14218377
A Methodology for Constructing Predicate Transition Net Specifications
In this paper, a methodology for constructing hierarchical and structured predicate transition net specifications is developed, which includes new systematic notation extensions for supporting various transformation techniques upon predicate transition nets and several rules for applying such transformation techniques. The levelling technique in data-flow diagrams is adapted in the refinement and the abstraction techniques, and the state decomposition idea in state-charts is employed in designing various label formulation operators. The methodology is illustrated through the specification of a lift system. The methodology can significantly reduce the constructing complexity and enhance the comprehensibility of large predicate transition net specifications.
28830107
Introducing software architecture specification and analysis in SAM through an example
Abstract Software architecture study has become one of the most active research areas in software engineering in the recent years. Although there have been many published results on specification and analysis method of software architectures, information on sound systematic methodology for modeling and analyzing software architectures is lacking. In this article, we present a formal systematic software architecture specification and analysis methodology called SAM and show how to apply SAM to specify a command control (C2) system and to analyze its real-time constraints.
20141160
A method for analyzing properties of hierarchical predicate transition nets
Hierarchical high level Petri nets have been proposed in recent years as a powerful formal method for modeling large concurrent and distributed systems but they are even more difficult to analyze than flat high level Petri nets, which are also lacking effective analysis methods themselves. A method for analyzing properties of hierarchical predicate transition Petri nets is proposed. The method employs two temporal induction techniques, one for safety properties and the other for liveness properties, without using temporal logic formalism. Within each induction technique, a hybrid reasoning technique combining net structural and behavioral reasoning, and ordinary first order logic reasoning is used.
28749706
An Improved Algorithm for Concurrency Control in Distributed Database Systems
Abstract Deadlock and livelock prevention is critical in concurrency control of distributed database systems. This paper presents an improved algorithm for concurrency control, which not only prevents deadlock and livelock problems but also improves system efficiency by lowering restart ratio and communication cost. The proposed algorithm is effective in environments where most transactions have one-way request (either forward waiting or backward waiting).
46303239
Object Orientation in Hierarchical Predicate Transition Nets
In this paper, an approach of using hierarchical predicate transition nets (HPrTNs in the sequel) for object-oriented specification is proposed. The realization of various object-oriented features (including encapsulation, inheritance, and polymorphism) in HPrTNs is presented and is demonstrated with examples. We believe that the approach can achieve the benefits of the object-oriented methods while maintaining the analyzability of HPrTNs.
2341522
PZ nets a formal method integrating Petri nets with Z
Abstract In this paper, a formal method (called PZ nets) for specifying concurrent and distributed systems is presented. PZ nets integrate two well-known existing formal methods Petri nets and Z such that Petri nets are used to specify the overall structure, control flows, causal relation, and dynamic behavior of a system; and Z is used to define tokens, labels and constrains of the system. The essence, benefits, and problems of the integration are discussed. A set of heuristics and transformations to develop PZ nets and a technique to analyze PZ nets are proposed and demonstrated through a well-known example.
39496103
An Application of Well-orderly Trees in Graph Drawing
Well-orderly tree is a powerful technique capable of deriving new results in graph encoding, graph enumeration and graph generation [3, 5]. In this paper, by using well-orderly trees, we prove that any plane graph G with n vertices has a visibility representation with height , which can be constructed in linear time. This improves the best previous bound of .
467326
An efficient parallel algorithm for finding rectangular duals of plane triangular graphs
We present an efficient parallel algorithm for constructing rectangular duals of plane triangular graphs. This problem finds applications in VLSI design and floor-planning problems. No NC algorithm for solving this problem was previously known. The algorithm takesO(log2n) time withO(n) processors on a CRCW PRAM, wheren is the number of vertices of the graph.
2934981
On Finding the Rectangular Duals of Planar Triangular Graphs
This paper presents a new linear-time algorithm for finding rectangular duals of planar triangular graphs. The algorithm is conceptually simpler than the previously known algorithm. The coordinates of the rectangular dual constructed by the new algorithm are integers and carry clear combinatorial meaning.
1887353
An Application of Well-Orderly Trees in Graph Drawing
Well-orderly trees seem to have the potential of becoming a powerful technique capable of deriving new results in graph encoding, graph enumeration and graph generation [3, 4]. In this paper, we reduce the height of the visibility representation of plane graphs from 5n/6 to (4n–1)/5, by using well-orderly trees.
5723803
Incremental semi-supervised subspace learning for image retrieval
Subspace learning techniques are widespread in pattern recognition research. They include Principal Component Analysis (PCA), Locality Preserving Projection (LPP), etc. These techniques are generally unsupervised which allows them to model data in the absence of labels or categories. In relevance feedback driven image retrieval system, the user provided information can be used to better describe the intrinsic semantic relationships between images. In this paper, we propose a semi-supervised subspace learning algorithm which incrementally learns an adaptive subspace by preserving the semantic structure of the image space, based on user interactions in a relevance feedback driven query-by-example system. Our algorithm is capable of accumulating knowledge from users, which could result in new feature representations for images in the database so that the system's future retrieval performance can be enhanced. Experiments on a large collection of images have shown the effectiveness and efficiency of our proposed algorithm.