On Languages for the Specification of Integrity Constraints in Spatial Conceptual Models

A model and a language for specifying spatial integrity constraints in pictorial databases are presented. Spatial integrity constraints are assertions about the correct behavior of database systems that specify acceptable database states and state transitions. They also serve as restrictions on positions occupied by objects in a picture. The language presented primarily uses pictures to specify constraints. The constraint pictures depict unacceptable database states. A data model provides the basic semantics for picture interpretation.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In the domain of GIS, large amounts of geographic data are increasingly shared, interchanged and analyzed in many different applications. Mobile GIS technologies bring lots of advantages for field survey and the datasets acquired by field based data collection in Mobile GIS are often employed for many purposes. Therefore, to ensure the quality of the collected datasets and tell the data users information about how reliable the datasets are, more concerns have to be contributed to spatial data quality issues. This paper will describe the open architecture of field based Mobile GIS and show the importance of spatial data quality issues. Then it explains how the spatial data quality concerns are considered for Mobile GIS aligning with international standards in geoinformatics, like ISO and OGC standards. Geographic data modeling with emphasis of spatial integrity constraints will be introduced here. In order that field users can simultaneously receive the warning of errors when they are entering the data in the field and correct them through seeing the reality, the data quality assurance work should be seamlessly integrated into the Mobile GIS. Therewith, the method of applying the spatial integrity constraints into the data collection work will also be explained.

In this paper, we present a system that translates first order logic (FOL) formulas into natural language (NL) sentences. The motivation comes from an intelligent tutoring system teaching logic as a knowledge representation language, where it is used as a means for feedback to the users. FOL to NL conversion is achieved by using a rule-based approach, where we exploit the pattern matching capabilities of rules. So, the system consists of a rule-based component and a lexicon. The rule-based unit implements the conversion process, which is based on a linguistic analysis of a FOL sentence, and the lexicon provides lexical and grammatical information that helps in producing the NL sentences. The whole system is implemented in Jess, a java-based expert system shell. The conversion process currently covers a restricted set of FOL formulas.

Class relations are used whenever the semantics of entire classes are described, independently of single entities. This chapter focuses on class relations that define cardinality restrictions for a certain instance relation (e.g., a topological relation) between all entities of the involved classes. Typical examples are spatial semantic integrity constraints or ontologies of geospatial entity classes. Reasoning on such class relations allows for the detection of inconsistencies and redundancies in sets of class relations. Therefore the logical properties of the applied instance relations and those of the cardinality restrictions have to be considered, in particular symmetry and compositions, but also other inferences. The chapter provides a summary of research and a discussion of open issues for future work on class relation reasoning.

The facilitation of interoperability requires a clear distinction if a relation refers to classes of individuals or to specific instances, in particular when it comes to the logical properties of the involved relations. Class relations are defined whenever the semantics of entire classes are described, independently of single instances. Typical examples are spatial semantic integrity constraints or ontologies of entity classes. The paper continues research on spatial class relations by deepening the analysis of the reasoning properties of class relations. The work is based on a set of 17 abstract class relations defined in [11]. The paper provides a complete composition table for the 17 abstract class relations and redefines the concept of conceptual neighbourhood for class relations. This approach can be used to find conflicts and redundancies in sets of semantic integrity constraints or other applications of spatial class relations.

This paper reviews the key features of an environment to support domain users in spatial information system (SIS) development. It presents a full design and prototype implementation of a repository system for the storage and management of metadata, focusing on a subset of spatial data integrity constraint classes. The system is designed to support spatial system development and customization by users within the domain that the system will operate.

The main goal of this chapter is to arrive at a coherent technology for deriving efficient SQL triggers from declarative specifications of arbitrary integrity constraints. The user may specify integrity constraints declaratively as closed queries in predicate calculus syntax (i.e., sentences in the language of first-order logic, abbr. FOL), as datalog denials, as query conditions in SQL WHERE clauses, or in some other, possibly more user-friendly manner (e.g., via a dialog-driven graphical or natural language interface which internally translates to equivalent WHERE clause conditions). As we are going to see, the triggers derived from such specifications behave such that whenever some update event would violate any of the integrity constraints, one or several of the triggers derived from that constraint are activated in order to enforce the constraint. That is, the violation is either prevented by rolling back the update or repaired instantly by subsequent further updates.

Typically, object-oriented schemas are lacking declarative specification of the schema integrity constraints. Object-oriented transactions are also typically missing a fundamental ACID requirement: consistency. We present a developed technology based on object-oriented assertion languages that overcomes these limitations of persistent and database object systems. This technology allows specification of objectoriented integrity constraints, their static verification and dynamic enforcement. Proof strategies that are based on static and dynamic verification techniques as they apply to verification of object-oriented transactions are presented in the paper. Most of this work has been motivated by the problems of object-oriented interfaces to XML that have not been able to express typical XML Schema constraints, database constraints in particular. The components of this technology are an object-oriented constraint language, a verification system with advanced typing and logic capabilities, predefined libraries of object-oriented specification and verification theories, and an extended virtual platform for integrating constraints into the run-time type system and their management.

The paper describes the design and implementation of a repository at the core of an integrated spatial information system (SIS) development environment. One of its major functions is the management of integrity constraints which include the non-standard spatial integrity constraints analogous to business rules in a mainstream database environment.

SUMMARYFeature modeling an attractive technique for capturing commonality as well as variability within an application domain for generative programming and software product line engineering. Feature models symbolize an overarching representation of the possible application configuration space, and can hence be customized based on specific domain requirements and stakeholder goals. Most interactive or semi‐automated feature model customization processes neglect the need to have a holistic approach towards the integration and satisfaction of the stakeholder's soft and hard constraints, and the application‐domain integrity constraints. In this paper, we will show how the structure and constraints of a feature model can be modeled uniformly through Propositional Logic extended with concrete domains, calledPscr(𝒩). Furthermore, we formalize the representation of soft constraints in fuzzy 𝒫(𝒩) and explain how semi‐automated feature model customization is performed in this setting. The model configuration derivation process that we propose respects the soundness and completeness properties. Copyright © 2011 John Wiley &amp; Sons, Ltd.

First-order logic augments the expressive power of propositional logic as it links the logical assertions to properties of objects of some non-empty universe: the domain of discourse. This is achieved by allowing the propositional symbols to take arguments that range over elements of the domain of discourse. These are now called predicate symbols and are interpreted as relations on the domain. Elements of the domain of discourse are denoted by terms built up from variables, constants, and functions applied to other terms. First-order logic also expands the lexicon of propositional logic with the quantifiers “for all” and “there exists” that are interpreted consistently with their natural language meaning.This chapter is devoted to classical first-order logic. Our presentation is similar to the one conducted for propositional logic. We first define the syntax of first-order logic, followed by its semantics. Next we define a proof system for it and present the fundamental theoretical results of soundness and completeness. We also discuss the decision problems related to this logic. The remaining sections of the chapter cover variations and extensions of first-order logic, as well as first-order theories.

Logic as a knowledge representation and reasoning language is a fundamental topic of an Artificial Intelligence (AI) course and includes a number of sub-topics. One of them, which brings difficulties to students to deal with, is converting natural language (NL) sentences into first-order logic (FOL) formulas. To assist students to overcome those difficulties, we developed the NLtoFOL system and equipped it with a strong assistance and feedback mechanism. In this work, first, we present that feedback mechanism. The mechanism can provide assistance before an answer is submitted, if requested, but mainly it provides assistance after an answer is submitted. To that end, it characterizes the answer in terms of completeness and accuracy to determine the level of incorrectness, based on an answer categorization scheme, introduced in this paper. The automatically generated natural language feedback sequences grow from general to specific and can include statements on a student’s metacognitive state. Feedback is provided as natural language sentences automatically generated through a template-based natural language generation mechanism. Second, we present an extensive evaluation of the effectiveness of the assistance and feedback mechanism on students’ learning. The evaluation of feedback with students showed that full feedback sequences lead to greater learning gains than sequences consisting of only flag feedback and bottom-out hints (n = 226), and that generic, template-based feedback sequences are comparable to the utility of problem-specific hints generated by human tutors (n = 120).

When employing the spatial diagnostics examined in Chapter 4, social scientists will often find evidence of spatial autocorrelation (see, e.g., Eff 2004). As discussed in Chapter 1, this predisposition of social science data toward spatial autocorrelation often results from interdependence between the units studied by social scientists. In other cases, social science data exhibit spatial dependence not as a result of behavioral interdependence but as a consequence of spatial clustering in the sources of behaviors of interest to social scientists. The spatial dependence, in short, may be consistent with either a spatial lag model or a spatial error model. Substantive theory will often lead scholars to believe that a spatial lag specification or a spatial error specification is more appropriate for their particular substantive application. Scholars may, for example, expect that a spatial diffusion process is at work and thus believe that a spatial lag model is warranted. Although such a specification may seem appropriate, such a theoretical expectation should not go untested. It would be inappropriate to estimate a diffusion model with a spatially lagged dependent variable if the spatial dependence diagnosed via, for example, the univariate Moran's I , is instead produced by spatial clustering in the sources of otherwise independent behaviors. This model misspecification will lead the researcher to inappropriate substantive inferences about the nature of the spatial dependence in her data. Inappropriate spatial model specification is all the more problematic because of the close mathematical relationship between a spatial lag model and a spatial error model with spatial autoregressive error dependence. As this chapter will discuss, a spatial autoregressive error model can be rewritten as a spatial Durbin model with both spatially lagged dependent and independent variables if a set of nonlinear common factor constraints are valid. Because of this close relationship between spatial autoregressive dependence in a spatial lag model and spatial autoregressive dependence in a spatial error model, a significant spatial parameter in a spatial lag model may reflect spatial clustering in omitted sources of the behavior of interest rather than true spatial lag dependence consistent with a diffusion process.

Summary form only given. As the fear of semantics is declining, let's formalize the unformalizable! The address is focusing on the feasibility and necessity of accessing directly the comprehensive meaning of natural language texts, data, images, etc., to emulate human understanding by the computer. It is based on the premise that, without such understanding, no real-life application can reach the precision that human users of computational systems require. Moreover, meaning access and processing goes well beyond natural language applications, even as the scope of those keeps growing. The main obstacle for this seemingly reasonable goal is the widespread prejudice against meaning as unknowable in principle because it is “subjective,” context-sensitive, complex, messy, etc. This has not been our experience, and it is worth talking, both practically and theoretically, about the know-how that a research group should acquire to approach the processing tasks semantically. A convincing case can be made for the feasibility and affordability of the approach as well, especially in the light of our planning to make our resources Open Source, a plan that may be followed by other computational semanticists. It is hoped, therefore, that this address will further reduce the fear of, and prejudice against semantics. It will focus on our Ontological Semantic Technology, a mature formal theory of meaning that has long overcome the crippling fixation on first-order logic as the only possible formalism for everything. This fixation has prevented generations of researchers to access natural language meaning to the grain size that makes meaning representation usable, both theoretically and in practical applications. A small set of predicates using a smaller number of variables as arguments may work well for formal languages but natural language defeats such an approach. The fear of semantics is based on the faulty conclusion that, therefore, natural language meaning is not formalizable. Throw away an insufficient method, and the task becomes feasible!

We address the key issues of temporal data modeling and temporal integrity constraints in the temporal extensions to the relational data model in attribute and tuple time stamping. These issues include representation of temporal data, temporal grouping identifiers, primary keys of temporal relations and other forms of temporal integrity constraints. We extend the traditional integrity constraints to the temporal domain. In tackling subtle issues temporality creates, we conceptualize that a temporal integrity constraint has a data and a temporal (time) component. The time component of a temporal integrity constraint limits the attribute values to the lifespan of an object (tuple). We also generalize single state integrity constraints to multi-state temporal integrity constraints and apply them to existential and referential integrity. We provide algorithms for enforcing the two types of temporal integrity constraints.