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.

/pdf/a-survey-of-approaches-to-automatic-schema-matching-4n2e5qtrpt.pdf

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

A WSN (Wireless Sensor Networks) consists of a large number of sensor nodes Each sensor node has limited battery, small storage, and short radio range Many researchers have proposed various methods to reduce energy consumption in sensor nodes, since it is difficult to replace sensor node power sources Generally, a sensor node consumes its energy during processing, receiving, transmitting and overhearing of messages that are directed to other nodes Among those, overhearing is not necessary for correct operation of sensor networks In this paper we propose a new synchronized wakeup scheme to reduce the overhearing energy consumption using different wakeup time scheduling for extending sensor network lifetime The results of our simulation show that there is a trade-off between reducing overhearing energy and delay time Therefore we propose Double Trees Structure, called DTS, having two routing trees, one based on Short Rings Topology and the other on Long Rings Topology DTS has multi routing paths from base station to children nodes If a node which is on the next routing path does not wakeup in time to receive the data, the sender node selects another path to connect to the destination We can save the wait time until the next destination node wakes up In the simulation result, our wakeup scheduling reduces overhearing energy consumption more than the S-MAC protocol Using the double trees structure reduces the delay time

/pdf/balancing-overhearing-energy-and-latency-in-wireless-sensor-47dk3iiwp4.pdf

Balancing Overhearing Energy and Latency in Wireless Sensor Networks

Comparing the Expressiveness of Temporal Data Models Based on Time Dimension Representation.

specification is a core component of the Semantic Web, and facilitates interoperability among different systems that use distinct models. Developing a spatial ontology will allow many applications that have spatial objects to interact. In this paper, we formalize 2-D spatial concepts and operations into a spatial ontology. We show how these concepts can be realized in Protege (30). The Protege spatial ontology provides spatial built-ins that can be used to provide a spatial dimension to other ontologies when needed. We give some examples of the use of our ontology, which is based on a standard Geometry class hierarchy (23), with a few modifications.

Formalization of 2-D Spatial Ontology and OWL/Protégé Realization Kulsawasd Jitkajornwanich

Web services have recently become a new trend for gathering biomedical information. However, it is not easy to integrate and obtain a concise/complete query result among thousands of service operations. In this dissertation, we propose a multi-level ontology-enabled service broker system for dynamically integrating web services in the biomedical domain (BioServiceBroker)[1, 2]. By introducing multi-level modeling concepts and intra/inter level relationships [3], our approach facilitates more accurate modeling of biomedical ontologies, which leads to a better understanding of the data stored in various biological data sources as well as the services provided by the data sources. In addition, we incorporate temporal concepts with new enhanced QoS measures [2], which allow service requesters to control more querying factors in order to precisely invoke corresponding service operations. We also define a Unified Biomedical Service Interface (UBSI) as a proposed service deployment standard. 
The multi-level ontology-enabled service broker system can be combined with other mediator systems [4] for cross referencing the diverse bioinformatics sources and can also be utilized in other application domains. Our ultimate goal is to construct a public, scalable, and interoperable biomedical service platform based on UBSI to benefit scientists in data searching and publishing.

Ramez Elmasri

Papers

Balancing Overhearing Energy and Latency in Wireless Sensor Networks

Comparing the Expressiveness of Temporal Data Models Based on Time Dimension Representation.

Formalization of 2-D Spatial Ontology and OWL/Protégé Realization Kulsawasd Jitkajornwanich

A multi-level biomedical ontology-enabled broker: dynamic service-based data source integration

Analyzing Semi-Structured Data for Ontological Information Extraction.