, the amount of genomic sequence data being produced continues to grow at an exponential rate. It is estimated that the entire genome of each individual human, each containing about 3 billion letters, could be made available in the next a few years. An increasingly pressing issue in genomics and medicine is how to efficiently store and query these massive amounts of sequence data. Recently a lossless compression technique has been proposed to drastically reduce the storage space of genomic sequences, taking advantage of the fact that any two genomes from the same species are highly similar and therefore only their differences need to be encoded. In this paper we study how to efficiently answer queries on the compressed sequences without first decompressing them. We study three important types of queries, including retrieving a subsequence, finding subsequences matching a given pattern, and finding subsequences similar to a pattern. We propose an index structure, filtering techniques, and efficient algorithms for answering these queries. We further demonstrate the utility of these algorithms using a real dataset.     

RDF partitioning for scalable SPARQL query processing

The volume of RDF data increases dramatically within recent years, while cloud computing platforms like Hadoop are supposed to be a good choice for processing queries over huge data sets for their wonderful scalability. Previous work on evaluating SPARQL queries with Hadoop mainly focus on reducing the number of joins through careful split of HDFS files and algorithms for generating Map/Reduce jobs. However, the way of partitioning RDF data could also affect system performance. Specifically, a good partitioning solution would greatly reduce or even totally avoid cross-node joins, and significantly cut down the cost in query evaluation. Based on HadoopDB, this work processes SPARQL queries in a hybrid architecture, where Map/Reduce takes charge of the computing tasks, and RDF query engines like RDF-3X store the data and execute join operations. According to the analysis of query workloads, this work proposes a novel algorithm for automatically partitioning RDF data and an approximate solution to physically place the partitions in order to reduce data redundancy. It also discusses how to make a good trade-off between query evaluation efficiency and data redundancy. All of these proposed approaches have been evaluated by extensive experiments over large RDF data sets.