There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Data Mining - Concepts and Techniques.

Schema matching is a basic problem in many database application domains, such as data integration, E-business, data warehousing, and semantic query processing. In current implementations, schema matching is typically performed manually, which has significant limitations. On the other hand, previous research papers have proposed many techniques to achieve a partial automation of the match operation for specific application domains. We present a taxonomy that covers many of these existing approaches, and we describe the approaches in some detail. In particular, we distinguish between schema-level and instance-level, element-level and structure-level, and language-based and constraint-based matchers. Based on our classification we review some previous match implementations thereby indicating which part of the solution space they cover. We intend our taxonomy and review of past work to be useful when comparing different approaches to schema matching, when developing a new match algorithm, and when implementing a schema matching component.

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A survey of approaches to automatic schema matching

Ontologies tend to be found everywhere. They are viewed as the silver bullet for many applications, such as database integration, peer-to-peer systems, e-commerce, semantic web services, or social networks. However, in open or evolving systems, such as the semantic web, different parties would, in general, adopt different ontologies. Thus, merely using ontologies, like using XML, does not reduce heterogeneity: it just raises heterogeneity problems to a higher level. Euzenat and Shvaikos book is devoted to ontology matching as a solution to the semantic heterogeneity problem faced by computer systems. Ontology matching aims at finding correspondences between semantically related entities of different ontologies. These correspondences may stand for equivalence as well as other relations, such as consequence, subsumption, or disjointness, between ontology entities. Many different matching solutions have been proposed so far from various viewpoints, e.g., databases, information systems, and artificial intelligence. The second edition of Ontology Matching has been thoroughly revised and updated to reflect the most recent advances in this quickly developing area, which resulted in more than 150 pages of new content. In particular, the book includes a new chapter dedicated to the methodology for performing ontology matching. It also covers emerging topics, such as data interlinking, ontology partitioning and pruning, context-based matching, matcher tuning, alignment debugging, and user involvement in matching, to mention a few. More than 100 state-of-the-art matching systems and frameworks were reviewed. With Ontology Matching, researchers and practitioners will find a reference book that presents currently available work in a uniform framework. In particular, the work and the techniques presented in this book can be equally applied to database schema matching, catalog integration, XML schema matching and other related problems. The objectives of the book include presenting (i) the state of the art and (ii) the latest research results in ontology matching by providing a systematic and detailed account of matching techniques and matching systems from theoretical, practical and application perspectives.

Ontology Matching

A federated database system (FDBS) is a collection of cooperating database systems that are autonomous and possibly heterogeneous. In this paper, we define a reference architecture for distributed database management systems from system and schema viewpoints and show how various FDBS architectures can be developed. We then define a methodology for developing one of the popular architectures of an FDBS. Finally, we discuss critical issues related to developing and operating an FDBS.

Federated database systems for managing distributed, heterogeneous, and autonomous databases

In home-based care, reliable contextual information of remotely monitored patients should be generated by correctly recognizing the activities to prevent hazardous situations of the patient. It is difficult to achieve a higher confidence level of contextual information for several reasons. First, low-level data from multisensors have different degrees of uncertainty. Second, generated contexts can be conflicting, even though they are acquired by simultaneous operations. We propose the static evidential fusion process (SEFP) as a context-reasoning method. The context-reasoning method processes sensor data with an evidential form based on the Dezert-Smarandache theory (DSmT). The DSmT approach reduces ambiguous or conflicting contextual information in multisensor networks. Moreover, we compare SEFP based on DSmT with traditional fusion processes such as Bayesian networks and the Dempster-Shafer theory to understand the uncertainty analysis in decision making and to show the improvement of the DSmT approach compared to the others.

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A Static Evidential Network for Context Reasoning in Home-Based Care

Reliable contextual information of remotely monitored patients should be generated to prevent hazardous situations and to provide pervasive services in home-based care This is difficult for several reasons First, low level data obtained from heterogeneous sensors have different degrees of uncertainty Second, generated contexts can be corrupted or conflicted even if they are acquired by simultaneous operations In this paper, we utilize Dezert-Smarandache Theory (DSmT) as an evidence fusion approach to reduce ambiguous or imperfect information then to get higher belief levels in the data fusion process of contextual information To analyze the improvement of DSmT fusion process, we compare DSmT with Dempster-Shafer Theory (DST) using PCR5 rule of combination and Dempster's rule of combination respectively

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Sensor Data Fusion Using DSm Theory for Activity Recognition under Uncertainty in Home-Based Care

XML was initially developed for document management, but it is becoming increasingly used for storing and exchanging all kinds of data on the Internet. In this paper, we introduce a design methodology for XML schemas that is based upon well-understood conceptual modeling methodologies. Because XML is hierarchical (tree-structured), many different XML schemas (or document structures) can be generated from the same conceptual database schema. We describe algorithms for generating customized hierarchical views from EER model, creating XML schemas from hierarchical views, and creating XML instance documents.

Conceptual modeling for customized XML schemas

This paper presents an infrastructure and methodology to extract conceptual structure from Web pages, which are mainly constructed by HTML tags and incomplete text. Human beings can easily read Web pages and grasp an idea about the conceptual structure of underlying data, but cannot handle excessive amounts of data due to lack of patience and time. However, it is extremely difficult for machines to accurately determine the content of Web pages due to lack of understanding of context and semantics. Our work provides a methodology and infrastructure to process Web data and extract the underlying conceptual structure, in particular relationships between ontological concepts using Inductive Logic Programming in order to help with automating the processing of the excessive amount of Web data by capturing its conceptual structures.

Learning Rules for Conceptual Structure on the Web

XML was initially developed for document management, but it is becoming increasingly used for storing and exchanging all kinds of data on the Internet. In this paper, we introduce a design methodology for XML schemas that is based upon well-understood conceptual modeling methodologies. Because XML is hierarchical (tree-structured), many different XML schemas (or document structures) can be generated from the same conceptual database schema. We describe algorithms for generating customized hierarchical views from EER model, creating XML schemas from hierarchical views, generating SQL queries corresponding to the XML schemas, and creating XML instance documents from the query results.

Ramez Elmasri

Papers

A Static Evidential Network for Context Reasoning in Home-Based Care

Sensor Data Fusion Using DSm Theory for Activity Recognition under Uncertainty in Home-Based Care

Conceptual modeling for customized XML schemas

Learning Rules for Conceptual Structure on the Web

Conceptual modeling for customized XML schemas