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

We present in this paper novel context-driven search techniques that compute the semantic similarities among different GO ontology terms. We implemented these techniques in a middleware search engine called GOcSim, which resides between user application and GO database. Most current research is focused on determining semantic similarities between GO ontology terms based solely on their IDs and proximity to one another in GO graph structure, while overlooking the contexts of the terms, which may lead to erroneous results. The context of a term T is determined by the set of other terms, whose existence is dependent on T. We propose novel techniques that determine the contexts of terms based on the concept of existence dependency. We present a stack-based sort-merge algorithm employing these techniques for determining the semantic similarities between GO terms. We evaluated GOcSim experimentally and compared it with four other methods. The results showed marked improvement.

GOcSim: GO context-driven similarity

In our previous work, we described various aspects of our approach in converting big raw rainfall data into meaningful storm concepts. Three concepts were defined: local, hourly, and overall storms. The latter describes overall spatio-temporal characteristics of a storm as it progresses over time. We previously described MapReduce-based algorithms for local and hourly storm identification. Overall storms are the most complex to identify, and are at the core of the storm identification system. Multiple consecutive hourly storms that have spatial overlap are combined to create storm-centric characteristics of the whole storm, which could not be captured in most existing hydrology research. In this paper, we propose a MapReduce-based overall storm identification algorithm, which is based on iteration on MapReduce framework. This greatly improves performance when compared to the depth-first search (DFS) graph traveling approach as introduced in our previous work. In addition, additional essential storm characteristics of hourly and overall storms are introduced in this paper. Examples include storm center concepts for hourly storms and storm track and speed for overall storms.

Complete storm identification algorithms from big raw rainfall data using MapReduce framework

Fragmentation and query decomposition are considered important issues in a distributed database environment For a semantic data model (ECR data model), the types of fragmentation and the criteria they should follow is provided along with how global queries in a high-level language, GORDAS, are decomposed into local queries on the fragments This technique may be used in a heterogeneous distributed database, where the global schema presented to the user is in the high-level ECR model

Fragmentation and query decomposition in the ECR model

SEEC: A Dual Search Engine for Business Employees and Customers

Some types of contextual information are more important than others for inferring a situation of the patient in home-based care. The weight of this information may be changed due to the variations of the sensed values over time. Some researches applied a static weighting based fusion process to their systems to model these variations. However, this fusion process sometimes reduces the reliability of contextual information, since it does not consider a dynamic change of the importance over time. We propose the dynamic weighting based evidential fusion process (DWEFP) as a context reasoning method. DWEFP applies a normalized weighting technique to dynamic evidential networks for improving the confidence level of contextual information. To show the improvement of the proposed method, we compare our DWEFP with previous static weighting based fusion methods such as Bayesian Networks (BNs), Dempster-Shafer Theory (DST), Static Evidential Fusion Process (SEFP), Dynamic Evidential Fusion Process (DEFP), and Dynamic Bayesian Networks (DBNs).

Ramez Elmasri

Papers

GOcSim: GO context-driven similarity

Complete storm identification algorithms from big raw rainfall data using MapReduce framework

Fragmentation and query decomposition in the ECR model

SEEC: A Dual Search Engine for Business Employees and Customers

A Dynamic Normalized Weighting Based Context Reasoning in Home-Based Care