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Abstract
ABSTRACTBig data have analytical potential that was hard to realize with available technologies. After new storage paradigms intended for big data such as NoSQL databases emerged, traditional systems got pushed out of the focus. The current research is focused on their reconciliation on different levels or paradigm replacement. Similarly, the emergence of NoSQL databases has started to push traditional (relational) data warehouses out of the research and even practical focus. Data warehousing is known for the strict modelling process, capturing the essence of the business processes. For that reason, a mere integration to bridge the NoSQL gap is not enough. It is necessary to deal with this issue on a higher abstraction level during the modelling phase. NoSQL databases generally lack clear, unambiguous schema, making the comprehension of their contents difficult and their integration and analysis harder. This motivated involving semantic web technologies to enrich NoSQL database contents by additional meaning and context. This paper reviews the application of semantics in data integration and data warehousing and analyses its potential in integrating NoSQL data and traditional data warehouses with some focus on document stores. Also, it gives a proposal of the future pursuit directions for the big data warehouse modelling phases.
Highlights
For decision makers, having the correct information at the right moment is crucial
In the discussion we suggest ordering of data warehouses (DWHs) design phases that is different from all design approaches that featured a NoSQL source so far, and by this methodological suggestion, we pave the way for our further research
We identify three basic scenarios of new multidimensional concepts (MDCs) created by big data: (1) new attributes (Figure 6) (2) new dimensions (3) new fact table
Summary
For decision makers, having the correct information at the right moment is crucial. Context captured by such information must be analytically relevant to give them additional, hidden knowledge needed for quality decision-making. To create such information, data scattered in various storages must often be integrated. Data warehousing is an example of integration aimed for analytical purposes. Data warehouses (DWHs) were designed for business (retail) analysis but were later applied in other domains as well. DWH’s relational implementation starts with the design of its schema, which includes the analysis of involved data sources’ schemas
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