Semantic data models have emerged from a requirement for more expressive conceptual data models. Current generation data models lack direct support for relationships, data abstraction, inheritance, constraints, unstructured objects, and the dynamic properties of an application. Although the need for data models with richer semantics is widely recognized, no single approach has won general acceptance. This paper describes the generic properties of semantic data models and presents a representative selection of models that have been proposed since the mid-1970s. In addition to explaining the features of the individual models, guidelines are offered for the comparison of models. The paper concludes with a discussion of future directions in the area of conceptual data modeling. INTRODUCTIONAlthough the relational model has provided database practitioners with a modeling methodology independent of the details of the physical implementation, many designers believe that the relational model does not offer a sufficiently rich conceptual model for problems that do not map onto tables in a straightforward fashion. The past decade has seen the emergence of numerous data models with the aims of providing increased expressiveness to the modeler and incorporating a richer set of semantics into the database. This collection of data models can be loosely categorized as "semantic" data models since their one unifying characteristic is that they attempt to provide more semantic content than the relational model. The first research papers on semantic data models appeared approximately 7 years after Codd's initial publications describing the relational model. Thus, in perhaps another 5-7 years, one of the modeling methodologies discussed here may attain commercial viability. This survey selects a representative sampling of the new generation of data
Protein fluorescence is a powerful tool for studying protein structure and dynamics if we have a means to interpret the spectral data in terms of protein structural properties. Our previous research successfully provided this support through the development of individual software modules implementing the algorithms for fluorescence and structural analyses. Now we have integrated the developed software modules, introduced a new program for the assignment of tryptophan residues to spectral‐structural classes, and created a web‐based toolkit PFAST: protein fluorescence and structural toolkit: http://pfast.phys.uri.edu/. PFAST contains three modules: (1) FCAT is a fluorescence‐correlation analysis tool, which decomposes protein fluorescence spectra to reveal the spectral components of individual tryptophan residues or groups of tryptophan residues located close to each other, and assigns spectral components to one of five previously established spectral‐structural classes. (2) SCAT is a structural‐correlation analysis tool for the calculation of the structural parameters of the environment of tryptophan residues from the atomic structures of the proteins from the Protein Data Bank (PDB), and for the assignment of tryptophan residues to one of five spectral‐structural classes. (3) The last module is a PFAST database that contains protein fluorescence and structural data obtained from results of the FCAT and SCAT analyses. Proteins 2008. © 2008 Wiley‐Liss, Inc.
To address the alarming decrease in students in Rhode Island computer science programs and the under-representation of women and minorities, we have devised a program to introduce students to research in computer graphics, art and new media. This program integrates good mentoring practice and pedagogy, including problem-based learning. Special attention is paid to creating a cohort of students who come together every week to learn about the research process, and ethical and societal issues related to it. Each student takes a small project from the proposal stage, through design and implementation, to publication and presentation. We report on the first two years of the program.
Abstract. A real-time database is a database in which both the data and the operations upon the data may h a ve timing constraints. We have i n tegrated real-time, object-oriented, semantic and active database approaches to develop a formal model called RTSORAC for real-time databases. This paper describes the components of the RTSORAC model including objects, relationships, constraints, updates, and transactions.
Abstract.We describe the conceptual model of SORAC, a data modeling system developed at the University of Rhode Island. SORAC supports both semantic objects and relationships, and provides a tool for modeling databases needed for complex design domains. SORAC's set of built-in semantic relationships permits the schema designer to specify enforcement rules that maintain constraints on the object and relationship types. SORAC then automatically generates C+ + code to maintain the specified enforcement rules, producing a schema that is compatible with Ontos. This facilitates the task of the schema designer, who no longer has to ensure that all methods on object classes correctly maintain necessary constraints. In addition, explicit specification of enforcement rules permits automated analysis of enforcement propagations. We compare the interpretations of relationships within the semantic and object-oriented models as an introduction to the mixed model that SORAC supports. Next, the set of built-in SORAC relationship types is presented in terms of the enforcement rules permitted on each relationship type. We then use the modeling requirements of an architectural design support system, called ArchObjects, to demonstrate the capabilities of SORAC. The implementation of the current SORAC prototype is also briefly discussed.
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