Jorge Oliveira e Sá

Data Warehouse Methodology: A Process Driven Approach

The current methods of the development and implementation of a Data Warehouse don’t consider the integration with the organizational-processes and their respective data. In addition to these current methods, based on demand- driven, data-driven and goal-driven, we will introduce in this paper a new approach to DW development and implementation. This proposal will be based on the integration of organizational processes and their data, denote by: Integrated-Process-Driven (IPD. The principles of this approach are founded on the relation-ships between business-processes and Entity-Relationship-Models (ERM), the Relational Database (RDB) data models. These relationships are originated in the Architecture of Integrated Information Systems (ARIS) methodology. IPD will use the information extracted from the data-driven, on the one side, to match (or define) the AS-IS business processes model. On the other hand, IPD will use the information returned from the demand-driven (required by the DW users) to define the TO-BE business process model based also on the AS-IS model. IPD will integrate the new data models, originated in the TO-BE business processes model, with the DW requirements. The aim of IPD is to define (or to redefine) the organizational processes which will supply the DW with data. The added-value of this approach will be the integration of the previous methods (demand-driven and data-driven) with organizational processes that will treat these sets of informations to be used by the DW. Our approach is also a trigger for business processes reengineering and optimization. Finally, the goal-driven will verify if the IPD achieves the business goals.

A Big Data system supporting Bosch Braga Industry 4.0 strategy

Abstract People, devices, infrastructures and sensors can constantly communicate exchanging data and generating new data that trace many of these exchanges. This leads to vast volumes of data collected at ever increasing velocities and of different variety, a phenomenon currently known as Big Data. In particular, recent developments in Information and Communications Technologies are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining volume, variety and velocity of data, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the Future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. Thereby, this paper addresses this key challenge, proposing and implementing a Big Data Analytics architecture, using a multinational organisation (Bosch Car Multimedia – Braga) as a case study. In this work, all the data lifecycle, from collection to analysis, is handled, taking into consideration the different data processing speeds that can exist in the real environment of a factory (batch or stream).

A Big Data Analytics Architecture for Industry 4.0

In an era in which people, devices, infrastructures and sensors can constantly communicate exchanging data and, also, generating new data that traces many of these exchanges, vast volumes of data is generated giving the context for the emergence of the Big Data concept. In particular, recent developments in Information and Communications Technology (ICT) are pushing the fourth industrial revolution, Industry 4.0, being data generated by several sources like machine controllers, sensors, manufacturing systems, among others. Joining the volume and variety of data, arriving at high velocity, with Industry 4.0, makes the opportunity to enhance sustainable innovation in the Factories of the future. In this, the collection, integration, storage, processing and analysis of data is a key challenge, being Big Data systems needed to link all the entities and data needs of the factory. In this context, this paper proposes a Big Data Analytics architecture that includes layers dedicated to deal with all data needs, from collection to analysis and distribution.

A Data Warehouse Model for Business Processes Data Analytics

Business Process Management and Business Intelligence initiatives are commonly seen as separated organizational projects, suffering from lack of coordination, leading to a poor alignment between strategic management and operational business processes execution. Researchers and professionals of information systems have recognized that business processes are the key for identifying the user needs for developing the software that supports those needs. In this case, a process-driven approach could be used to obtain a Data Warehouse model for the Business Intelligence supporting software. This paper presents a process-based approach for identifying an analytical data model using as input a set of interrelated business processes, modeled with Business Process Model and Notation version 2.0, and the corresponding operational data model. The proposed approach ensures the identification of an analytical data model for a Data Warehouse repository, integrating dimensions, facts, relationships and measures, providing useful data analytics perspectives of the data under analysis.

Big Data in Cloud: A Data Architecture

This work was financed by FCT - Fundacao para a Ciencia e Tecnologia for the project: PEst-OE/EEI/UI0319/2014

Exploring Data Analytics of Data Variety.

The Internet allows organizations managers access to large amounts of data, and this data are presented in different formats, i.e., data variety, namely structured, semi-structured and unstructured. Based on the Internet, this data variety is partly derived from social networks, but not only, machines are also capable of sharing information among themselves, or even machines with people. The objective of this paper is to understand how to retrieve information from data analysis with data variety. An experiment was carried out, based on a dataset with two distinct data types, images and comments on cars. Techniques of data analysis were used, namely Natural Language Processing to identify patterns, and Sentimental and Emotional Analysis. The image recognition technique was used to associate a car model with a category. Next, OLAP cubes and their visualization through dashboards were created. This paper concludes that it is possible to extract a set of relevant information, namely identifying which cars people like more/less, among other information.

Baby Steps in E-Health: Internet of Things in a Doctor’s Office

IoT is a very common buzzword in e-Health because the creation of new and better healthcare services are possible through technology-push. There are great expectations in using IoT in healthcare, but most studies describe very futuristic and disruptive solutions for the healthcare real world. In a near future these solutions could be implemented, but now we need some “baby steps” to reach an e-Health with IoT technology. In this paper it is presented a solution to transform a doctor’s office in an IoT office by helping the doctor attend a patien. This solution has impact in the doctor’s work because (s)he has a limited time to attend a patient and a big part of this time is spent writing the results of the appointment in a computer record - the electronic patient record. By other hand the patient improve their experience because the doctor will give them more attention and care.

Who can assess HR performance in IT/IS projects: a review

Acting in markets characterized by a growing demand, organizations need to manage their human resources effectively and nowadays recognize that human resources (HR) are key elements to obtain success. In fact, organizations, by looking for operation optimization, have interest in enhancing the performance of human resources through systematic appraisals, by collecting and using information from individual and team performance. In the context of information technology/information systems (IT/IS) projects, the research that focuses on HR performance appraisal is scarce. To help fill this gap this paper presents a review of sources on performance information, which are applicable to these kind of projects.

Process-driven data analytics supported by a data warehouse model

Business process management and business intelligence initiatives are commonly seen as separated organisational projects, suffering from lack of coordination, leading to a poor alignment between strategic management and operational business processes execution. Information systems researchers and professionals have recognised that business processes are the key for identifying the user needs for developing the software that supports those requirements. This paper presents a process based approach for identifying an analytical data model using as input a set of interrelated business processes, modelled with business process model and notation (BPMN), and the corresponding persistent operational data model. This process-based approach extends the BPMN language allowing the integration of behavioural aspects and processes performance measures in the persistent operational data model. The proposed approach ensures the identification of an analytical data model for a data warehouse, integrating dimensions, facts, relationships and measures, providing useful data analytics perspectives of the data under analysis.

Process-Based Information Systems Development: Taking Advantage of a Component-Based Infrastructure

Abstract Background: Owing to the highly competitive business environment in which contemporary organizations have to operate, a quick and effective way of developing and maintaining information systems is of utmost importance to their success. Obviously, the computerized information systems that support the everyday operations and management of organizations play a critical role in their competitiveness. For that reason, nowadays, more than ever, information systems have to be quickly developed, rapidly reconfigured, and easily maintained. Objectives: We aim to define a technological infrastructure, accompanied by a set of methodological development requirements, which might help to fulfil those needs. Methods/Approach: In this work, we followed a Design Science Research (DSR) approach. Results: We propose a specific IT infrastructure, inspired by the concept of a business process and using the functionalities provided by collaborative and workflow technologies, which allows the development of distributed IT solutions, Process-Based Information Systems (PBIS), in a component-based fashion. In the context of PBIS, we also propose a set of development requirements. Conclusions: We claim that Process-Based Information Systems allow organizations to evolve quickly and smoothly in face of changing business requirements, facilitating the integration of existing and future IT artefacts, while simplifying the overall development and maintenance effort of information systems.