The COVID-19 pandemic has resulted in more than 440 million confirmed cases globally and almost 6 million reported deaths as of March 2022. Consequently, the world experienced grave repercussions to citizens’ lives, health, wellness, and the economy. In responding to such a disastrous global event, countermeasures are often implemented to slow down and limit the virus’s rapid spread. Meanwhile, disaster recovery, mitigation, and preparation measures have been taken to manage the impacts and losses of the ongoing and future pandemics. Data-driven techniques have been successfully applied to many domains and critical applications in recent years. Due to the highly interdisciplinary nature of pandemic management, researchers have proposed and developed data-driven techniques across various domains. However, a systematic and comprehensive survey of data-driven techniques for pandemic management is still missing. In this article, we review existing data analysis and visualization techniques and their applications for COVID-19 and future pandemic management with respect to four phases (namely, Response, Recovery, Mitigation, and Preparation) in disaster management. Data sources utilized in these studies and specific data acquisition and integration techniques for COVID-19 are also summarized. Furthermore, open issues and future directions for data-driven pandemic management are discussed.

From the start, the airline industry has remarkably connected countries all over the world through rapid long-distance transportation, helping people overcome geographic barriers. Consequently, this has ushered in substantial economic growth, both nationally and internationally. The airline industry produces vast amounts of data, capturing a diverse set of information about their operations, including data related to passengers, freight, flights, and much more. Analyzing air travel data can advance the understanding of airline market dynamics, allowing companies to provide customized, efficient, and safe transportation services. Due to big data challenges in such a complex environment, the benefits of drawing insights from the air travel data in the airline industry have not yet been fully explored. This article aims to survey various components and corresponding proposed data analysis methodologies that have been identified as essential to the inner workings of the airline industry. We introduce existing data sources commonly used in the papers surveyed and summarize their availability. Finally, we discuss several potential research directions to better harness airline data in the future. We anticipate this study to be used as a comprehensive reference for both members of the airline industry and academic scholars with an interest in airline research.

The price of an airline ticket is affected by a number of factors, such as flight distance, purchasing time, fuel price, etc. Each carrier has its own proprietary rules and algorithms to set the price accordingly. Recent advance in Artificial Intelligence (AI) and Machine Learning (ML) makes it possible to infer such rules and model the price variation. This paper proposes a novel application based on two public data sources in the domain of air transportation: the Airline Origin and Destination Survey (DB1B) and the Air Carrier Statistics database (T-100). The proposed framework combines the two databases, together with macroeconomic data, and uses machine learning algorithms to model the quarterly average ticket price based on different origin and destination pairs, as known as the market segment. The framework achieves a high prediction accuracy with 0.869 adjusted R squared score on the testing dataset.

In disaster management, people are interested in the development and the evolution of the disasters. If they intend to track the information of the disaster, they will be overwhelmed by the large number of disaster-related documents, microblogs, and news, etc. To support disaster management and minimize the loss during the disaster, it is necessary to efficiently and effectively collect, deliver, summarize, and analyze the disaster information, letting people in affected area quickly gain an overview of the disaster situation and improve their situational awareness. To present an integrated solution to address the information explosion problem during the disaster period, we designed and implemented DI-DAP, an efficient and effective disaster information delivery and analysis platform. DI-DAP is an information centric information platform aiming to provide convenient, interactive, and timely disaster information to the users in need. It is composed of three separated but complementary services: Disaster Vertical Search Engine , Disaster Storyline Generation , and Geo-Spatial Data Analysis Portal . These services provide a specific set of functionalities to enable users to consume highly summarized information and allow them to conduct ad-hoc geospatial information retrieval tasks. To support these services, DI-DAP adopts FIU-Miner , a fast, integrated, and user-friendly data analysis platform, which encapsulated all the computation and analysis workflow as well-defined tasks. Moreover, to enable ad-hoc geospatial information retrieval, an advanced query language MapQL is used and the query template engine is integrated. DI-DAP is designed and implemented as a disaster management tool and is currently been exercised as the disaster information platform by more than 100 companies and institutions in South Florida area.

Techniques to efficiently discover, collect, organize, search, and disseminate real-time disaster information have become national priorities for efficient crisis management and disaster recovery tasks. We have developed techniques to facilitate information sharing and collaboration between both private and public sector participants for major disaster recovery planning and management. We have designed and implemented two parallel systems: a web-based prototype of a Business Continuity Information Network system and an All-Hazard Disaster Situation Browser system that run on mobile devices. Data mining and information retrieval techniques help impacted communities better understand the current disaster situation and how the community is recovering. Specifically, information extraction integrates the input data from different sources; report summarization techniques generate brief reviews from a large collection of reports at different granularities; probabilistic models support dynamically generating query forms and information dashboard based on user feedback; and community generation and user recommendation techniques are adapted to help users identify potential contacts for report sharing and community organization. User studies with more than 200 participants from EOC personnel and companies demonstrate that our systems are very useful to gain insights about the disaster situation and for making decisions.

With the rise of heterogeneous information delivering platform, the process of collecting, integrating, and analyzing disaster related information from diverse channels becomes more difficult and challenging. Further, information from multiple sources brings up new challenges for information presentation. In this paper, we design and implement a Disaster Situation Reporting System (Disaster SitRep) that is essentially a disaster information collecting, integration, and presentation platform to address three critical tasks that can facilitate information acquisition, integration and presentation by utilizing domain knowledge as well as public and private web resources for major disaster recovery planning and management. Our proposed techniques create a disaster domain-specific search engine and a geographical information presentation and navigation platform using advanced data mining and information retrieval techniques for disaster preparedness and recovery that helps impacted communities better understand the current disaster situation. Specifically, hierarchical clustering with constraints are used to automatically update existing disaster concept hierarchy; taxonomy-based focused crawling component is developed to automatically detect, parse and filter those relevant web resources; a domain-oriented skeleton for each type of disasters is used to extract disaster events from disaster documents by defining the set of structural attributes. Furthermore, the platform can perform not only as a domain-specific search engine but also as an information monitoring and analysis tool for decision support during recovery phase of disasters.

With the proliferation of smart devices, disaster responders and community residents are capturing footage, pictures and video of the disaster area with mobile phones and wireless tablets. This multimedia disaster situation information is critical for assisting emergency management (EM) personnel to effectively respond in a timely manner. Currently, however the data is not integrated in incident command systems where situation reports, incidence action plans, etc. are being held. Therefore, we have designed and developed a Multimedia-Aided Disaster information Integration System (MADIS), which utilizes advanced data mining techniques to analyze situation reports and pictures as well as text captured in the field and automatically link the reports directly to relevant multimedia content. Specifically, a dynamic hierarchical image classification approach is proposed to categorize disaster images into different subjects by fusing image and text information. Situation reports are analyzed using advanced document processing techniques and then associated with processed multimedia data. In order to seamlessly incorporate user interactive activities for improving information integration, a user feedback processing scheme is proposed to refine the association between situation reports and images as well as the affinity among images. The system is developed on Apple's mobile operating system (iOS) and runs on iPad tablets, and its usefulness is evaluated by domain experts from the local EM department.

Sustainable building has emerged as an important topic due to the fact that it can significantly reduce the impact of buildings and their operation on the natural environment and more efficiently utilize resources throughout a building's life-cycle. When compared with a traditional buildingdesign process, integrated project delivery has proven to be more efficient, and is thus gaining wider acceptance for many sustainable building projects. However, managing design and construction from different disciplines is still challenging. Conflicts among constraints are often not identified at the right design stage, which results in multiple iterations of the design process. In this paper, a novel constrain-driven model that enhances design processes through better management of constraints and thus delivers optimal design solutions with higher energy performance is proposed. Multiple Correspondence Analysis was applied to capture the correlations between different items (parameter-value pairs) and classes (constraints). Meanwhile, it integrated Collaborative Filtering methods and Constraint Satisfaction Problem to train and refine the proposed model. Finally, we have applied our model to a synthetic data sets to demonstrate its performance.

In this paper, a hierarchical disaster image classification (HDIC) framework based on multi-source data fusion (MSDF) and multiple correspondence analysis (MCA) is proposed to aid emergency managers in disaster response situations. The HDIC framework classifies images into different disaster categories and sub-categories using a pre-defined semantic hierarchy. In order to effectively fuse different sources (visual and text) of information, a weighting scheme is presented to assign different weights to each data resource depending on the hierarchical structure. The experimental analysis demonstrates that the proposed approach can effectively classify disaster images at each logical layer. In addition, the paper also presents an iPad application developed for situation report management using the proposed HDIC framework.

The improvement of Crisis Management and Disaster Recovery techniques are national priorities in the wake of man-made and nature inflicted calamities of the last decade. Our prior work has demonstrated that the efficiency of sharing and managing information plays an important role in business recovery efforts after disaster event. With the proliferation of smart phones and wireless tablets, professionals who have an operational responsibility in disaster situations are relying on such devices to maintain communication. Further, with the rise of social media, technology savvy consumers are also using these devices extensively for situational updates. In this paper, we address several critical tasks which can facilitate information sharing and collaboration between both private and public sector participants for major disaster recovery planning and management. We design and implement an All-Hazard Disaster Situation Browser (ADSB) system that runs on Apple's mobile operating system (iOS) and iPhone and iPad mobile devices. Our proposed techniques create a collaborative solution on a mobile platform using advanced data mining and information retrieval techniques for disaster preparedness and recovery that helps impacted communities better understand the current disaster situation and how the community is recovering. Specifically, hierarchical summarization techniques are used to generate brief reviews from a large collection of reports at different granularities; probabilistic models are proposed to dynamically generate query forms based on user's feedback; and recommendation techniques are adapted to help users identify potential contacts for report sharing and community organization. Furthermore, the developed techniques are designed to be all-hazard capable so that they can be used in earthquake, terrorism, or other unanticipated disaster situations.

We present a novel visual analytics system and multimedia enabled mobile application that allows emergency management (EM) personnel access to timely and relevant disaster situation information. The system is able to semantically integrate text-based emergency management disaster situation reports with related disaster imagery taken in the field by EM responders and community residents. In addition, through an intuitive and seamless Apple iPad application, users are able to interact with the system in diverse places and conditions and thus provide a more effective response. The system is demonstrated via its iPad application which aims at providing relevant and actionable information.

The area of disaster management receives increasing attention from multiple disciplines of research. A key role of computer scientists has been in devising ways to manage and analyze the data produced in disaster management situations. In this paper we make an effort to survey and organize the current knowledge in the management and analysis of data in disaster situations, as well as present the challenges and future research directions. Our findings come as a result of a thorough bibliography survey as well as our hands-on experiences from building a Business Continuity Information Network (BCIN) with the collaboration with the Miami-Dade county emergency management office. We organize our findings across the following Computer Science disciplines: data integration and ingestion, information extraction, information retrieval, information filtering, data mining and decision support. We conclude by presenting specific research directions.

Crisis Management and Disaster Recovery have gained immense importance in the wake of recent man and nature inflicted calamities. A critical problem in a crisis situation is how to efficiently discover, collect, organize, search and disseminate real-time disaster information. In this paper, we address several key problems which inhibit better information sharing and collaboration between both private and public sector participants for disaster management and recovery. We design and implement a web based prototype implementation of a Business Continuity Information Network (BCIN) system utilizing the latest advances in data mining technologies to create a user-friendly, Internet-based, information-rich service and acting as a vital part of a company's business continuity process. Specifically, information extraction is used to integrate the input data from different sources; the content recommendation engine and the report summarization module provide users personalized and brief views of the disaster information; the community generation module develops spatial clustering techniques to help users build dynamic community in disasters. Currently, BCIN has been exercised at Miami-Dade County Emergency Management.

This Partnership for International Research and Education (PIRE) is a 5-year long project funded by the National Science Foundation that aims to provide 196 international research and training experiences to its participants by leveraging the established programs, resources, and community of the Latin American Grid (LA Grid, an international academic and industry partnership designed to promote research, education and workforce development at major institutions in the USA, Mexico, Argentina, Spain, and other locations around the world). In return, PIRE will take LA Grid to the next level of research and education excellence. Top students, particularly underrepresented minorities, are engaged and each participant will receive multiple perspectives in each of three different aspects of collaboration as they work with (1) local and international researchers, in (2) academic and industrial research labs, and on (3) basic and applied research projects. PIRE participants will engage not only in computer science research topics focused on transparent cyberinfrastructure enablement, but will also be exposed to challenging scientific areas of national importance such as meteorology, bioinformatics, and healthcare. During the first year of this project, 18 students out of a pool of 68 applicants were selected; they participated in complementary PIRE research projects, visited 7 international institutions (spanning 5 countries and 4 continents), and published 9 papers.

Crisis Management and Disaster Recovery have gained immense importance in the wake of recent man and nature inflicted calamities such as the terrorist attacks of September 11th 2001 and hurricanes/earthquakes i.e. Katrina (2005), Wilma (2005) and Indian Ocean Tsunami (2004). Most of the recent work has been conducted for crisis management under terrorist attacks and emergency management services under natural disasters with private business continuity and disaster recovery a secondary concern. In this paper, we propose a model for pre-disaster preparation and post-disaster business continuity/rapid recovery. The model is utilized to design and develop a web based prototype of our Business Continuity Information Network (BCIN) system facilitating collaboration among local, state, federal agencies and the business community for rapid disaster recovery. We present our model and prototype with Hurricane Wilma as the case study.

The Latin American Grid (LA Grid) joint research program fosters collaborative research across eleven universities and IBM Research with the objective of developing innovative grid technologies and applying them to solve challenging problems in the application areas of bioinformatics and hurricane mitigation. This paper describes some of these innovative technologies, such as the support for transparent to the application expert grid enablement, meta-scheduling, job flows, data integration, and custom visualization, and shows how these technologies will be leveraged in the LA Grid infrastructure to provide solutions to pharmagenomics problems and hurricane prediction ensemble simulations.