Providing an integrated access to multiple heterogeneous sources is a challenging issue in global information systems for cooperation and interoperability. In this context, two fundamental problems arise. • First, how to determine if the sources contain semantically related information, that is, information related to the same or similar real-world concept(s). Second, how to handle semantic heterogeneity to support integration and uniform query interfaces. Complicating factors with respect to conventional view integration techniques are related to the fact that the sources to be integrated already exist and that semantic heterogeneity occurs on the large-scale, involving terminology, structure, and context of the involved sources, with respect to geographical, organizational, and functional aspects related to information use. Moreover, to meet the requirements of global, Internet-based information systems, it is important that tools developed for supporting these activities are semi-automatic and scalable as much as possible. The goal of this paper is to describe the MOMIS [4, 5] (Mediator envirOnment for Multiple Information Sources) approach to the integration and query of multiple, heterogeneous information sources, containing structured and semistructured data. MOMIS has been conceived as a joint collaboration between University of Milano and Modena in the framework of the INTERDATA national research project, aiming at providing methods and tools for data management in Internet-based information systems. Like other integration projects [1, 10, 14], MOMIS follows a "semantic approach" to information integration based on the conceptual schema, or metadata, of the information sources, and on the following architectural elements: i) a common object-oriented data model, defined according to the ODLxs language, to describe source schemas for integration purposes. The data model and ODLxs have been defined in MOMIS as subset of the ODMG-93 ones, following the proposal for a standard mediator language developed by the I3/POB working group [7]. In addition, ODLxs introduces new constructors to support the semantic integration process [4, 5]; ii) one or more wrappers, to translate schema descriptions into the common ODLIs representation; iii) a mediator and a query-processing component, based on two p~e-existing tools, namely ARTEMIS [8] and ODB-Tools [3] (available on Internet at http://sparc20.dsi.unimo.it/), to provide an 13 architecture for integration and query optimization. In this paper, we focus on capturing and reasoning about semantic aspects of schema descriptions of heterogeneous information sources for supporting integration and query optimization. Both semistructured and structured data sources are taken into account [5]. A Common Thesaurus is constructed, which has the role of a shared ontology for the information sources. The Common Thesaurus is built by analyzing ODLI3 descriptions of the sources, by exploiting the Description Logics OLCD (Object Language with Complements allowing Descriptive cycles) [...

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Keyword queries offer a convenient alternative to traditional SQL in querying relational databases with large, often unknown, schemas and instances. The challenge in answering such queries is to discover their intended semantics, construct the SQL queries that describe them and used them to retrieve the respective tuples. Existing approaches typically rely on indices built a-priori on the database content. This seriously limits their applicability if a-priori access to the database content is not possible. Examples include the on-line databases accessed through web interface, or the sources in information integration systems that operate behind wrappers with specific query capabilities. Furthermore, existing literature has not studied to its full extend the inter-dependencies across the ways the different keywords are mapped into the database values and schema elements. In this work, we describe a novel technique for translating keyword queries into SQL based on the Munkres (a.k.a. Hungarian) algorithm. Our approach not only tackles the above two limitations, but it offers significant improvements in the identification of the semantically meaningful SQL queries that describe the intended keyword query semantics. We provide details of the technique implementation and an extensive experimental evaluation.

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Entity Resolution (ER) is the task of finding entity profiles that correspond to the same real-world entity. Progressive ER aims to efficiently resolve large datasets when limited time and/or computational resources are available. In practice, its goal is to provide the best possible partial solution by approximating the optimal comparison order of the entity profiles. So far, Progressive ER has only been examined in the context of structured (relational) data sources, as the existing methods rely on schema knowledge to save unnecessary comparisons: they restrict their search space to similar entities with the help of schema-based blocking keys (i.e., signatures that represent the entity profiles). As a result, these solutions are not applicable in Big Data integration applications, which involve large and heterogeneous datasets, such as relational and RDF databases, JSON files, Web corpus etc. To cover this gap, we propose a family of schema-agnostic Progressive ER methods, which do not require schema information, thus applying to heterogeneous data sources of any schema variety. First, we introduce two naïve schema-agnostic methods, showing that straightforward solutions exhibit a poor performance that does not scale well to large volumes of data. Then, we propose four different advanced methods. Through an extensive experimental evaluation over 7 real-world, established datasets, we show that all the advanced methods outperform to a significant extent both the naïve and the state-of-the-art schema-based ones. We also investigate the relative performance of the advanced methods, providing guidelines on the method selection.