What’s Up in Business Intelligence? A Contextual and Knowledge-Based Perspective

A methodology to automatically translate user requirements into visualizations: Experimental validation

“…Visualizations are required to be precise and easy to comprehend by users in order to minimize the interpretation errors made by users as well as designers. Visualizations must also contemplate the changing needs of users, considering high-level semantics, and reasoning about unstructured and structured data, providing easier access and better understanding of the data [20]. Moreover, although often overlooked in visualization design, requirement modeling is a paramount activity [21] that compensates for the little attention usually paid to (explicitly) representing the reasons, i.e., the why, in terms of motivations, rationale, objectives, and requirements.…”

Internet of Things Data Visualization for Business Intelligence

“…Data visualization is essential in this process, since it allows managers to gain a proper understanding of the underlying patterns and results obtained by analysis algorithms (Lavalle, 2019). Data Visualization in BI must consider the business objectives and the evolving needs of users, taking into account high-level semantics, reasoning about unstructured and structured data, and providing simplified access and a better understanding of data (Aufaure, 2013).…”

Research on conceptual modeling: Themes, topics, and introduction to the special issue

“…However, issues of content and semantics have been, and continue to be, major challenges [11,51]. Business intelligence and data analytics initiatives have been developing web analytics techniques to mine usergenerated content in the form of unstructured data to perform sentiment analysis and other types of intelligence analysis on consumer opinions and needs, in an attempt to create new business opportunities [4,12]. The era of big data is here to stay.…”

“…These were: David Embley's "Big Data: Conceptual Modeling to the Rescue" [18]; Marie-Aude Aufaure's "What's Up in Business Intelligence? A Contextual and Knowledge-based Perspective" [4]; and Surajit Chaudhuri's "Big Data and Enterprise Analytics". Additional industry presentations echoed this theme.…”

“…As our understanding and advancement of tools and techniques to deal with big data for predictive analytics [17] and other applications increases, accurate representation and modeling of data is crucial (see, e.g. [4,5]). With respect to conceptual modeling, an important first observation [18] is that the main thrust of conceptual modeling has always been about organizing data [13].…”