Implementing Multidimensional Data Warehouses into NoSQL

International audienceNot only SQL (NoSQL) databases are becoming increasingly popular and have some interesting strengths such as scalability and flexibility. In this paper, we investigate on the use of NoSQL systems for implementing OLAP (On-Line Analytical Processing) systems. More precisely, we are interested in instantiating OLAP systems (from the conceptual level to the logical level) and instantiating an aggregation lattice (optimization). We define a set of rules to map star schemas into two NoSQL models: columnoriented and document-oriented. The experimental part is carried out using the reference benchmark TPC. Our experiments show that our rules can effectively instantiate such systems (star schema and lattice). We also analyze differences between the two NoSQL systems considered. In our experiments, HBase (columnoriented) happens to be faster than MongoDB (document-oriented) in terms of loading time

Entrepôts de données multidimensionnelles NoSQL

International audienceLes données des systèmes d'analyse en ligne (OLAP, On-Line Analytical Processing) sont traditionnellement gérées par des bases de données relationnelles. Malheureusement, il devient difficile de gérer des mégadonnées (de gros volumes de données, « Big Data »). Dans un tel contexte, comme alternative, les environnements « Not-Only SQL » (NoSQL) peuvent fournir un passage à l'échelle tout en gardant une certaine flexibilité pour un système OLAP. Nous définissons ainsi des règles pour convertir un schéma en étoile, ainsi que son optimisation, le treillis d'agrégats pré-calculés, en deux modèles logiques NoSQL : orienté-colonnes ou orienté-documents. En utilisant ces règles, nous implémentons et analysons deux systèmes décisionnels, un par modèle, avec MongoDB et HBase. Nous comparons ces derniers sur les phases de chargement des données (générées avec le benchmark TPC-DS), de calcul d'un treillis et d'interrogation

Implementation of multidimensional databases in column-oriented NoSQL systems

International audienceNoSQL (Not Only SQL) systems are becoming popular due to known advantages such as horizontal scalability and elasticity. In this paper, we study the implementation of multidimensional data warehouses with columnoriented NoSQL systems. We define mapping rules that transform the conceptual multidimensional data model to logical column-oriented models. We consider three different logical models and we use them to instantiate data warehouses. We focus on data loading, model-to-model conversion and OLAP cuboid computation

Benchmark for OLAP on NoSQL Technologies

International audienceThe plethora of data warehouse solutions has created a need comparing these solutions using experimental benchmarks. Existing benchmarks rely mostly on the relational data model and do not take into account other models. In this paper, we propose an extension to a popular benchmark (the Star Schema Benchmark or SSB) that considers non-relational NoSQL models. To avoid data post-processing required for using this data with NoSQL systems, the data is generated in different formats. To exploit at best horizontal scaling, data can be produced in a distributed file system, hence removing disk or partition sizes as limit for the generated dataset. Experimental work proves improved performance of our new benchmark

Document-oriented data warehouses : complex hierarchies and summarizability

There is an increasing interest in implementing data warehouses with NoSQL document-oriented systems. In the ideal case, data can be analysed on different dimensions. These dimensions follow strict hierarchies that we can use to roll-up and drill-down on analysis axes. In this paper, we deal with non-strict and non-covering hierarchies, common issues in data warehousing a.k.a. summarizability issues. We show how to model these hierarchies in document-oriented systems and we propose an algorithm that can deal with summarizability issues. The new approach is tested and compared to existing approaches

Energy and Processing Time Efficiency for an Optimal Offloading in a Mobile Edge Computing Node

This article describes a processing time, energy and computing resources optimization in a Mobile Edge Computing (MEC). We consider a mobile user MEC system, where a smart mobile device (SMD) demand computation offloading to a MEC server. For that, we consider a SMD contains a set of heavy tasks that can be offloadable. The formulated optimization problem takes into account both the dedicated energy capacity and the processing times. We proposed a heuristic solution schema. To evaluate our solution, we realized a range of simulation experiments. The results obtained in terms of treatment time and energy consumption are very

Time and resource constrained offloading with multi-task in a mobile edge computing node

In recent years, the importance of the mobile edge computing (MEC) paradigm along with the 5G, the Internet of Things (IoT) and virtualization of network functions is well noticed. Besides, the implementation of computation-intensive applications at the mobile device level is limited by battery capacity, processing capabalities and execution time. To increase the batteries life and improve the quality of experience for computationally intensive and latency-sensitive applications, offloading some parts of these applications to the MEC is proposed. This paper presents a solution for a hard decision problem that jointly optimizes the processing time and computing resources in a mobile edge-computing node. Hence, we consider a mobile device with an offloadable list of heavy tasks and we jointly optimize the offloading decisions and the allocation of IT resources to reduce the latency of tasks’ processing. Thus, we developped a heuristic solution based on the simulated annealing algorithm, which can improve the offloading rate and reduce the total task latency while meeting short decision time. We performed a series of experiments to show its efficiency. Finally, the obtained results in terms of full-time treatrement are very encouraging. In addition, our solution makes offloading decisions within acceptable and achievable deadlines

Offloading Decisions in a Mobile Edge Computing Node with Time and Energy Constraints

This article describes a simulated annealing based offloading decision with processing time, energy consumption and resource constraints in a Mobile Edge Computing Node. Edge computing mostly deals with mobile devices subject to constraints. Especially because of their limited processing capacity and the availability of their battery, these devices have to offload some of their heavy tasks, which require a lot of calculations. We consider a single mobile device with a list of heavy tasks that can be offloadable. The formulated optimization problem takes into account both the dedicated energy capacity and the total execution time. We proposed a heuristic solution schema. To evaluate our solution, we performed a set of simulation experiments. The results obtained in terms of processing time and energy consumption are very encouraging

Implantation Not Only SQL des bases de données multidimensionnelles

International audienceLes systèmes NoSQL (Not Only SQL) se développent notamment grâce à leur capacité à gérer facilement de grands volumes de données, et leur flexibilité en terme de type de données. Dans cet article, nous étudions l'implantation d'un entrepôt de données multidimensionnelles avec un système NoSQL orienté documents. Nous proposons des règles de transformation qui permettent de passer d'un modèle conceptuel multidimensionnel vers un modèle logique NoSQL orienté documents. Nous proposons trois types de transformation pour implanter les entrepôts de données multidimensionnelles. Nous expérimentons ces trois approches avec le système MongoDB, et étudions le chargement des données, les processus de transformation d'un type d'implantation à un autre ainsi que le pré-calcul d'agrégats inhérents aux entrepôts de données multidimensionnelles

Efficient Multi-task offloading with energy and computational resources optimization in a mobile edge computing node

With the fifth-generation (5G) networks, Mobile edge computing (MEC) is a promising paradigm to provide near computing and storage capabilities to smart mobile devices. In addition, mobile devices are most of the time battery dependent and energy constrained while they are characterized by their limited processing and storage capacities. Accordingly, these devices must offload a part of their heavy tasks that require a lot of computation and are energy consuming. This choice remains the only option in some circumstances, especially when the battery drains off. Besides, the local CPU frequency allocated to processing has a huge impact on devices energy consumption. Additionally, when mobile devices handle many tasks, the decision of the part to offload becomes critical. Actually, we must consider the wireless network state, the available processing resources at both sides, and particularly the local available battery power. In this paper, we consider a single mobile device that is energy constrained and that retains a list of heavy offloadable tasks that are delay constrained. Therefore, we formulated the corresponding optimization problem, and proposed a Simulated Annealing based heuristic solution scheme. In order to evaluate our solution, we carried out a set of simulation experiments. Finally, the obtained results in terms of energy are very encouraging. Moreover, our solution performs the offloading decisions within an acceptable and feasible timeframes