Aomar Osmani

bitSPADE: A Lattice-based Sequential Pattern Mining Algorithm Using Bitmap Representation

Sequential pattern mining allows to discover temporal relationship between items within a database. The patterns can then be used to generate association rules. When the databases are very large, the execution speed and the memory usage of the mining algorithm become critical parameters. Previous research has focused on either one of the two parameters. In this paper, we present bitSPADE, a novel algorithm that combines the best features of SPAM, one of the fastest algorithm, and SPADE, one of the most memory efficient algorithm. Moreover, we introduce a new pruning strategy that enables bitSPADE to reach high performances. Experimental evaluations showed that bitSPADE ensures an efficient tradeoff between speed and memory usage by outperforming SPADE by both speed and memory usage factors more than 3.4 and SPAM by a memory consumption factor up to more than an order of magnitude.

Mining Association Rules in Temporal Sequences

Mining association rules is an important technique for discovering meaningful patterns in datasets. Temporal association rule mining can be decomposed into two phases: finding temporal frequent patterns and finding temporal rules construction. Till date, a large number of algorithms have been proposed in the area of mining association rules. However, most of these algorithms consider patterns as a collection of point primitives and their three basic relations (<, =, >). Several applications consider patterns with duration and need to reason about intervals and their thirteen possible relationships. In this paper we investigate properties of temporal sequences represented as a collection of intervals. We present a simple framework for temporal sequence and describe DATTES (Discovering pATterns in TEmporal Sequences), an innovative algorithm using interval properties to mine temporal patterns. The framework can be used to mine temporal association rules. According to some interval algebra properties, this paper introduces a new confidence evaluation function for mining temporal rules. Experiments on real dataset (human face identification problem) show the effectiveness and the performances of this approach.

Introduction to reasoning about cyclic intervals

This paper introduces a new formalism for representation and reasoning about time and space. Allen’s algebra of time intervals is well known within the constraint-based spatial and temporal reasoning community. The algebra assumes standard time, i.e., time is viewed as a linear order. Some real applications, however, such as reasoning about cyclic processes or cyclic events, need a representational framework based on (totally ordered) cyclic time. The paper describes a still-in-progress work on an algebra of cyclic time intervals, which can be looked at as the counterpart of Allen’s linear time algebra for cyclic time.

Model-based diagnosis for fault management in ATM networks

ATM networks require powerful management capabilities for configuration, performance, security and particularly fault management. International standard organizations and particularly the ATM Forum and ITU-T organization have proposed several recommendations to manage faults. However, the large size and the high level of complexity of such networks requires new intelligent approaches. This article gives some examples of the existing formalisms and proposes an intelligent approach to build rule-based systems used to detect and to localize faults in ATM networks.

A Model to Study Phase Transition and Plateaus in Relational Learning

The feasibility of symbolic learning strongly relies on the efficiency of heuristic search in the hypothesis space. However, recent works in relational learning claimed that the phase transition phenomenon which may occur in the subsumption test during search acts as a plateau for the heuristic search, strongly hindering its efficiency. We further develop this point by proposing a learning problem generator where it is shown that top-down and bottom-up learning strategies face a plateau during search before reaching a solution. This property is ensured by the underlying CSP generator, the RB model, that we use to exhibit a phase transition of the subsumption test. In this model, the size of the current hypothesis maintained by the learner is an order parameter of the phase transition and, as it is also the control parameter of heuristic search, the learner has to face a plateau during the problem resolution. One advantage of this model is that small relational learning problems with interesting properties can be constructed and therefore can serve as a benchmark model for complete search algorithms used in learning. We use the generator to study complete informed and non-informed search algorithms for relational learning and compare their behaviour when facing a phase transition of the subsumption test. We show that this generator exhibits the pathological case where informed learners degenerate into non-informed ones.

Modeling and simulating breakdown situations in telecommunication networks

In this article we describe a framework for model-based diagnosis of telecommunication management networks.

Empirical study of relational learning algorithms in the phase transition framework

Relational Learning (RL) has aroused interest to fill the gap between efficient attribute-value learners and growing applications stored in multi-relational databases. However, current systems use general- purpose problem solvers that do not scale-up well. This is in contrast with the past decade of success in combinatorics communities where studies of random problems, in the phase transition framework, allowed to evaluate and develop better specialised algorithms able to solve real-world applications up to millions of variables. A number of studies have been proposed in RL, like the analysis of the phase transition of a NP-complete sub-problem, the subsumption test, but none has directly studied the phase transition of RL. As RL, in general, is

Reasoning about generalized intervals

{\it \Sigma}_2-hard

Model driven data warehouse using MDA and 2TUP

, we propose a first random problem generator, which exhibits the phase transition of its decision version, beyond NP. We study the learning cost of several learners on inherently easy and hard instances, and conclude on expected benefits of this new benchmarking tool for RL.

A constraint-based approach to simulate faults in telecommunication networks

Extending previous notions of generalized intervals, this paper defines the generalized interval as a tuple of solutions of some consistent interval network. It studies the possible relations between such generalized intervals and introduces the notion of a generalized interval network. It proves the tractability of the problem of the consistency of a generalized network which constraints are preconvex.