Mirko Orsini

A semantic approach to ETL technologies

Data warehouse architectures rely on extraction, transformation and loading (ETL) processes for the creation of an updated, consistent and materialized view of a set of data sources. In this paper, we support these processes by proposing a tool that: (1) allows the semi-automatic definition of inter-attribute semantic mappings, by identifying the parts of the data source schemas which are related to the data warehouse schema, thus supporting the extraction process; and (2) groups the attribute values semantically related thus defining a transformation function for populating with homogeneous values the data warehouse. Our proposal couples and extends the functionalities of two previously developed systems: the MOMIS integration system and the RELEVANT data analysis system. The system has been experimented within a real scenario concerning the creation of a data warehouse for enterprises working in the beverage and food logistic area. The results showed that the coupled system supports effectively the extraction and transformation processes.

Keymantic: semantic keyword-based searching in data integration systems

We propose the demonstration of Keymantic, a system for keyword-based searching in relational databases that does not require a-priori knowledge of instances held in a database. It finds numerous applications in situations where traditional keyword-based searching techniques are inapplicable due to the unavailability of the database contents for the construction of the required indexes.We propose the demonstration of Keymantic, a system for keyword-based searching in relational databases that does not require a-priori knowledge of instances held in a database. It finds numerous applications in situations where traditional keyword-based searching techniques are inapplicable due to the unavailability of the database contents for the construction of the required indexes.

A location aware role and attribute based access control system

In this paper, we follow the role-based access control (RBAC) approach and extend it to provide for the dynamic association of roles with users. In our framework, privileges associated with resources are assigned depending on the attribute values of the resources, attribute values associated with users determine the association of users with privileges, and a location mapping function between physical and logical locations allows to enable/disable roles depending on the logical location of the users and thus preserve the privacy of the location. We use Semantic Web technologies and a graphical user interface based on the Google Maps API.

A Constraint and Attribute Based Security Framework for Dynamic Role Assignment in Collaborative Environments

We investigate a security framework for collaborative applications that relies on the role-based access control (RBAC) model. In our framework, roles are pre-defined and organized in a hierarchy (partial order). However, we assume that users are not previously identified, therefore the actions that they can perform are dynamically determined based on their own attribute values and on the attribute values associated with the resources. Those values can vary over time (e.g., the user’s location or whether the resource is open for visiting) thus enabling or disabling a user’s ability to perform an action on a particular resource. In our framework, constraint values form partial orders and determine the association of actions with the resources and of users with roles. We have implemented our framework by exploring the capabilities of semantic web technologies, and in particular of OWL 1.1, to model both our framework and the domain of interest and to perform several types of reasoning. In addition, we have implemented a user interface whose purpose is twofold: (1) to offer a visual explanation of the underlying reasoning by displaying roles and their associations with users (e.g., as the user’s locations vary); and (2) to enable monitoring of users that are involved in a collaborative application. Our interface uses the Google Maps API and is particularly suited to collaborative applications where the users’ geospatial locations are of interest.

Instances Navigation for Querying Integrated Data from Web-Sites

Research on data integration has provided a set of rich and well understood schema mediation languages and systems that provide a meta-data representation of the modeled real world, while, in general, they do not deal with data instances.

From Data Integration to Big Data Integration

The Database Group (DBGroup, www.dbgroup.unimore.it) and Information System Group (ISGroup, www.isgroup.unimore.it) research activities have been mainly devoted to the Data Integration Reserach Area. The DBGroup designed and developed the MOMIS data integration system, giving raise to a successful innovative enterprise DataRiver (www.datariver.it), distributing MOMIS as open source. MOMIS provides an integrated access to structured and semistructured data sources and allows a user to pose a single query and to receive a single unified answer. Description Logics, Automatic Annotation of schemata plus clustering techniques constitute the theoretical framework. In the context of data integration, the ISGroup addressed problems related to the management and querying of heterogeneous data sources in large-scale and dynamic scenarios. The reference architectures are the Peer Data Management Systems and its evolutions toward dataspaces. In these contexts, the ISGroup proposed and evaluated effective and efficient mechanisms for network creation with limited information loss and solutions for mapping management query reformulation and processing and query routing. The main issues of data integration have been faced: automatic annotation, mapping discovery, global query processing, provenance, multidimensional Information integration, keyword search, within European and national projects. With the incoming new requirements of integrating open linked data, textual and multimedia data in a big data scenario, the research has been devoted to the Big Data Integration Research Area. In particular, the most relevant achieved research results are: a scalable entity resolution method, a scalable join operator and a tool, LODEX, for automatically extracting metadata from Linked Open Data (LOD) resources and for visual querying formulation on LOD resources. Moreover, in collaboration with DATARIVER, Data Integration was successfully applied to smart e-health.

An ETL Tool Based on Semantic Analysis of Schemata and Instances

In this paper we propose a system supporting the semi-automatic definition of inter-attribute mappings and transformation functions used as an ETL tool in a data warehouse project. The tool supports both schema level analysis, exploited for the mapping definitions amongst the data sources and the data warehouse, and instance level operations, exploited for defining transformation functions that integrate data coming from multiple sources in a common representation. Our proposal couples and extends the functionalities of two previously developed systems: the MOMIS integration system and the RELEVANT data analysis system.

KEYMANTIC: A KEYWORD-BASED SEARCH ENGINE USING STRUCTURAL KNOWLEDGE

Traditional techniques for query formulation need the knowledge of the database contents, i.e. which data are stored in the data source and how they are represented. In this paper, we discuss the development of a keyword-based search engine for structured data sources. The idea is to couple the ease of use and flexibility of keyword-based search with metadata extracted from data schemata and extensional knowledge which constitute a semantic network of knowledge. Translating keywords into SQL statements, we will develop a search engine that is effective, semantic-based, and applicable also when instance are not continuously available, such as in integrated data sources or in data sources extracted from the deep web.

An Ontology-Based Data Integration System for Data and Multimedia Sources

Data integration is the problem of combining data residing at distributed heterogeneous sources, including multimedia sources, and providing the user with a unified view of these data. Ontology based Data Integration involves the use of ontology(s) to effectively combine data and information from multiple heterogeneous sources. Ontologies, with respect to the integration of data sources, can be used for the identification and association of semantically corresponding information concepts, i.e. for the definition of semantic mappings among concepts of the information sources. MOMIS is a Data Integration System which performs information extraction and integration from both structured and semistructured data sources. In MOMIS was extended to manage “traditional” and “multimedia” data sources at the same time. STASIS is a comprehensive application suite which allows enterprises to simplify the mapping process between data schemas based on semantics. Moreover, in STASIS, a general framework to perform Ontology-driven Semantic Mapping has been pro-posed. This paper describes the early effort to combine the MOMIS and the STASIS frameworks in order to obtain an effective approach for Ontology-Based Data Integration for data and multimedia sources.

A Secure Mediator for Integrating Multiple Level Access Control Policies

We present a method for mapping security levels among the components of a distributed system where data in the local sources are represented in XML. Distributed data is integrated using a semantic-based approach that maps each XML schema into an RDF schema and subsequently integrates those schemas into a global RDF schema using a global as view (GAV) approach. We transform the security levels defined on the XML schema elements of each local source into security levels on the triples of the local RDF schemas, which form a lattice. We show how the merged data in the global schema can be classified in different security classes belonging to the global partially ordered security graph.