SUMMARYData Grids are an emerging technology for managing large amounts of distributed data. This technology is highly anticipated by scientific communities, such as in the area of astronomy and high-energy physics, because their experiments generate massive amounts of data which need to be shared and analysed. Since it is not feasible to test different usages on real testbeds, it is easier to use simulations as a means of studying complex scenarios. This paper presents our work on incorporating data Grids features as an extension to GridSim, a computational Grid simulator. The extension provides essential building blocks for simulating various data Grids scenarios. Moreover, it is designed to be easily extended. This approach makes it easy to try various strategies and to add functionalities to suit the needs of other communities. This paper also gives a detailed description of the design and usage examples demonstrating the versatility of this tool.

In Grids, users may require assurance for completing their jobs on shared resources. Such guarantees can only be provided by reserving resources in advance. However, if many reservation requests arrive at a resource simultaneously, the overhead of providing such service due to adding, deleting and searching, will be significant. An efficient data structure for managing these reservations plays an important role in order to minimise the time required for searching available resources, adding and deleting reservations. In this paper, we present new approaches to advance reservation in order to deal with the limitations of the existing data structures, such as Segment Tree and Calendar Queue in similar problems. We propose a Grid advanced reservation Queue (GarQ), which is a new data structure that improves some weaknesses of the aforementioned data structures. We demonstrate the superiority of the proposed structure by conducting a detailed performance evaluation on real workload traces.

Cell counting in microscopic images is one of the fundamental analysis tools in life sciences, but is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand additional training or data input from the user. Most of them do not allow the users to online monitor the counting results, either. Therefore, we designed two straightforward, simple-to-use cell-counting programs that also allow users to correct the detection results. In this paper, we present the Cellcounter and Learn123 programs for automatic and semiautomatic counting of objects in fluorescent microscopic images (cells or cell nuclei) with a user-friendly interface. Although Cellcounter is based on predefined and fine-tuned set of filters optimized on sets of chosen experiments, Learn123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. Cellcounter also includes an extension for analysis of overlaying images. The efficiency of both programs was assessed on images of cells stained with different fluorescent dyes by comparing automatically obtained results with results that were manually annotated by an expert. With both programs, the correlation between automatic and manual counting was very high (R(2) < 0.9), although Cellcounter had some difficulties processing images with no cells or weakly stained cells, where sometimes the background noise was recognized as an object of interest. Nevertheless, the differences between manual and automatic counting were small compared to variations between experimental repeats. Both programs significantly reduced the time required to process the acquired images from hours to minutes. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Moreover, Cellcounter overlay extension also enables fast analysis of related images that would otherwise require image merging for accurate analysis, whereas Learn123's evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings.

Abstract-Motivated by improving the efficiency of pattern matching on graphs, we define a new kind of equivalence on graph vertices. Since it can be used in various graph algorithms that explore graphs, we call it exploratory equivalence. The equivalence is based on graph automorphisms. Because many similar equivalences exist (some also based on automorphisms), we argue that this one is novel. For each graph, there are many possible exploratory equivalences, but for improving the efficiency of the exploration, some are better than others. To this end, we define a goal function that models the reduction of the search space in such algorithms. We describe two greedy algorithms for the underlying optimization problem. One is based directly on the definition using a straightforward greedy criterion, whereas the second one uses several practical speedups and a different greedy criterion. Finally, we demonstrate the huge impact of exploratory equivalence on a real application, i.e., graph grammar parsing.

The subgraph isomorphism problem is one of the most important problems for pattern recognition in graphs. Its applications are found in many di®erent disciplines, including chemistry, medicine, and social network analysis. Because of the N P-completeness of the problem, the existing exact algorithms exhibit an exponential worst-case running time. In this paper, we propose several improvements to the well-known Ullmann's algorithm for the problem. The improvements lower the time consumption as well as the space requirements of the algorithm. We experimentally demonstrate the e±ciency of our improvement by comparing it to another set of improvements called FocusSearch, as well as other state-of-the-art algorithms, namely VF2 and LAD. 1 row = select an unmatched row; 2 for col ∈ U :M d (row, col) = 1 do 3 if d = n then 4 subgraph isomorphism found; 5 else 6 M tmp = filter(M d ); 7 M d+1 = refine(M tmp ); 8 findIso(M d+1 , d + 1);