Social Business Intelligence - OLAP Applied to User Generated Contents

2014 ◽  
Author(s):  
Matteo Golfarelli
Keyword(s):  
Business Intelligence ◽  
Social Business

scholarly journals Modeling Analytical Streams for Social Business Intelligence

2018 ◽  
Author(s):  
Indira Lanza-Cruz ◽  
Rafael Berlanga ◽  
María José Aramburu
Keyword(s):  
Social Network ◽  
Business Intelligence ◽  
Semantic Representation ◽  
Social Indicators ◽  
Open Data ◽  
Multidimensional Analysis ◽  
Social Business ◽  
Strategic Goals ◽  
Real Scenario ◽  
Definition Of

Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network contents and the company analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector.

scholarly journals Social Business Intelligence: A New Perspective for Decision Makers

2014 ◽  
Vol 124 ◽  
pp. 562-567 ◽  
Author(s):  
Mihaela Muntean ◽  
Liviu Gabriel Cabău ◽  
Vlad Rînciog
Keyword(s):  
Business Intelligence ◽  
Decision Makers ◽  
Social Business ◽  
New Perspective

scholarly journals Social business intelligence: Review and research directions

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Helena Gioti ◽  
Stavros T. Ponis ◽  
Nikolaos Panayiotou
Keyword(s):  
Social Media ◽  
Business Intelligence ◽  
Scientific Field ◽  
Added Value ◽  
Data Systems ◽  
Social Business ◽  
Research Directions ◽  
Wide Range ◽  
Initial Shock ◽  
Media Data

Social business intelligence (SBI) is a rather novel discipline, emerged in theacademic and business literature as a result of the convergence of two distinct researchdomains: business intelligence (BI) and social media. Traditional BI scientists and practitioners,after an inevitable initial shock, are currently discovering and acknowledge the potential of usergenerated content (UGD) published in social media as an invaluable and inexhaustible sourceof information capable of supporting a wide range of business activities. The confluence of thesetwo emerging domains is already producing new added value organizational processes andenhanced business capabilities utilized by companies all over the world to effectively harnesssocial media data and analyze them in order to produce added value information such ascustomer profiles and demographics, search habits, and social behaviors. Currently the SBIdomain is largely uncharted, characterized by controversial definitions of terms and concepts,fragmented and isolated research efforts, obstacles created by proprietary data, systems andtechnologies that are not mature yet. This paper aspires to be one of the few -to our knowledge contemporaryefforts to explore the SBI scientific field, clarify definitions and concepts,structure the documented research efforts in the area and finally formulate an agenda of futureresearch based on the identification of current research shortcomings and limitations.

scholarly journals Modeling Analytical Streams for Social Business Intelligence

Informatics ◽  
2018 ◽  
Vol 5 (3) ◽  
pp. 33 ◽  
Author(s):  
Indira Lanza-Cruz ◽  
Rafael Berlanga ◽  
María Aramburu
Keyword(s):  
Business Intelligence ◽  
Semantic Representation ◽  
Social Indicators ◽  
Open Data ◽  
Multidimensional Analysis ◽  
Social Business ◽  
Strategic Goals ◽  
Real Scenario ◽  
Definition Of ◽  
Analysis Models

Social Business Intelligence (SBI) enables companies to capture strategic information from public social networks. Contrary to traditional Business Intelligence (BI), SBI has to face the high dynamicity of both the social network’s contents and the company’s analytical requests, as well as the enormous amount of noisy data. Effective exploitation of these continuous sources of data requires efficient processing of the streamed data to be semantically shaped into insightful facts. In this paper, we propose a multidimensional formalism to represent and evaluate social indicators directly from fact streams derived in turn from social network data. This formalism relies on two main aspects: the semantic representation of facts via Linked Open Data and the support of OLAP-like multidimensional analysis models. Contrary to traditional BI formalisms, we start the process by modeling the required social indicators according to the strategic goals of the company. From these specifications, all the required fact streams are modeled and deployed to trace the indicators. The main advantages of this approach are the easy definition of on-demand social indicators, and the treatment of changing dimensions and metrics through streamed facts. We demonstrate its usefulness by introducing a real scenario user case in the automotive sector.

Advanced topic modeling for social business intelligence

2015 ◽  
Vol 53 ◽  
pp. 87-106 ◽  
Author(s):  
Enrico Gallinucci ◽  
Matteo Golfarelli ◽  
Stefano Rizzi
Keyword(s):  
Business Intelligence ◽  
Topic Modeling ◽  
Social Business ◽  
Advanced Topic

scholarly journals Business Intelligence Model to analyze Social Media through Big Data analytics

2021 ◽  
Vol 119 ◽  
pp. 07006
Author(s):  
Kawtar Mouyassir ◽  
Mohamed Hanine ◽  
Hassan Ouahmane
Keyword(s):  
Social Media ◽  
Big Data ◽  
Business Intelligence ◽  
Qualitative Data ◽  
Big Data Analytics ◽  
Decision Makers ◽  
Social Business ◽  
Social Media Networks ◽  
Big Data Technologies ◽  
Voice Of Customer

Business Intelligence (BI) is a collection of tools, technologies, and practices that include the entire process of collecting, processing, and analyzing qualitative information, to help entrepreneurs better understand their business and marketplace. Every day, social networks expand at a faster rate and pace, which sees them as a source of Big Data. Therefore, BI is developed in the same way on VoC (Voice of Customer) expressed in social media as qualitative data for company decision-makers, who desire to have a clear perception of customers’ behaviour. In this article, we present a comparative study between traditional BI and social BI, then examine an approach to social business intelligence. Next, we are going to demonstrate the power of Big Data that can be integrated into BI so that we can finally describe in detail how Big Data technologies, like Apache Flume, help to collect unstructured data from various sources such as social media networks and store it in Hadoop storage.

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