Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

Maturity model to position and orient organizations through the process automation implementation

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business AnalyticsNowadays, companies are seeking for processes done with a zero error rate enhancing their service quality, while the demand for costs reduction and speed is also increasing. For these reasons, the value of Artificial Intelligence is raising, namely in the area of optimization and processes automation. These concepts lead to a hot topic: Hyperautomation, which aims to achieve an environment where machines are working together with each other or alongside human employees. However, it is not clear what does the introduction of intelligence means in processes. Previous studies have defined maturity models regarding Business Process Management or Industry 4.0, but there is a gap in this topic for the automation area. The Design Science Research Methodology (DSRM) was applied to build a Maturity Model that can help position and orient organizations through the process automation implementation. The model aims to be a framework where the companies can rely to be successful in the journey of automating and optimizing business processes not only by understanding their position but also finding the actions needed to improve. Thus, the Maturity Model incorporates a taxonomy to classify each level as well as a description of what each level represents. Additionally, the proposed Maturity Model provides an evaluation framework

How machine learning informs ride-hailing services: A survey

In recent years, online ride-hailing services have emerged as an important component of urban transportation system, which not only provide significant ease for residents’ travel activities, but also shape new travel behavior and diversify urban mobility patterns. This study provides a thorough review of machine-learning-based methodologies for on-demand ride-hailing services. The importance of on-demand ride-hailing services in the spatio-temporal dynamics of urban traffic is first highlighted, with machine-learning-based macro-level ride-hailing research demonstrating its value in guiding the design, planning, operation, and control of urban intelligent transportation systems. Then, the research on travel behavior from the perspective of individual mobility patterns, including carpooling behavior and modal choice behavior, is summarized. In addition, existing studies on order matching and vehicle dispatching strategies, which are among the most important components of on-line ride-hailing systems, are collected and summarized. Finally, some of the critical challenges and opportunities in ride-hailing services are discussed

STAY FLEXIBLE: A PRESCRIPTIVE PROCESS MONITORING APPROACH FOR ENERGY FLEXIBILITY-ORIENTED PROCESS SCHEDULES

The transition of energy supply from fossil fuels to renewable energy sources poses major challenges for balancing increasingly weather-dependent energy supply and demand. Demand-side energy flexibility, offered particularly by companies, is seen as a promising and necessary approach to address these challenges. Process mining provides significant potential to prevent a deterioration of product quality or process flows due to flexibilization and allows for exploiting monetary benefits associated with flexible process operation. Hence, we follow the design science research paradigm to develop PM4Flex, a prescriptive process monitoring approach, that generates recommendations for pending process flows optimized under fluctuating power prices by implementing established energy flexibility measures. Thereby, we consider company- and process-specific constraints and historic event logs. We demonstrate and evaluate PM4Flex by implementing it as a software prototype and applying it to exemplary data from a heating and air conditioning company, observing considerable cost-savings of 1.42ct per kWh or 7.89%

A Business Intelligence Framework to Provide Performance Management through a Holistic Data Mining View

Traditional views of business intelligence have mainly focused on the physical and human aspects of the organization. This paper tries to show that a new information view of business activities can make a platform for developing business intelligence and support performance management. To do that, the paper proposes a new framework that can be used to provide high level of business intelligence for performance management usage. The framework introduces a hierarchy of performance influencers and a new methodology for managing them. The new methodology introduces a holistic view towards data mining concepts. The framework can be served as a blueprint for the companies which use any of ecommerce business models