A use case represents a unit of the functionality specification of a system. Industrial object-oriented projects have applied use cases to capture user requirements. Use cases can be used throughout the whole process of object-oriented software development [14]. But, the problem of how to write use cases is still puzzling even software experts [6]. It reflects the lack of a systematic approach to capturing use cases. The set of use cases in a use case model is unstructured regarding the structure of the problem addressed by an application. This paper presents a notion of a use case pattern and proposes using a use case pattern to guide use case capturing. A use case pattern encodes reusable knowledge on the structure and function of a specific class of applications. It guides the work of use case gathering. We illustrate the approach to use case modeling with several use case patterns.

We present a semi-automatic approach to translate a use case to a sequence diagram, which can be easily used in software design. It needs to normalize a use case manually. It accommodates user instructions on how to translate some parts of the use case description while producing message sends from other parts mechanically.

Essential modeling has been the cornerstone of structured analysis. We define the notion of an essential model in terms of object-oriented artifacts and describe how to create essential models for object-oriented projects.

The formalism of nonmonotonic reasoning has been integrated into logic programming to define semantics for logic program with negation. Because a Petri net provides a uniform model for both the logic of knowledge and the control of inference, the class of high-level Petri nets called predicate/transition nets (PrT-nets) has been employed to study production rule based expert systems and Horn clause logic programs. We show that a PrT-net can implement the nonmonotonicity associated with a logic program with negation as well as the monotonicity of Horn clause logic program. In particular, we define a semantics for a normal logic program and implement it with PrT-net. We demonstrate that in the presence of inconsistency in a normal logic program, the semantics still works well by deducing meaningful answers. The variations and potential applications of the PrT-net are also addressed.>

Several fast and space-optimal sequential and parallel algorithms for solving the satisfaction problem of functional and multivalued dependencies (FDs and MVDs) are presented. Two frameworks to verify an MVD for a relation and their implementation by exploring the existing fast space-optimal sorting techniques are described. The space optimality means that only a constant amount of extra memory space is needed for the sequential implementations, and O(M) amount of extra memory space for parallel algorithms that use M processors. This feature makes the algorithms attractive whenever space is a critical resource and I/O transfers should be reduced to the minimal, as is often the case for relational database systems. The time requirements for in-place FD and MVD verification are given in terms of M and of N, which is the number of tuples in a relation. The effect of relation modification on FD and MVD verification is examined.>

The relationship between Reiter's default logic and general logic programs, which may contain negative subgoals in rule bodies, has been discussed in the literature by translating logic programs to default logic theories. Here, we present a method to translate some default logic theories to general logic programs, and study the extensions of default logic theories with the stable model semantics of logic programming. Based on the translation method, we show that another semantics of logic programming, the well‐founded semantics, can be used to define a new version of default logic, which is more cautious than the original one. This enables the existing proof procedures for the well‐founded semantics to perform default inference. We also study the property of cumulative monotonicity for both default logic theories and general logic programs under the two different semantics. As a direct application of the translation method, logic programs can be used to make default inference for semantic networks with exceptions. La relation entre la logique par défaut de Reiter et les programmes logiques généraux, qui peuvent comporter des sous‐buts négatifs dans les ensembles de règies, a déjàété discutée en traduisant les programmes logiques en théories de la logique par défaut. Dans cet article, les auteurs proposent une méthode pour traduire certaines théories de la logique par defaut en programmes logiques généraux et étudient les extensions des theories de la logique par defaut avec la sémantique de modéle stable de la programmation logique. En se basant sur la méthode de traduction, les auteurs démontrent qu'une autre sémantique de la programmation logique, la sémantique bien fondée, peut ětre utilisée pour définir une nouvelle version de la logique par défaut, qui est plus prudente que la première. Cela permet aux procédures de preuve existantes de la sémantique bien fondée d' effectuer l' inférence par défaut. Les auteurs examinent également les caracteristiques de la monotonicité cumulative pour les theories de la logique par défaut et les programmes logiques généraux selon les deux différentes sémantiques. Une application directe de la méthode de traduction est la possibilityé d' utiliser les programmes logiques pour effectuer de l' inférence par défaut pour les réseaux sémantiques avec exceptions.

In this paper, we consider the problem of generating, ranking, and unranking of AVL-trees with n leaves. We represent AVL-trees by integer-pair sequences, called LDP-sequences. Then we propose a linear ordering among these sequences, i.e., among the AVL-trees. The problem of ranking is to determine the order number (rank) of a given tree in this ordering, unranking means constructing the tree of a given rank. The main result is that ranking and unranking can be done in $0(n\log ^2 n)$ and $0(n\log ^3 n)$ time, respectively, after a preprocessing step that takes $0(n^2 \log n)$ time.