Schema domain

Young et al. (2003 14-17) identify 18 early maladaptive schemata (EMS) in five schema domains. Briefly summarized, these are ... 

The characteristic of a relational database model is the organization of data in different tables that have relationships with each other. A table is a two-dimensional consti uction of rows and columns. All the entries in one column have an equivalent meaning (c.g., name, molecular weight, etc. and represent a particular attribute of the objects (records) of the table (file) (Figure 5-9). The sequence of rows and columns in the tabic is irrelevant. Different tables (e.g., different objects with different attributes) in the same database can be related through at least one common attribute. Thus, it is possible to relate objects within tables indirectly by using a key. The range of values of an attribute is called the domain, which is defined by constraints. Schemas define and store the metadata of the database and the tables. 

IX 1.0. Nous appellerons schema de Krull un schema qui admet un recouvrement par des ouverts affines, spectres d anneaux de Krull (Bourbaki, alg. com. chap. VII 1). Si A est un anneau de Krull, on sait (loc. cit.) que le localise de A par une partie multiplicative, 1 anneau des polynSmes A[T], et le normalise de A dans une extension algebrique finie du corps des fractions de A, sont des anneaux de Krull. II en resulte que si S est un schema de Krull, tout schema X lisse sur S est un schema de Krull de plus, les points de codi— mension 1 de X sont les points de codimension 1 des fibres maximales de X et les points maximaux des fibres de X au-dessus des points de codimension 1 de S. Proposition IX 1.1. Soient S un schema, X un S-schema lisse sur S, h fibres connexes, I un S-schema localement de type fini, e S —> X et 6 S — I. deux S-sections, f une S-application rationnelle de X dans I, U son domaine de definition. On suppose que pour tout point x de... 

There are a few design options for the Data Persistence Layer. Here we use the Data Mapper Pattern (Fowler, 2003b). The reason is that we want to separate the domain layer and the database schema and allow them to evolve independently. 

To begin, however, we must first have an idea of how space could be used in abstract cognition. The first part of this introduction seeks to illustrate what is meant by spatial schemas, what is meant by abstract cognition, and how the latter may benefit from the former. In the second part, the aim is to identify two important points in formulating accounts of spatial schemas. First, our account of spatial schemas explores the idea that space can be a mechanism for cognition, not merely a metaphor for cognition. Second, our account of spatial schemas aims to identify the nature of the correspondences between space and other domains, and how those correspondences, or mappings, are established. The third part of this introduction proceeds to provide an overview of the contents of the book. 

Ample evidence in the literature and reviewed in this volume indicates that people use spatial representations to deal with problems in other cognitive domains. This section discusses the possibility that animals also might use spatial schemas to deal with problems in other cognitive domains. The fundamental nature of spatial representations for the survival of organisms suggests that such representations might be found in both humans and nonhumans. Two speculative examples will be discussed here. 

Introduction of XML formats was a very important step toward better intercomputer communication, but it is not a miraculous solution to all problems. Not every possible relation is easily expressed in XML (Wang et al. 2005), so specifications usually contain many implicit assumptions that are not properly formalized. The Resource Description Framework (RDF) provides a very powerful yet simple model for this formalization (Manola and Miller 2004). In this framework, any information is transformed to basic units called triplets that are combined to map the available information. This unifying mechanism can be used to express hierarchical vocabularies for domain knowledge description, as in RDF Schema (Brickley and Guha 2004) or its extension, Web Ontology Language (OWL) (Smith et al. 2004), both standardized by the W3C. 

Aggregation layer Software, intelligent agents, and data schemas customized for particular domains, applications, and user. 

The remainder of this book focuses on these critical issues. In the chapters that follow, I explain the theory and model of schema development that have guided my research, and I describe a number of experimental studies designed to test specific aspects of the theory in the domain of arithmetic story problems. Underlying these chapters are two important premises First, the study of the schema will yield theoretical findings of value to the study of cognition, and second, the schema is the means by which theory can guide practice. With respect to the latter premise, the schema has the potential of being a theoretical construct with practical appli-... 

Several levels of schema connectivity exist. First, schemas from the same subject domain undoubtedly are connected to each other. Second, components within a single schema have many connections, else the schema could not hold together. And third, within each components there are connections among the elements. The... 

Schemas can be large or small. Some are very broad and some are highly specific. One slot of a schema may be filled by another schema. As far as I know, no one has studied the optimal size of a schema. In models of schema performance, most restrictions on size have been made because of computing limitations rather than because of psychological necessity. It seems reasonable to speculate that there is an optimal size for a schema, and that when any schema exceeds the optimum, the individual tends to subdivide the schema, making several new ones. I give examples of how this may occur in story problems in the discussion of the schemas of that domain (see chapter 3).

The approach described here derives from the idea that in any problem-solving domain, only a limited set of basic situations will occur. The number of basic situations is presumed to be small, but many complex situations can arise because the basic ones can be logically combined in many ways. Given that there is but a small collection of basic situations, the objective is to delineate one and only one schema for each of them. Just as basic situations combine to create complex situations, the corresponding basic schemas are combined in solving complex problems. 

In many domains, a person may develop a basis set of schemas quite naturally. In a number of educational domains, however, we have evidence that students are not developing powerful schemas... 

The first step, situation description, lays the basic framework for the set of situations to which the schemas will pertain. At this point, one specifies the details of each major situation in the domain, paying particular attention to its identifying characteristics and discriminating features. 

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