Sebti Foufou

A survey of clustering algorithms for big data: Taxonomy and empirical analysis

Clustering algorithms have emerged as an alternative powerful meta-learning tool to accurately analyze the massive volume of data generated by modern applications. In particular, their main goal is to categorize data into clusters such that objects are grouped in the same cluster when they are similar according to specific metrics. There is a vast body of knowledge in the area of clustering and there has been attempts to analyze and categorize them for a larger number of applications. However, one of the major issues in using clustering algorithms for big data that causes confusion amongst practitioners is the lack of consensus in the definition of their properties as well as a lack of formal categorization. With the intention of alleviating these problems, this paper introduces concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as providing a comparison, both from a theoretical and an empirical perspective. From a theoretical perspective, we developed a categorizing framework based on the main properties pointed out in previous studies. Empirically, we conducted extensive experiments where we compared the most representative algorithm from each of the categories using a large number of real (big) data sets. The effectiveness of the candidate clustering algorithms is measured through a number of internal and external validity metrics, stability, runtime, and scalability tests. In addition, we highlighted the set of clustering algorithms that are the best performing for big data.

OntoSTEP: Enriching product model data using ontologies

The representation and management of product lifecycle information is critical to any manufacturing organization. Different modeling languages are used at different lifecycle stages, for example STEPs EXPRESS may be used at a detailed design stage, while UML may be used for initial design stages. It is necessary to consolidate product information created using these different languages to build a coherent knowledge base. In this paper, we present an approach to enable the translation of STEP schema and its instances to Ontology Web Language (OWL). This gives a model-which we call OntoSTEP-that can easily be integrated with any OWL ontologies to create a semantically rich model. As an example, we combine geometry information represented in STEP with non-geometry information, such as function and behavior, represented using the NISTs Core Product Model (CPM). A plug-in for Protege is developed to automate the different steps of the translation. As additional benefits, reasoning, inference procedures, and queries can be performed on enriched legacy CAD models. We describe the rules for the translation from EXPRESS to OWL, and illustrate the benefits of OWL translation with an example. We will also describe how these mapping rules can be implemented through meta-model based transformations, which can be used to map other languages to OWL.

A Model for Capturing Product Assembly Information

The important issue of mechanical assemblies has been a subject of intense research over the past several years. Most electromechanical products are assemblies of several components, for various technical as well as economic reasons. This paper provides an object-oriented definition of an assembly model called the Open Assembly Model (OAM) and defines an extension to the NIST Core Product Model (NIST-CPM). The assembly model represents the function, form, and behavior of the assembly and defines both a system level conceptual model and associated hierarchical relationships. The model provides a way for tolerance representation and propagation, kinematics representation, and engineering analysis at the system level. The assembly model is open so as to enable plug-and-play with various applications, such as analysis (FEM, tolerance, assembly), process planning, and virtual assembly (using VR techniques). With the advent of the Internet more and more products are designed and manufactured globally in a distributed and collaborative environment. The class structure defined in OAM can be used by designers to collaborate in such an environment. The proposed model includes both assembly as a concept and assembly as a data structure. For the latter it uses STEP. The OAM together with CPM can be used to capture the assembly evolution from the conceptual to the detailed design stages. It is expected that the proposed OAM will enhance the assembly information content in the STEP standard. A case study example is discussed to explain the Usecase analysis of the assembly model.

Product lifecycle management support: a challenge in supporting product design and manufacturing in a networked economy

In this paper, we provide an overview of the changing design and manufacturing landscape in the 21st century that has come about because of IT and the changing global conditions. Based on this overview and a review of the current state of IT for PLM support in the design and manufacturing sector, we identify the areas of need for standards. A review of areas covered by standards leads us to the development of an initial typology of standards and a potential path for bringing convergence of these standards in support of PLM. We make a case throughout the paper that given the nature of the task we need to aspire to create open standards with wide participation. We conclude by arguing that there is an important role to be played in this context by institutions such as NIST as a neutral party in the standards debates and implementations.

A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living

This research aims to describe pattern recognition models for detecting behavioural and health-related changes in a patient who is monitored continuously in an assisted living environment. The early anticipation of anomalies can improve the rate of disease prevention. Here we present different learning techniques for predicting abnormalities and behavioural trends in various user contexts. In this paper we described a Hidden Markov Model based approach for detecting abnormalities in daily activities, a process of identifying irregularity in routine behaviours from statistical histories and an exponential smoothing technique to predict future changes in various vital signs. The outcomes of these different models are then fused using a fuzzy rule-based model for making the final guess and sending an accurate context-aware alert to the health-care service providers. We demonstrated the proposed techniques by evaluating some case studies for different patient scenarios in ambient assisted living. HighlightsWe developed a novel change detection framework for context-aware monitoring.We described a Hidden Markov Model for detecting abnormalities in daily activities.We developed a statistical process to identify irregularity in routine behaviours.We presented a disease forecasting technique using Holt?s liner trend method.We built a rule-based fuzzy fusion model for making context-aware decision.

A multiagent system approach for image segmentation using genetic algorithms and extremal optimization heuristics

We propose a new distributed image segmentation algorithm structured as a multiagent system composed of a set of segmentation agents and a coordinator agent. Starting from its own initial image, each segmentation agent performs the iterated conditional modes method, known as ICM, in applications based on Markov random fields, to obtain a sub-optimal segmented image. The coordinator agent diversifies the initial images using the genetic crossover and mutation operators along with the extremal optimization local search. This combination increases the efficiency of our algorithm and ensures its convergence to an optimal segmentation as it is shown through some experimental results.

MESH COMPARISON USING ATTRIBUTE DEVIATION METRIC

We propose a mesh comparison method using a new attribute deviation metric. The considered meshes contain geometrical and appearance attributes (material color, texture, temperature, etc.). The proposed deviation metric computes local differences between the attributes of two meshes. A mesh comparison assessment can be done easily and quickly using this metric. The techniques proposed are applicable in a number of ways, e.g. 3D matching and registration, and the example described in the paper is the simplification of a surface by iteratively reducing its complexity according to an error metric. The results are presented showing the success of the algorithm through comparisons with other measures and with three different simplification algorithms.

Geometric constraint solving: The witness configuration method

Geometric constraint solving is a key issue in CAD, CAM and PLM. The systems of geometric constraints are today studied and decomposed with graph-based methods, before their numerical resolution. However, graph-based methods can detect only the simplest (called structural) dependences between constraints; they cannot detect subtle dependences due to theorems. To overcome these limitations, this paper proposes a new method: the system is studied (with linear algebra tools) at a witness configuration, which is intuitively similar to the unknown one, and easy to compute.

Numerical decomposition of geometric constraints

Geometric constraint solving is a key issue in CAD/CAM. Since Owens seminal paper, solvers typically use graph based decomposition methods. However, these methods become difficult to implement in 3D and are misled by geometric theorems. We extend the Numerical Probabilistic Method (NPM), well known in rigidity theory, to more general kinds of constraints and show that NPM can also decompose a system into rigid subsystems. Classical NPM studies the structure of the Jacobian at a random (or generic) configuration. The variant we are proposing does not consider a random configuration, but a configuration similar to the unknown one. Similar means the configuration fulfills the same set of incidence constraints, such as collinearities and coplanarities. Jurzaks prover is used to find a similar configuration.

Cloud-Ready Biometric System for Mobile Security Access

In this contribution, we introduce an application that allows a mobile phone to be used as a biometric-capture device for secure access to the cloud. In this application, the biometric capture and recognition are performed during a standard web session, using JQuery, a new mobile web framework which provides the technology needed to build web pages that act more like mobile applications rather than traditional web pages. The mobile user obtains service catalog through an interface developed for the Android system. We have used Hadoop, an open source cloud computing environment, to establish the connection between mobile user and server in the cloud via Ethernet, WiFi or 3G.