Abstract
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.
Highlights
The main objective of Business Intelligence (BI) is extracting strategic knowledge from the information provided by different data sources to help during the decision-making process and achieve the strategic goals of a company
With the aim of developing a generic architecture that covers a wide range of Big Data analysis tasks, we have developed a state of the art identifying the main tasks and the most used methodologies for processing them
We have identified some important dimensions of analysis that group and distinguish the work reviewed, namely: model type, whether the system does streaming processing, Key Framework
Summary
The main objective of Business Intelligence (BI) is extracting strategic knowledge from the information provided by different data sources to help during the decision-making process and achieve the strategic goals of a company. The processing and analysis of massive data oriented to BI has evolved in recent years. The most commonly used approaches have combined data warehouse (DW), online analytical processing (OLAP), and multidimensional (MD) technologies [1], on very specific scenarios, making use of static and well-structured data sources of corporate nature, being all the information fully materialized and periodically processed in batch mode for future analysis. New technologies for Exploratory OLAP have been introduced, aimed at exploiting semi-structured external data sources (e.g., XML, RDF) for the discovery and acquisition of relevant data that can be combined with corporate data for decision making process. Social networks are a fundamental part of the information ecosystem, social media platforms have achieved an unprecedented reach for users, consumers, and companies providing a useful information channel for any professional environment
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