In this section you will find EURA NOVA’s scientific publications and reports.
In July, our R&D engineer Katherine Krasnoschok was in Melbourne, Australia to attend the ACL conference. She presented her poster on topic modelling. Her paper, co-written with Salim Jouili, indicates that involving more named entities positively influences the overall quality of topics.
News-related content has been extensively studied in both topic modeling research and named entity recognition. However, expressive power of named entities and their potential for improving the quality of discovered topics has not received much attention. In this paper, we use named entities as domain-specific terms for news-centric content and present a new weighting model for Latent Dirichlet Allocation. Our experimental results indicate that involving more named entities in topic descriptors positively influences the overall quality of topics, improving their interpretability, specificity and diversity.
A few weeks ago, Sabri Skhiri and Florian Demesmaeker were in London to attend the Spark+AI summit. They came back with a lot to say about the new features of Spark and the presented use cases! In this article, they will give you their opinion about Databricks’ main announcement, the intakes of their favourite talks and training, and what they thought of the new name of the conference.
A new name
This year, Spark expanded the summit’s scope and renamed it “Spark + AI Summit”. The goal of Databricks, announced by its co-founder Ali Ghodsi, is to incorporate unified aspects of data and AI.
Florian Demesmaeker, our R&D engineer, explains: “In some of the keynote talks, the speakers talked about use cases where the job of the data engineer is strongly reduced. The data scientists can easily experiment with data, travelling back and forth in time. This means more focus on AI, rather than on the data engineering part that makes all data accessible to the data scientists”.
In line with this change of name, Databricks announced the release of a complete data science lifecycle on the cloud.
Sabri Skhiri, our R&D Director, explains “It is interesting to see that the change in the event name is actually very visible in the change of Databricks’ strategy. Their tools are now completely dedicated to stream ETL, and there is a huge focus on integrated data management”.
Databricks’ new features include Databricks Delta which creates data pipeline and provides data views and exploration features. Secondly, the Databricks Runtime ML is a ready-to-use environment providing a set of pre-loaded ML frameworks where the data scientist can play with data. Finally, the MLflow tool allows to simplify the ML models development at enterprise scale.
Our R&D Director precises: “Together, these features provide a complete and unified approach to machine learning lifecycle and pipeline automation. This looks like a very competitive SaaS offer for integrated data management, available on AWS and Azure. However, the metadata management and the security aspect is still the missing piece”.
The training day
The first day of the conference was dedicated to training workshops that include a mix of instruction and hands-on exercises to help attendants improve their Apache Spark skills.
Florian gives insights into his favourite training Tuning and Best Practices. He explains: “The aim of the training was to make programmers aware of how Spark works internally, in order to be able to write optimised applications. They presented a few situations, each one showing one relatively slow process. Then they presented a step-by-step procedure to debug the situation and to find the points that could be improved in the current situation. In summary, tips and tricks to adapt to different situations”.
The sessions at the conference covered data engineering and data science contents along with best practices for productionising AI. The talks were divided into roughly two categories: Spark programming and deployment, and applications on top of Spark (AI applications).
Florian Demesmaeker explains: “I attended 28 talks. The keynotes from Databricks were quite interesting, they presented Delta and MLflow. I also enjoyed the talks about tools to optimise the internals of Spark, these provided good technical details. Other talks were about use cases on top of Spark, it was interesting to see what challenges other companies face and how they address them”.
Sabri Skhiri adds: “The talk Learning to Rank Datasets for Search was very inspiring. Oscar Castañeda-Villagrán, a data scientist working at Xoom (a Paypal service) talked about learning to rank R data set. The idea is that we can extract metadata when the data pipeline is arriving in the lake. Going further, you can not only extract metadata but also calculate a kind of judgment relevance score that will be used for bootstrapping the learning to rank process. In this way, a user can search and retrieve the relevant R data set in the lake. A very good idea for the metadata-driven exploration”.
Early September 2018, 8 EURA NOVA engineers travelled to Berlin to attend the Flink Forward Conference, dedicated to Apache Flink users and stream processing communities. You can read their feedback here.
Our paper “Data Mining and Machine Learning Techniques supporting Time-based Separation Concept Deployment”, co-written with Eurocontrol and WaPT, has been accepted by the 37th Digital Avionics Systems Conference (DASC) in London, U.K.
The paper presents two methods to allow air traffic controllers to deliver separation minima accurately and safely, on the basis of time intervals instead of distances.
Importantly, in strong headwind conditions, the aircraft’s groundspeed during approach decreases, meaning that keeping the distance-based separation method results in lower landing rates. At a time of intensified air traffic, this situation leads to considerable delays at airports with significant costs to operators and travellers.
With the new methods presented in the paper, capacity can increase by up to 14% in strong wind conditions, and by up to 8% in moderate wind conditions.
The paper will be presented in September at DASC 2018, but you can already read the abstract below. If you wish to go deeper into the subject, do not hesitate to contact our research department at email@example.com.
The Time-Based Separation (TBS) concept consists in the definition of separation minima for aircraft on the final approach to a runway based on time intervals instead of distances, as applied in Distance-Based Separation (DBS) operations.
TBS allows for dynamic distance separation reductions in strong headwind conditions so as to preserve time spacing across all wind conditions. However, TBS application entails the use of a support tool providing separation distance indicators depending on the applicable time separation minimum, the aircraft speed profile which also depends on the headwind conditions.
This paper details two methodologies allowing a system to compute those TBS indicators so as to allow Air Traffic Controllers to accurately and safely deliver the TBS minima using a separation delivery support tool. The first approach is based on “analytical” data mining and modelling whereas the second one is based on a Machine Learning (M/L) procedure.
In the framework of the deployment of the TBS concept in Vienna airport (LOWW), those approaches are developed and tested using a database covering one year of traffic and corresponding local meteorological data.
The operation of TBS with indicators computed using either approaches leads to substantial diminution of time separations compared to a DBS strategy. However, given the large uncertainties related both to leader and follower aircraft speed profiles, the buffers could be designed only for the most frequent pairs. With the M/L approach (resp. the “analytical” approach), the capacity benefits related to the application of TBS with a separation support tool are of the order of 8% (resp. 2%) in moderate wind conditions, and up to 14% (resp. 10%) in strong wind conditions.
Our paper “Graph BI & Analytics: Current State and Future Challenges” has been accepted for publication at the 20th International Conference on Big Data Analytics and Knowledge Discovery, taking place in Regensburg, Germany.
The paper presents the state of the art of graph BI & analytics, with a focus on graph warehousing. We survey the topics of graph modelling, management, querying, and processing in graph warehouses. Then we conclude by discussing future research directions for solving complex graph problems, building native graph components and intelligent techniques to assist end-users in building and analysing the graph.
More importantly, the paper calls for the development of intelligent, efficient and industry-grade graph data warehousing systems to support the structure-driven management and analytics of data efficiently. While adopting a template that is similar to the traditional BI systems, the graph BI that is presented here extends current systems with graph analytics capabilities that deliver graph-derived insights. The paper will be presented in September at DaWak 2018, but you can already read the abstract bellow. If you wish to go deeper into the subject, don’t hesitate to contact our research department at firstname.lastname@example.org.
Abstract. In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI & analytics framework.
Due to the increasing importance and volume of highly interconnected data, such as in social or information networks, a plethora of graph mining techniques have been designed to enable the analysis of such data. In this work, we focus on the mining of associations between entity features in networks. We model each entity feature as a dimension to be analyzed. Consequently we build our approach on top of the existing graph cube framework which is an extension of the concept of the data cube to networks. Our task is particularly challenging because it requires the analysis of both the initial multidimensional network and all its subsequent aggregate forms. As soon as we deal with a big data situation it is impossible for an analyst to consider manually all the possible views of the network data. The aim of this work is to design an algorithm for the discovery of interesting patterns in large graph cubes. Thus, instead of examining all the possible aggregations manually, the proposed technique leads the analyst to the interesting associations or patterns in the multidimensional network. Furthermore, we study the application of existing algorithms from the frequent itemset mining literature on graph data and propose a mapping between the two settings.
Florian Demesmaeker, Amine Ghrab, Siegfried Nijssen, Sabri Skhiri: Discovering interesting patterns in large graph cubes. 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 2017, pp. 3322-3331.
Iterative-convergent algorithms represent an im-portant family of applications in big data analytics. These aretypically run on distributed processing frameworks deployed on a cluster of machines. On the other hand, we are witnessing the move towards data center operating systems (OS), where resources are unified by a resource manager and processing frameworks coexist with each other. In this context, different processing framework job tasks can be scheduled on the same machine and slow down a worker (straggler problem). Existing work has shown that an iteration model with relaxed consistency such as the Stale Synchronous Parallel (SSP) model, while still guaranteeing convergence, is able to cope with stragglers. In this paper we propose a model for the integration of the SSP model on a pipelined distributed processing framework. We then apply SSP on a distributed version of the Frank-Wolfe algorithm. We theoretically show its sparsity bounds and convergence under SSP. Finally, we experimentally show that the Frank-Wolfe algorithm applied on LASSO regression under SSP is able to converge faster than its BSP counterpart, especially under load conditions similar to those encountered in a data center OS.
Nam-Luc Tran, Thomas Peel, Sabri Skhiri, Distributed Frank-Wolfe under Pipelined Stale Synchronous Parallelism, proceedings of the 2015 IEEE Conference on Big Data, November 2015, Santa Clara, CA, USA.
Graphs are widespread structures providing a powerful abstraction for modeling networked data. Large and complex graphs have emerged in various domains such as social networks, bioinformatics, and chemical data. However, current warehousing frameworks are not equipped to handle efficiently the multidimensional modeling and analysis of complex graph data. In this paper, we propose a novel framework for building OLAP cubes from graph data and analyzing the graph topological properties. The framework supports the extraction and design of the candidate multidimensional spaces in property graphs. Besides property graphs, a new database model tailored for multidimensional modeling and enabling the exploration of additional candidate multidimensional spaces is introduced. We present novel techniques for OLAP aggregation of the graph, and discuss the case of dimension hierarchies in graphs.
Furthermore, the architecture and the implementation of our graph warehousing framework are presented and show the effectiveness of our approach.
Amine Ghrab, Oscar Romero, Sabri Skhiri, Alejandro Vaisman, and Esteban Zimany, A Framework for Builidng OLAP Cubes on Graphs, proceedings of the 19th East-European Conference on Advances in Databases and Information Systems, Poitiers, France, September 2015.
We are witnessing the move towards data center operating systems (OS), where resources are unified and processing frameworks coexist with each other. In this context it has been shown that an iteration model with relaxed consistency such as the Stale Synchronous Parallel (SSP) model, while still guaranteeing convergence, is able to cope with the straggler problem for converging iterative algorithms. In this poster we present a model for the integration of the SSP model on a pipelined processing framework. We then apply the SSP on a distributed version of the Frank-Wolfe algorithm and empirically show its convergence under stress situations similar to those encountered in a data center OS.
Thomas Peel, and Nam-Luc Tran, Distributed Frank-Wolfe under Pipelined Stale Synchronous Parallelism, poster at the Greed is Great ICML’15 Workshop, Lille, France, July 2015
In the context of the recent policies concerning anti-money laundering and counter terrorist financing defined by the Financial Action Task Force Recommendation 16, it is the responsibility of the financial institution to monitor the quality of the information present in wire transfers. To that end we present in this paper an approach to automate the monitoring and the validation of the information contained in interbank transfer messages. The approach is backed by a solution built around an event-driven architecture where the data is processed as a stream and transformed at each stage. This architecture is in line with the latest research in data warehouses with stream data processing. We show that our approach is suitable to the requirements and the standards in the banking industry.
Nam-Luc Tran, Analysis of Interbank Messages for the Enforcement of Financial Regulations, proceedings of Journées francophones sur les Entrepôts de Données et l’Analyse en ligne, Bruxelles, Belgium, April 2015.
Over the past years there has been significant enthusiasm for development of parallel computing on Graphics Processing Units (GPU) which have now become powerful and affordable hardware equipping data centers and research clusters. Our earlier research has explored the ways to exploit the parallel compute performance of the GPU along the CPU in the same cluster. We have proposed a model for processing distributed machine learning tasks leveraging both the CPU and the GPU equipped on the nodes. Still in this direction, we present in this paper our approach for optimizing the performance of the previously proposed framework. We then further present our approach for integrating this processing model into a more general dataflow graph processing framework by extending it with support for GPU tasks and resources. In addition we have developed a k-nearest neighbors implementation demonstrating all the features. We then present our model based on flow networks for the efficient scheduling on this heterogeneous framework.
Nam-Luc Tran, Sabri Skhiri, Arnaud Schils, and Egar Isaac Hiroshi Leon Saiki, An Approach for Maximizing Performance on Heterogeneous Clusters of CPU and GPU. EURA NOVA technical series.
Graphs are a fundamental structure for modeling many real world domains and applications. They have emerged in various fields such as social, informational and transportation networks. The hetero geneity and dynamicity of these networks pose challenges to traditional techniques for data modeling, storage and analysis of data.
Managing graph-structured data using native graph structures and algorithms is the key for its efficient analysis. Therefore, the graph should be modeled using nodes and edges, and explored using graph algorithms, such as pattern matching and k-neighborhood.
In this paper, we introduce a novel model for management of graph data. The aim of our model is to provide analysts with a set of simple, well-defined, and adaptable components to perform complex graph modeling and analysis tasks.
Amine Ghrab, Oscar Romero, Sabri Skhiri, and Esteban Zimanyi, Analytics-Aware Graph Database Modeling, EURA NOVA technical series.
In the context of processing high volumes of data, the recent developments have led to numerous models and frameworks of distributed processing running on clusters of commodity hardware. On the other side, the Graphics Processing Unit (GPU) has seen much enthusiastic development as a device for general-purpose intensive parallel computation. In this paper we propose a framework which combines both approaches and evaluates the relevance of having nodes in a distributed processing cluster that make use of GPU units for further fine-grained parallel processing. We have engineered parallel and distributed versions of two data mining problems, the naive Bayes classifier and the k-means clustering algorithm, to run on the framework and have evaluated the performance gain. Finally, we also discuss the requirements and perspectives of integrating GPUs in a distributed processing cluster, introducing a fully distributed heterogeneous computing cluster.
Nam-Luc Tran, Quentin Dugauthier, and Sabri Skhiri, A Distributed Data Mining Framework Accelerated with Graphics Processing Units, proceedings of the 2013 International Conference on Cloud Computing and Big Data (CloudCom-Asia), FuZhou, China, December 2013.
The importance of graphs as the fundamental structure underpinning many real world applications is no longer to be proved. Large graphs have emerged in various fields such as biological, social and transportation networks. The sheer volume of these networks poses challenges to traditional techniques for storage and analysis of graph data. In particular, OLAP analysis requires access to large portions of data to extract key information and to feed strategic decision making. OLAP provides multilevel, multiperspective views of the data. Most of the current techniques are optimized for centralized graph processing. A distributed approach providing horizontal scalability is required in order to handle the analysis workload.
In this paper, we focus on applying OLAP analysis on large, distributed graph data. We describe Distributed Graph Cube, our distributed framework for graph-based OLAP cubes computation and aggregation. Experimental results on large, real-world datasets demonstrate that our method significantly outperforms its centralized counterparts. We also evaluate the performance of both Hadoop and Spark for distributed cubes computations.
Benoît Denis, Amine Ghrab, and Sabri Skhiri, A Distributed Approach for Graph-Oriented Multidimensional Analysis, proceedings of the 2013 IEEE International Conference on Big Data, Santa Clara, CA, USA, October 2013.
Diverse applications including cyber security, social networks, protein networks, recommendation systems or citation networks work with inherently graph-structured data. The graphs modeling the data of these applications are large by nature so the efficient processing of them becomes challenging.
In this paper we present imGraph, a graph system that addresses the challenge of efficient processing of large graphs by using a distributed in-memory storage. We use this type of storage to obtain fast random data access which is mostly required for graph exploration. imGraph uses a native graph data model to ease the implementation of graph algorithms. On top of it, we design and implement a traversal engine that achieves high performance by efficient memory access, distribution of the workload, and optimizations on network communications. We run a set of experiments on real graph datasets of different sizes to assess the performance of imGraph in relation to other graph systems. The results show that imGraph gets better performance on traversals on large graphs than its counterparts.
Salim Jouili, and Aldemar Reynaga, imGraph: A distributed in-memory graph database, proceedings of the 2013 ASE/IEEE International Conference on Big Data, Washington D.C., USA, September 2013.
In recent years, more and more companies provide services that can not be anymore achieved efficiently using relational databases. As such, these companies are forced to use alternative database models such as XML databases, object-oriented databases, document-oriented databases and, more recently graph databases. Graph databases only exist for a few years. Although there have been some comparison attempts, they are mostly focused on certain aspects only.
In this paper, we present a distributed graph database comparison framework and the results we obtained by comparing four important players in the graph databases market: Neo4j, OrientDB, Titan and DEX.
Salim Jouili, and Valentin Vansteenberghe, An empirical comparison of graph databases, proceedings of the 2013 ASE/IEEE International Conference on Big Data, Washington D.C., USA, September 2013.
Graphs are ubiquitous data structures commonly used to represent highly connected data. Many real-world applications, such as social and biological networks, are modeled as graphs. To answer the surge for graph data management, many graph database solutions were developed. These databases are commonly classified as NoSQL graph databases, and they provide better support for graph data management than their relational counterparts. However, each of these databases implement their own operational graph data model, which differ among the products. Further, there is no commonly agreed conceptual model for graph databases.
In this paper, we introduce a novel conceptual model for graph databases. The aim of our model is to provide analysts with a set of simple, welldefined, and adaptable conceptual components to perform rich analysis tasks. These components take into account the evolving aspect of the graph. Our model is analytics-oriented, flexible and incremental, enabling analysis over evolving graph data. The proposed model provides a typing mechanism for the underlying graph, and formally defines the minimal set of data structures and operators needed to analyze the graph.
Amine Ghrab, Sabri Skhiri, Salim Jouili, and Esteban Zimányi, An Analytics-Aware Conceptual Model For Evolving Graphs, proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery – DaWak 2013, Prague, Czech Republic, August 2013.
Migrating services to the cloud brings all the benefits of elasticity, scalability and cost-cutting. However, migrating services among different cloud infrastructures or outside of the cloud is not an obvious task. In addition, distributing services among multiple cloud providers, or on a hybrid installation requires a custom implementation effort that must be repeated at each infrastructure change. This situation raises the lock-in problem and discourages cloud adoption. Cloud computing open standards were designed to face this situation and to bring interoperability and portability to cloud environments. However, they target isolated resources, and do not take into account the notion of complete services. In this paper, we introduce an extension to OCCI, a cloud computing open standard, in order to support complete service definition and management automation. We support this proposal with an open-source framework for service management through compliant cloud infrastructures.
Amine Ghrab, Sabri Skhiri, Hervé Kœner, and Guy Ledu, Towards A Standards-Based Cloud Service Manager, proceedings of the 3rd International Conference on Cloud Computing and Services Science, CLOSER 2013, Aachen, Germany, May 2013.
The development in computational processing has driven towards distributed processing frameworks performing tasks in parallel setups. The recent advances in Cloud Computing have widely contributed to this tendency. The MapReduce model proposed by Google is one of the most popular despite the well-known limitations inherent to the model which constrain the types of jobs that can be expressed. On the other hand models based on Data Flow Graphs (DFG) for the processing and the definition of the jobs, while more complex to express, are more general and suitable for a wider range of tasks, including iterative and pipelined tasks. In this paper we present AROM, a framework for large scale distributed processing based on DFG to express the jobs and which uses paradigms from functional programming to define the operators. The former leads to more natural handling of pipelined tasks while the latter enhances genericity and reusability of the operators, as shown by our tests on a parallel and pipelined job performing the calculation of PageRank.
Nam-Luc Tran, Sabri Skhiri, Esteban Zimányi, and Arthur Lesuisse. AROM: Processing Big Data With Data Flow Graphs and Functional Programming, proceedings of the 4th IEEE International Conference on Cloud Computing Technology and Science, IEEE CloudCom 2012. IEEE Computer Society Press, Taipei, Taiwan, December 2012.
With the recent growth of the graph-based data, the large graph processing becomes more and more important. In order to explore and to extract knowledge from such data, graph mining methods, like community detection, is a necessity. The legacy graph processing tools mainly rely on single machine computational capacity, which cannot process large graphs with billions of nodes. Therefore, the main challenge of new tools and frameworks lies on the development of new paradigms that are scalable, efficient and flexible. In this paper, we review the new paradigms of large graph processing and their applications to graph mining domains using the distributed and shared nothing approach used for large data by internet players.
Sabri Skhiri, and Salim Jouili, Large Graph Mining: Recent Developments, Challenges and Potential Solutions, presentation during the European Business Intelligence Summer School (eBISS 2012) organized by the Université Libre de Bruxelles and the Ecole Centrale Paris, Brussels, Belgium, July 2012.
The use of trust in recommender systems has been shown to improve the accuracy of rating predictions, especially in the case where a user’s rating significantly differs from the average. Different techniques have been used to incorporate trust into recommender systems, each showing encouraging results. However, the lack of trust information available in public datasets has limited the empirical analysis of these techniques and trust-based recommendation in general, with most analysis limited a single dataset.
In this paper, we provide a more complete empirical analysis of trust-based recommendation. By making use of a method that infers trust between users in a social graph, we are able to apply trust-based recommendation techniques to three separate datasets. From this, we measure the overall accuracy of each technique in terms of the Mean Absolute Error (MAE), the Root Mean Square Error (RMSE) as well as measuring the prediction coverage of each technique. We thus provide a comparison and analysis of each technique on all three datasets.
Daire O’Doherty, Salim Jouili, and Peter Van Roy, Trust-based recommendation: an empirical analysis, proceedings of the 6th ACM SIGKDD Workshop on Social Network Mining and Analysis SNA-KDD, Beijing, China, ACM, July 2012.
The emergence of trust as a key link between users in social networks has provided an effective means of enhancing the personalization of online user content. However, the availability of such trust information remains a challenge to the algorithms that use it, as the majority of social networks do not provide a means of explicit trust feedback. This paper presents an investigation into the inference of trust relations between actor pairs of a social network, based solely on the structural information of the bipartite graph typical of most on-line social networks. Using intuition inspired from real life observations, we argue that the popularity of an item in a social graph is inversely related to the level of trust between actor pairs who have rated it. From an existing bipartite social graph, this method computes a new social graph, linking actors together by means of symmetric weighted trust relations. Through a set of experiments performed on a real social network dataset, our method produces statistically significant results, showing strong trust prediction accuracy.
Daire O’Doherty, Salim Jouili, and Peter Van Roy, Towards trust inference in bipartite social networks, proceedings of the 2d ACM SIGMOD Workshop on Databases and Social Networks, DBSocial 2012, Scottsdale, USA, ACM, June 2012.
In this paper, we introduce a novel method for graph indexing. We propose a hypergraph-based model for graph data sets by allowing cluster overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database. We consider clusters as hyperedges in our hypergraph model and we index the graph set by the hyperedge centroids. This model is interesting to traverse the data set and efficient to retrieve graphs.
Salim Jouili, and Salvatore Tabbone, Hypergraph-based image retrieval for graph-based representation. Journal of the Pattern Recognition Society, April 2012. © 2012 Elsevier Ltd.
With the emergence of cloud computing, on-demand resources usage is made possible. This allows applications to elastically scale out according to the load. One design pattern that suits this paradigm is the event-driven architecture (EDA) in which messages are sent asynchronously between distributed application instances using message queues. However, existing message queues are only able to scale for a certain number of clients and are not able to scale out elastically. We present the Elastic Queue Service (EQS), an elastic message queue architecture and a scaling algorithm which can be adapted to any message queue in order to make it scale elastically. EQS architecture is layered onto multiple distributed components and its management components can be integrated with the cloud infrastructure management. We have implemented a prototype of EQS and deployed it on a cloud infrastructure. A series of load testings have validated our elastic scaling algorithm and show that EQS is able to scale out in order to adapt to an applied load. We then discuss about the elastic scaling of the management layers of EQS and their possible integration with the cloud infrastructure management.
Nam-Luc Tran, Sabri Skhiri, and Esteban Zimány, EQS: An Elastic and Scalable Message Queue for the Cloud, proceedings of the 3rd International IEEE conference on Cloud computing technology and science (IEEE CloudCom 2011), Athens, Greece, November 2011.
SWIFT is a member-owned cooperative providing secure messaging capabilities to the financial services industry. One critical mission of SWIFT is the standardization of the message flows between the industry players. The model-driven approach naturally came as a solution to the management of these message definitions. However, one of the most important challenges that SWIFT has been facing is the global governance of the message repository and the management of each element. Nowadays modeling tools exist but none of them enables the management of the complete life-cycle of the message models. In this paper wepresent the challenges that SWIFT had to face in the development of a dedicated platform.
Sabri Skhiri, Marc Delbaere, Yves Bontemps, Grégoire de Hemptinne, and Nam-Luc Tran, Governance issues on heavy models in an industrial context. Advances in Conceptual Modeling. Recent Developments and New Directions ER 2011, Brussels, Belgium, November 2011.
The rise of the Internet and the multiplication of data sources have multiplied the number of “Bigdata” storage problems. These data sets are not only very big but also tend to grow very fast, sometimes in a short period. Distributed databases that work well for such data sets need to be not only scalable but also elastic to ensure a fast response to growth in demand of computing power or storage. The goal of this article is to present measurement results that characterize the elasticity of three databases. We have chosen Cassandra, HBase, and mongoDB as three representative popular horizontally scalable NoSQL databases that are in production use. We have made measurements under realistic loads up to 48 nodes, using the Wikipedia database to create our dataset and using the Rackspace cloud infrastructure. We define precisely our methodology and we introduce a new dimensionless measure for elasticity to allow uniform comparisons of different databases at different scales. Our results show clearly that the technical choices taken by the databases have a strong impact on the way they react when new nodes are added to the clusters.
Thibault Dory, Boris Mejías, Peter Van Roy, and Nam-Luc Tran, Measuring Elasticity for Cloud Databases, proceedings of the Cloud Computing 2011 (Second International Conference on Cloud Computing, GRIDs, and Virtualization), Rome, Italy, September 2011.