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

This thesis develops an integration methodology for logical database design of large, integrated, multi-user database systems. Such database systems are often used by large organizations. The traditional approach to logical database design assumes the existence of a database administrator who

On the design, use, and integration of data models

OOXKSearch is a semantic search engine that answers XML keyword-based queries as well as loosely structured queries using Object Oriented techniques. There has been extensive research in XML keyword-based and loosely structured querying. Some frameworks work well for certain types of XML data models while fail in others. The reason is that the proposed techniques are based solely on establishing relationships between individual elements while overlooking the context of these elements. The context of a data element is determined by its parent, because it specifies one of the characteristics of the parent. Since data elements are nothing but characteristics of their parents, we observe that we could treat each parent-children set of elements as one unified entity. We then find semantic relationships between the different unified entities. If two distinct unified entities are semantically related, their data elements are also semantically related. The search performance and quality of OOXKSearch were evaluated experimentally and compared with three recent proposed systems. The results showed marked improvement.

OOXKSearch: A Search Engine for Answering XML Keyword and Loosely Structured Queries Using OO Techniques

This paper addresses the issues of declustering temporal index and access structures for a single processor multiple independent disk architecture. The temporal index is the Monotonic B/sup +/-Tree which uses the time index temporal access structure. We devise a new algorithm, called multi-level round robin, for assigning tree nodes to multiple disks. The multi-level round robin declustering technique takes advantage of the append-only nature of temporal databases to achieve uniform load distribution, decrease response time, and increase the fanout of the tree by eliminating the need to store disk numbers within the tree nodes. We propose two declustering techniques for the time index access structures; one considers only time proximity while declustering, whereas the other considers both time proximity and data size. We investigate their performance over different types of temporal queries and show that various temporal queries have conflicting allocation criteria for the time index buckets. In addition, we devise two disk partition techniques for the time index buckets. The mutually exclusive technique partitions the disks into disjoint groups, whereas the shared disk technique allows the different types of buckets to share all disks. >

Declustering techniques for parallelizing temporal access structures

This chapter was a summary of our work in temporal conceptual models and query languages [173, 178, 174, 179], Our model distinguishes between conceptual and temporal objects, and characterizes the properties of entities (conceptual objects), entity roles (temporal objects), and (temporal and non-temporal) attributes. It also defines temporal constraints among entity roles, differentiates between temporal and conceptual relationships, and provides rules for preserving temporal integrity constraints.

A Temporal Query Language For A Conceptual Model

Clustering of sub-trajectories is a very useful method to extract important information from vast amounts of trajectory data. Existing trajectory clustering algorithms have focused on geometric properties and spatial features of trajectories and sub-trajectories. In contrast to the existing trajectory clustering algorithms, we propose a new framework to cluster sub-trajectories based on a combination of their spatial and non-spatial features. This algorithm combines techniques from grid based approaches, spatial geometry and string processing. First, we convert each trajectory into a representative sequence that captures the trajectory direction and location. We identify common sub-trajectories from all the sequences using a modified string matching algorithm. Then, we extract non-spatial features from the common sub-trajectories. Finally, we present a density based clustering algorithm to cluster the sub-trajectories. Experimental results show that our framework correctly discovers groups of similar sub-trajectories with their similar non-spatial features.

Ramez Elmasri

Papers

On the design, use, and integration of data models

OOXKSearch: A Search Engine for Answering XML Keyword and Loosely Structured Queries Using OO Techniques

Declustering techniques for parallelizing temporal access structures

A Temporal Query Language For A Conceptual Model

A Novel Approach to Trajectory Analysis Using String Matching and Clustering