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。     

一种面向对象GIS数据模型的研究

在对传统的空间数据结构和数据模型研究的基础上,提出了一种以面向对象的方式组织的数据模型,并针对GIS数据的组织、底层对象的结构、O/R映射方式进行了讨论。通过使用这种模型,更有利于在上层软件中采用面向对象的设计和实现。     

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.     

Discovering interesting inclusion dependencies: application to logical database tuning

Inclusion dependencies together with functional dependencies form the most important data dependencies used in practice. Inclusion dependencies are important for various database applications such as database design and maintenance, semantic query optimization and efficient view maintenance of data warehouse. Existing approaches for discovering inclusion dependencies consist in producing the whole set of inclusion dependencies holding in a database, leaving the task of selecting the interesting ones to an expert user.In this paper, we take another look at the problem of discovering inclusion dependencies. We exploit the logical navigation, inherently available in relational databases through workloads of SQL statements, as a guess to automatically find out only interesting inclusion dependencies. This assumption leads us to devise a tractable algorithm for discovering interesting inclusion dependencies. Within this framework, approximate dependencies, i.e. inclusion dependencies which almost hold, are also considered.As an example, we present a novel application, namely self-tuning the logical database design, where the discovered inclusion dependencies can be used effectively.     

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     

An object-oriented hydrogeological data model for groundwater projects

Geological and hydrogeological data are expensive to obtain in the field but are crucial for specific hydrogeological studies, from hydrogeological water balances to groundwater flow modelling and contaminant transport, or for more integrated environmental investigations where groundwater plays a role. In this context, hydrogeological data are collected, transformed and exchanged at different scales, from local to international levels and between numerous institutions ranging from environmental consulting companies to the national and international environmental administrations. To guarantee that these exchanges are possible and meaningful, a clear structure and meta-information on applied hydrogeological data models is required. To make one step towards seamless management of groundwater projects, a new hydrogeological data model has been developed: H^g₂O. It is described using object-oriented paradigms and it follows the recommendations of the International Organization for Standardization (ISO/TC211), the Open Geospatial Consortium (OGC), and the European Geospatial Information Working Group. Hydrogeological features are organized in packages of spatial feature datasets. The observations and measurements related to these features are organized in a separate package. A particular focus is on specialized hydrogeological field experiments such as hydraulic and tracer tests. Two first implementations in the proprietary desktop ArcGIS environment and in the open source web-based Web2GIS platform are presented, focussing on their respective standards support.     

Erratum to “Reformulating table constraints using functional dependencies—an application to explanation generation”

We had claimed that arc-consistency is preserved in a constraint reformulation relying on functional dependencies (Theorem 2 of Cambazard and O’Sullivan Constraints 13(3):385–406 (2008)). We show that the statement of this theorem was too strong by providing a counter-example. However, the result holds for dependencies between pairs of variables, and more generally in restricted settings.     

面向对象数字地质图数据模型的研究

本文结合国内外的研究成果,吸收面向对象的思想和方法,利用统一建模语言UML,完成了面向对象数字地质图数据模型的建立。从制图学的角度出发,设计了数字地质图数据的概念模型、逻辑模型,定义了构成数字地质图各要素间的逻辑关系,并简要叙述了物理模型的实现平台和建立过程。面向对象的数字地质图数据模型打破了关系模型范式的限制,更加合理地将空间数据和属性数据有效地融合在一起,为数字地质图的高效存储、管理、分析、应用、可视化表达提供一种科学、合理、可靠、可操作性强的机制和模式,作为一种尝试和探索,具体的设计和实施还有待于进一步研究。     

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.     

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.     

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     

A fuzzy object-oriented data model for managing vague and uncertain information

In this paper, a fuzzy Object-Oriented Data model (FOOD) is defined based on the extension of a Graph-based Object model (D. Lucarella and A. Zanzi “A graph-oriented data model,” in Database and Expert Systems Applications, R. Wagner and H. Toma, Eds., Springer-Verlag, Berlin, 1996, pp. 197–206), in order to manage both crisp and imperfect information. These capabilities are requisites of many current applications dealing with data of different nature and with complex interrelationships. The model is based on a visual paradigm which supports both the representation of the data semantics and the direct browsing of the information. In the extended model both the database scheme and instances are represented as directed labeled graphs in which the fuzzy and uncertain information has its own representation. ©1999 John Wiley & Sons, Inc.     

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.