Roberto De Virgilio

Converting relational to graph databases

Graph Database Management Systems provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In this framework, the conversion of the persistent layer of an application from a relational to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a relational to a graph database by exploiting the schema and the constraints of the source. The approach supports the translation of conjunctive SQL queries over the source into graph traversal operations over the target. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database.

Semantic Web Information Management

Of course, from childhood to forever, we are always thought to love reading. It is not only reading the lesson book but also reading everything good is the choice of getting new inspirations. Religion, sciences, politics, social, literature, and fictions will enrich you for not only one aspect. Having more aspects to know and understand will lead you become someone more precious. Yea, becoming precious can be situated with the presentation of how your knowledge much.

A general methodology for context-aware data access

In this paper, we present an extensible approach to the adaptation of Web information delivery according to different and possibly heterogeneous contexts. The approach is based on a general notion of profile that can be used to represent a variety of contexts at different level of details. Each profile is associated with a configuration that specifies, in abstract terms, how information has to be delivered by taking into account the requirements of adaptation for the profile. We present a general and extensible methodology for content adaptation based on the generation and management of configurations. The genericity of the approach guarantees that different types of contexts and orthogonal requirements of adaptation, possibly not fixed in advance, can be taken into account in the adaptation process. We also describe architecture and functionality of a prototype implementing the proposed methodology. We finally provide a number of experimental results of the system to support the effectiveness and efficiency of the approach.

Cluster-Based Exploration for Effective Keyword Search over Semantic Datasets

The amount of data available in the Web, in databases as well as other systems, is constantly increasing as increasing is the number of users that wish to access such data. Data is available in forms that may not be of easy access for not expert users. Keyword Search approaches are an effort to abstract from specific data representations, allowing users to retrieve information by providing a few terms of interest. Many solutions build on dedicated indexing techniques as well as search algorithms aiming at finding substructures that connect the data elements matching the keywords. In this paper, we present the development of Yaanii, a tool for effective Keyword Search over semantic datasets. Yaaniiis based on a novel keyword search paradigm for graph-structured data, focusing in particular on the RDF data model. We provide a clustering technique that identifies and groups graph substructures based on template match. A scoring function, IR inspired, evaluates the relevance of the substructures and of the clusters, and supports the generation of Top-k solutions during its execution in the first k steps. Experiments demonstrate the effectiveness of our approach.

Modeling heterogeneous context information in adaptive web based applications

In this paper we propose a comprehensive framework for therepresentation and translation of context information in adaptiveWeb based applications.We consider the various formats used today to express context data (HTTPheader, XML files over specific DTDs, RDF, CC/PP, and so on) and show thatthe primitives adopted by all of them can be classified into a ratherlimited set of basic types. We then define, building on these basic types,a general formalism for context information called GPM that embeds theseheterogeneous formats. It turns out that GPM can be used to model a varietyof contexts in a uniform way and therefore provides a powerful toolsupporting the design and analysis of context aware applications.We also address the problem of translating context information from onerepresentation into another. This is achieved by using GPM as anintermediate representation level. Finally, we describe architecture andfunctionality of a prototype implementing the translation process andillustrate experimental results supporting the effectiveness of the overallapproach.

NYAYA: A System Supporting the Uniform Management of Large Sets of Semantic Data

We present NYAYA, a flexible system for the management of large-scale semantic data which couples a general-purpose storage mechanism with efficient ontological query answering. NYAYA rapidly imports semantic data expressed in different formalisms into semantic data kiosks. Each kiosk exposes the native ontological constraints in a uniform fashion using data log±, a very general rule-based language for the representation of ontological constraints. A group of kiosks forms a semantic data market where the data in each kiosk can be uniformly accessed using conjunctive queries and where users can specify user-defined constraints over the data. NYAYA is easily extensible and robust to updates of both data and meta-data in the kiosk and can readily adapt to different logical organizations of the persistent storage. In the demonstration, we will show the capabilities of NYAYA over real-world case studies and demonstrate its efficiency over well-known benchmarks.

Semantic data markets: a flexible environment for knowledge management

We present Nyaya, a system for the management of Semantic-Web data which couples a general-purpose and extensible storage mechanism with efficient ontology reasoning and querying capabilities. Nyaya processes large Semantic-Web datasets, expressed in multiple formalisms, by transforming them into a collection of Semantic Data Kiosks. Nyaya uniformly exposes the native meta-data of each kiosk using the datalog+- language, a powerful rule-based modelling language for ontological databases. The kiosks form a Semantic Data Market where the data in each kiosk can be uniformly accessed using conjunctive queries and where users can specify user-defined constraints over the data. Nyaya is easily extensible and robust to updates of both data and meta-data in the kiosk and can readily adapt to different logical organization of the persistent storage. The approach has been experimented using well-known benchmarks, and compared to state-of-the-art research prototypes and commercial systems.

Rule-based Adaptation of Web Information Systems

Mobile devices provide a variety of ways to access information resources available on the Web and a high level of adaptability to different aspects of the context (such as the device capabilities, the network QoS, the user preferences, and the location) is strongly required in this scenario. In this paper, we present a rule-based approach supporting the automatic adaptation of content delivery in Web Information Systems. The approach relies on the general notions of profile and configuration. The former is used to model a variety of context characteristics in a uniform way. The latter describes, in abstract terms, how to build the various levels of a suitable Web interface (content, navigation and presentation). We propose an original notion of adaptation rule that can be used to specify, in a declarative way, how to build a configuration that satisfies the requirements of adaptation for a profile. The evaluation process defined for these rules supports: (1) the handling of many separately specified adaptation requirements according to different aspects of the context, possibly not fixed in advance, and (2) their integration into one coherent recipe for adaptation. We also describe the architecture and functionality of a prototype implementing the proposed approach and illustrate experimental results supporting its flexibility and efficiency.

A path-oriented RDF index for keyword search query processing

Most of the recent approaches to keyword search employ graph structured representation of data. Answers to queries are generally sub-structures of the graph, containing one or more keywords. While finding the nodes matching keywords is relatively easy, determining the connections between such nodes is a complex problem requiring on-the-fly time consuming graph exploration. Current techniques suffer from poorly performing worst case scenario or from indexing schemes that provide little support to the discovery of connections between nodes. In this paper, we present an indexing scheme for RDF that exposes the structural characteristics of the graph, its paths and the information on the reachability of nodes. This knowledge is exploited to expedite the retrieval of the substructures representing the query results. In addition, the index is organized to facilitate maintenance operations as the dataset evolves. Experimental results demonstrates the feasibility of our index that significantly improves the query execution performance.

A similarity measure for approximate querying over RDF data

Approximate query answering relies on a similarity measure that evaluates the relevance, for a given query, of a set of data extracted from the underlying database. In the context of graph-modeled data, many methods (such as, subgraph isomorphism, graph edit distance, and maximum common subgraph) have been proposed to face this problem. Unfortunately, they are usually hard to compute and when they are used on RDF data, several drawbacks arise. In this paper, we propose a measure to evaluate the similarity between a (small) graph representing a query and a portion of a (large) graph representing an RDF data set. We show that this measure: (i) can be evaluated in linear time with respect to the size of the given graphs and, (ii) guarantees other interesting properties. In order to show the feasibility of our approach, we have used such similarity measure in a technique for approximate query answering. The technique has been implemented in a prototypical system and a number of experimental results obtained with this system confirm the effectiveness of the proposed measure.