Técnicas para Detecção de Anomalias em Padrões de Séries Espaço-Temporais: Uma Revisão Sistemática de Literatura

Justificativa: O uso de técnicas para detecção de anomalias e seus impactos é um dos fatores determinantes para a avaliação das condições de tráfego urbano. Objetivo: Investigar técnicas para detecção de padrões e anomalias em séries espaçotemporais. Método: Uma revisão sistemática foi realizada, a partir dos achados em uma base de dados científica (Scopus). Utilizou-se uma string de busca e, em seguida, a leitura e aplicação dos critérios de inclusão e exclusão para seleção dos estudos primários. Resultados: Trinta e dois artigos foram recuperados, sendo que destes, somente doze foram incluídos nesta revisão (38%). Esses diferentes estudos possuem alguns aspectos em comum. O principal é a necessidade de seleção inicial / preparação dos dados a serem coletados e analisados. Esta é uma etapa primordial para determinação dos padrões e anomalias no tráfego de veículos. Diferentes técnicas para identificação de anomalias foram analisadas, sendo as mais predominantes o cálculo da distância euclidiana e o uso de uma arquitetura orientada a serviços e a eventos. Essas técnicas demonstraram ser eficientes no fornecimento de fluxos de tráfego que causam anomalias. Conclusão: O uso dessas técnicas é essencial para a coleta de informações necessárias para o processo de tomada de decisão, em busca da melhoria da qualidade e mobilidade urbana

The competitive productivity (CP) of tourism destinations: an integrative conceptual framework and a reflection on big data and analytics

Purpose: The purpose of this study is twofold. First, this study elaborates an integrative conceptual framework of tourism destination competitive productivity (TDCP) by blending established destination competitiveness frameworks, the competitive productivity (CP) framework and studies pertaining to big data and big data analytics (BDA) within destination management information systems and smart tourism destinations. Second, this study examines the drivers of TDCP in the context of the ongoing 4th industrial revolution by conceptualizing the destination business intelligence unit (DBIU) as a platform able to create sustained destination business intelligence under the guise of BDA, useful to support destination managers to achieve the tourism destination’s economic objectives. Design/methodology/approach: In this work, the authors leverage both extant literature (under the guise of research on CP, tourism destination competitiveness [TDC] and destination management information systems) and empirical work (in the form of interviews and field work involving destination managers and chief executive officers of destination management organizations and convention bureaus, as well as secondary data) to elaborate, develop and present an integrative conceptual framework of TDCP. Findings: The integrative conceptual framework of TDCP elaborated has been found helpful by a number of destination managers trying to understand how to effectively and efficiently manage and market a tourism destination in today’s fast-paced, digital and hypercompetitive environment. While DBIUs are at different stages of implementation, often as part of broader smart destination initiatives, it appears that they are increasingly fulfilling the purpose of creating sustained destination business intelligence by means of BDA to help tourism destinations achieve their economic goals. Research limitations/implications: This work bears several practical implications for tourism policymakers, destination managers and marketers, technology developers, as well as tourism and hospitality firms and practitioners. Tourism policymakers could embed TDCP into tourism and economic policies, and destination managers and marketers might build and make use of platforms such as the proposed DBIU. Technology developers need to understand that designing destination management information systems in general and more specifically DBIUs requires an in-depth analysis of the stakeholders that are going to contribute, share, control and use BDA. Originality/value: To the best of the authors’ knowledge, this study constitutes the first attempt to integrate the CP, TDC and destination management information systems research streams to elaborate an integrative conceptual framework of TDCP. Second, the authors contribute to the Industry 4.0 research stream by examining the drivers of tourism destination CP in the context of the ongoing 4th industrial revolution. Third, the authors contribute to the destination management information systems research stream by introducing and conceptualizing the DBIU and the related sustained destination business intelligence

The competitive productivity (CP) of tourism destinations: an integrative conceptual framework and a reflection on big data and analytics

Purpose The purpose of this study is twofold. First, this study elaborates an integrative conceptual framework of tourism destination competitive productivity (TDCP) by blending established destination competitiveness frameworks, the competitive productivity (CP) framework and studies pertaining to big data and big data analytics (BDA) within destination management information systems and smart tourism destinations. Second, this study examines the drivers of TDCP in the context of the ongoing 4th industrial revolution by conceptualizing the destination business intelligence unit (DBIU) as a platform able to create sustained destination business intelligence under the guise of BDA, useful to support destination managers to achieve the tourism destination’s economic objectives. Design/methodology/approach In this work, the authors leverage both extant literature (under the guise of research on CP, tourism destination competitiveness [TDC] and destination management information systems) and empirical work (in the form of interviews and field work involving destination managers and chief executive officers of destination management organizations and convention bureaus, as well as secondary data) to elaborate, develop and present an integrative conceptual framework of TDCP. Findings The integrative conceptual framework of TDCP elaborated has been found helpful by a number of destination managers trying to understand how to effectively and efficiently manage and market a tourism destination in today’s fast-paced, digital and hypercompetitive environment. While DBIUs are at different stages of implementation, often as part of broader smart destination initiatives, it appears that they are increasingly fulfilling the purpose of creating sustained destination business intelligence by means of BDA to help tourism destinations achieve their economic goals. Research limitations/implications This work bears several practical implications for tourism policymakers, destination managers and marketers, technology developers, as well as tourism and hospitality firms and practitioners. Tourism policymakers could embed TDCP into tourism and economic policies, and destination managers and marketers might build and make use of platforms such as the proposed DBIU. Technology developers need to understand that designing destination management information systems in general and more specifically DBIUs requires an in-depth analysis of the stakeholders that are going to contribute, share, control and use BDA. Originality/value To the best of the authors’ knowledge, this study constitutes the first attempt to integrate the CP, TDC and destination management information systems research streams to elaborate an integrative conceptual framework of TDCP. Second, the authors contribute to the Industry 4.0 research stream by examining the drivers of tourism destination CP in the context of the ongoing 4th industrial revolution. Third, the authors contribute to the destination management information systems research stream by introducing and conceptualizing the DBIU and the related sustained destination business intelligence

A Multidisciplinary Perspective of Big Data in Management Research

In recent years, big data has emerged as one of the prominent buzzwords in business and management. In spite of the mounting body of research on big data across the social science disciplines, scholars have offered little synthesis on the current state of knowledge. To take stock of academic research that contributes to the big data revolution, this paper tracks scholarly work's perspectives on big data in the management domain over the past decade. We identify key themes emerging in management studies and develop an integrated framework to link the multiple streams of research in fields of organisation, operations, marketing, information management and other relevant areas. Our analysis uncovers a growing awareness of big data's business values and managerial changes led by data-driven approach. Stemming from the review is the suggestion for research that both structured and unstructured big data should be harnessed to advance understanding of big data value in informing organisational decisions and enhancing firm competitiveness. To discover the full value, firms need to formulate and implement a data-driven strategy. In light of these, the study identifies and outlines the implications and directions for future research

Understanding Network Dynamics in Flooding Emergencies for Urban Resilience

Many cities around the world are exposed to extreme flooding events. As a result of rapid population growth and urbanization, cities are also likely to become more vulnerable in the future and subsequently, more disruptions would occur in the face of flooding. Resilience, an ability of strong resistance to and quick recovery from emergencies, has been an emerging and important goal of cities. Uncovering mechanisms of flooding emergencies and developing effective tools to sense, communicate, predict and respond to emergencies is critical to enhancing the resilience of cities. To overcome this challenge, existing studies have attempted to conduct post-disaster surveys, adopt remote sensing technologies, and process news articles in the aftermath of disasters. Despite valuable insights obtained in previous literature, technologies for real-time and predictive situational awareness are still missing. This limitation is mainly due to two barriers. First, existing studies only use conventional data sources, which often suppress the temporal resolution of situational information. Second, models and theories that can capture the real-time situation is limited. To bridge these gaps, I employ human digital trace data from multiple data sources such as Twitter, Nextdoor, and INTRIX. My study focuses on developing models and theories to expand the capacity of cities in real-time and predictive situational awareness using digital trace data. In the first study, I developed a graph-based method to create networks of information, extract critical messages, and map the evolution of infrastructure disruptions in flooding events from Twitter. My second study proposed and tested an online network reticulation theory to understand how humans communicate and spread situational information on social media in response to service disruptions. The third study proposed and tested a network percolation-based contagion model to understand how floodwaters spread over urban road networks and the extent to which we can predict the flooding in the next few hours. In the last study, I developed an adaptable reinforcement learning model to leverage human trace data from normal situations and simulate traffic conditions during the flooding. All proposed methods and theories have significant implications and applications in improving the real-time and predictive situational awareness in flooding emergencies