A Process Study of the Development of Capabilities for Replication in an Evolving Franchise Chain

The purpose of this paper is to extend insight about processes and routines needed for franchise replication and makes an important contribution to understanding ways through which dynamic capabilities are created. Based on a grounds-up study, this paper utilizes a storyline approach (Miles & Huberman, 1994) to present interview data obtained from interviews with elite informants (IEIs) of the host company and its archival data. Evolution of capabilities, both substantive and dynamic, are captured in the findings section. Of the seventeen interviews, seven subjects were location heads and ten corporate executives. Interviews with corporate executives included questions pertaining to their functional specialty as well. Finally, implications for future research and practice are discussed.

Reconhecimento de atividades humanas baseado na análise de fluxo contínuo de dados simbólicos

Smartphones sensing capabilities have enabled the development of Human Activity Recognition (HAR) solutions for better understanding human behavior through computational techniques. However, these solutions have been difficult to perform in dynamic scenarios because they do not observe data evolution over time and the high consumption of computational resources, such as memory, processing and energy. This occurs because the HAR problem for smartphones has been solved through classification models generated by offline machine learning algorithms that, in this case, are limited by a data history with little information about human activities. The problem with this approach is that human activities change constantly over time and are strongly influenced by the physical environment and the user’s profile. To overcome these problems this doctoral thesis proposes a new approach to recognize human activities based on the symbolic data streaming analysis. Our approach enables the development of low-cost, scalable HAR systems capable of adapting to data change over time. In this con- text, this thesis proposes a framework called DISTAR (DIscrete STream learning for Activity Recognition), responsible for standardizing the analysis of data stream process and generation of adaptive models that observe the data evolution over time without storing a data history. The DISTAR framework uses the symbolic representation algorithms known for reducing the dimensionality and numerosity of the data. In addition, this thesis also proposes a new adaptive online algorithm, called NOHAR (NOvelty discrete data stream for Human Activity Recognition), which uses as basis the DISTAR framework. Experimental results using three databases show that NOHAR is 13 times faster compared to the state of the art and is able to reduce memory consumption by an average of 99.97.

Link maintenance for integrity in linked open data evolution: Literature survey and open challenges

RDF data has been extensively deployed describing various types of resources in a structured way. Links between data elements described by RDF models stand for the core of Semantic Web. The rising amount of structured data published in public RDF repositories, also known as Linked Open Data, elucidates the success of the global and unified dataset proposed by the vision of the Semantic Web. Nowadays, semi-automatic algorithms build connections among these datasets by exploring a variety of methods. Interconnected open data demands automatic methods and tools to maintain their consistency over time. The update of linked data is considered as key process due to the evolutionary characteristic of such structured datasets. However, data changing operations might influence well-formed links, which turns difficult to maintain the consistencies of connections over time. In this article, we propose a thorough survey that provides a systematic review of the state of the art in link maintenance in linked open data evolution scenario. We conduct a detailed analysis of the literature for characterising and understanding methods and algorithms responsible for detecting, fixing and updating links between RDF data. Our investigation provides a categorisation of existing approaches as well as describes and discusses existing studies. The results reveal an absence of comprehensive solutions suited to fully detect, warn and automatically maintain the consistency of linked data over time.

Strategy Synthesis for Data-Aware Dynamic Systems with Multiple Actors

The integrated modeling and analysis of dynamic systems and the data they manipulate has been long advocated, on the one hand, to understand how data and corresponding decisions affect the system execution, and on the other hand to capture how actions occurring in the systems operate over data. KR techniques proved successful in handling a variety of tasks over such integrated models, ranging from verification to online monitoring. In this paper, we consider a simple, yet relevant model for data-aware dynamic systems (DDSs), consisting of a finite-state control structure defining the executability of actions that manipulate a finite set of variables with an infinite domain. On top of this model, we consider a data-aware version of reactive synthesis, where execution strategies are built by guaranteeing the satisfaction of a desired linear temporal property that simultaneously accounts for the system dynamics and data evolution.

COVID-19 Deaths Previsions With Deep Learning Sequence Prediction

In this study, the authors use deep learning sequence prediction models for the continuous monitoring of the epidemic while considering the potential impacts of Bacille Calmette-Guérin (BCG) vaccination and tuberculosis (TB) infection rates in populations. Three models were built based on the epidemic data evolution in several countries between the date of their first case and April 1, 2020. The data was based on 14 variables for cases prediction, 15 variables for recoveries prediction, and 16 variables for deaths prediction. Prevision results were very promising, and the suspicions on the BCG vaccination and TB infections rates' implications turned out to be warranted. The model can evolve by continuously updating and enriching data, adding the experiences of all affected countries.