91,274 research outputs found
A Generic Language for Query and Viewtype Generation By-Example
In model-driven engineering, powerful query/view languages exist to compute result sets/views from underlying models. However, to use these languages effectively, one must understand the query/view language concepts as well as the underlying models and metamodels structures. Consequently, it is a challenge for domain experts to create queries/views due to the lack of knowledge about the computer-internal abstract representation of models and metamodels. To better support domain experts in the query/view creation, the goal of this paper is the presentation of a generic concept to specify queries/views on models without requiring deep knowledge on the realization of modeling languages. The proposed concept is agnostic to specific modeling languages and allows the query/view generation by-example with a simple mechanism for filtering model elements. Based on this generic concept, a generic query/view language is proposed that uses role-oriented modeling for its non-intrusive application for specific modeling languages. The proposed language is demonstrated based on the role-based single underlying model (RSUM) approach for AutomationML to create queries/views by-example, and subsequently, associated viewtypes to modify the result set or view
Modular Web Queries â From Rules to Stores
Even with all the progress in Semantic technology, accessing Web
data remains a challenging issue with new Web query languages and approaches
appearing regularly. Yet most of these languages, including W3C approaches
such as XQuery and SPARQL, do little to cope with the explosion of the data
size and schemata diversity and richness on the Web. In this paper we propose
a straightforward step toward the improvement of this situation that is simple to
realize and yet effective: Advanced module systems that make partitioning of (a)
the evaluation and (b) the conceptual design of complex Web queries possible.
They provide the query programmer with a powerful, but easy to use high-level
abstraction for packaging, encapsulating, and reusing conceptually related parts
(in our case, rules) of a Web query. The proposed module system combines ease
of use thanks to a simple core concept, the partitioning of rules and their consequences
in flexible âstoresâ, with ease of deployment thanks to a reduction
semantics. We focus on extending the rule-based Semantic Web query language
Xcerpt with such a module system though the same approach can be applied to
other (rule-based) languages as well
Queries, rules and definitions as epistemic statements in concept languages
Concept languages have been studied in order to give a formal account of the basic features of frame-based languages. The focus of research in concept languages was initially on the semantical reconstruction of frame-based systems and the computational complexity of reasoning. More recently, attention has been paid to the formalization of other aspects of frame-based languages, such as non-monotonic reasoning and procedural rules, which are necessary in order to bring concept languages closer to implemented systems. In this paper we discuss the above issues in the framework of concept languages enriched with an epistemic operator. In particular, we show that the epistemic operator both introduces novel features in the language, such as sophisticated query formulation and closed world reasoning, and makes it possible to provide a formal account for some aspects of the existing systems, such as rules and definitions, that cannot be characterized in a standard first-order framework
ON PERIODICITY IN TEMPORAL DATABASES
The issue of periodicity is generally understood to be a desirable property of temporal
data that should be supported by temporal database models and their query
languages. Nevertheless, there has so far not been any systematic examination of how
to incorporate this concept into a temporal DBMS. In this paper we describe two concepts
of periodicity, which we call strong periodicity and near periodicity, and discuss
how they capture formally two of the intuitive meanings of this term. We formally
compare the expressive power of these two concepts, relate them to existing temporal
query languages, and show how they can be incorporated into temporal relational
database query languages, such as the proposed temporal extension to SQL, in a clean
and straightforward manner.Information Systems Working Papers Serie
Computable queries for relational data bases
AbstractThe concept of âreasonableâ queries on relational data bases is investigated. We provide an abstract characterization of the class of queries which are computable, and define the completeness of a query language as the property of being precisely powerful enough to express the queries in this class. This definition is then compared with other proposals for measuring the power of query languages. Our main result is the completeness of a simple programming language which can be thought of as consisting of the relational algebra augmented with the power of iteration
Database Queries that Explain their Work
Provenance for database queries or scientific workflows is often motivated as
providing explanation, increasing understanding of the underlying data sources
and processes used to compute the query, and reproducibility, the capability to
recompute the results on different inputs, possibly specialized to a part of
the output. Many provenance systems claim to provide such capabilities;
however, most lack formal definitions or guarantees of these properties, while
others provide formal guarantees only for relatively limited classes of
changes. Building on recent work on provenance traces and slicing for
functional programming languages, we introduce a detailed tracing model of
provenance for multiset-valued Nested Relational Calculus, define trace slicing
algorithms that extract subtraces needed to explain or recompute specific parts
of the output, and define query slicing and differencing techniques that
support explanation. We state and prove correctness properties for these
techniques and present a proof-of-concept implementation in Haskell.Comment: PPDP 201
A Theory of Formal Synthesis via Inductive Learning
Formal synthesis is the process of generating a program satisfying a
high-level formal specification. In recent times, effective formal synthesis
methods have been proposed based on the use of inductive learning. We refer to
this class of methods that learn programs from examples as formal inductive
synthesis. In this paper, we present a theoretical framework for formal
inductive synthesis. We discuss how formal inductive synthesis differs from
traditional machine learning. We then describe oracle-guided inductive
synthesis (OGIS), a framework that captures a family of synthesizers that
operate by iteratively querying an oracle. An instance of OGIS that has had
much practical impact is counterexample-guided inductive synthesis (CEGIS). We
present a theoretical characterization of CEGIS for learning any program that
computes a recursive language. In particular, we analyze the relative power of
CEGIS variants where the types of counterexamples generated by the oracle
varies. We also consider the impact of bounded versus unbounded memory
available to the learning algorithm. In the special case where the universe of
candidate programs is finite, we relate the speed of convergence to the notion
of teaching dimension studied in machine learning theory. Altogether, the
results of the paper take a first step towards a theoretical foundation for the
emerging field of formal inductive synthesis
MDDQL: an ontology driven, multi-lingual query language and system for an integrated view of heterogeneous data sources
Query languages and keywords based search engines are
conventionally specified and implemented with the
emphasis put on syntactic rules to which query typing and
answering must be bound. MDDQL is a query language
and system that operates on a semantic model in terms of a
graph based ontology. As a software technology, MDDQL
allows the meaning of/and associations between
information to be known and processed at execution time at
following levels: (a) driving the user to the construction of,
as meaningful as possible, queries with an advanced
concept-based search method, (b) resolving high level
queries into various data source specific query statements.
In addition, queries can be posed in more than one natural
sub-language. The major goal behind this approach has
been the simplification and scalability of both tasks: query
construction, even within multi-lingual user communities,
and addressing of a large number of possibly semantically
heterogeneous data sources in a distributed environment
Reasoning & Querying â State of the Art
Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF
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