含模糊语言量词的SQL查询技术研究

目前 SQL模糊扩展查询已经研究了带权重的模糊简单查询、复合查询、多表查询、子查询、全关系查询等,在一定程度上解决了查询中的模糊型问题 ,但对于带模糊语言量词的查询研究不多。基于 Zadeh的模糊理论将模糊理论与 SQL系统函数相结合 ,提出了一种解决含模糊语言量词的 SQL查询方法 ,进一步丰富了 SQL模糊查询体系 ,扩展了 SQL模糊查询的应用范围 ,提高了查询能力。     

Rule-based composite event queries: the language XChangeEQ and its semantics

Web systems, Web services, and Web-based publish/subscribe systems communicate events as XML messages and in many cases, require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. This entails a need for expressive, high-level languages for querying composite events. Emphasizing language design and formal semantics, we describe the rule-based composite event query language XChange^EQ. XChange^EQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as a model theory with accompanying fixpoint theory, an approach that is established for rule languages but has not been applied to event queries so far. Because they are highly declarative, thus easy to understand and well suited for query optimization, such semantics are desirable for event queries.     

Predicate logic with flexibly binding operators and natural language semantics

A new formalism for predicate logic is introduced, with a non-standard method of binding variables, which allows a compositional formalization of certain anaphoric constructions, including donkey sentences and cross-sentential anaphora. A proof system in natural deduction format is provided, and the formalism is compared with other accounts of this type of anaphora, in particular Dynamic Predicate Logic.Both authors have been supported by grants from the Swedish Council for Research in the Humanities and Social Sciences.     

Extending the fuzzy database with fuzzy numbers

The fuzzy relational database model originated by the authors permits fuzzy domain values from a discrete, finite universe. The model is extended here by demonstrating that fuzzy numbers may be employed as domain values without loss of consistency with respect to representation or the relational algebra. Where equivalence is required in an ordinary relational database, similarity is employed in a fuzzy relational database. For discrete, finite universes, similarity between atomic elements is described via a fuzzy similarity relation with max-min transitivity. Two or more fuzzy numbers are defined to be α-similar if their union forms a continuous α-level set over the real line. This convention effects the partitioning of fuzzy number domains that is necessary to assure the well-definedness of the fuzzy relational algebra.     

General predicate transformer and the semantics of a programming language with go to statement

Acta Informatica - We analyze storage utilization in random B *-trees (trees, where all data are stored in the leaf nodes). Extending a proposal of Bayer and McCreight, in case of insertion into a...     

An investigation into applying UML to the Zachman framework

The Zachman framework is considered to be the most referenced framework for the purpose of enterprise architecture. It is commonplace to compare other frameworks with this basic one in order to show correctness and usability of those frameworks. However, this is more than a fashion, the Zachman framework is actually the best one. Despite of its popularity, the Zachman framework could be a challengeable one in practical situations because there are not enough well-known methods and tools covering all of its aspects. Three major challenges in using this framework, are discussed in this article. These challenges are lack of a methodology, a well-defined repository and a popular modeling notation. Focus of this article is on solving the last problem with the help of notations in UML (Unified Modeling Language) and UML Business Profile. At the first glance the topic seems to be already researched by others, but there are some major distinctions between this work and the others', which make it a unique one. Most of the other work tried to cover the framework using multiple class diagrams stereotyped in different ways. This work tries to cover the Zachman framework using all of the UML features, especially those, which are convenient in common modeling tools as well as ignoring unfamiliar symobls as it is used by some authors. A case study is used upon which we show how to apply the selected notation on a sample enterprise to develop cells in second and third rows of the framework. Models are tested to consider if they are supporting Zachman rules governing the framework. Furthermore, in order to see if they could be convincing enough, a statistical study is employed. Although results of these tests are relatively acceptable, the problem of inventing new modeling notations is mentioned as an open problem.     

On the capability of fuzzy set theory to represent concepts

The aim of this paper is to examine the conclusions drawn by Osherson and Smith ["On the adequacy of prototype theory as a theory of concepts", Cognition 9 (1981), pp. 35-58] concerning the inadequacy of the apparatus of fuzzy set theory to represent concepts. Since Osherson and Smith derive their conclusions from specific examples, we show for each of these examples that the respective conclusion they arrive at is not warranted. That is, we demonstrate that fuzzy set theory is sufficiently expressive to represent the various strong intuitions and experimental evidence regarding the relation between simple and combined concepts that are described by Osherson and Smith. To pursue our arguments, we introduce a few relevant notions of fuzzy set theory.     

Extending the rand,adjusted rand and jaccard indices to fuzzy partitions

The first stage of knowledge acquisition and reduction of complexity concerning a group of entities is to partition or divide the entities into groups or clusters based on their attributes or characteristics. Clustering is one of the most basic processes that are performed in simplifying data and expressing knowledge in a scientific endeavor. It is akin to defining classes. Since the output of clustering is a partition of the input data, the quality of the partition must be determined as a way of measuring the quality of the partitioning (clustering) process. The problem of comparing two different partitions of a finite set of objects reappears continually in the clustering literature. This paper looks at some commonly used clustering measures including the rand index (RI), adjusted RI (ARI) and the jaccuard index(JI) that are already defined for crisp clustering and extends them to fuzzy clustering measures giving FRI,FARI and FJI. These new indices give the same values as the original indices do in the special case of crisp clustering. The extension is made by first finding equivalent expressions for the parameters, a, b, c, and d of these indices in the case of crisp clustering. A relationship called bonding that describes the degree to which two cluster members are in the same cluster or class is first defined. Through use in crisp clustering and fuzzy clustering the effectiveness of the indices is demonstrated.     

A relational tsetlin machine with applications to natural language understanding

Journal of Intelligent Information Systems - Tsetlin machines (TMs) are a pattern recognition approach that uses finite state machines for learning and propositional logic to represent patterns. In...     

Approximation theorem of the fuzzy transform in fuzzy reasoning and its application to the scheduling problem

In this paper, the theoretical foundation of fuzzy reasoning is analyzed, and the idea that the fuzzy transform given in the fuzzy reasoning method should be continuous with respect to a certain fuzzy distance is proposed. Also, the fuzzy transforms given in the two fuzzy reasoning methods, the Mamdani method and the III method, are proved to be continuous. Based on the continuity of the fuzzy transform, the approximation theorem of the continuous fuzzy number transform is proven. Then, on the basis of the approximation theorem, a simple fuzzy number transform is constructed in order to implement the fuzzy reasoning based on multiple rules. At last, the fuzzy reasoning based on multiple rules implemented by a simple fuzzy number transform is applied to machine scheduling problems, and numerical computational results of different scale scheduling problems with the objective of minimizing the total number of tardy jobs show that it is more effective than usual heuristics based on rules, and in the computational time it has the obvious advantage over the reasoning by fuzzy rules directly.     

Adding semantics to image-region annotations with the Name-It-Game

In this paper we present the Name-It-Game, an interactive multimedia game fostering the swift creation of a large data set of region-based image annotations. Compared to existing annotation games, we consider an added semantic structure, by means of the WordNet ontology, the main innovation of the Name-It-Game. Using an ontology-powered game, instead of the more traditional annotation tools, potentially makes region-based image labeling more fun and accessible for every type of user. However, the current games often present the players with hard-to-guess objects. To prevent this from happening in the Name-It-Game, we successfully identify WordNet categories which filter out hard-to-guess objects. To verify the speed of the annotation process, we compare the online Name-It-Game with a desktop tool with similar features. Results show that the Name-It-Game outperforms this tool for semantic region-based image labeling. Lastly, we measure the accuracy of the produced segmentations and compare them with carefully created LabelMe segmentations. Judging from the quantitative and qualitative results, we believe the segmentations are competitive to those of LabelMe, especially when averaged over multiple games. By adding semantics to region-based image annotations, using the Name-It-Game, we have opened up an efficient means to provide precious labels in a playful manner.     

Approaches to the representations and logic operations of fuzzy concepts in the framework of axiomatic fuzzy set theory I

In this paper, the representations of fuzzy concepts based on raw data have been investigated within the framework of AFS (Axiomatic Fuzzy Set) theory. First, a brief review of AFS theory is presented and a completely distributive lattice, the E^#I algebra, is proposed. Secondly, two kinds of E^#I algebra representations of fuzzy concepts are derived in detail. In order to represent the membership functions of fuzzy concepts in the interval [0, 1], the norm of AFS algebra is defined and studied. Finally, the relationships of various representations with their advantages and drawbacks are analyzed.     

An alternative use of fuzzy transform with application to a class of delay differential equations

Fuzzy transforms (or F-transforms for short) are an approximation technique recently introduced. The main application is referred to image and data compression. There are really few works devoted to the use of F-transform for solving ordinary differential equations. In the present paper, an F-transform-based Picard-like numerical scheme is proposed in order to solve a class of delay differential equations. For linear cases, the proposed approach leads to a non-recursive approximate solution by means of operational matrices and vectors of known quantities. Numerical results show the good performance of the proposed method against known solutions.     

UML 2.0 and agents: how to build agent-based systems with the new UML standard

Unified Modeling Language (UML 2.0) is the upcoming standard of the Object Management Group for specifying object-oriented software systems. In this paper, we will show how UML 2.0 can be applied for the specification of agent-based systems. Moreover, we will give a short overview on existing agent methodologies to have a reference what has to be specified in such systems. The paper concludes with some outlook for further research and open issues for specifying agents with UML 2.0.