Ronan Tournier

Algebraic and Graphic Languages for OLAP Manipulations

This article deals with multidimensional analyses. Analyzed data are designed according to a conceptual model as a constellation of facts and dimensions, which are composed of multi-hierarchies. This model supports a query algebra defining a minimal core of operators, which produce multidimensional tables for displaying analyzed data. This user-oriented algebra supports complex analyses through advanced operators and binary operators. A graphical language, based on this algebra, is also provided to ease the specification of multidimensional queries. These graphical manipulations are expressed from a constellation schema and they produce multidimensional tables.

Finding an application-appropriate model for XML data warehouses

Decision support systems help the decision making process with the use of OLAP (On-Line Analytical Processing) and data warehouses. These systems allow the analysis of corporate data. As OLAP and data warehousing evolve, more and more complex data is being used. XML (Extensible Markup Language) is a flexible text format allowing the interchange and the representation of complex data. Finding an appropriate model for an XML data warehouse tends to become complicated as more and more solutions appear. Hence, in this survey paper we present an overview of the different proposals that use XML within data warehousing technology. These proposals range from using XML data sources for regular warehouses to those using full XML warehousing solutions. Some researches merely focus on document storage facilities while others present adaptations of XML technology for OLAP. Even though there are a growing number of researches on the subject, many issues still remain unsolved.

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.

Graphical querying of multidimensional databases

This paper provides an answer-oriented multidimensional analysis environment. The approach is based on a conceptual point of view. We define a conceptual model that represents data through a constellation of facts and dimensions and we present a query algebra handling multidimensional data as well as multidimensional tables. Based on these two propositions, in order to ease the specification of multidimensional analysis queries, we define a formal graphical language implemented within a prototype: GraphicOLAPSQL.

Top_Keyword: An Aggregation Function for Textual Document OLAP

For more than a decade, researches on OLAP and multidimensional databases have generated methodologies, tools and resource management systems for the analysis of numeric data. With the growing availability of digital documents, there is a need for incorporating text-rich documents within multidimensional databases as well as an adapted framework for their analysis. This paper presents a new aggregation function that aggregates textual data in an OLAP environment. The Top_Keyword function ( Top_Kw for short) represents a set of documents by their most significant terms using a weighing function from information retrieval: tf.idf.

A conceptual model for multidimensional analysis of documents

Data warehousing and OLAP are mainly used for the analysis of transactional data. Nowadays, with the evolution of Internet, and the development of semi-structured data exchange format (such as XML), it is possible to consider entire fragments of data such as documents as analysis sources. As a consequence, an adapted multidimensional analysis framework needs to be provided. In this paper, we introduce an OLAP multidimensional conceptual model without facts. This model is based on the unique concept of dimensions and is adapted for multidimensional document analysis. We also provide a set of manipulation operations.

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.

Designing and Implementing OLAP Systems from XML Documents

There has been a lot of research on OLAP (On-Line Analytical Processing) systems during the past decade. These systems allow decision makers to improve their decisions. Despite numerous multidimensional conceptual models, none tackle the problem of analysing data extracted from text-rich XML documents. These documents represent a lot of unavailable information for actual OLAP systems. Moreover, the implementation of such a system requires an adapted design process. In this paper, we present an adapted “galaxy” model for the analysis of text-rich XML documents. This model is associated to an adapted design process and a tool that takes in charge all automated tasks of the process.

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.