A graph-based meta-model for heterogeneous data management

The wave of interest in data-centric applications has spawned a high variety of data models, making it extremely difficult to evaluate, integrate or access them in a uniform way. Moreover, many recent models are too specific to allow immediate comparison with the others and do not easily support incremental model design. In this paper, we introduce GSMM, a meta-model based on the use of a generic graph that can be instantiated to a concrete data model by simply providing values for a restricted set of parameters and some high-level constraints, themselves represented as graphs. In GSMM, the concept of data schema is replaced by that of constraint, which allows the designer to impose structural restrictions on data in a very flexible way. GSMM includes GSL, a graph-based language for expressing queries and constraints that besides being applicable to data represented in GSMM, in principle, can be specialised and used for existing models where no language was defined. We show some sample applications of GSMM for deriving and comparing classical data models like the relational model, plain XML data, XML Schema, and time-varying semistructured data. We also show how GSMM can represent more recent modelling proposals: the triple stores, the BigTable model and Neo4j, a graph-based model for NoSQL data. A prototype showing the potential of the approach is also described

Modeling temporal dimensions of semistructured data

In this paper we propose an approach to manage in a correct way valid time semantics for semistructured temporal clinical information. In particular, we use a graph-based data model to represent radiological clinical data, focusing on the patient model of the well known DICOM standard, and define the set of (graphical) constraints needed to guarantee that the history of the given application domain is consistent

Data mining by means of generalized patterns

The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces

Modeling Semistructured Data by using graph-based constraints

In this paper, we introduce the General Semistructured Meta-Model (GSMM), a simple meta-model for semistructured information which can be applied for the translation to a common formalism of the various abstract models proposed in literature; this approach fosters easy a priori comparison and discussion of concrete models’ features, such as allowed sets of values, handling of object identifiers, relationships representation; moreover, it supports effective inter-model translation and design

Modeling Semistructured Data by using graph-based constraints

In this paper, we introduce the General Semistructured Meta-Model (GSMM), a simple meta-model for semistructured information which can be applied for the translation to a common formalism of the various abstract models proposed in literature; this approach fosters easy a priori comparison and discussion of concrete models\u2019 features, such as allowed sets of values, handling of object identifiers, relationships representation; moreover, it supports effective inter-model translation and design