Social Media Multidimensional Analysis for Intelligent Health Surveillance

Background: Recent work in social network analysis has shown the usefulness of analysing and predicting outcomes from user-generated data in the context of Public Health Surveillance (PHS). Most of the proposals have focused on dealing with static datasets gathered from social networks, which are processed and mined off-line. However, little work has been done on providing a general framework to analyse the highly dynamic data of social networks from a multidimensional perspective. In this paper, we claim that such a framework is crucial for including social data in PHS systems. Methods: We propose a dynamic multidimensional approach to deal with social data streams. In this approach, dynamic dimensions are continuously updated by applying unsupervised text mining methods. More specifically, we analyse the semantics and temporal patterns in posts for identifying relevant events, topics and users. We also define quality metrics to detect relevant user profiles. In this way, the incoming data can be further filtered to cope with the goals of PHS systems. Results: We have evaluated our approach over a long-term stream of Twitter. We show how the proposed quality metrics allow us to filter out the users that are out-of-domain as well as those with low quality in their messages. We also explain how specific user profiles can be identified through their descriptions. Finally, we illustrate how the proposed multidimensional model can be used to identify main events and topics, as well as to analyse their audience and impact. Conclusions: The results show that the proposed dynamic multidimensional model is able to identify relevant events and topics and analyse them from different perspectives, which is especially useful for PHS systems

Psychology of Business Intelligence Tools: Needs-Affordances-Features Perspective

This study applied the Needs-Affordances-Features (NAF) framework to study psychological motivations behind the use of Business Intelligence (BI) tools especially when the use of such tools is voluntary. Our findings suggest that psychological needs motivate the use of BI tools that provide 13 affordances to fulfill five psychological needs, namely autonomy, competence, relatedness, having a place and self-realization. These affordances were identified through a review of six publicly available BI tools. This study posits that three groups of affordances––creation, collaboration, and communication––explain the relationship between psychological needs and applications of BI. This study generates important implications for BI research by providing an overarching framework for the affordances of BI tools as a whole and explaining the importance of psychological needs that motivate the use of BI tools. The results also provide a new lens and common vocabulary for future studies and design of BI tools

Context-aware OLAP for textual data warehouses

Decision Support Systems (DSS) that leverage business intelligence are based on numerical data and On-line Analytical Processing (OLAP) is often used to implement it. However, business decisions are increasingly dependent on textual data as well. Existing research work on textual data warehouses has the limitation of capturing contextual relationships when comparing only strongly related documents. This paper proposes an Information System (IS) based context-aware model that uses word embedding in conjunction with agglomerative hierarchical clustering algorithms to dynamically categorize documents in order to form the concept hierarchy. The results of the experimental evaluation provide evidence of the effectiveness of integrating textual data into a data warehouse and improving decision making through various OLAP operations

Metadata-Based Integration of Qualitative and Quantitative Information Resources Approaching Knowledge Management

This paper presents a concept for the integration of quantitative and qualitative information sources with their accompanying management support functionalities from navigation and retrieval up to analysis and business intelligence. The integration is realized by a common keyword-based metadata base, retrievable and extendible by the end user on a web-based platform. This enables a dynamic acquisition of supplementary information on the usage, usability and benefit of basic and derived information objects, e.g. data warehouses, data marts, OLAP cubes, reports or (textual) documents. Being extended by functions to automatically catch contextual links during system usage, the concept is discussed as a contribution to the implementation of knowledge management. The concept is being developed and successfully tested in the practical environment of a reference project for the implementation of an IT-infrastructure to support decentralized decision-making at a German university

Incorporation of ontologies in data warehouse/business intelligence systems - A systematic literature review

Semantic Web (SW) techniques, such as ontologies, are used in Information Systems (IS) to cope with the growing need for sharing and reusing data and knowledge in various research areas. Despite the increasing emphasis on unstructured data analysis in IS, structured data and its analysis remain critical for organizational performance management. This systematic literature review aims at analyzing the incorporation and impact of ontologies in Data Warehouse/Business Intelligence (DW/BI) systems, contributing to the current literature by providing a classification of works based on the field of each case study, SW techniques used, and the authors’ motivations for using them, with a focus on DW/BI design, development and exploration tasks. A search strategy was developed, including the definition of keywords, inclusion and exclusion criteria, and the selection of search engines. Ontologies are mainly defined using the Ontology Web Language standard to support multiple DW/BI tasks, such as Dimensional Modeling, Requirement Analysis, Extract-Transform-Load, and BI Application Design. Reviewed authors present a variety of motivations for ontology-driven solutions in DW/BI, such as eliminating or solving data heterogeneity/semantics problems, increasing interoperability, facilitating integration, or providing semantic content for requirements and data analysis. Further, implications for practice and research agenda are indicated.info:eu-repo/semantics/publishedVersio