Max Chevalier

Implementing Multidimensional Data Warehouses into NoSQL

Not 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: column-oriented 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 (column-oriented) happens to be faster than MongoDB (document-oriented) in terms of loading time.

An annotation management system for multidimensional databases

This paper deals with an annotation-based decisional system. The decisional system we present is based on multidimensional databases, which are composed of facts and dimensions. The expertise of decision-makers is modelled, shared and stored through annotations. These annotations allow decisionmakers to make an active reading and to collaborate with other decisionmakers about a common analysis project.

Benchmark for OLAP on NoSQL technologies comparing NoSQL multidimensional data warehousing solutions

The 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.

Implementation of Multidimensional Databases in Column-Oriented NoSQL Systems

NoSQL (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.

Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling

Professionals are continually presented with numerous information sources creating the need to determine their relevance within the huge amount of available information. Collaborative and Social Information Retrieval and Access: Techniques for Improved User Modeling presents current state-of-the-art developments including case studies, challenges, and trends. Covering topics such as recommender systems, user profiles, and collaborative filtering, this book informs and educates academicians, researchers, and field practitioners on the latest advancements in information retrieval.

Personalized information access through flexible and interoperable profiles

When searching information, any user has to face huge cognitive efforts to obtain accurate and relevant results. The search task includes a set of complementary sub-tasks in which the user needs to be necessarily involved. But, the real place of the users is not obvious without an effective knowledge of their context, environment, and so on. So we assume that a better knowledge of the user and of available information should make it possible to implement techniques aimed at adapting the retrieved information contents, as well as the search process itself. This personalization mainly relies on the definition of profiles. Since applications principally manage specific user/information profiles (structure and content), we propose in this paper a generic and a flexible profile model. This latter facilitates the construction and the interoperability of various profiles coming from different applications and/or having different structure/content. This paper presents the way the different resources (user, information...) can be modeled within the information search process and its related tasks. Then, we discuss the usefulness of profiles in such processes/tasks. Finally we present the generic and the flexible profile model we propose.

An original usage-based metrics for building a unified view of corporate documents

Nowadays, organizational members manage the huge amount of digital documents that they exploit at work. To do that, they organize documents into individual hierarchies. Actually, these documents are really parts of a companys capital as they reflect past experiences, present competences and impending expertise. Unfortunately, even if corporate documents represent high value-added material, they still mostly remain unknown from the organization as a whole. That is the reason why this paper proposes to build a unified view of corporate documents. Our approach is complementary to current content-based ones because it relies on an original metrics related to documents usage within an organization.

How Can We Implement a Multidimensional Data Warehouse Using NoSQL

The traditional OLAP (On-Line Analytical Processing) systems store data in relational databases. Unfortunately, it is difficult to manage big data volumes with such systems. As an alternative, NoSQL systems (Not-only SQL) provide scalability and flexibility for an OLAP system. We define a set of rules to map star schemas and its optimization structure, a precomputed aggregate lattice, into two logical NoSQL models: column-oriented and document-oriented. Using these rules we analyse and implement two decision support systems, one for each model (using MongoDB and HBase).We compare both systems during the phases of data (generated using the TPC-DS benchmark) loading, lattice generation and querying.

Implementation of Multidimensional Databases with Document-Oriented NoSQL

NoSQL (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 data warehouses with document-oriented NoSQL systems. We propose mapping rules that transform the multidimensional data model to logical document-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.

Information Retrieval and Folksonomies together for Recommender Systems

The powerful and democratic activity of social tagging allows the wide set of Web users to add free annotations on resources. Tags express user interests, preferences and needs, but also automatically generate folksonomies. They can be considered as gold mine, especially for e-commerce applications, in order to provide effective recommendations. Thus, several recommender systems exploit folksonomies in this context. Folksonomies have also been involved in many information retrieval approaches. In considering that information retrieval and recommender systems are siblings, we notice that few works deal with the integration of their approaches, concepts and techniques to improve recommendation. This paper is a first attempt in this direction. We propose a trail through recommender systems, social Web, e-commerce and social commerce, tags and information retrieval: an overview on the methodologies, and a survey on folksonomy-based information retrieval from recommender systems point of view, delineating a set of open and new perspectives.