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).
20452185
A methodology for test selection
Software creation requires not only testing during the development cycle by the development staff, but also independent testing following the completion of the implementation. However in the latter case, the amount of testing that can be carried out is often limited by time and resources. At the very most, independent testing can be expected to provide 100% test coverage of the test requirements (or specifications) associated with the software element with the minimum of effort. This paper describes a methodology employing integer programming by which the amount of testing required to provide the maximum possible test coverage of the test requirements (for the given test set) is assured while at the same time minimizing the total number of tests to be included in a test suite. A collateral procedure provides recommendations on which tests might be eliminated if less than 100% test coverage of the test requirements is permitted. This latter procedure will be useful in determining the risk of not running the minimum set of tests for 100% test coverage. A third process selects from the test matrix the set of tests to be applied to the system following maintenance modification of any test requirements-- that is, to provide a submatrix for regression testing. The potential benefits for applying the integer programming technique in test data selection is also discussed.
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.
17445618
A Formal Definition of Hierarchical Predicate Transition Nets
Hierarchical predicate transition nets have recently been introduced as a visual formalism for specifying complex reactive systems. They extend predicate transition nets with hierarchical structures so that large systems can be specified and understood stepwisely, and thus are more suitable for real-world applications. In this paper, we provide a formal syntax and an algebraic semantics for hierarchical predicate transition nets, which establish the theory of hierarchical predicate transition nets for precise specification and formal reasoning.
31879969
A Method for Constructing Algebraic Petri Nets
Algebraic Petri nets are a powerful formal method for specifying concurrent and distributed systems. We present a new method of constructing algebraic Petri net specifications by establishing a relationship between algebraic Petri nets with structured analysis methods. Algebraic Petri net specifications are systematically derived from data flow diagrams, a data dictionary, and decision table process specifications. Our results facilitate the application of algebraic Petri nets and also provide a potential analysis technique for informal specifications written in structured analysis methods.
11889495
On Even Triangulations of 2-Connected Embedded Graphs
Recently, Hoffmann and Kriegel proved an important combinatorial theorem [SIAM J. Discrete Math., 9 (1996), pp. 210--224]: Every 2-connected bipartite plane multigraph G without 2-cycle faces has a triangulation in which all vertices have even degree (this is called an even triangulation). Combined with the classical Whitney's theorem, this result implies that every such graph has a 3-colorable plane triangulation. Using this theorem, Hoffmann and Kriegel significantly improved the upper bounds of several art gallery and prison guard problems. A complicated O(n2) time algorithm was obtained in [SIAM J. Discrete Math., 9 (1996), pp. 210--224] for constructing an even triangulation of G. Hoffmann and Kriegel conjectured that there is an O(n3/2) time algorithm for solving this problem. 
In this paper, we develop a simple proof of the above theorem. Our proof reveals and relies on a natural correspondence between even triangulations of G and certain orientations of G. Based on this new proof, we obtain a very simple O(n) time algorithm for finding an even triangulation of G. We also extend our proof to show the existence of even triangulations for similar graphs on high genus surface.
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.
9650912
Learning an image manifold for retrieval
We consider the problem of learning a mapping function from low-level feature space to high-level semantic space. Under the assumption that the data lie on a submanifold embedded in a high dimensional Euclidean space, we propose a relevance feedback scheme which is naturally conducted only on the image manifold in question rather than the total ambient space. While images are typically represented by feature vectors in Rn, the natural distance is often different from the distance induced by the ambient space Rn. The geodesic distances on manifold are used to measure the similarities between images. However, when the number of data points is small, it is hard to discover the intrinsic manifold structure. Based on user interactions in a relevance feedback driven query-by-example system, the intrinsic similarities between images can be accurately estimated. We then develop an algorithmic framework to approximate the optimal mapping function by a Radial Basis Function (RBF) neural network. The semantics of a new image can be inferred by the RBF neural network. Experimental results show that our approach is effective in improving the performance of content-based image retrieval systems.
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.
14270830
Laplacian Score for Feature Selection
In supervised learning scenarios, feature selection has been studied widely in the literature. Selecting features in unsupervised learning scenarios is a much harder problem, due to the absence of class labels that would guide the search for relevant information. And, almost all of previous unsupervised feature selection methods are "wrapper" techniques that require a learning algorithm to evaluate the candidate feature subsets. In this paper, we propose a "filter" method for feature selection which is independent of any learning algorithm. Our method can be performed in either supervised or unsupervised fashion. The proposed method is based on the observation that, in many real world classification problems, data from the same class are often close to each other. The importance of a feature is evaluated by its power of locality preserving, or, Laplacian Score. We compare our method with data variance (unsupervised) and Fisher score (supervised) on two data sets. Experimental results demonstrate the effectiveness and efficiency of our algorithm.