# Towards Interactive Analytics over RDF Graphs
## 2 Requirements and Related Work
### Requirements  
In decision-support systems, to extract useful information from the data of an application, it is necessary to analyze large amounts of data accumulated over time--typically  
Figure 1: An Overview of the Access Methods over RDF.  
over a period of several months. This data is usually stored in a so-called \"data warehouse\" and analysed along various dimensions and at various levels in each dimension [16]. The end users of such warehouses are mainly analysts and decision-makers, who invariably ask for data aggregations (e.g., total sales by branch).  
During last decade the development of Semantic Web data has led to the emergence of semantic warehouses; specific-domain warehouses [17; 18] or general-purpose knowledge bases (e.g., DBpedia and WikiData ([[https://www.wikidata.org](https://www.wikidata.org)]([https://www.wikidata.org](https://www.wikidata.org)))). Thus, it would be useful if the data of these warehouses could be analyzed to extract valuable information (e.g., identify patterns, predict values, discover data correlations), check the quality of semantic integration activities (e.g., for measuring the commonalities between several data sets [19; 20]) or monitor the content and the quality of them (e.g., by evaluating the completeness or the representativeness of its data etc.).  
However, the analysis of such warehouses introduces several challenges [21]. The data heterogeneity, its lack of a strict structure, its rich semantics and the possibility of incomplete data sources significantly complicates their analysis. For example, although one can reasonably expect to find information about a particular concept, they cannot always find specific information for all the instances of it (e.g., the opening hours or closing days of all branch stores). Moreover, data warehouses follow a star schema and thus, the facts can be analyzed based on certain dimensions and measures, the choice of which is made at the data warehouse design time (e.g., if \"branch\" and \"product\" have been defined as dimensions, then aggregations over them are not allowed; one cannot find \"the number of branches established in 2020, since \"branch\" is a dimension and relational data cubes do not allow aggregating over dimensions). In addition, different concepts (e.g., \"branches\", \"products\", \"people\") can be analyzed, only if each of them is modeled by a different schema and stored in a distinct data warehouse. Finally, even though such warehouses host data published along with a schema (which can facilitate the understanding of data), the structure of it tends to be complex. Please also note that the end-users, who are usually non-specialists, are unable to read the schema and formulate the queries necessary for their data analysis. Thus, it would be useful if apart from native RDF data, one could analyze and deduce further knowledge (inference) from RDF schemas, too (e.g., ask for all the relationships linking products to other entities).  
Therefore, there is a need to be able to apply analytics to any kind of RDF graph--not only to multidimensional data expressed in RDF, but also to domain-specific or general-purpose semantic data; a way that will be applicable to several RDF data sets, as well as to any data source. In general, we need an analytical tool that will allow the user to select the desired data set(s) or desired parts thereof, define the features (s)he is interested in at the query time, formulate an analytic query without having any programming background knowledge and will display the results in the form of tables, plots or any other kind of visualization for intuitive exploration.