NOSQL design for analytical workloads: Variability matters

Big Data has recently gained popularity and has strongly questioned relational databases as universal storage systems, especially in the presence of analytical workloads. As result, co-relational alternatives, commonly known as NOSQL (Not Only SQL) databases, are extensively used for Big Data. As the primary focus of NOSQL is on performance, NOSQL databases are directly designed at the physical level, and consequently the resulting schema is tailored to the dataset and access patterns of the problem in hand. However, we believe that NOSQL design can also benefit from traditional design approaches. In this paper we present a method to design databases for analytical workloads. Starting from the conceptual model and adopting the classical 3-phase design used for relational databases, we propose a novel design method considering the new features brought by NOSQL and encompassing relational and co-relational design altogether.Peer ReviewedPostprint (author's final draft

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

Applying Operational Business Intelligence in Production Environments

Operational Business Intelligence (OpBI) discusses a possible support of production-specific decisions by integrating and analyzing production data. The discussion of OpBI focusses thereby rather on common applicability aspects than on certain implementation strategies. This is however less conclusive for a functional reliability of OpBI in production environments and for associated efforts. Therefore, we introduce an OpBI framework to integrate and analyze data of production processes automatically. Following principles of design science research, framework evaluation refers to real-world data from a rod and wire rolling process. In conclusion, our OpBI framework improves information quality perceived by end users analyzing a steel’s rolling behavior

CFBM - A Framework for Data Driven Approach in Agent-Based Modeling and Simulation

Recently, there has been a shift from modeling driven approach to data driven approach in Agent Based Modeling and Simulation (ABMS). This trend towards the use of data-driven approaches in simulation aims at using more and more data available from the observation systems into simulation models [1, 2]. In a data driven approach, the empirical data collected from the target system are used not only for the design of the simulation models but also in initialization, evaluation of the output of the simulation platform. That raises the question how to manage empirical data, simulation data and compare those data in such agent-based simulation platform. In this paper, we first introduce a logical framework for data driven approach in agent-based modeling and simulation. The introduced framework is based on the combination of Business Intelligence solution and a multi-agent based platform called CFBM (Combination Framework of Business intelligence and Multi-agent based platform). Secondly, we demonstrate the application of CFBM for data driven approach via the development of a Brown Plant Hopper Surveillance Models (BSMs), where CFBM is used not only to manage and integrate the whole empirical data collected from the target system and the data produced by the simulation model, but also to initialize and validate the models. The successful development of the CFBM consists not only in remedying the limitation of agent-based modeling and simulation with regard to data management but also in dealing with the development of complex simulation systems with large amount of input and output data supporting a data driven approach

Graph BI & analytics: current state and future challenges

In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer ReviewedPostprint (author's final draft