Quality management in social business intelligence projects

Social networks have become a new source of useful information for companies. Increasing the value of social data requires, first, assessing and improving the quality of the relevant data and, subsequently, developing practical solutions that apply them in business intelligence tasks. This paper focuses on the Twitter social network and the processing of social data for business intelligence projects. With this purpose, the paper starts by defining the special requirements of the analysis cubes of a Social Business Intelligence (SoBI) project and by reviewing previous work to demonstrate the lack of valid approaches to this problem. Afterwards, we present a new data processing method for SoBI projects whose main contribution is a phase of data exploration and profiling that serves to build a quality data collection with respect to the analysis objectives of the project

Sentiment Analysis and Classifying Hashtags in Social Media Using Data Mining Techniques

Big data is one of the important topics which is still open for a wide range of applications for extracting useful information and knowledge for supporting organizations by planning and decision-making. Social media as a technology is an important resource of data, especially because it has been widely used in the last years. A Hashtag is recently one of the most popular features provided by Social media and is used by most social media users to express, share, and retrieve opinions and feelings regarding a specific theme. Hashtag features in social media are used more and more in recent years to discuss and debate important current events by public audience. This paper sheds light on how business can use such sources of information and how needed technical processes can be implemented accordingly. The paper demonstrates sentiment analysis as a scenario for such implementation. The main innovation in this paper is not limited to the technical method used, but rather to focus on the idea of using hashtags as information source in business, which is still rarely addressed in science. This paper will provide a novel model based on text mining techniques to provide a sentiment analysis for classifying business-related Hashtags posted on social media from the customers. The results will be presented and verified through samples of positive, and negative classified comments extracted from the Hashtags for supporting the organization by planning and decision making for generating completive advantages

Impact of data quality in real-time big data systems

Data Quality is one of the main challenges in any type of Big Data System. Timeliness is one of the main factors in real-time Big Data. Limiting data quality evaluations to data sources may be insufficient in Big Data Systems with high Velocity and Variability. On the other hand, real-time Data Quality evaluations throughout the Big Data Pipeline can be costly (i.e., latency introduced by Data Quality Evaluations). This paper identifies four categories – embedded, parallel, in-line, and independent– of approaches for Big Data Quality Evaluation available in the literature. A real-time Big Data System based on the SmartCambridge Real-Time Data Platform is deployed and used as basis to implement a representative case for each one of the four categories identified. An application for bus catching dynamic prediction is used as case study to quantify the impact of these Data Quality Evaluations in the Real-Time Data Platform in terms of latency introduced in the system. Results suggests that the impact of Data Quality Evaluations differ depending on the type of method used, and that the main factors are the data transfers between Data Quality modules and the data processing algorithms, the synchronisation of messages, and the complexity of the Data Quality algorithms

Facing Big Data System Architecture Deployments: Towards an Automated Approach Using Container Technologies for Rapid Prototyping

Within the last decade, big data became a promising trend for many application areas, offering immense potential and a competitive edge for various organizations. As the technical foundation for most of today´s data-intensive projects, not only corresponding infrastructures and facilities but also the appropriate knowledge is required. Currently, several projects and services exist that not only allow enterprises to utilize but also to deploy related technologies and systems. However, at the same time, the use of these is accompanied by various challenges that may result in huge monetary expenditures, a lack of modifiability, or a risk of vendor lock-ins. To overcome these shortcomings, in the contribution at hand, modern container and task automation technologies are used to wrap complex big data technologies into re-usable and portable resources. Those are subsequently incorporated in a framework to automate the deployment of big data architectures in private and limited resources

Quality Indicators for Social Business Intelligence

Comunicació presentada a 2019 Sixth International Conference on Social Networks Analysis, Management and Security (SNAMS) (Granada, Spain, 22-25 Oct. 2019)The main purpose of Social Business Intelligence is to help companies in making decisions by performing multidimensional analysis of the relevant information disseminated on social networks. Although data quality is a general issue in SBI, few approaches have aimed at assessing it for any data collection, being this a context dependent task. In this paper, we define a quality indicator as a metric that serves to assess the overall quality of a collection, and that integrates the measures obtained by several quality criteria applied to filter the posts relevant for a SBI project. The selection of the best quality criteria to include in each quality indicator is a complex task that requires a deep understanding of both the context and objectives of analysis. In this paper, we propose a new methodology to design quality indicators for SBI projects whose quality criteria consider contents coherence and data provenance. Thus, for the context defined by an objective of analysis, this methodology helps users to find the quality criteria that best suit both the users and the available data, and then integrate them into a valid quality indicator

Social Media Usages in Small Not-For-Profit Organizations

Effective social media management takes time, effort, and intentionality. Small not-for-profit organizations would benefit from doing this, but often have difficulty with being able to do so. In order to find out how small not-for-profit organizations use their social media and how social media could best be used to accomplish their mission, the following research questions were asked: What communications policies and procedures are followed by small not-for-profits organizations? Are the current social media practices of these organizations effective? What are the primary needs of these organizations in terms of social media and how can they be addressed? In order to address these questions, relevant existing literature was reviewed, six small not-for-profit organizations were randomly selected, social media data was collected and analyzed concerning these six organizations, and the managers of these six small not-for-profit organizations were interviewed. Literature strongly supports the notion that social media management is of great importance for the success of an organization. The data from the 6 not-for-profit organizations’ social media accounts show some areas do not match recommended usage while managers offered insights into how social media content is produced. By bringing the importance of social media management to the managers’ attentions and providing them with up-to-date solutions to common problems, small not-for-profit organizations will be better equipped to fulfill their missions. Key findings include: unsuccessfully satisfying ideal balance between types of posts (shares and originals), organizations under use appropriate platforms, organizations fail to post at the ideal rate in order to increase interactions, content is not ideally strategized to align with recommended purpose

Adapting a quality model for a Big Data application: the case of a feature prediction system

En la última década hemos sido testigos del considerable incremento de proyectos basados en aplicaciones de Big Data. Algunos de los tipos más populares de esas aplicaciones han sido: los sistemas de recomendaciones, la predicción de características y la toma de decisiones. En este nuevo auge han surgido propuestas de implementación de modelos de calidad para las aplicaciones de Big data que por su gran heterogeneidad se hace difícil la selección del modelo de calidad ideal para el desarrollo de un tipo específico de aplicación de Big Data. En el presente Trabajo de Fin de Máster se realiza un estudio de mapeo sistemático (SMS, por sus siglas en inglés) que parte de dos preguntas clave de investigación. La primera trata sobre cuál es el estado en la identificación de riesgos, problemas o desafíos en las aplicaciones de Big Data. La segunda, trata sobre qué modelos de calidad se han aplicado hasta la fecha a las aplicaciones de Big Data, específicamente a los sistemas de predicción de características. El objetivo final es analizar los modelos de calidad disponibles y adaptar un modelo de calidad a partir de los existentes que se puedan aplicar a un tipo específico de aplicación de Big Data: los sistemas de predicción de características. El modelo definido comprende un conjunto de características de calidad definidas como parte del modelo y métricas de calidad para evaluarlas. Finalmente, se realiza una aproximación a un caso de estudio donde se aplica el modelo y se evalúan las características de calidad definidas a través de sus métricas de calidad presentándose los resultados obtenidos.In the last decade, we have been witnesses of the considerable increment of projects based on big data applications. Some of the most popular types of those applications have been: Recommendations, Feature Predictions, and Decision making. In this new context, several proposals have arisen for the implementation of quality models applied to Big Data applications. As part of the current Master thesis, a Systematic Mapping Study (SMS) is conducted which starts from two key research questions. The first one is about what is the state of the art about the identification of risks, issues, problems, or challenges in big data applications. The second one, is about which quality models have been applied up to date to big data applications, specifically to feature prediction systems. The main objective is to analyze the available quality models and adapt a quality model from the existing ones that can be applied to a specific type of Big Data application: The Feature Prediction Systems. The defined model comprises a set of quality characteristics defined as part of the model and a set of quality metrics to evaluate them. Finally, an approach is made to a case study where the model is applied, and the quality characteristics defined through its quality metrics are evaluated. The results are presented and discussed.Departamento de Informática (Arquitectura y Tecnología de Computadores, Ciencias de la Computación e Inteligencia Artificial, Lenguajes y Sistemas Informáticos)Máster en Ingeniería Informátic