Towards a Methodology for Social Business Intelligence in the Era of Big Social Data Incorporating Trust and Semantic Analysis

2019 ◽  
pp. 519-527 ◽  
Author(s):  
Bilal Abu Salih ◽  
Pornpit Wongthongtham ◽  
Seyed-Mehdi-Reza Beheshti ◽  
Behrang Zajabbari
Keyword(s):  
Business Intelligence ◽  
Semantic Analysis ◽  
Social Business ◽  
Social Data

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 CredSaT: Credibility ranking of users in big social data incorporating semantic analysis and temporal factor

2018 ◽  
Vol 45 (2) ◽  
pp. 259-280 ◽  
Author(s):  
Bilal Abu-Salih ◽  
Pornpit Wongthongtham ◽  
Kit Yan Chan ◽  
Dengya Zhu
Keyword(s):  
Semantic Analysis ◽  
Social Trust ◽  
Viral Marketing ◽  
Social Data ◽  
Fine Grained ◽  
Computer Scientists ◽  
Real World Datasets ◽  
Information Consumers ◽  
Expertise Retrieval ◽  
Temporal Factor

The widespread use of big social data has influenced the research community in several significant ways. In particular, the notion of social trust has attracted a great deal of attention from information processors and computer scientists as well as information consumers and formal organisations. This attention is embodied in the various shapes social trust has taken, such as its use in recommendation systems, viral marketing and expertise retrieval. Hence, it is essential to implement frameworks that are able to temporally measure a user’s credibility in all categories of big social data. To this end, this article suggests the CredSaT (Credibility incorporating Semantic analysis and Temporal factor), which is a fine-grained credibility analysis framework for use in big social data. A novel metric that includes both new and current features, as well as the temporal factor, is harnessed to establish the credibility ranking of users. Experiments on real-world datasets demonstrate the efficacy and applicability of our model in determining highly domain-based trustworthy users. Furthermore, CredSaT may also be used to identify spammers and other anomalous users.

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.

Discovering Data and Information Quality Research Insights Gained through Latent Semantic Analysis

2012 ◽  
Vol 3 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Roger Blake ◽  
Ganesan Shankaranarayanan
Keyword(s):  
Business Intelligence ◽  
Latent Semantic Analysis ◽  
Information Quality ◽  
Semantic Analysis ◽  
Recent Decade ◽  
Research Area ◽  
Quality Research ◽  
The Core ◽  
Data Querying ◽  
Do So

In the recent decade, the field of data and information quality (DQ) has grown into a research area that spans multiple disciplines. The motivation here is to help understand the core topics and themes that constitute this area and to determine how those topics and themes from DQ relate to business intelligence (BI). To do so, the authors present the results of a study which mines the abstracts of articles in DQ published over the last decade. Using Latent Semantic Analysis (LSA) six core themes of DQ research are identified, as well as twelve dominant topics comprising them. Five of these topics--decision support, database design and data mining, data querying and cleansing, data integration, and DQ for analytics--all relate to BI, emphasizing the importance of research that combines DQ with BI. The DQ topics from these results are profiled with BI, and used to suggest several opportunities for researchers.

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