We discuss a proposal for the implementation of the model management operator ModelGen, which translates schemas from one model to another, for example from object-oriented to SQL or from SQL to XML Schema Descriptions. The operator can be used to generate database wrappers (e.g., object-oriented or XML to relational), default user interfaces (e.g., relational to forms), or default database schemas from other representations. The approach translates schemas from a model to another, within a predefined, but large and extensible, set of models: given a source schema S expressed in a source model, and a target model TM, it generates a schema S expressed in TM that is "equivalent" to S. A wide family of models is handled by using a metamodel in which models can be succinctly and precisely described. The approach expresses the translation as Datalog rules and exposes the source and target of the translation in a generic relational dictionary. This makes the translation transparent, easy to customize and model-independent. The proposal includes automatic generation of translations as composition of basic steps.
Abstract. Model management is a metadata-based approach to database problems aimed at supporting the productivity of developers by providing schema manipulation operators. Here we propose MISM (Model Independent Schema Management), a platform for model management offering a set of operators to manipulate schemas, in a manner that is both model-independent (in the sense that operators are generic and apply to schemas of different data models) and model-aware (in the sense that it is possible to say whether a schema is allowed for a data model). This is the first proposal for model management in this direction. We consider the main operators in model management: merge, diff, and modelgen. These operators play a major role in solving various problems related to schema evolution (such as data integration, data exchange or forward engineering), and we show in detail a solution to a major representative of the class, the round-trip engineering problem.
Abstract. Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontologybased Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.
Many experts predict that the next huge step forward in Web information technology will be achieved by adding semantics to Web data, and will possibly consist of (some form of) the Semantic Web. In this paper, we present a novel approach to Semantic Web search, called Serene, which allows for a semantic processing of Web search queries, and for evaluating complex Web search queries that involve reasoning over the Web. More specifically, we first add ontological structure and semantics to Web pages, which then allows for both attaching a meaning to Web search queries and Web pages, and for formulating and processing ontology-based complex Web search queries (i.e., conjunctive queries) that involve reasoning over the Web. Here, we assume the existence of an underlying ontology (in a lightweight ontology language) relative to which Web pages are annotated and Web search queries are formulated. Depending on whether we use a general or a specialized ontology, we thus obtain a general or a vertical Semantic Web search interface, respectively. That is, we are actually mapping the Web into an ontological knowledge base, which then allows for Semantic Web search relative to the underlying ontology. The latter is then realized by reduction to standard Web search on standard Web pages and logically completed ontological annotations. That is, standard Web search engines are used as the main inference motor for ontologybased Semantic Web search. We develop the formal model behind this approach and also provide an implementation in desktop search. Furthermore, we report on extensive experiments, including an implemented Semantic Web search on the Internet Movie Database.
Multimodal interfaces can be profitably used to support increasingly complex services in assistive environments. In particular, sketch-based interfaces offer users an effortless and powerful communication way to represent concepts and commands on different devices. Unlike other modalities, sketch-based interaction can be easily fitted according to heterogeneous services. Moreover it can be quickly personalized according to the user needs. Developing a sketch-based interface for a specific service is a time-consuming operation that requires the re-engineering and/or the re-designing of the whole recognizer framework. This paper describes a definitive framework by which the user, simply by using freehand drawing, can define every kind of sketch-based interface. The definition of the interface and its recognition process are performed by using our developed Sketch Modeling Language (SketchML).
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