Towards a query language for annotation graphs

The multidimensional, heterogeneous, and temporal nature of speech databases raises interesting challenges for representation and query. Recently, annotation graphs have been proposed as a general-purpose representational framework for speech databases. Typical queries on annotation graphs require path expressions similar to those used in semistructured query languages. However, the underlying model is rather different from the customary graph models for semistructured data: the graph is acyclic and unrooted, and both temporal and inclusion relationships are important. We develop a query language and describe optimization techniques for an underlying relational representation.Comment: 8 pages, 10 figure

Tailoring temporal description logics for reasoning over temporal conceptual models

Temporal data models have been used to describe how data can evolve in the context of temporal databases. Both the Extended Entity-Relationship (EER) model and the Unified Modelling Language (UML) have been temporally extended to design temporal databases. To automatically check quality properties of conceptual schemas various encoding to Description Logics (DLs) have been proposed in the literature. On the other hand, reasoning on temporally extended DLs turn out to be too complex for effective reasoning ranging from 2ExpTime up to undecidable languages. We propose here to temporalize the ‘light-weight’ DL-Lite logics obtaining nice computational results while still being able to represent various constraints of temporal conceptual models. In particular, we consider temporal extensions of DL-Lite^N_bool, which was shown to be adequate for capturing non-temporal conceptual models without relationship inclusion, and its fragment DL-Lite^N_core with most primitive concept inclusions, which are nevertheless enough to represent almost all types of atemporal constraints (apart from covering)

DATA MINING LANGUAGES STANDARDS

The increasing of the database dimension creates many problems, especially when we need to access, use and analyze data. The data overflow phenomenon in database environments imposes the application of different data mining methods, in order to find relevant information from large databases. A lot of data mining tools emerged in the last years. The standardization of data mining languages become in the last years a very important topic. The paper presents Predictive Model Markup Language (PMML) standards from the Data Mining Group. PMML, a standard language for defining data mining models, which allows users to develop models within one vendor's application, and use other vendors' applications to visualize, analyze, evaluate or otherwise use the models.

Introducing new constructs for data modelling and column generation in LP modelling languages

Through popular implementation of structured query language (SQL) and query-by-example(QBE) relational databases have become the de-facto industry standard for data modelling.We consider the indices, sets, and the declarative form of Linear Programming (LP) modelling languages and introduce new constructs which provide direct link to the database systems. The models constructed in this way are data driven and display a dynamicstructure. We then show how this approach can be naturally extended to include column generation features stated in procedural forms within an otherwise declarative modelling paradigm

Translation of Heterogeneous Databases into RDF, and Application to the Construction of a SKOS Taxonomical Reference

International audienceWhile the data deluge accelerates, most of the data produced remains locked in deep Web databases. For the linked open data to benefit from the potential represented by this huge amount of data, it is crucial to come up with solutions to expose heterogeneous databases as linked data. The xR2RML mapping language is an endeavor towards this goal: it is designed to map various types of databases to RDF, by flexibly adapting to heterogeneous query languages and data models while remaining free from any specific language. It extends R2RML, the W3C recommendation for the mapping of relational databases to RDF, and relies on RML for the handling of various data formats. In this paper we present xR2RML, we analyse data models of several modern databases as well as the format in which query results are returned , and we show how xR2RML translates any result data element into RDF, relying on existing languages such as XPath and JSONPath when necessary. We illustrate some features of xR2RML such as the generation of RDF collections and containers, and the ability to deal with mixed data formats. We also describe a real-world use case in which we applied xR2RML to build a SKOS thesaurus aimed at supporting studies on History of Zoology, Archaeozoology and Conservation Biology

On the Semantics of "Now" in Databases

While "now" is expressed in SQL as CURRENT-TIMESTAMP within queries, this value cannot be stored in the database. However, this notion of an ever-increasing current-time value has been reflected in some temporal data models by inclusion of database-resident variables, such as "now," "until-changed," "â," "@" and "-." Time variables are very desirable, but their use also leads to a new type of database, consisting of tuples with variables, termed a variable database. This paper proposes a framework for defining the semantics of the variable databases of temporal relational data models. A framework is presented because several reasonable meanings may be given to databases that use some of the specific temporal variables that have appeared in the literature. Using the framework, the paper defines a useful semantics for such databases. Because situations occur where the existing time variables are inadequate, two new types of modeling entities that address these shortcomings, timestamps which we call now-relative and now-relative indeterminate, are introduced and defined within the framework. Moreover, the paper provides a foundation, using algebraic bind operators, for the querying of variable databases via existing query languages. This transition to variable databases presented here requires minimal change to the query processor. Finally, to underline the practical feasibility of variable databases, we show that database variables can be precisely specified and efficiently implemented in conventional query languages, such as SQL, and in temporal query languages, such as TSQL2.Information Systems Working Papers Serie

Approximation in Databases

One source of partial information in databases is the need to combine information from several databases. Even if each database is complete for some world , the combined databases will not be, and answers to queries against such combined databases can only be approximated. In this paper we describe various situations in which a precise answer cannot be obtained for a query asked against multiple databases. Based on an analysis of these situations, we propose a classification of constructs that can be used to model approximations. One of the main goals is to show that most of these models of approximations possess universality properties. The main motivation for doing this is applying the data-oriented approach, which turns universality properties into syntax, to obtain languages for approximations. We show that the languages arising from the universality properties have a number of limitations. In an attempt to overcome those limitations, we explain how all the languages can be embedded into a language for conjunctive and disjunctive sets from [21], and demonstrate its usefulness in querying independent databases

xR2RML: Relational and Non-Relational Databases to RDF Mapping Language

This document describes xR2RML, a language for expressing customized mappings from various types of databases (XML, object-oriented, NoSQL) to RDF datasets. xR2RML flexibly adapts to heterogeneous query languages and data models while remaining free from any specific language or syntax. It extends R2RML, the W3C recommendation for the mapping of relational databases to RDF, and relies on RML for the handling of various data representation formats.Web version available at: http://i3s.unice.fr/~fmichel/xr2rml_specification_v5.htm

A software service supporting software quality forecasting

Software repositories such as source control, defect tracking systems and project management tools, are used to support the progress of software projects. The exploitation of such data with techniques like forecasting is becoming an increasing need in several domains to support decision-making processes. However, although there exist several statistical tools and languages supporting forecasting, there is a lack of friendly approaches that enable practitioners to exploit the advantages of creating and using such models in their dashboard tools. Therefore, we have developed a modular and flexible forecasting service allowing the interconnection with different kinds of databases/data repositories for creating and exploiting forecasting models based on methods like ARIMA or ETS. The service is open source software, has been developed in Java and R and exposes its functionalities through a REST API. Architecture details are provided, along with functionalities’ description and an example of its use for software quality forecasting.Peer ReviewedPostprint (author's final draft

Knowledge-preserving Certain Answers for SQL-like Queries

International audienceAnswering queries over incomplete data is based on finding answers that are certainly true, independently of how missing values are interpreted. This informal description has given rise to several different mathematical definitions of certainty. To unify them, a framework based on "explanations", or extra information about incomplete data, was recently proposed. It partly succeeded in justifying query answering methods for relational databases under set semantics, but had two major limitations. First, it was firmly tied to the set data model, and a fixed way of comparing incomplete databases with respect to their information content. These assumptions fail for reallife database queries in languages such as SQL that use bag semantics instead. Second, it was restricted to queries that only manipulate data, while in practice most analytical SQL queries invent new values, typically via arithmetic operations and aggregation. To leverage our understanding of the notion of certainty for queries in SQL-like languages, we consider incomplete databases whose information content may be enriched by additional knowledge. The knowledge order among them is derived from their semantics, rather than being fixed a priori. The resulting framework allows us to capture and justify existing notions of certainty, and extend these concepts to other data models and query languages. As natural applications, we provide for the first time a well-founded definition of certain answers for the relational bag data model and for valueinventing queries on incomplete databases, addressing the key shortcomings of previous approaches