Abstract Grid computing is the aggregation of the power of heterogeneous, geographically distributed computing resources to provide high-performance computing. To benefit from the grid computing capabilities, effectual scheduling algorithms are primarily essential. This paper presents a GA-based approach, called Grid Workflow Tasks Scheduling Algorithm (GWTSA), for scheduling workflow tasks on grid services based on users’ QoS (quality of service) constraints in terms of cost and time. For a given set of inter-dependent workflow tasks, it generates an optimal schedule, which minimizes the execution time and cost, such that the optimized time be within the time constraints (deadline) imposed by the user. In GWTSA, the workflow tasks are modeled as a DAG, which is divided, then the optimal sub-schedules of all task divisions are computed and used to obtain the execution schedule of the entire workflow. A GA-based technique is employed in GWTSA to compute the optimal execution sub-schedule for each branch division that consists of a set of sequential tasks. In this technique, the chromosome represents a branch division, where each gene holds the id of the service provider chosen to execute the corresponding task in the branch; and the fitness function is formulated as a multi-objective function of time and cost, this gives users the ability to determine their requirements if speed against cost or vice versa, by changing the weighting coefficients in the fitness function. The paper also exhibits the experimental results of assessing the performance of GWTSA with workflow samples of different sizes.

Compressing an image is a significant technique for the sake of saving a little more bandwidth or storage space. However, one of the main problems in vector quantization (VQ) is the high computational complexity of searching for the appropriate codeword in its codebook. To overcome this problem a fast search algorithm based on Tree-Hadamard Transform of the mean pyramid levels of the codewords is proposed. The algorithm uses tree structure of the highest level for all codewords to speed up the searching process. The proposed algorithm is more efficient than using only Hadamard transformed mean pyramids especially in high dimension cases.

This paper presents an automatic test-data generation technique that uses a genetic algorithm (GA) to generate test data that satisfy data-flow coverage criteria. The technique applies the concepts of dominance relations between nodes to define a new multi-objective fitness function to evaluate the generated test data. The paper also presents the results of a set of empirical studies conducted on a set of programs that evaluate the effectiveness of our technique compared to the random-testing technique. The studies show the effective of our technique in achieving coverage of the test requirements, and in reducing the size of test suites, the search time, and the number of iterations required to satisfy the data-flow criteria.

The Internet presents a huge amount of useful information which is usually formatted for its users, which makes it difficult to extract relevant data from various sources. Therefore, the availability of robust, flexible Information Extraction (IE) systems that transform the Web pages into program-friendly structures such as a relational database will become a great necessity. Although many approaches for data extraction from Web pages have been developed, there has been limited effort to compare such tools. Unfortunately, in only a few cases can the results generated by distinct tools be directly compared since the addressed extraction tasks are different. This paper surveys the major Web data extraction approaches and compares them in three dimensions: the task domain, the automation degree, and the techniques used. The criteria of the first dimension explain why an IE system fails to handle some Web sites of particular structures. The criteria of the second dimension classify IE systems based on the techniques used. The criteria of the third dimension measure the degree of automation for IE systems. We believe these criteria provide qualitatively measures to evaluate various IE approaches.

Text categorization is the automated assigning of documents to predefined categories based on their contents. It involves two main tasks — feature selection and document classification. This paper discusses the weak points of the text categorization technique developed by Maron and modified by Lewis. Then, it introduces a technique for text categorization that uses new formulas for feature selection and document classification. These formulas have been formulated to overcome the weak points of Maron's and Lewis' techniques. Also, the paper describes the design of an experimental text categorization system that is composed of the same set of processes as the MAXCAT system developed by Lewis. The paper presents and analyses the results of applying the system on a set of training and test documents by using Lewis' and the proposed formulas. In addition, a method for separately evaluating the effectiveness of feature selection is given. Finally, the impact of the feature set size on the effectiveness of the classification system is investigated, using the system and applying one of the proposed classification formulas with different feature set sizes.

Abstract The utilization is described of a data flow path selection criterion in a symbolic execution system. The system automatically generates a subset of program paths according to a certain control flow criterion. This subset is called the ZOT‐subset, since it requires paths that traverse loops zero, one and two times. Experience indicates that traversing this subset of program paths is enough to cover most control flow and data flow components of the program. The problem with the ZOT‐subset is that it might contain, for large programs, a large number of paths. The number of paths in this subset can be reduced by concentrating on executable paths that cover vital components of programs such as data flow components. This object is achieved by employing a data flow path selection criterion in the system. The system symbolically executes the paths of the ZOT‐subset, and creates a system of branch conditions for each one. The user determines infeasible paths by checking the consistency of each system of conditions. The system selects feasible paths from the ZOT‐subset that cover the data flow criterion. Solving the systems of conditions of the selected paths provides the user with test data to fulfil the given data flow criterion.

In reference to the above-titled paper by R.A. DeMillo and A.J. Offutt (see ibid., vol.17, no.9, p.900-10, Sept. 1991), the commenter rates that he and M.R. Woodward (1985) implemented a system for FORTRAN-77 programs that integrates weak mutation and data flow analysis. He reports here that experiments have been carried out by them (1986), using the system to compare the error exposing ability of weak mutation, data flow, and control flow testing strategies.>