Samba Ndiaye

Database replication in large scale systems: optimizing the number of replicas

In distributed systems, replication is used for ensuring availability and increasing performances. However, the heavy workload of distributed systems such as web2.0 applications or Global Distribution Systems, limits the benefit of replication if its degree (i.e., the number of replicas) is not controlled. Since every replica must perform all updates eventually, there is a point beyond which adding more replicas does not increase the throughput, because every replica is saturated by applying updates. Moreover, if the replication degree exceeds the optimal threshold, the useless replica would generate an overhead due to extra communication messages. In this paper, we propose a suitable replication management solution in order to reduce useless replicas. To this end, we define two mathematical models which approximate the appropriate number of replicas to achieve a given level of performance. Moreover, we demonstrate the feasibility of our replication management model through simulation. The results expose the effectiveness of our models and their accuracy.

Contribution to SMS management over 4G network in distance education context

In parallel to the using of resources implemented for distance learning, learners appreciate send and receive SMS. Although 4G offers very high data rates and adaptable quality of service, it does not support the SMS legacy service of GSM. 3GPP is currently specifying two architectures for SMS transmission: SMS over SGs, and SMS over IMS. However, when the learner uses a 4G service, to send or receive a SMS, he is first switched to 3G, which interrupts the current service on 4G. It is in this context that we propose a solution that permits to learner to send or receive SMS while continuing to use his service on the 4G network.

Pastoral mobility optimization based on a mobile Geographic Information System (GIS) for rural development

This paper explores the use of mobile Geographic Information System (GIS) computing in its application in water point management. Applications based on mobile GIS are the best way to provide appropriate solutions for rural populations such as breeders in Senegal. In fact, the breeders have many problems to find water points for their cattle group. So far, the potential geographic location of water points is transmitting orally between breeders. It is worth noticing that this knowledge, in rural areas, is not stored for instance in databases and depends only on the availability of breeders that own these information. Many solutions for setting up rural information systems have been proposed, but most of them are either PC-based, or rely on proprietary software, or do not take into account electricity constraints which are crucial in rural areas. In this paper, we present an information system based on Short Message Service (SMS) technology and mobile GIS for pastoral mobility (breeders) in Senegal. The system is based on Scalable Vector Graphics (SVG) [1] format, adapted to mobile phone. Our proposed solution is able to keep up-to-date water points status as well to optimize the seasonal migration of breeders.

A Novel Term Weighting Scheme Model

The use of textual data has increased exponentially in recent years due to the networking infrastructure such as Facebook, Twitter, Wikipedia, Blogs, and so one. Analysis of this massive textual data can help to automatically categorize and label new content. Before classification process, term weighting scheme is the crucial step for representing the documents in a way suitable for classification algorithms. In this paper, we are conducting a survey on the term weighting schemes and we propose an efficient term weighting scheme that provide a better classification accuracy than those obtained with the famous TF-IDF, the recent IF-IGM and the others term weighting schemes in the literature.

A Parallelized Spark Based Version of mRMR

Nowadays, we are surrounded by enormous large-scale high dimensional data called big data and it is crucial to reduce the dimensionality of data for machine learning problems. That’s why feature selection plays a vital role in the process of machine learning because it aims to reduce high-dimensionality by removing irrelevant and redundant features from original data. However some characteristics of big data like data velocity, volume and data variety have brought new challenges in the field of feature selection. In fact, most of existing feature selection algorithms were designed for running on a single machine (centralized computing architecture) and do not scale well when dealing with big data. Their efficiency may significantly deteriorate to the point of becoming inapplicable. For this reason, there is an increasing need for scalable yet efficient feature selection methods. That’s why we present here a distributed and effective version of the mRMR (Max-Relevance and Min-Redundancy) algorithm to face real-world problems of data mining and evaluate the empirical performance of the proposed algorithms in selecting features in several public datasets. When we compared the efficiency and the scalability of our parallelized method in comparison with the centralized one we have found out that our parallelized method have given better results.

S-FPG: A parallel version of FP-Growth algorithm under Apache Spark™

Frequent Itemsets Mining (FIM) is an essential data mining task, with many real world applications such as market basket analysis, outlier detection, and so one. Many efficient single-node FIM algorithms such as the well-known FP-Growth algorithm have been proposed in the last two decades. However, as large-scale datasets are usually adopted nowadays, these algorithms become inefficient to mine frequent itemsets over big data. Scalable parallel algorithms hold the key to solving the problem in this context. However, the existing parallel versions of FP-Growth algorithm implemented with the disk-based MapReduce model are not efficient enough for iterative computation. In this paper, we propose an implementation of scalable parallel FP-Growth using the inmemory parallel computing framework Apache Spark™. Our experimental results demonstrated that the proposed algorithm can scale well and efficiently process large datasets.

Proposal for a monitoring system of the inbound traffic

This article studies problems related to the control of inbound traffic in a given country. Its objective is to give a visibility on the inbound traffic. With this intention, we suggest an architecture allowing controlling the incoming calls effectively. The proof of the good working order of our system was made by the use of the servers linked with activation of CDR. Then, a monitoring interface which suggests facilitating the analysis of the results. This control of the international traffic will make it possible at the State level to have additional resources, in terms of Tax on Added-value (VAT) and of taxation, to finance development projects in particular in the field of health, education or of research etc. the management of litigations between operators will be as well handled. The system will also allow to the State to be able to trace calls, if necessary, to track a criminal.

Green and Distributed Architecture for Managing Big Data of Biodiversity

The biodiversity term refers to the totality of genes, species, and ecosystems of a region or the globe. Biodiversity’s impact on the human health and the ecosystem is without a doubt very significative. Therefore, the conservation of the biodiversity is becoming an international political and scientific issue since it may have a drawback on climate and the human health or survival. For a sustainable development perspective, several ongoing studies are conducted to analyze, predict, and face biodiversity changes. Such studies require a huge volume of data collected, stored, shared, and exploited intensively by researchers through the world by using web technologies and information systems as GEOBON, LifeWacth, GBIF, MosquitoMap. These systems handle an important amount of computing and database resources that must be optimized for avoiding maintaining useless resources while reducing considerably the energy usage. Actually, the goal of such optimization that we propose in this chapter is to adapt (increase or decrease) the number of resources for dealing with data of biodiversity based on the current load (or number of requests) while ensuring good performances. The benefits of doing so are manifold. First, it fits perfectly with the objectives of green computing or green IT that suggest to define computing systems efficiently and effectively with minimal or no impact on the environment. Second, it is well suited for African developing countries that encounter frequently energy problems and that miss enough funds to maintain complex infrastructures.

Decision Making Environment on Rift Valley Fever in Ferlo (Senegal)

The Rift Valley fever (RVF), which first appeared in Kenya in 1912, is an anthropozoonosis widespread in tropical areas. In Senegal, it is particularly felt in the Ferlo area where a strong presence of ponds shared by humans, cattle and vectors is noted. As part of the studies carried out on the environmental factors which favour its start and propagation, the focus of this paper is put on the decision making process to evaluate the impacts, the interactions and to make RVF monitoring easier. The present paper proposes a model based on data mining techniques and dedicated to trade experts. This model integrates all the involved data and the results of the analyses made on the characteristics of the surrounding ponds. This approach presents some advantage in revealing the relationship between environmental factors and RVF transmission vectors for space–time epidemiology monitoring purpose.

Optimizing Pastoral Mobility Based on Mobile Geographic Information Systems (MGIS)

In the agropastoral areas of Eastern Senegal, the goal of pastoral units (PU) is to improve the daily life of breeders and farmers. PUs are characterized by a low population density, and inhabitants are scattered throughout the region. In such cases, it is difficult for breeders and people living in these areas to share information. Many pastoral breeding systems in Africa are conditioned either by the dry season or by the rainy season. This approach incorporates local knowledge covering two complementary strategies: mobility and geographic localization of the resources that should be used.