Claudine Badue

Distributed query processing using partitioned inverted files

In this paper; we study query processing in a distributed text database. The novelty is a real distributed architecture implementation that oflers concurrent query service. The distributed system adopts a network of workstations model and the client-server paradigm. The document collection is indexed with an imerted

Automated multi-label text categorization with VG-RAM weightless neural networks

le. We adopt two distinct strategies of index partitioning in the distributed system, namely local index partitioning and global indexpartitioning. In both strategies, documents are ranked using the vector space model along with a documentfiltering technique for fast ranking. We evaluate and compare the impact of the two index partitioning strategies on query processing per

Analyzing imbalance among homogeneous index servers in a web search system

ormance. Experimental results on retrieval eficiency show that, within our framework, the global index partitioning outpe~orms the local index partitioning.

Basic issues on the processing of web queries

In automated multi-label text categorization, an automatic categorization system should output a label set, whose size is unknown a priori, for each document under analysis. Many machine learning techniques have been used for building such automatic text categorization systems. In this paper, we examine virtual generalizing random access memory weightless neural networks (VG-RAM WNN), an effective machine learning technique which offers simple implementation and fast training and test, as a tool for building automatic multi-label text categorization systems. We evaluated the performance of VG-RAM WNN on two real-world problems:, (i) categorization of free-text descriptions of economic activities and (ii) categorization of Web pages, and compared our results with that of the multi-label lazy learning approach (Multi-Label K-Nearest Neighbors, ML-KNN). Our experimental comparative analysis showed that, on average, VG-RAM WNN either outperforms ML-KNN or show similar categorization performance.

Face Recognition with VG-RAM Weightless Neural Networks

The performance of parallel query processing in a cluster of index servers is crucial for modern web search systems. In such a scenario, the response time basically depends on the execution time of the slowest server to generate a partial ranked answer. Previous approaches investigate performance issues in this context using simulation, analytical modeling, experimentation, or a combination of them. Nevertheless, these approaches simply assume balanced execution times among homogeneous servers (by uniformly distributing the document collection among them, for instance)-a scenario that we did not observe in our experimentation. On the contrary, we found that even with a balanced distribution of the document collection among index servers, correlations between the frequency of a term in the query log and the size of its corresponding inverted list lead to imbalances in query execution times at these same servers, because these correlations affect disk caching behavior. Further, the relative sizes of the main memory at each server (with regard to disk space usage) and the number of servers participating in the parallel query processing also affect imbalance of local query execution times. These are relevant findings that have not been reported before and that, we understand, are of interest to the research community.

Modeling performance-driven workload characterization of web search systems

In this paper we study three basic and key issues related to Web query processing: load balance, broker behavior, and performance by individual index servers. Our study, while preliminary, does reveal interesting tradeoffs: (1) load unbalance at low query arrival rates can be controlled with a simple measure of randomizing the distribution of documents among the index servers, (2) the broker is not a bottleneck, and (3) disk utilization is higher than CPU utilization.

A Comparison between a KNN Based Approach and a PNN Algorithm for a Multi-label Classification Problem

Virtual Generalizing Random Access Memory Weightless Neural Networks ( Vg-ram wnn ) are effective machine learning tools that offer simple implementation and fast training and test. We examined the performance of Vg-ram wnn on face recognition using a well known face database--the AR Face Database. We evaluated two Vg-ram wnn architectures configured with different numbers of neurons and synapses per neuron. Our experimental results show that, even when training with a single picture per person, Vg-ram wnn are robust to various facial expressions, occlusions and illumination conditions, showing better performance than many well known face recognition techniques.

Traffic sign recognition with VG-RAM Weightless Neural Networks

In this paper we model workloads for a web search system from the performance point of view. We analyze both workload intensity and service demand parameters expressed in the context of web search systems as the distribution of the interarrival times of queries and the per-query execution time, respectively. Our results are derived from experiments in an information retrieval testbed fed with real-world experimental data. Our findings unveil a certain number of unexpected and interesting features. We verify in practice that there is a high variability in both interarrival times of queries reaching a search engine and service times of queries processed in parallel by a cluster of index servers. We also show that this highly variable behavior can be accurately captured by hyperexponential distributions. These results shed light on the usual assumption taken by previous analytical models for web search systems found in the literature that interarrival times and service times are exponentially distributed. We find evidence that the intensity and service demand workloads of a typical web search system present long-range dependence characteristics, leading to self-similar behavior. This finding is important because, in the presence of long-range dependence and self-similarity, exponential-based models tend to underestimate response times as self-similarity leads to increased queueing delays, resulting in significant performance degradation. Based on our findings, we also discuss possible steps toward a generative model for synthetic workloads.

Image-based global localization using VG-RAM Weightless Neural Networks

Techniques for categorization and clustering, range from support vector machines, neural networks to Bayesian inference and algebraic methods. The k-Nearest Neighbor Algorithm (KNN) is a popular example of the latter class of these algorithms. Recently, a slight modification of it has been proposed so that the Multi-Label k-Nearest Neighbor Algorithm (ML-KNN) can deal better with multi-label classification problems. In this paper we are interested in automatic text categorization, which are becoming more and more important as the amount of text in electronic format grows and the access to it becomes more necessary and widespread. We proposed a Probabilistic Neural Network Algorithm (PNN) tailored to also deal with multi-label classification problems, and compared it against the ML-KNN algorithm. Our implementation surpass the ML-KNN algorithm in four metrics typically used in the literature for multi-label categorization problems.

A Model-Predictive Motion Planner for the IARA autonomous car

Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) is an effective machine learning technique that offers simple implementation and fast training and test. In this paper, we present a new approach for traffic sign recognition based on VG-RAM WNN. We evaluate its performance using the German Traffic Sign Recognition Benchmark (GTSRB), a large multi-class classification benchmark. Our experimental results showed that our VG-RAM WNN architecture for traffic sign recognition was able to rank at 4th position in the GTSRB evaluation system, with a recognition rate of 98.73%, and was overcome by only one automatic approach.