Nadia Kabachi

Columnar NoSQL CUBE: Agregation operator for columnar NoSQL data warehouse

The emergence of large volumes of data imposed by the major players of the web requires new management models and new data storage architectures and treatment able to find information quickly in a large volume of data. The column-oriented NoSQL (Not Only SQL) database provide for big data the most suitable model to the data warehouse and the structure of multidimensional data in OLAP cube form. However, in the absence of OLAP cube computation operators, we propose in this paper, a new aggregation operator called CN-CUBE (Columnar NoSQL CUBE), which allows data cubes to be computed from data warehouses stored in column-oriented NoSQL database management system. We implemented the CNCUBE operator using the SQL Phoenix interface of HBase DBMS and conducted experiments on a public data warehouse in a distributed environment produced using the Hadoop platform. Thus we have shown that our CN-CUBE operator has OLAP cubes computation times very suitable for NoSQL warehouses.

Community Extraction Based on Topic-Driven-Model for Clustering Users Tweets

Twitter has become a significant means by which people communicate with the world and describe their current activities, opinions and status in short text snippets. Tweets can be analyzed automatically in order to derive much potential information such as, interesting topics, social influence, user’s communities, etc. Community extraction within social networks has been a focus of recent work in several areas. Different from the most community discovery methods focused on the relations between users, we aim to derive user’s communities based on common topics from user’s tweets. For instance, if two users always talk about politic in their tweets, thus they can be grouped in the same community which is related to politic topic. To achieve this goal, we propose a new approach called CETD: Community Extraction based on Topic-Driven-Model. This approach combines our proposed model used to detect topics of the user’s tweets based on a semantic taxonomy together with a community extraction method based on the hierarchical clustering technique. Our experimentation on the proposed approach shows the relevant of the users communities extracted based on their common topics and domains.

Towards an OLAP Environment for Column-Oriented Data Warehouses

Column-oriented database systems offer decision-makers the most appropriate model for data warehouse storage. However, in the absence of on-line analytical operators, the only, very costly, way of constructing OLAP cubes involves using the UNION operator for group by queries in order to obtain all the Group By required to compute the OLAP cube. To solve this problem, in this article we propose a new aggregation operator, called C-CUBE (Columnar-CUBE), which allows data cubes to be computed using column-oriented data warehouses. We implemented the C-CUBE operator within the column-oriented DBMS, MonetDB and conducted experiments on the benchmark SSBM (Star Schema Benchmark). Thus we have shown that C-CUBE has OLAP cubes computation times reduced by up to 60% compared with the SQL Server CUBE operator in a 1TB warehouse.

Optimizing OLAP Cubes Construction by Improving Data Placement on Multi-nodes Clusters

The increasing volumes of relational data let us find an alternative to cope with them. The Hadoop framework - which is an open source project based on the MapReduce paradigm - is a popular choice for big data analytics. However, the performance gained from Hadoops features is currently limited by its default block placement policy, which does not take any data characteristics into account. Indeed, the efficiency of many operations can be improved by a careful data placement, including indexing, grouping, aggregation and joins. In this paper we propose a data warehouse placement policy to improve query gain performances on multi nodes clusters, especially Hadoop clusters. We investigate the performance gain for OLAP cube construction query with and without data organization. And this, by varying the number of nodes and data warehouse size. It has been found that, the proposed data placement policy has lowered global execution time for building OLAP data cubes up to 20 percent compared to default data placement.

A multiagent system to aggregate preferences

In this paper we build a multi-agent system to aggregate preferences of decision-makers. In our system, a decision-maker is a cognitive agent who debates with other agents in view to obtain a consensus about the preference between two objects. Our approach is based on the simulation of debates where preferences of these agents can changed according to influences or coalitions between agents. Our multiagent system gives us an iterative process to aggregate preferences and displays some informations about the processes of negotiation between decision-makers.

Intelligent multi agent system based solution for data protection in the cloud

Cloud computing services have been adopted to provide the necessary tools and resources to face the emergence of data that needs to be stored and processed properly. However, these promising services, raise the issue of security and reliability in terms of data confidentiality, control and loss of intellectual property. In this work, we exploit the characteristics of multi agent systems to deliver an optimal and secure solution for data storage and exploration in the Cloud. Our solution is based on an encryption process before storage, while an intelligent multi agent system was designed and simulated to optimize the exploration of the data in a Cloud environment. Our architecture aims to use adaptive agents able to predict alerts, make decisions and block any intrusion.

Intentional Data Placement Optimization for Distributed Data Warehouses

Parallel computing is a fundamental technique in the management of large quantities of data as it leverages on the concurrent utilization of multiple computing resources. One of the technologies that made big data analytics popular and accessible to enterprises of all sizes is MapReduce (and its open-source Hadoop implementation). With the ability to automatically parallelize the application on a cluster of commodity hardware, MapReduce allows enterprises to analyze terabytes and petabytes of data more conveniently than ever. However, the performance gained from Hadoops features is currently limited by its default block placement policy, which does not take any data characteristics into account. Indeed, the efficiency of many operations can be improved by a careful data placement, including indexing, grouping, aggregation and joins. In this paper, we present a MapReduce data blocks allocation approach to improve MapReduce jobs execution and query performances on multi-nodes clusters, especially Hadoop clusters. Based on k-means clustering method that allows to master the number of clusters through its k parameter, we study the influence of number of clusters on queries execution instead of queries performances with and without data organization. For this, we used well-known, large-scale data analysis benchmark: TPC-H. Our experiments suggest that defining a good data placement on a cluster during the implementation of a data warehouse increase significantly the OLAP cube construction and querying performances.

Contributions from ADBIS 2018 Workshops

The ADBIS conferences provide an international forum for the presentation of research on database theory, development of advanced DBMS technologies, and their applications. The 22nd edition of ADBIS, held on September 2–5, 2018, in Budapest, Hungary, includes six thematic workshops collecting contributions from various domains representing new trends in the broad research areas of databases and information systems.

Alteration Agent for Cloud Data Security

In the big data era, the cloud computing services have been adopted to face the emergence of data that needs to be stored and processed properly. However, these services need to provide safety mechanisms to insure its secure adoption. Thus, several solutions have been proposed including the use of secure architectures by customers. In that context, an architecture based on multi-agent systems has been proposed which aims to secure both storage and exploration of data hosted in the Cloud. In this paper, we present a brief synthesis of data security methods. We then focus on the multi-agent system architecture. Finally, we propose our solution considering the design and implementation in Java of an alteration agent which will ensure the secure storage of data stored in the Cloud. We finally present the test results of this agent on real datasets.

A Data Mining-based Blocks Placement Optimization for Distributed Data Warehouses

The amount of data that is captured and generated by modern computing devices has augmented exponentially over the last years. The Hadoop framework - an open source project based on the MapReduce paradigm - is a popular choice for processing these large volumes of data or big data. However, the performance gained from Hadoops features is currently limited by its default block placement policy, which does not take any data characteristics into account. This is particularly true for relational data bases and data warehouses. Indeed, the efficiency of many operations can be improved by a careful data placement, including indexing, grouping, aggregation and joins. In this paper we propose a data warehouse distribution strategy to improve query gain performances on multi-nodes clusters, especially Hadoop clusters. Based on k-means clustering method that allows to master the number of clusters through its k parameter, we investigate the performance gain for OLAP cube construction with and without data organization. And this, by varying the number of clusters and data warehouse size. Our experiments suggest that a good data placement on a cluster during the implementation of the data warehouse increase significantly the OLAP cube construction and querying performances.