Abstract-This paper describes the convergence of some of the most influential technologies in the last few years, namely data warehousing (DW), On-Line Analytical Processing (OLAP), and the Semantic Web (SW). OLAP is used by enterprises to derive important business-critical knowledge from data inside the company. However, the most interesting OLAP queries can no longer be answered on internal data alone, external data must also be discovered (most often on the Web), acquired, integrated, and (analytically) queried, resulting in a new type of OLAP, exploratory OLAP. When using external data, an important issue is knowing the precise semantics of the data. Here, SW technologies come to the rescue, as they allow semantics (ranging from very simple to very complex) to be specified for web-available resources. SW technologies do not only support capturing the "passive" semantics, but also support active inference and reasoning on the data. The paper first presents a characterization of DW/OLAP environments, followed by an introduction to the relevant SW foundation concepts. Then, it describes the relationship of multidimensional (MD) models and SW technologies, including the relationship between MD models and SW formalisms. Next, the paper goes on to survey the use of SW technologies for data modeling and data provisioning, including semantic data annotation and semantic-aware extract, transform, and load (ETL) processes. Finally, all the findings are discussed and a number of directions for future research are outlined, including SW support for intelligent MD querying, using SW technologies for providing context to data warehouses, and scalability issues.

Some research efforts have proposed the automation of the data warehouse design in order to free this task of being (completely) performed by an expert and facilitate the whole process. Most advanced approaches exclusively work over relational sources and perform a detailed analysis of the data sources to identify the multidimensional concepts in a reengineering process. Starting from a logical schema, however, may present some inconveniences. A logical schema is tied to the design decisions made when devising the system and these decisions either made to fulfill the system requirements (for instance, improve query answering, avoid insertion / deletion anomalies, preserve features inherited from legacy systems, etc.) or naively made by nonexpert users, have a big impact on the quality of the multidimensional schemas got by current automatable approaches.In this paper, we introduce our approach for automatically deriving the multidimensional schema from a domain ontology. Our goals are mainly two: i) we want to improve the quality of the output got (by working over a conceptual formalization of the domain instead of a logical one) and ii) we want to automate the process. This second goal is the main reason for choosing ontologies instead of other conceptual formalizations, as ontology languages provide reasoning services that will facilitate the automation of our task.

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Big Data architectures allow to flexibly store and process heterogeneous data, from multiple sources, in their original format. The structure of those data, commonly supplied by means of REST APIs, is continuously evolving. Thus data analysts need to adapt their analytical processes after each API release. This gets more challenging when performing an integrated or historical analysis.To cope with such complexity, in this paper, we present the Big Data Integration ontology, the core construct to govern the data integration process under schema evolution by systematically annotating it with information regarding the schema of the sources. We present a query rewriting algorithm that, using the annotated ontology, converts queries posed over the ontology to queries over the sources. To cope with syntactic evolution in the sources, we present an algorithm that semi-automatically adapts the ontology upon new releases. This guarantees ontology-mediated queries to correctly retrieve data from the most recent schema version as well as correctness in historical queries. A functional and performance evaluation on real-world APIs is performed to validate our approach.

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