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Business Intelligence and Data Science

Recently, the field of Data is increasingly being interested by many young people and wants to pursue their career path. However, there are many small areas related to Data that can be confusing: Data Science vs. Business Intelligence. Generally speaking, in order to interpret the situation accurately, two aspects should be considered: similarity and differences between data science and business intelligence. Prior to this article, most people think that "data science" and "business intelligence" are completely different roles. However, if you carefully analyze these different positions, it turns out that this view is not absolutely accurate! For example, some products use the technology of artificial intelligence-based data mining for mining raw data; In addition to the raw data storage platform without processing requirements (storage), a typical processing platform can only manage a single kind of work release experience management system; Other cloud applications may have greater functionality or integration options such as real time processing or mobile interface capabilities But by its nature, compared to traditional methods such as statistical analysis mathematical model analysis on large amounts of historical data It should be noted that due to its high computational requirements This type of application has nothing to do with traditional business intelligence (BI) platforms available today

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Multidimensional Modeling for Social Business Intelligence

In this paper, we describe the concept of social business intelligence (SBI) in order to describe its scope and aim. The applicability of SBI was explained, followed by an outline of its benefits and drawbacks. Moreover, we discuss topics as the main component of a topic hierarchy and outline its challenges. Afterwards, we introduce a reference architecture for SBI that has been formulated based on the core concepts outlined above. We illustrate the utilization of our proposed architecture by describing two particular implementations specific to query performances and to our meta-star approach. Finally, some experimental results are presented for topic queries within our system as well as for various query executions performed with different volumes of textual data available for aggregation in a real context (Filipino auctioning sites).

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