the intrinsic characteristics of the data and are relatively stable to database updates. To reduce the size of index structure, two techniques, size-increasing support constraint and discriminative fragments, are introduced. Our performance study shows that gIndex has 10 times smaller index size, but achieves

that predicts whether a novel molecule will be active or inactive, so that future chemical tests can be focused on the most promising candidates. In [1] an algorithm for constructing such a classifier was proposed that uses molecular fragments to discriminate between active and inactive molecules. In this paper

to identify an interpretable discriminative structure which describes the most discriminant structural elements with respect to some target. The application of ILP in an interactive software for discriminative min-ing of chemical fragments is presented in this paper. In particular, it is described

Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the wellknown National Cancer Institute’s HIV-screening dataset. KEY WORDS Distributed computing, frequent subgraph mining, dynamic load balancing, biochemical databases. 1

distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the wellknown National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing

to extract class-discriminative boundary fragments. In addition to their shape, these “codebook ” entries also determine the object’s centroid (in the manner of Leibe et al. [19]). Boosting is used to select discriminative combinations of boundary fragments (weak detectors) to form a strong “Boundary-Fragment

The goal of this paper is event detection and recounting using a representation of concept detector scores. Differ-ent from existing work, which encodes videos by averaging concept scores over all frames, we propose to encode videos using fragments that are discriminatively learned per event. Our

The goal of this paper is event detection and recounting using a representation of concept detector scores. Differ-ent from existing work, which encodes videos by averaging concept scores over all frames, we propose to encode videos using fragments that are discriminatively learned per event. Our

The goal of this work is to discriminatively learn contour fragment descriptors for the task of object detection. Unlike previous methods that incorporate learning techniques only for object model generation or for verification after detection, we present a holistic object detection system using

images; the second stage bootstraps these detections to learn an improved classifier while explicitly training against clutter. The detectors are learned with a boosting algorithm which creates a location-sensitive classifier using a discriminative set of features from a randomly chosen dictionary