For many problems arising in the setting of graph querying (such as finding semantic associations in RDF graphs, exact and approximate pattern matching, sequence alignment, etc.), the power of standard languages such as the widely studied conjunctive regular path queries (CRPQs) is insufficient in at least two ways. First, they cannot output paths and second, more crucially, they cannot express relations among paths.

Abstract In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.

The resource description framework (RDF) is a metadata model and language recommended by the W3C. This paper presents a framework to incorporate temporal reasoning into RDF, yielding temporal RDF graphs. We present a semantics for these kinds of graphs which includes the notion of temporal entailment and a syntax to incorporate this framework into standard RDF graphs, using the RDF vocabulary plus temporal labels. We give a characterization of temporal entailment in terms of RDF entailment and show that the former does not yield extra asymptotic complexity with respect to nontemporal RDF graphs. We also discuss temporal RDF graphs with anonymous timestamps, providing a theoretical framework for the study of temporal anonymity. Finally, we sketch a temporal query language for RDF, along with complexity results for query evaluation that show that the time dimension preserves the tractability of answers

In multidimensional data models intended for online analytic processing (OLAP), data are viewed as points in a multidimensional space. Each dimension has structure, described by a directed graph of categories, a set of members for each category, and a child/parent relation between members. An important application of this structure is to use it to infer summarizability, that is, whether an aggregate view defined for some category can be correctly derived from a set of precomputed views defined for other categories. A dimension is called structurally heterogeneous if two members in a given category are allowed to have ancestors in different categories. In this article, we propose a class of integrity constraints, dimension constraints , that allow us to reason about summarizability in heterogeneous dimensions. We introduce the notion of frozen dimensions which are minimal homogeneous dimension instances representing the different structures that are implicitly combined in a heterogeneous dimension. Frozen dimensions provide the basis for efficiently testing the implication of dimension constraints and are a useful aid to understanding heterogeneous dimensions. We give a sound and complete algorithm for solving the implication of dimension constraints that uses heuristics based on the structure of the dimension and the constraints to speed up its execution. We study the intrinsic complexity of the implication problem and the running time of our algorithm.

The Semantic Web is based on the idea of adding more machine-readable semantics to web information via annotations written in a language called the Resource Description Framework (RDF). RDF resembles a subset of binary first-order logic including the ability to refer to anonymous objects. Its extended version, RDFS, supports reification, typing and inheritance. These features introduce new challenges into the formal study of sets of RDF/RDFS statements and languages for querying them. Although several such query languages have been proposed, there has been little work on foundational aspects. We investigate these, including computational aspects of testing entailment and redundancy. We propose a query language with well-defined semantics and study the complexity of query processing, query containment, and simplification of answers.

A common technique for speeding up OLAP query processing is to materialize (pre-compute) some aggregate views, called cube views, and use them for the derivation of other cube views. The derivations are inferred from the dimension hierarchies, and are usually represented as a relation between cube views called cube dependence relation, also called summarizability relation for a single dimension hierarchy. In this paper, we study the problem of computing the summarizability relation of dimension schemas that model structural irregularities of dimensions by means of integrity constraints. We study the intrinsic complexity of the problem, and present an algorithm, which uses deep structural properties of dimension hierarchies and constraints to solve the problem. Finally, we give an extension of the algorithm to compute the dependence relation for the multidimensional case.

In multidimensional data models intended for online analytic processing (OLAP), data are viewed as points in a multidimensional space. Each dimension has structure, described by a directed graph of categories, a set of members for each category, and a child/parent relation between members. An important application of this structure is to use it to infer summarizability, that is, whether an aggregate view defined for some category can be correctly derived from a set of precomputed views defined for other categories. A dimension is called heterogeneous if two members in a given category are allowed to have ancestors in different categories. In previous work, we studied the problem of inferring summarizability in a particular class of heterogeneous dimensions. In this paper, we propose a class of integrity constraints and schemas that allow us to reason about summarizability in general heterogeneous dimensions. We introduce the notion of frozen dimensions, which are minimal homogeneous dimension instances representing the different structures that are implicitly combined in a heterogeneous dimension. Frozen dimensions provide the basis for efficiently testing implication of dimension constraints, and are useful aid to understanding heterogeneous dimensions. We give a sound and complete algorithm for solving the implication of dimension constraints, that uses heuristics based on the structure of the dimension and the constraints to speed up its execution. We study the intrinsic complexity of the implication problem, and the running time of our algorithm.

OLAP systems support data analysis through a multidimensional data model, according to which data facts are viewed as points in a space of application-related “dimensions” , organized into levels which conform a hierarchy. Although the usual assumption is that these points reflect the dynamic aspect of the data warehouse while dimensions are relatively static, in practice it turns out that dimension updates are often necessary to adapt the multidimensional database to changing requirements. These updates can take place either at the structural level (e.g. addition of categories or modification of the hierarchical structure) or at the instance level (elements can be inserted, deleted, merged, etc.). They are poorly supported (or not supported at all) in current commercial systems and have not been addressed in the literature. In a previous paper we introduced a formal model supporting dimension updates. Here, we extend the model, adding a set of semantically meaningful operators which encapsulate common sequences of primitive dimension updates in a more efficient way. We also formally define two mappings (normalized and denormalized) from the multidimensional to the relational model, and compare an implementation of dimension updates using these two approaches.

OLAP systems support data analysis through a multidimensional data model, according to which data facts are viewed as points in a space of application-related "dimensions", organized into levels which conform to a hierarchy. The usual assumption is that the data points reflect the dynamic aspect of the data warehouse, while dimensions are relatively static. However, in practice, dimension updates are often necessary to adapt the multidimensional database to changing requirements. Structural updates can also take place, like addition of categories or modification of the hierarchical structure. When these updates are performed, the materialized aggregate views that are typically stored in OLAP systems must be efficiently maintained. These updates are poorly supported (or not supported at all) in current commercial systems, and have received little attention in the research literature. We present a formal model of dimension updates in a multidimensional model, a collection of primitive operators to perform them, and a study of the effect of these updates on a class of materialized views, giving an algorithm to efficiently maintain them.