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An enterprise database contains a global, integrated, and consistent representation of a company’s data. Multi-level modeling facilitates the definition and maintenance of such an integrated conceptual data model in a dynamic environment of changing data requirements of diverse applications. Multi-level models transcend the traditional separation of class and object with clabjects as the central modeling primitive, which allows for a more flexible and natural representation of many real-world use cases. In deep instantiation, the number of instantiation levels of a clabject or property is indicated by a single potency. Dual deep modeling (DDM) differentiates between source potency and target potency of a property or association and supports the flexible instantiation and refinement of the property by statements connecting clabjects at different modeling levels. DDM comes with multiple generalization of clabjects, subsetting/specialization of properties, and multi-level cardinality constraints. Examples are presented using a UML-style notation for DDM together with UML class and object diagrams for the representation of two-level user views derived from the multi-level model. Syntax and semantics of DDM are formalized and implemented in F-Logic, supporting the modeler with integrity checks and rich query facilities. [ABSTRACT FROM AUTHOR]

In this work we establish and investigate connections between causes for query answers in databases, database repairs with respect to denial constraints, and consistency-based diagnosis. The first two are relatively new research areas in databases, and the third one is an established subject in knowledge representation. We show how to obtain database repairs from causes, and the other way around. Causality problems are formulated as diagnosis problems, and the diagnoses provide causes and their responsibilities. The vast body of research on database repairs can be applied to the newer problems of computing actual causes for query answers and their responsibilities. These connections are interesting per se. They also allow us, after a transition inspired by consistency-based diagnosis to computational problems on hitting-sets and vertex covers in hypergraphs, to obtain several new algorithmic and complexity results for database causality. [ABSTRACT FROM AUTHOR]

The article studies the rewriting mechanisms for intensional documents in the Active XML framework, abstracted in the form of active context-free games. The safe rewriting problem studied in this article is to decide whether the first player, JULIET, has a winning strategy for a given game and (nested) word; this corresponds to a successful rewriting strategy for a given intensional document. The article examines several extensions of active context-free games. The primary extension allows for more expressive schemas (namely XML schemas and regular nested word languages) for both target and replacement languages and has the effect that games are played on nested words instead of (flat) words as in previous studies. Other extensions consider validation of input parameters of web services, and an alternative semantics based on insertion of values returned by the services. In general, the complexity of the safe rewriting problem is highly intractable (doubly exponential time), but the article identifies relevant tractable cases. [ABSTRACT FROM AUTHOR]

Large scale of short text records are now prevalent, such as news highlights, scientific paper citations, and posted messages in a discussion forum, and are often stored as set records in hidden-Web databases. Many interesting information retrieval tasks are correspondingly raised on the correlation query over these short text records, such as finding hot topics over news highlights and searching related scientific papers on a certain topic. However, current relational database management systems (RDBMS) do not directly provide support on set correlation query. Thus, in this paper, we address both the effectiveness and the efficiency issues of set correlation query over set records in databases. First, we present a framework of set correlation query inside databases. To the best of our knowledge, only the Pearson's correlation can be implemented to construct token correlations by using RDBMS facilities. Thereby, we propose a novel correlation coefficient to extend Pearson's correlation, and provide a pure-SQL implementation inside databases. We further propose optimal strategies to set up correlation filtering threshold, which can greatly reduce the query time. Our theoretical analysis proves that with a proper setting of filtering threshold, we can improve the query efficiency with a little effectiveness loss. Finally, we conduct extensive experiments to show the effectiveness and the efficiency of proposed correlation query and optimization strategies. [ABSTRACT FROM AUTHOR]

In the last decades the popularity of natural language interfaces to databases (NLIDBs) has increased, because in many cases information obtained from them is used for making important business decisions. Unfortunately, the complexity of their customization by database administrators make them difficult to use. In order for a NLIDB to obtain a high percentage of correctly translated queries, it is necessary that it is correctly customized for the database to be queried. In most cases the performance reported in NLIDB literature is the highest possible; i.e., the performance obtained when the interfaces were customized by the implementers. However, for end users it is more important the performance that the interface can yield when the NLIDB is customized by someone different from the implementers. Unfortunately, there exist very few articles that report NLIDB performance when the NLIDBs are not customized by the implementers. This article presents a semantically-enriched data dictionary (which permits solving many of the problems that occur when translating from natural language to SQL) and an experiment in which two groups of undergraduate students customized our NLIDB and English language frontend (ELF), considered one of the best available commercial NLIDBs. The experimental results show that, when customized by the first group, our NLIDB obtained a 44.69 % of correctly answered queries and ELF 11.83 % for the ATIS database, and when customized by the second group, our NLIDB attained 77.05 % and ELF 13.48 %. The performance attained by our NLIDB, when customized by ourselves was 90 %. [ABSTRACT FROM AUTHOR]

Query formalisms and facilities have received significant attention in the past decades resulting in the development of query languages with varying characteristics; many of them resemble sql. Query facilities typically ship as part of database management systems or, sometimes, bundled with programming languages. For applications written in imperative programming languages, database management systems impose an expensive model transformation. In-memory data structures can represent sophisticated relationships in a manner that is efficient in terms of storage and processing overhead, but most general purpose programming languages lack an interpreter and/or an expressive query language for manipulating interactive queries. Issuing interactive ad-hoc queries on program data structures is tough. This work presents a method and an implementation for representing an application's arbitrary imperative programming data model as a queryable relational one. The Pico COllections Query Library ( pi co ql) uses a domain specific language to define a relational representation of application data structures and an sql interface implementation. Queries are issued interactively and are type safe. We demonstrate our relational representation for objects and the library's usefulness on three large c++ projects. pi co ql enhances query expressiveness and boosts productivity compared to querying via traditional programming constructs. [ABSTRACT FROM AUTHOR]

The framework of database repairs is a principled approach to managing inconsistency in databases. In particular, the consistent answers of a query on an inconsistent database provide sound semantics and the guarantee that the values obtained are those returned by the query on every repair of the given inconsistent database. In this paper, we carry out a systematic investigation of the data complexity of the consistent answers of conjunctive queries for set-based repairs and with respect to classes of constraints that, in recent years, have been extensively studied in the context of data exchange and data integration. Our results, which range from polynomial-time computability to undecidability, complement or improve on earlier work, and provide a fairly comprehensive picture of the data complexity of consistent query answering. We also address the problem of finding a 'representative' or 'useful' repair of an inconsistent database. To this effect, we introduce the notion of a universal repair, as well as relaxations of it, and then apply it to the investigation of the data complexity of consistent query answering. [ABSTRACT FROM AUTHOR]

Networks of relational transducers can serve as a formal model for declarative networking, focusing on distributed database querying applications. In declarative networking, a crucial property is eventual consistency, meaning that the final output does not depend on the message delays and reorderings caused by the network. Here, we formalize eventual consistency as a confluence notion, meaning that finite executions of the system can always be extended to yield the desired output. We show that confluence is decidable when the transducers satisfy some syntactic restrictions, some of which have also been considered in earlier work on automated verification of relational transducers. This simple class of transducer networks computes exactly all distributed queries expressible by unions of conjunctive queries with negation. [ABSTRACT FROM AUTHOR]