The increasing demand for home care due to the growing number of older people with an increased incidence of multiple chronic conditions requires the adoption of technological innovation that can improve the integration and coordination of health and social services. This paper presents the H@H platform that aims to improve the coordination and cooperation among stakeholders facilitating the provision of care services in a continuity of care framework. Moreover, a conceptual model that combines the main concepts of the ContSys standard with social care components and with assistive domotics linked to the lifestyle and healthy behavior is proposed. Finally, a scenario that depicts the application of the integrated approach to a subject affected from metabolic syndrome is presented.

Semantic similarity search is one of the most promising methods for improving the performance of retrieval systems. This paper presents a new probabilistic method for ontology weighting based on a Bayesian approach. In particular, this work addresses the semantic search method SemSim for evaluating the similarity among a user request and semantically annotated sources. Each resource is annotated with a vector of features (annotation vector), i.e., a set of concepts defined in a reference ontology. Analogously, a user request is represented by a collection of desired features. The paper shows, on the bases of a comparative study, that the adoption of the Bayesian weighting method improves the performance of the SemSim method.

In this article, we propose a novel approach for measuring word association based on the joint occurrences distribution in a text. Our approach relies on computing a sum of distances between neighboring occurrences of a given word pair and comparing it with a vector of randomly generated occurrences. The idea behind this assumption is that if the distribution of co‐occurrences is close to random or if they tend to appear together less frequently than by chance, such words are not semantically related. We devise a distance function S that evaluates the words association rate. Using S, we build a concept tree, which provides a visual and comprehensive representation of keywords association in a text. In order to illustrate the effectiveness of our algorithm, we apply it to three different texts, showing the consistency and significance of the obtained results with respect to the semantics of documents. Finally, we compare the results obtained by applying our proposed algorithm with the ones achieved by both human experts and the co‐occurrence correlation method. We show that our method is consistent with the experts' evaluation and outperforms with respect to the co‐occurrence correlation method.

In this paper, we combine the characteristics of three fundamental data models in order to represent their semantics in a common framework. These fundamental data models include the familiar concepts of modeling (1) object classes (or entities), their properties (attributes) and relationships between them, (2) multidimensional objects and attributes that can be summarized over the dimensions, and (3) hierarchical structures. This model, called the Composite Data Model, facilitates combinations of these three model structures to be represented jointly in a single schema, thus providing more expressive and natural queries over them. The main advantage of the composite data model (CDM), and a composite query language (CQL) over it, is that any combination of the three fundamental models can be represented jointly based on explicit semantics of each of the fundamental data models. This is unlike existing data models that represent each data model individually or obscure the semantics of additional features being modeled. In order to develop a query language over the combined schemas, we introduce a new concept, referred to as anchor, which is an object class that acts as the focus of the query. We provide in the query language path structures relative to the anchor that facilitate data navigation and data manipulation. We develop the syntax and semantics of the proposed language, and illustrate its expressive power through numerous query examples, and comparisons to three other query languages: OQL, SPARQL, and XQuery.

In this paper, we investigate the problem of estimation of a target database from summary databases derived from a base data cube. We show that such estimates can be derived by choosing a primary database which uses a proxy database to estimate the results. This technique is common in statistics, but an important issue we are addressing is the accuracy of these estimates. Specifically, given multiple primary and multiple proxy databases, that share the same summary measure, the problem is how to select the primary and proxy databases that will generate the most accurate target database estimation possible. We propose an algorithmic approach for determining the steps to select or compute the source databases from multiple summary databases, which makes use of the principles of information entropy. We show that the source databases with the largest number of cells in common provide the more accurate estimates. We prove that this is consistent with maximizing the entropy. We provide some experimental results on the accuracy of the target database estimation in order to verify our results.

Given an OLAP query expressed over multiple source OLAP databases, we study the problem of estimating the resulting OLAP target database. The problem arises when it is not possible to derive the result from a single database. The method we use is linear indirect estimation, commonly used for statistical estimation. We examine two obvious computational methods for computing such a target database, called the full cross-product (F) and preaggregation (P) methods. We study the accuracy and computational cost of these methods. While the F method provides a more accurate estimate, it is more expensive computationally than P. Our contribution is in proposing a third, new method, called the partial preaggregation method (PP), which is significantly less expensive than F, but just as accurate. We prove formally that the PP method yields the same results as the F method, and provide analytical and experimental results on the accuracy and computational benefits of the PP method.

OLAP and object data models represent different logical concepts and structures, and therefore separate database systems with different query languages were developed based on these models. We show in this paper that it is desirable and possible to combine these models to represent realistic modeling requirements. We define in this paper an OLAP-object data model that combines the main characteristics of OLAP and object data models in order to represent their functionalities in a common framework. We use three different types of object classes: primitive, regular and composite. In the OLAP-object data model, primitive and regular classes which represent object structures can be used for form composite classes that represent OLAP structures. We define a query language that uses path structures to facilitate data navigation and data manipulation. The proposed language uses the concept of an anchor. An anchor is an object class (primitive, regular or composite) that is selected as a starting node from which paths structures can be formed to express queries. The power of the proposed query language is illustrated through numerous examples. The syntax and semantics of the proposed language are developed

Given an OLAP query expressed over a collection of source OLAP databases, we study the problem of computing the result OLAP target database when the underlying detail data of the source databases are not available. The problem arises when the source databases do not have the same category attributes, and it is not possible to derive the result from a single database. The method we use is the linear indirect estimator. We consider two obvious methods for computing such a target database, called the full-cross-product (F) and the pre-aggregation (P) methods. As for the accuracy and computational performance of these methods, while the method F provides the most accurate estimate possible, it is more expensive computationally than P. Our contribution is in proposing a third new method, called the shortcut method, which is less expensive than F, but is as accurate.

The concepts abstracted from reality and represented through the dimensions in a Statistical Database (SDB) support the user to use them in query formulation and processing. Instead, all those useful properties involved in a query that cannot be obtained through dimension/s in aN SDB (for example, the concept of adjacency) can be presented in a Geographical Database (GDB). This paper presents a conceptual approach to allow the end user, working in a Geographic Database (GDB) environment to use data cube stored in a Statistical Database (SDB). In this context we need to extend the geographic data structure with some special "functional attributes": They support links between environments mentioned above through geographic dimensions always implicitly or explicitly present in SDB. Therefore, the main objective of this paper is to propose a solution to answer queries involving data stored in both environments in a transparent way to the user. Then, a query language to support the integration of multidimensional operators with geographic operators is proposed. Finally, the main characteristics of the proposed approach are illustrated through some examples.

Recently numerous proposals for modelling and querying Multidimensional Databases (MDDB) are proposed. Among the still open problems there is a rigorous classification of the different types of hierarchies. In this paper we propose and discuss some different types of hierarchies within a single dimension of a cube. These hierarchies divide in different levels of aggregation a single dimension. Depending on them, we discuss the characterization of some OLAP operators which refer to hierarchies in order to maintain the data cube consistency. Moreover, we propose a set of operators for changing the hierarchy structure. The issues discussed provides modelling flexibility during the scheme design phase and correct data analysis.