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

Time is an attribute of all real-world phenomena and many real-world database applications naturally involve time-varying information. As in traditional databases, joins are the most important and costly operators in temporal databases. Hence, efficient processing strategies for temporal joins are needed. The Entity Join operation is used to group temporal attributes of an entity into a single relation, and is important because those temporal attributes are likely to reside in separate relations due to normalization. In this paper, we propose an efficient Entity-join algorithm for both non-ordered and append-only temporal databases. The proposed approach can be applied to other temporal join operations, such as valid-time equijoin.

Efficient temporal join processing using time index

Improving energy efficiency in intelligent buildings is an important issue in reducing global warming. Real smart energy efficiency is a combination of energy saving and providing for the satisfaction of occupants. However, the comfort of occupants has an uncertainty in prediction and can conflict with preferences of other occupants. So the "Conflict Resolution Architecture" (CRA) is introduced in this paper to solve the conflict among occupants using the integration of RFID systems and sensor network systems. A CRA consists of two control systems that are applied to two separate types of locations. One is the central rule- based control system in a public zone and the other is the individual utility-based control system in a private zone. Two methods that consist of a priority approach and a privilege-average approach are suggested for the CRA control. Then a CRA is able to make conflict resolution among occupants in an intelligent office.

A conflict resolution architecture for the comfort of occupants in intelligent office

We proposed previously in [9] an XML semantic search engine called OOXSearch, which answers loosely structured queries. It takes into account the semantic relationships between data elements based on their contexts. The context of a data element is determined by its parent element. The framework of OOXSearch treats each parent-children set of elements as a single unified entity. OOXSearch works well for all types of XML trees, except when the tree contains a parent that has a child interior element, whose type is the same as the type of its parent (e.g. the parent is "professor and its child interior element is "student" - both professor and student belong to the "person" type). In this paper, we propose an XML search engine called CXLEngine, which is an improvement over OOXSearch. It adopts all the techniques of OOXSearch in addition to new techniques that handle the types of XML trees described above, which OOXSearch does not handle well. We evaluated CXLEngine by comparing it experimentally with OOXSearch and with two other proposed systems, XSEarch [5] and Schema-Free XQuery [8]. The results showed marked improvement.

CXLEngine: a comprehensive XML loosely structured search engine

Reliable contextual information should be generated to provide pervasive services to the occupant in smart spaces. This is difficult for several reasons. First, the number of ways to describe an event or an object is unlimited and there is no standard regarding granularity of context information in context classification schemes. Second, the quality of a given piece of contextual information is not guaranteed by uncertainty. In this paper, we propose a pragmatic context classification and a generalized context modeling scheme based on sensor fusion techniques. To make a pragmatic context classification, we introduce two approaches, “occupant-centered pragmatic approach” and “relation-dependency” approach. To improve the quality of given contextual information by reducing uncertainty, we introduce “state-space based sensor fusion modeling” as a generalized context modeling. Finally, we show an example within the applied scenario as an evidential network.

A classification and modeling of the quality of contextual information in smart spaces

We present in this paper, a context-driven search engine called XCDSearch for answering XML Keyword-based queries as well as Loosely Structured queries, using a stack-based sort-merge algorithm. Most current research is focused on building relationships between data elements based solely on their labels and proximity to one another, while overlooking the contexts of the elements, which may lead to erroneous results. Since a data element is generally a characteristic of its parent, its context is determined by its parent. We observe that we could treat each set of elements consisting of a parent and its children data elements as one unified entity, and then use a stack-based sort-merge algorithm employing context-driven search techniques for determining the relationships between the different unified entities. We evaluated XCDSearch experimentally and compared it with five other search engines. The results showed marked improvement.

Ramez Elmasri

Papers

Efficient temporal join processing using time index

A conflict resolution architecture for the comfort of occupants in intelligent office

CXLEngine: a comprehensive XML loosely structured search engine

A classification and modeling of the quality of contextual information in smart spaces

XCDSearch: An XML Context-Driven Search Engine