Sabeur Aridhi

Density-based data partitioning strategy to approximate large-scale subgraph mining

Recently, graph mining approaches have become very popular, especially in certain domains such as bioinformatics, chemoinformatics and social networks. One of the most challenging tasks is frequent subgraph discovery. This task has been highly motivated by the tremendously increasing size of existing graph databases. Due to this fact, there is an urgent need of efficient and scaling approaches for frequent subgraph discovery. In this paper, we propose a novel approach for large-scale subgraph mining by means of a density-based partitioning technique, using the MapReduce framework. Our partitioning aims to balance computational load on a collection of machines. We experimentally show that our approach decreases significantly the execution time and scales the subgraph discovery process to large graph databases.Recently, graph mining approaches have become very popular, especially in domains such as bioinformatics, chemoinformatics and social networks. In this scope, one of the most challenging tasks is frequent subgraph discovery. This task has been motivated by the tremendously increasing size of existing graph databases. Since then, an important problem of designing efficient and scaling approaches for frequent subgraph discovery in large clusters, has taken place. However, failures are a norm rather than being an exception in large clusters. In this context, the MapReduce framework was designed so that node failures are automatically handled by the framework. In this paper, we propose a large-scale and fault-tolerant approach of subgraph mining by means of a density-based partitioning technique, using MapReduce. Our partitioning aims to balance computation load on a collection of machines. We experimentally show that our approach decreases significantly the execution time and scales the subgraph discovery process to large graph databases.

A MapReduce-based approach for shortest path problem in large-scale networks

The cloud computing allows to use virtually infinite resources, and seems to be a new promising opportunity to solve scientific computing problems. The MapReduce parallel programming model is a new framework favoring the design of algorithms for cloud computing. Such framework favors processing of problems across huge datasets using a large number of heterogeneous computers over the web. In this paper, we are interested in evaluating how the MapReduce framework can create an innovative way for solving operational research problems. We proposed a MapReduce-based approach for the shortest path problem in large-scale real-road networks. Such a problem is the cornerstone of any real-world routing problem including the dial-a-ride problem (DARP), the pickup and delivery problem (PDP) and its dynamic variants. Most of efficient methods dedicated to these routing problems have to use the shortest path algorithms to construct the distance matrix between each pair of nodes and it could be a time-consuming task on a large-scale network due to its size. We focus on the design of an efficient MapReduce-based approach since a classical shortest path algorithm is not suitable to accomplish efficiently such task. Our objective is not to guarantee the optimality but to provide high quality solutions in acceptable computational time. The proposed approach consists in partitioning the original graph into a set of subgraphs, then solving the shortest path on each subgraph in a parallel way to obtain a solution for the original graph. An iterative improvement procedure is introduced to improve the solution. It is benchmarked on a graph modeling French road networks extracted from OpenStreetMap. The results of the experiment show that such approach achieves significant gain of computational time.

Prediction of Ionizing Radiation Resistance in Bacteria Using a Multiple Instance Learning Model

Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. In this context, in silico methods of phenotypic prediction and genotype-phenotype relationship discovery are limited. In this work, we analyzed basal DNA repair proteins of most known proteome sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts this bacterial phenotype. We formulated the problem of predicting bacterial ionizing radiation resistance (IRR) as a multiple-instance learning (MIL) problem, and we proposed a novel approach for this purpose. We provide a MIL-based prediction system that classifies a bacterium to either IRRB or IRSB. The experimental results of the proposed system are satisfactory with 91.5% of successful predictions.

The uncertain cloud: State of the art and research challenges

Abstract During the last decade, cloud computing became a natural choice to host and provide various computing resources as on-demand services. The correct characterization and management of cloud environment objects (clouds, data centers, providers, services, data, users, etc.) is the first step towards effective provisioning and integration of cloud services. However, cloud computing environment is often subject to uncertainty. This could be attributed to the incompleteness and imprecision of cloud available information, as well as the highly changing conditions. The purpose of this survey is to study, criticize and classify the already existing works that deal with uncertainty in the cloud. We present a taxonomy on the uncertainty in the cloud and we study how such concept was tackled by researchers in cloud environments. Finally, we identify the challenges and the requirements to deal with uncertain data in the cloud, as well as the future directions.

Computational phenotype prediction of ionizing-radiation-resistant bacteria with a multiple-instance learning model

Ionizing-radiation-resistant bacteria (IRRB) are important in biotechnology. The use of these bacteria for the treatment of radioactive wastes is determined by their surprising capacity of adaptation to radionuclides and a variety of toxic molecules. In silico methods are unavailable for the purpose of phenotypic prediction and genotype-phenotype relationship discovery. We analyze basal DNA repair proteins of most known proteomes sequences of IRRB and ionizing-radiation-sensitive bacteria (IRSB) in order to learn a classifier that correctly predicts unseen bacteria. In this work, we formulate the problem of predicting IRRB as a multiple-instance learning (MIL) problem and we propose a novel approach for predicting IRRB. We use a local alignment technique to measure the similarity between protein sequences to predict ionizing-radiation-resistant bacteria. The first results are satisfactory and provide a MIL-based prediction system that predicts whether a bacterium belongs to IRRB or to IRSB. The proposed system is available online.