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

Information flow up and down the supply chain, particularly for complex products such as automobiles, aircraft, and weapons systems is complex, difficult, fraught with errors and time consuming. In the defense business, for example, just the engineering trade-off process in the conceptual design phase can take up to a year before all the information requested is available for the design engineering team to compare alternatives (Schwart97). Some of the problems encountered include the inability of small suppliers far down the chain to access technical data created with CAD systems to which they do not have access, inaccurate or wrong versions of technical data when the supplier eventually obtains it, and difficulty in contacting people to request clarification. EDI and geometric modeling standards (e.g. STEP) and Email have started to address these problems, but they do not address all of them. Email, for instance often results in huge CAD files bogging down a suppliers email system (often using a dial up modem) only to have it discarded because it was not appropriate to their business. EDI/EC and STEP standards focus on standards for the exchange and sharing of data, not on their smooth and efficient movement up and down the chain.

/pdf/aeroweb-an-information-infrastructure-for-the-supply-chain-2f1pa5giq9.pdf

AeroWEB: An Information Infrastructure for the Supply Chain

The main purpose of spatio-temporal database systems is combining the spatial and temporal features of data. Almost all spatio-temporal applications—such as mobile communication systems, traffic control systems, and GIS with moving objects—have a common basis, which is the requirement to handle both space and time characteristics of the data. Similar to other data types, spatio-temporal data are required to be accurately modeled, structured, and queried efficiently. In this paper, we survey data models, related operations, data structures and access methods for spatial, temporal, and spatio-temporal data types. These access methods basically are enhanced variations of the well-known R-tree.

A Survey of Spatio-Temporal Database Research

People communicate with each other using email with great convenience, but there has been no research effort for utilizing email systems for Data Processing Systems (DPS). A certain class of DPS applications, such as order processing or sales reporting, do not require real-time response, so they may utilize email or protocols that allow delaying or relaying of messages. Lit this paper, we propose two models of DPS that utilize a MTA (Mail Transport Agent) to transport data and data processing commands. We introduce the concept of a DUA (Data User Agent), which we call Senddata, that interacts with a MTA, users, and applications. Our approach helps to reduce certain problems that can occur in DPS, such as overloaded server, heavy traffic on the Internet, and querying a database. We also discuss how these protocols can enhance security by utilizing a three-tier model.

Senddata: an agent for data processing systems using email

Summary form only given. Biological data such as nucleic acids (DNA or RNA), proteins, and pathways require certain conceptual modeling characteristics that are not available in the popular ER (entity-relationship) model and its variations, such as the EER (extended-ER) models. In this paper, we try to make minimal changes to the EER model by introducing special relationships for ordering and process input/output. This allows more accurate modeling of bioinformatics structures that can be used in bioinformatics ontologies and mediator systems. We propose some EER schemas for DNA sequence ordering, the structures of proteins, and pathway processes

Incorporating concepts for bioingormatics data modeling into EER models

POI recommendation provides users personalized location recommendation. It helps users to explore new locations and filter uninteresting places that do not match with their interests. Multiple factors influence users to choose a POI, such as user's categorical preferences, temporal activities and location preferences as well as popularity of a POI. In this work, we define a unified framework that takes all these factors into consideration. None of the previous POI recommendation systems consider all four factors: Personal preferences, spatial (location) preferences, temporal influences and POI popularity. This method aims to provide users with a list of recommendation of POIs within a geo-spatial range that should match with their temporal activities and categorical preferences. Experimental results on real-world data show that the proposed recommendation framework outperforms the baseline approaches.

/pdf/preference-aware-poi-recommendation-with-temporal-and-2eow2pl7ew.pdf

Ramez Elmasri

Papers

AeroWEB: An Information Infrastructure for the Supply Chain

A Survey of Spatio-Temporal Database Research

Senddata: an agent for data processing systems using email

Incorporating concepts for bioingormatics data modeling into EER models

Preference-Aware POI Recommendation with Temporal and Spatial Influence