Fusion Cubes

A Unified View of Data-Intensive Flows in Business Intelligence Systems: A Survey

“…The typical challenges of querying the data in the next generation BI setting concern the ability of the system to adapt and complement users' analytical needs by means of discovering related, external data, and the usability of a BI system for posing analytical needs by endusers. The former challenge may span from the traditional DW systems that typically answer user's OLAP queries solely by exploiting the data previously loaded into a DW (by means of an ETL process), to situation-(context-)aware approaches that considering end user queries, explore, discover, acquire, and integrate external data [1,2]. Regarding the latter challenge, we can also observe two extreme cases: traditional querying by means of standard, typically declarative query languages (e.g., SQL, MDX), and approaches that enable users to express their (often incomplete) analytical needs in a more natural and human-preferred manner (e.g., keyword search, natural language).…”

“…In Phase I, we focused on the keywords for finding seminal works in the DW and ETL field (i.e., "data warehousing", "ETL", "business intelligence"), as well as the most relevant works on the next generation BI (i.e., "next generation business intelligence", "BI 2.0", "data-intensive flows", "operational business intelligence"). While in the case of traditional DW and ETL approaches we encountered and targeted the most influential books from the field (e.g., [53,46]) and some extensive surveys (e.g., [96]), in the case of the next generation BI approaches, we mostly selected surveys or visionary papers on the topic (e.g., [1,2,17,15,40]). Furthermore, the following phases included keyword search based on the terminology found in the created study outline (see Figure 4), as well as the identified dimensions (see Figure 5).…”

“…The most commonly discussed challenge when extracting data (e.g., [1,53]) is related to the format in which the data are provided, i.e., structuredness.…”

“…The next generation BI setting goes by various names: operational BI (e.g., [15,22]), live BI (e.g., [17]), collaborative BI (e.g., [6]), self-service BI (e.g., [1]), situational BI (e.g., [63]). While these works look at the problem from different perspectives, in general, they all aim at enabling the broader spectrum of business users to access a plethora of heterogeneous sources (not all being under the control of the user's organization and known in advance), and to extract, transform and combine these data, in order to make right-time decisions.…”

“…Finally, as we mentioned above, to make the enormously growing data volumes on the Web available and potentially useful for the enterprise analytical and data discovery actions, the linked and open data movement (e.g., [7]) has proposed a completely uncoupled environment (i.e., Low coupledness) with the general idea of having huge amounts of distributed data on the Web semantically interlinked and preferably provided in easily parseable formats (e.g., XML, RDF), see Figure 4(E). Approaches that argue for such Low coupled scenarios, envision architectures that can take the advantage of existing logic-based solutions for enabling data exploration actions over the external sources [2], and provide more context-aware data analysis [1].…”