Towards association rules for XML documents

In this work we propose a flexible approach to extract and evaluate association rules on XML documents. We describe two kinds of association rules: structural associations and value associations. A structural association allows one to capture the similarity of an XML document with respect to a given structure, while a value association allows one to capture the similarity of the information contained in the XML document with respect to a given scenario. Moreover, we show how it possible to compose these associations in order to describe complex association rules on XML documents

Merging Multimedia Presentations and Semistructured Temporal Data: a Graph-based Model and its Application to Clinical Information

Semistructured data are data having some structure, that may be irregular or incomplete and does not necessarily conform to a fixed schema. Semistructured data often contain the description of histories of the considered real world. The eXtensible Markup Language (XML) is becoming a cross compatible and standardized means for representing semistructured clinical data. In the field of medical informatics, there are many ongoing activities concerning XML. In the field of multimedia database systems, the topic related to the integration of several media objects (with their temporal aspects) have been considered both for data modeling and querying issues and for modeling multimedia presentations. In this paper, we focus on the issue of providing physicians with the capability of representing in a seamless way both temporal aspects of multimedia semistructured data and their temporal presentation requirements

Querying temporal clinical databases on granular trends

This paper focuses on the identification of temporal trends involving different granularities in clinical databases, where data are temporal in nature: for example, while follow-up visit data are usually stored at the granularity of working days, queries on these data could require to consider trends either at the granularity of months (‘‘find patients who had an increase of systolic blood pressure within a single month’’) or at the granularity of weeks (‘‘find patients who had steady states of diastolic blood pressure for more than 3 weeks’’). Representing and reasoning properly on temporal clinical data at different granularities are important both to guarantee the efficacy and the quality of care processes and to detect emergency situations. Temporal sequences of data acquired during a care process provide a significant source of information not only to search for a particular value or an event at a specific time, but also to detect some clinically-relevant patterns for temporal data. We propose a general framework for the description and management of temporal trends by considering specific temporal features with respect to the chosen time granularity. Temporal aspects of data are considered within temporal relational databases, first formally by using a temporal extension of the relational calculus, and then by showing how to map these relational expressions to plain SQL queries. Throughout the paper we consider the clinical domain of hemodialysis, where several parameters are periodically sampled during every session

A Uniform Algebraic Characterization of Temporal Functional Dependencies

In the database literature, different types of temporal functional dependencies (TFDs) have been proposed to constrain the temporal evolution of information. Unfortunately, the lack of a common notation makes it difficult to compare, to integrate, and to possibly extend the various proposals. In this paper, we outline a unifying algebraic framework for TFDs. We first introduce the proposed approach, then we use it to give a uniform account of existing TFDs, and finally we show that it allows one to easily express new meaningful TFDs

Mining Violations to Relax Relational Database Constraints

Frequent constraint violations on the data stored in a database may suggest that the represented reality is changing, and thus the database does not reflect it anymore. It is thus desirable to devise methods and tools to support (semi-)automatic schema changes, in order for the schema to mirror the new situation. In this work we propose a methodology and the RELACS tool, based on data mining, to maintain the domain and tuple integrity constraints specified at design time, in order to adjust them to the evolutions of the modeled reality that may occur during the database life. The approach we propose allows to isolate frequent and meaningful constraint violations and, consequently, to extract novel rules that can be used to update or relax the no longer up-to-date integrity constraints. \ua9 2009 Springer Berlin Heidelberg

Context−aware views for mobile users

Independent, heterogeneous, distributed, sometimes transient and mobile data sources produce an enormous amount of information that should be semantically integrated and filtered, or, as we say, tailored, based on the users’ interests and context. We propose to exploit knowledge about the user, the adopted device, and the environment - altogether called context - to the end of information tailoring. This paper presents the Context Dimension Tree, a context model which is the basis for solving the information tailoring problem, along with its role in the framework of the Context-ADDICT architecture

Context−aware views for mobile users

Independent, heterogeneous, distributed, sometimes transient and mobile data sources produce an enormous amount of information that should be semantically integrated and filtered, or, as we say, tailored, based on the users’ interests and context. We propose to exploit knowledge about the user, the adopted device, and the environment - altogether called context - to the end of information tailoring. This paper presents the Context Dimension Tree, a context model which is the basis for solving the information tailoring problem, along with its role in the framework of the Context-ADDICT architecture

And what can context do for data?

Common to all actors in today’s information world is the problem of lowering the “information noise”, both reducing the amount of data to be stored and accessed, and enhancing the “precision” according to which the available data fit the application requirements. Thus, fitting data to the application needs is tantamount to fitting a dress to a person, and will be referred to as data tailoring. The context will be our scissors to tailor data, possibly assembled and integrated from many data sources