Mohammed El Malki

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.

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.

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.

Document-oriented Models for Data Warehouses

There is an increasing interest in NoSQL (Not Only SQL) systems developed in the area of Big Data as candidates for implementing multidimensional data warehouses due to the capabilities of data structuration/storage they offer. In this paper, we study implementation and modeling issues for data warehousing with document-oriented systems, a class of NoSQL systems. We study four different mappings of the multidimensional conceptual model to document data models. We focus on formalization and cross-model comparison. Experiments go through important features of data warehouses including data loading, OLAP cuboid computation and querying. Document-oriented systems are also compared to relational systems.

Document-oriented data warehouses: Models and extended cuboids, extended cuboids in oriented document

Within the Big Data trend, there is an increasing interest in Not-only-SQL systems (NoSQL). These systems are promising candidates for implementing data warehouses particularly due to the data structuration/storage possibilities they offer. In this paper, we investigate data warehouse instantiation using a document-oriented system (a special class of NoSQL systems). On the one hand, we analyze several issues including modeling, querying, loading data and OLAP cuboids. We compare document-oriented models (with and without normalization) to analogous relational database models. On the other hand, we suggest improvements in order to benefit from document-oriented features. We focus particularly on extended versions of OLAP cuboids that exploit nesting and arrays. They are shown to work better on workloads with drill-down queries. Research in this direction is new. As existing work focuses on feasibility issues, document-specific implementation features, modeling and cross-model comparison.

Querying Heterogeneous Data in Graph-Oriented NoSQL Systems.

NoSQL systems are based on a “schemaless” approach that not does require schema specification before writing data, allowing a wide variety of representations. This flexibility leads to a large volume of heterogeneous data, which makes their querying more complex for users who are compelled to know the different forms (i.e. the different schemas) of these data. This paper addresses this issue focusing on simplifying the heterogeneous data querying. Our work specially concerns graph-oriented NoSQL systems.

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.