Converting a fuzzy data model to an object-oriented design for managing GIS data files

A fuzzy entity relationship diagram (ERD) data model for managing information about sets of related data files was converted to an object design. To do this, we developed new methods incorporating object model flattening, entity payload data containerization, and a nonintegrated object model design to allow expression of fuzziness.     

Versioning and configuration management in an object-oriented data model

Many database applications require the storage and manipulation of different versions of data objects. To satisfy the diverse needs of these applications, current database systems support versioning at a very low level. This article demonstrates that application-independent versioning can be supported at a significantly higher level. In particular, we extend the EXTRA data model and EXCESS query language so that configurations can be specified conceptually and non-procedurally. We also show how version sets can be viewed multidimensionally, thereby allowing configurations to be expressed at a higher level of abstraction. The resulting model integrates and generalizes ideas, in CAD systems, CASE systems, and temporal databases.     

关系数据模型与面向对象数据模型的关系探讨

面向对象数据模型的应用打破了油田单一关系数据库体系架构。如何在面向对象数据模型和关系数据模型并存条件下构建和优化协调统一的数据库体系架构,是油田数据库建设的重要技术方向之一。本文从油田数据库应用的角度概要对比了关系数据模型和面向对象数据模型的特点,对关系数据模型和面向对象数据模型共存条件下建立油田数据库体系架构的可能性进行了探讨。     

An enhanced neutral object-oriented data model

A neutral object-oriented data model is necessary to provide separation of application data views from physical data storage in industrial software systems. This short note suggests that a semantically enhanced object data model beyond that of current language-oriented models will be necessary to provide robust capabilities.     

Relational similarity-based model of data part 2: dependencies in data

This paper is a sequel to our previous paper on relational similarity-based model of data and its fundamental query systems. The present paper elaborates on the dependency theory in the similarity-based model, focusing mainly on similarity-based functional dependencies, their semantic entailment, model-theoretic properties, complete axiomatizations, characterization of nonredundant bases, computational issues, and related algorithms. The paper shows that various aspects of dependencies in ranked data tables over domains with similarities can be properly formalized using complete residuated lattices as structures for similarities and ranks. In addition to their theoretical importance, the results can be directly applied in the areas of similarity-based constraints, query result analysis, and knowledge discovery from relational data which involves similarity-based reasoning. We assume that readers are acquainted with the prequel of this paper.     

Adding cardinality constraints to integer programs with applications to maximum satisfiability

Max-SAT-CC is the following optimization problem: Given a formula in CNF and a bound k, find an assignment with at most k variables being set to true that maximizes the number of satisfied clauses among all such assignments. If each clause is restricted to have at most ? literals, we obtain the problem Max-?SAT-CC. Sviridenko [Algorithmica 30 (3) (2001) 398-405] designed a (1−e⁻¹)-approximation algorithm for Max-SAT-CC. This result is tight unless P=NP [U. Feige, J. ACM 45 (4) (1998) 634-652]. Sviridenko asked if it is possible to achieve a better approximation ratio in the case of Max-?SAT-CC. We answer this question in the affirmative by presenting a randomized approximation algorithm whose approximation ratio is . To do this, we develop a general technique for adding a cardinality constraint to certain integer programs. Our algorithm can be derandomized using pairwise independent random variables with small probability space.     

Uncertainty management issues in the object-oriented data model

This paper fully develops a previous approach by George et al. (1993) to modeling uncertainty in class hierarchies. The model utilizes fuzzy logic to generalize equality to similarity which permitted impreciseness in data to be represented by uncertainty in classification. In this paper, the data model is formally defined and a nonredundancy preserving primitive operator, the merge, is described. It is proven that nonredundancy is always preserved in the model. An object algebra is proposed, and transformations that preserve query equality are discussed     

Case-based process planning using an object-oriented model representation

This research focuses on the development of a process-planning system. This system utilizes case-based techniques for process selection and sequencing to combine the advantages of the variant and generative approaches to process planning. The case-based process planner utilizes an object-oriented model representation to operate on general three-dimensional prismatic parts, which are represented by a collection of features (e.g. slots, pockets, holes, etc.). Each feature subplan is developed by the case-based planner. Then the feature subplans are combined into the global process plan for the part via a hierarchical plan-merging mechanism. Abstracted feature subplans correspond to cases, which are used in subsequent planning operations to solve new problems. The abstracting and storing of feature subplans as cases is the primary mechanism by which the planner learns from its previous experiences to become more effective and efficient.     

Adding data abstraction to Fortran software

The Fortran Abstract Data (FAD) system was designed to facilitate the structured reuse of Fortran-callable software. FAD uses data abstraction to implement information hiding. It lets the implementer of a data type explicitly control how instances of that type are used, while itself utilizing Fortran's separate compilation and the vast collection of available Fortran libraries. The principle and methods of information hiding are discussed. The use of FAD, which is designed for situations where the user of an abstract data type is distinct from the implementer of the abstract data type, is described. Some related work is examined     

Numeric semantics of class diagrams with multiplicity and uniqueness constraints

We translate class diagrams with multiplicity constraints and uniqueness attributes to inequalities over non-negative integers. Based on this numeric semantics we check the satisfiability and consistency of class diagrams and compute minimal models. We show that this approach is efficient and provides succinct user feedback in the case of errors. In an experimental section we demonstrate that general off-the-shelf solvers for integer linear programming perform as well on real-world and synthetic benchmarks as specialised algorithms do, facilitating the extension of the formal model by further numeric constraints like cost functions. Our results are embedded in a research programme on reasoning about class diagrams and are motivated by applications in configuration management. Compared to other (for instance logic-based) approaches our aim is to hide the complexity of formal methods behind familiar user interfaces like class diagrams and to concentrate on problems that can be solved efficiently in order to be able to provide immediate feedback to users.     

An object-oriented fuzzy data model for similarity detection in image databases

We introduce a fuzzy set theoretic approach for dealing with uncertainty in images in the context of spatial and topological relations existing among the objects in the image. We propose an object-oriented graph theoretic model for representing an image and this model allows us to assess the similarity between images using the concept of (fuzzy) graph matching. Sufficient flexibility has been provided in the similarity algorithm so that different features of an image may be independently focused upon.     

Missing data mask estimation with frequency and temporal dependencies

Automatic speech recognition (ASR) has reached a very high level of performance in controlled situations. However, the performance degrades drastically when environmental noise occurs during recognition. Nowadays, the major challenge is to reach a good robustness to adverse conditions. Missing data recognition has been developed to deal with this challenge. Unlike other denoising methods, missing data recognition does not match the whole data with the acoustic models, but instead considers part of the signal as missing, i.e. corrupted by noise. The main challenge of this approach is to identify accurately missing parts (also called masks). The work reported here focuses on this issue. We start from developing Bayesian models of the masks, where every spectral feature is classified as reliable or masked, and is assumed independent of the rest of the signal. This classification strategy results in sparse and isolated masked features, like the squares of a chess-board, while oracle reliable and unreliable features tend to be clustered into consistent time–frequency blocks. We then propose to take into account frequency and temporal dependencies in order to improve the masks’ estimation accuracy. Integrating such dependencies leads to a new architecture of a missing data mask estimator. The proposed classifier has been evaluated on the noisy Aurora2 (digits recognition) and Aurora4 (continuous speech) databases. Experimental results show a significant improvement of recognition accuracy when these dependencies are considered.     

Margin-maximizing classification of sequential data with infinitely-long temporal dependencies

Generative models for sequential data are usually based on the assumption of temporal dependencies described by a first-order Markov chain. To ameliorate this shallow modeling assumption, several authors have proposed models with higher-order dependencies. However, the practical applicability of these approaches is hindered by their prohibitive computational costs in most cases. In addition, most existing approaches give rise to model training algorithms with objective functions that entail multiple spurious local optima, thus requiring application of tedious countermeasures to avoid getting trapped to bad model estimates. In this paper, we devise a novel margin-maximizing model with convex objective function that allows for capturing infinitely-long temporal dependencies in sequential datasets. This is effected by utilizing a recently proposed nonparametric Bayesian model of label sequences with infinitely-long temporal dependencies, namely the sequence memoizer, and training our model using margin maximization and a versatile mean-field-like approximation to allow for increased computational efficiency. As we experimentally demonstrate, the devised margin-maximizing construction of our model, which leads to a convex optimization scheme, without any spurious local optima, combined with the capacity of our model to capture long and complex temporal dependencies, allow for obtaining exceptional pattern recognition performance in several applications.     

Formalization of object-oriented database model with rules

The paper investigates knowledge representation in an object-oriented database management system first within the data model with rules and second in the computational model by using logic. Issues of structure, integrity, and retrieval are focused on. The proposed system provides object-oriented concepts for describing complex structured data, rules for expressing object-dependent constraints and object associations, and, finally, logic for inference and retrieval.     

Experience with an object-oriented virtual machine

The implementation of a virtual machine for X2, an object-based programming language, is presented. The X2 virtual machine, is similar to the Smalltalk-80 virtual machine, but X2 does not perform message look-up. The implementation differs from most Smalitalk-80 systems in that objects are paged and in the ability to save changes to an image instead of the whole image. These differences allow larger images to be handled.     

Adding agility to CAD: Integrating product data management tools into an organization

The integration of product data management (PDM) software tools into a manufacturing company requires both technical and organizational considerations. Using an information processing paradigm, the impact of PDM on the design process and group activities are assessed. Using ongoing work with the furniture industry in the Southeastern United States as an example, the relationship of job design and the product development process to the introduction of PDM is examined. © 1997 John Wiley & Sons, Inc.. Inc.     

Using NNF to transform conceptual data models to object-oriented database designs

More work is needed on devising practical, but theoretically well-founded procedures for doing object-oriented database (OODB) design [17]. Design procedures should also be flexible enough to take into account various application characteristics (such as whether objects are very large or are read-only). In this paper, we present and discuss an OODB design procedure that addresses these problems. The procedure is practical in the sense that it is based on a common family of conceptual models and in the sense that it does not expect users to supply esoteric, difficult-to-discover, and hard-to-understand constraints (such as multivalued dependencies), nor does it make hard-to-check and easy-to-overlook assumptions (such as the universal relation scheme assumption). At the same time, the procedure is well-founded and formal, being based on NNF (Nested Normal Form [21]), a new theoretical result that characterizes properties of interest in designing complex objects. It is also adaptable to various applications characteristics.     

Adding phonetic similarity data to a lexical database

As part of a project to construct an interactive program which would encourage children to play with language by building jokes, we developed a lexical database, starting from WordNet. To the existing information about part of speech, synonymy, hyponymy, etc., we have added phonetic representations and phonetic similarity ratings for pairs of words/phrases.