166 research outputs found
Model-Checking Based Data Retrieval: an application to semistructured and temporal data
No full textThe book deals with the problems of characterizing the semantics of and assuring efficient execution for database query languages, where the database contains semistructured and time-varying information. This area of technology is of much interest and significance for databases and knowledge bases; it also presents many challenging research problems deserving an in-depth investigation
Discovering Contextual Association Rules in Relational Databases
No full textContextual association rules represent co-occurrences between contexts and properties of data, where the context is a set of environmental or user personal features employed to customize an application. Due to their particular structure, these rules can be very tricky to mine, and if the process is not carried out with care, an unmanageable set of not significant rules may be extracted. In this paper we survey two existing algorithms for relational databases and present a novel algorithm that merges the two proposals overcoming their limitations
Filtering mobile data by means of context: a methodology
No full textThe goal of this paper is the introduction of a methodol- ogy for designing context-aware data selection for portable devices, where computation, memory, power and connectivity resources are limited, and thus, the possibility to tailor the available, usually too rich, data according to context is a mandatory task. First of all, we will introduce the concept of context and its model, a data structure that expresses knowledge on the user, the environment and the pos- sible scenarios. We will then focus on the proposed methodology for selecting, by means of such information, the relevant data to be made available on a user device. An overall picture of the complete scenario for this context-aware data design and tailoring system will be also provided
Graph transformation to infer schemata from XML documents.
No full textSemi-structured data are characterized by the lack of a predefined schema. This heterogeneity simplifies the management of such data, but analysis and queries become more difficult and demand for schemata that describe these data. Super-imposed structures cannot be as general as predefined ones, but ease the retrieval of the information embedded in such data. The paper adopts XML as the language to render semi-structured data and proposes an approach - based on graph transformation techniques - to infer the schemata of XML documents
Context-Driven Data Filtering: A Methodology
No full textThe goal of this paper is the introduction of a methodology for designing context-driven data selection, that is the possibility to tailor the available, usually too rich, data to be held on portable mobile devices, according to context. First of all, we will introduce the concept of context and its model, a data structure that expresses knowledge on the user, the environment and the possible scenarios. We will then focus on the proposed methodology for selecting, by means of such information, the relevant data to be made available on a user device. An application of the proposed methodology is the possibility to select data of interest for portable devices, where computation, memory, power and connectivity resources are limited, and thus, tailororing the available, usually too rich, data according to context is a mandatory task
A Graph-Based Model for Semistructured Temporal Data
No full textWe sketch here the semistructured temporal data model GEM (Graphical sEmistructured teMporal), which is a graph-based data model and allows one to uniformly capture different temporal aspects of semistructured data, such as valid and transaction times
Modeling temporal dimensions of semistructured data
No full textIn 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
Mining flexible association rules from XML
No full textThe role of the eXtensible Markup Language (XML) is becoming very important in the research fields focusing on the representation, the exchange, and the integration of information coming from different data sources and containing information related to various contexts such as, for example, medical and biological data. Extracting knowledge from XML datasets is an important issue that may be difficult because of the semistructured intrinsic nature of XML; indeed documents can have an implicit and irregular structure, not defined in advance.
In this paper, we propose a novel approach for discovering frequent, but approximate, information in XML documents, based on Flexible Tree Rules taking into account both structure and content of the analyzed data. Our proposal is flexible enough to be adapted to both documents with a regular structure and documents with a highly heterogeneous structure, and can be used to evaluate the similarity of XML documents. Moreover, we describe an algorithm to evaluate the similarity degree of a Flexible Tree Rule with respect to an XML document
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