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-\section{Overview}
-
-{\MathQL}%
-\footnote{See \CURI{http://helm.cs.unibo.it/mathql}.}
-is a query language for {\RDF} \cite{RDF,RDFS} databases, developed in the
-context of the {\HELM}%
-\footnote{See \CURI{http://helm.cs.unibo.it}.}
-project \cite{APSCGS03}.
-Its name suggests that it is supposed to be the first of a group of query
-languages for retrieving information from distributed digital libraries of
-formal mathematical knowledge by means of content-aware requests, but no other
-languages of this proposal have been implemented yet except for {\MathQL} that
-is not Mathematics-oriented. So the name is a bit misleading.
-This proposal has several domains of application and may be useful for
-database or on-line libraries reviewers, for proof assistants or
-proof-checking systems, and also for learning environments because these
-applications require features for classifying, searching and browsing
-mathematical information in a semantically meaningful way.
-Other languages to be defined in the context of the MathQL proposal may be
-suitable for queries about the semantic structure of mathematical data:
-this includes content-based pattern-matching and possibly other forms of
-formal matching involving for instance isomorphism, unification and
-$\delta$-expansion%
-\footnote{By $\delta$-expansion we mean the expansion of definitions.}.
-In this perspective the role of a query on metadata is that of producing a
-filtered knowledge base containing relevant information for subsequent queries
-of other kind (see \cite{GSC03} for a more detailed description of this
-approach).
-
-{\MathQL} is carefully designed for making up for two limitations that seem to
-characterize several implementations and proposals of current {\RDF}-oriented
-query languages, namely the insufficient compliance with the most requested
-features and the poor attention paid to query result management.
-Thus the language has the following design goals:
-
-\begin{enumerate}
-
-\item
-compliance with the main requirements stated by the {\RDF} community;
-
-\item
-native support for post-processing the query results;
-
-\item
-{\HELM}-independent implementation of the query engine.
-
-\end{enumerate}
-
-We will briefly analyze these features in the remaining part of this
-section.
-
-\vspace{-1pc}
-
-\subsubsection*{The main requirements from the RDF community}
-
-As a query language for {\RDF} databases, {\MathQL} has a well-conceived
-semantics, defined in term of an abstract metadata model, according to which
-queries return exhaustive solutions.
-The language provides facilities for imposing query constraints based on
-{\RDFS} \cite{RDFS} and for the traversal of compound values of properties.
-It also provides a full set of Boolean operators to compose the query
-constraints and facilities for selecting resources or literals by means of
-{\POSIX} regular expressions.
-Moreover the language allows to customize the query results specifying what
-part of a solution should be preserved, and supports a machine-processable
-{\XML} \cite{XML} syntax as well as a human-readable textual syntax to achieve
-the best usability.
-The two syntaxes concern both queries and results, making {\MathQL} usable in
-a distributed environment where query engines are implemented as stand-alone
-components. In this setting in fact both the queries and their results must be
-exchanged by the system's components and thus need to be clearly encoded.
-
-{\MathQL} provides a graph-oriented access to the {\RDF} metadata, based on
-tree instantiation.
-This approach has the advantage of providing an abstraction over the
-concrete representation of the {\RDF} database (that can consist of {\RDF}
-triples and {\XML} files simultaneously) at the user level, and this is
-definitely desirable especially in a distributed context.
-
-{\MathQL} query results are meant to capture the structure of trees coming
-from an {\RDF} graph and for this purpose a standard $1$- or $2$-dimensional
-organization (as provided by most {\RDF}-oriented query languages) is not
-satisfactory. {\MathQL} approach is to use a $4$-dimensional organization
-for its query results.
-
-\vspace{-1pc}
-
-\subsubsection*{Post-processing and code generation capabilities}
-
-The {\MathQL} query engine, that is written in {\CAML}%
-\footnote{See \CURI{http://caml.inria.fr}.}
-for an easy integration with the {\HELM} software, provides two ways of
-processing the query results: at {\CAML} side and natively.
-
-At {\CAML} side, an application issues a query calling a function of the
-engine and manipulates the result either operating directly on its internal
-representation (through a low-level interface), or using a set of dedicated
-functions specifically designed to manage the query results.
-This set of functions includes a basic library but is extensible depending
-on the {\CAML} modules included in the engine at compile-time. In this way
-an expert user can write a {\CAML} module with new dedicated functions and can
-include it in the engine recompiling it.
-
-{\MathQL} supports native post-processing of the query results including the
-standard constructions of an imperative Turing-complete programming language,
-whose aim is definitely not that of being all-purpose (the user can work at
-{\CAML} side for that), but of being optimized for the management of the
-query results.
-In this context an {\SQL}-like ``select-from-where'' construction is provided
-(as required by the {\RDF} community) as well as a mechanism for accessing the
-post-processing dedicated functions available to the engine.
-
-Moreover the language provides access to an extensible set of code-generating
-functions (also available at {\CAML} side) that the expert user can define
-writing suitable {\CAML} modules for the engine.
-Note that the generated code is always {\MathQL} code.
-The code generation features allow to build complex queries incrementally and
-in an automatic manner, as required by the needs of the {\HELM} project.
-Using the native programming language, instead, queries can include the
-post-processing algorithms on their results so the querying code and the
-subsequent processing code (if needed) are treated together as a
-self-contained object that can be computed by a single engine.
-In this sense the alternative of performing a complex query on a remote
-component issuing some {\MathQL} querying code followed by some {\CAML}
-post-processing code is really infeasible in a distributed context.
-
-\vspace{-1pc}
-
-\subsubsection*{Physical organization of the RDF database}
-
-The implementation of the {\MathQL} query engine does not depend on any
-software developed within the {\HELM} project, nor it depends on the {\HELM}
-metadata model in any way.
-
-However the engine does make few assumptions on the way metadata are
-physically organized and needs some user-provided knowledge about the concrete
-metadata representation.
-Metadata stored as {\RDF} triples are accessed through a {\MySQL}%
-\footnote{See \CURI{http://www.mysql.com}.}
-or a {\PostgreSQL}%
-\footnote{See \CURI{http://www.postgresql.org}.}
-engine, while metadata stored as {\RDF}/{\XML} files are accessed through a
-{\Galax}%
-\footnote{See \CURI{http://db.bell-labs.com/galax/}.}
-{\XQuery} \cite{XQuery} engine.
projects / helm.git / blobdiff