1. Introduction
Having access to reliable information during emergencies is essential for effective emergency management. New technologies have mainly changed the nature and quantity of information available from different actors, such as public authorities, media, citizens, and volunteer organizations.
The growth of social media, satellite remote sensing, sensor networks, and connected devices has contributed to a data deluge beyond what can be captured, processed, and interpreted with traditional tools, which is usually known as a big data problem. According to NIST’s big data definition [
1], “
Big Data consists of extensive datasets—primarily in the characteristics of volume, variety, velocity, and/or variability—that require a scalable architecture for efficient storage, manipulation, and analysis”. Thus, big data technologies have been widely used to process data and improve disaster management decision-making processes [
2,
3].
Besides, social media and crowdsourcing have significantly impacted how information is processed and decisions are made. Consequently, emergency management has evolved from centralized top-down models managed by public authorities to collaborative approaches where citizen participation is encouraged. These two models represent a continuum of existing emergency management models [
4]. At the end of the continuum lies the command and control approach [
5] (also called strategic [
6]), which follows an authoritarian model and divides competencies by level of command into strategic, tactical, and operational [
7]. At the other end of the continuum lies the emergent human resource model [
5] (also called people-centered [
7] or tactical [
6]), which tends to divide competencies by theme [
7], such as communication, logistics, and shelter.
A common view is that traditional top-down crisis management approaches are necessary but not sufficient [
8], and they should be complemented with the promotion of societal resilience. While top-down approaches can improve preparedness and planning of emergencies, an effective response during the immediate aftermath of a crisis is critically improved by citizens’ resilience.
The availability and adoption of Information and Communication Technologies (ICTs) have been among the reasons that have enabled this shift in emergency management [
9]. Society has accustomed to immediacy and to gather and deliver information in real-time. Even when landline phone networks are unavailable or intermittently available, fiber-optic connectivity and mobile phone networks exhibit a more resilient performance, especially to establish SMS and text-based short messaging communication. As stated by Eric Gujer [
10]:
“The Internet plays an increasingly important role in catastrophes and conflicts. Television fundamentally changed our perception of conflicts and disasters through live broadcasts from war zones in the nineties. The Internet, cell phones, and satellites are the next stage in the media revolution”. The effective use of social media has made possible phenomena, such as the Arab Spring. In the words of the protester Fawaz Rashed:
“We use Facebook to schedule the protests, Twitter to coordinate, and YouTube to tell the world”. In the emergency response domain, the effective usage of social media has also impacted emergency management. Disasters such as Haiti’s earthquake in 2010 “
have represented a paradigm shift in the use of social media for disaster response, as multiple web-based platforms emerged to collect, refine, and disseminate crisis-related social media” [
11].
Despite these advances, many challenges remain in leveraging the crowd’s wisdom and automatic information processing. The main identified shortfalls of crowdsourcing applications are scalability, quality control, coordination, safety, and forecasting capabilities. Several authors [
12,
13,
14] report that crowdsourcing applications such as Ushahidi have severe scalability issues, since their inflow rate of information can reach thousands of messages per minute, surpassing crowd processing capacity, resulting in an ever-growing backlog of unprocessed requests. Another frequently discussed downside is the need for better quality control and assurance [
12,
13]. Quality assurance is required to improve classifications and geo-location accuracy, reduce redundancy, and ensure critical control points. Concerning coordination, crowdsourcing applications have proven to be useful for gathering information during the disaster, but they do not support response coordination. Gao et al. [
12] proposed to integrate groupsourcing, so the system allows the separation of requests from crowds (end users suffering the catastrophe) and requests from groups (coordination messages of responding organizations). Besides, these platforms cannot forecast the evolution of incoming messages or emergencies in areas with limited or communication ability [
12].
In view of the above-identified shortfalls, it would be advantageous to provide methods and systems that would enable us to combine effectively big data-enabled automatic processing with the power of human-centered approaches in emergency management. In this paper, we aim at providing a reference architecture that enables the combination of the both approaches.
The remainder of this paper is organized as follows.
Section 2 reviews existing works.
Section 3 introduces the proposed Big Data Framework for Emergency Management that provides a panoramic overview of the different actors, data, tasks, and coordination means for emergency management.
Section 4 presents how the reference architecture is mapped onto a case study.
Section 5 analyses the results. Finally, the conclusions of the research are presented in
Section 6.
3. Big Data Reference Architecture for Emergency Management
This section presents the design of a big data reference architecture for emergencies based on NBDRA. The reference architecture shown in
Figure 2 has been constructed inductively based on the analysis of the literature previously presented. Analytical tasks have been classified according to the CommonKADS task hierarchy [
88], as explained below.
The proposed reference architecture aims at developing a shared understanding of the applications of big data for emergency management. This reference architecture can be used for knowledge management by collecting and organizing best practices and for its practical implementation.
Data providers introduce information feeds in the system. The proposed reference architecture extends previous taxonomies [
89,
90], and includes ICT systems that provide information to the big data system [
56]. Data providers have been classified as:
Digital sensors: data collected passively through the use of digital services (e.g., mobile phones, web searches).
Physical sensors: sensors [
90] (e.g., satellite [
91], wireless sensor networks [
90,
92] and geospatial) focused on remote sensing of changes in human activity.
Social media and news media: the information published on the Internet (e.g., blogs, Twitter) can be traced as social sensors of people’s opinions and intents. Especially relevant is geolocated social media [
72].
Open data: open information provided by governments (e.g., census, statistics) and organizations (e.g., Wikipedia).
Crowdsourcing: information produced actively by users in order to report information about a disaster (e.g., mobile phone reporting tool, emergency map).
Health Information Management Systems: health information for managing the disaster, mainly related to patients and hospital management systems.
GIS: geographical information provided by GIS systems.
The five processing activities within the big data application provider has been further detailed for emergency management.
The collection activity uses standard big data collection techniques for accessing data providers and persisting data in the big data framework provider. Depending on the disaster phase, the system orchestrator should configure access to data providers and the security and privacy fabric components to follow the established requirements and data policies. The main specificity for disaster management the integration with crowdworking software.
The preparation activity comprises data cleansing, standardization, validation, and enrichment. The proposed framework includes a list of microtasks derived from the literature review: filtering [
93], tagging [
94], translation [
94], geocoding [
95], geotagging [
96], validation to check the veracity or data correctness [
97], correction [
98], summarization [
99], and comparison [
100]. Many of these tasks can be done using crowdsourcing or automatic methods. For example, Imran et al. [
97] use automatic techniques for filtering and classifying images, and the classification is validated using crowdsourcing.
The analytics activity aims at extracting knowledge from the ingested data. Analytic tasks have been organized based on the CommonKADS task library [
88], since it provides a general framework for classifying the potential uses of big data analytics. This framework distinguishes two general task types: analytical and synthetic tasks. Analytical tasks produce a characterization of the system and are subdivided into prediction, classification, diagnosis, assessment, and monitoring. Synthetic tasks construct a description of the system and are subdivided into assignment, scheduling, planning, modeling, and design. This categorization has been used for classifying uses of big data according to NRF core capabilities in the different phases of disaster management: mitigation (
Table 2), preparedness (
Table 3), response (
Table 4), and recovery (
Table 5).
During the pre-disaster stage, big data analytics can contribute to building resilient infrastructures and communities, both in mitigation and preparedness activities. As shown in
Table 2, during the mitigation phase, big data technologies can help in reducing the impact of disasters by providing a long term hazards data collection system. Big data analytics can be used for risk assessment, in order to understand vulnerabilities to threats and hazards, and develop plans and strategies to manage them. In addition, monitoring and prediction analytic tasks are also relevant, since they can help decision makers to prioritize risks and make informed decisions. Regarding preparedness activities, big data technologies can improve decision making in planning, coordination and information activitiesm as shown in
Table 3.
During the disaster stage, big data technologies can provide real-time decision support for disaster management, since they can manage the variety, volumen, and velocity of the available data sources. As shown in
Table 4, the main purpose of analytic tasks is providing real-time assessment. In fact, the integration of big data has transformed the decision-making process that previously was based on historical data [
86]. Instead, now organizations can make more informed decisions and adapt their strategy when the situation changes. As illustrated in
Table 4, big data analytics can provide assessments for improving decision making in a wide range of activities, such as analysis of social media for emergency planning [
101], rescue team coordination [
102], and triage [
103]. In addition, analytic tasks can provide new insights, since they can detect hidden patterns that enable decision makers to gain a deeper understanding of the situation [
86]. Monitoring activities can benefit from the integration of heterogeneous sources [
104], and help in detecting trends and patterns to foresee potential issues [
86,
105,
106]. Moreover, big data technologies can not only improve situational awareness, but prediction analytic tasks can enable moving from hindsight to foresight, and anticipate the consequences of the current situation.
Finally, during the aftermath of the disaster, big data technologies can contribute to monitor its recovery status, and provide assessment to evaluate the socio-economic consequences and recovery efforts, as can be seen in
Table 5.
The visualization activity presents processed data to data consumers. The proposed reference architecture includes crisis maps since they are among the most popular visualization mechanisms for crowd data. They provide an overview of the emergency situation and include layers for organizing the information (e.g., incidents, safety, and security) [
107].
The access activity manages communication and interaction with data consumers. For disaster management, specific attention should be paid to the communication with crowdsourcing tools, and with visual analytics tools such as crisis mapping ones.
Finally, data consumers use the output of the big data system for managing the disaster. Data consumers of the Big Data System for Emergency Management are:
Government: governmental partners responsible for disaster management.
Media: mass media communication that contributed to information distribution and sharing during the emergency cycle.
NGOs: participating in the emergency as first responders.
Citizens: citizens affected or non-affected by the emergency.
Crowdsourcing: digital humanitarian organizations participating proactively in emergency management.
Health information management systems: health systems that can use the big data insights for their decision making processes.
GIS: GIS systems that can aggregate information from the big data system.
Social media management: social media management tools that can use big data insights for improving information sharing impact.
The proposed reference architecture enables the integration of automatic (big data-based) and crowdsourcing resources as follows.
Regarding big data processing, data pipelines correspond to the processing tasks carried out by big data application providers in NBDRA according to the requirements specified in the system orchestrator. The execution of data pipelines usually requires the system orchestrator’s interaction with other systems that play the role of big data application provider, management fabric, and security and privacy fabric. As Imran et al. [
84] point out, crowdsourcing systems are more suitable for data entry, binary classification, and n-ary classification microtasks. The use of automatic or human processing for these tasks depends on disaster requirements and resource availability.
With reference to the integration of crowdsourcing resources, digital sensors and social media have been identified as data providers, which corresponds to the crowdsourcing roles “crowd as a reporter”, “crowd as a sensor”, and “crowd as a social computer” according to the crowdsourcing role taxonomy defined by Poblet et al. [
76]. Besides, the activities defined in the big data application provider can be executed automatically by the big data system or orchestrated as microtasks, which corresponds to the crowdsourcing role “crowd as a microtasker” of the previously mentioned taxonomy. The access activity also considers the integration of interfaces with the crowdsourcing tools [
78], including the popular crisis mapping system [
108]. Finally, crowdsourcing also plays the role of data consumer. A digital humanitarian can benefit from the use of big data systems for optimizing their performance.
4. Case Study
This section describes a case study to show how the defined reference architecture can be mapped onto published disaster management architectures.
Kabir et al. [
136] proposed the system STIMULATE for coordinating rescue operations based on the information published by affected people in the social network Twitter. The system is deployed in a cloud environment using Hadoop and comprises three components: the tweet fetcher, tweet processing, and rescue scheduling.
The tweet fetcher component collects tweets using the Twitter streaming API. A Web interface allows filtering tweets using multiple keywords and locations. The location area can be selected on a map. Then tweets are preprocessed, replacing emojis, jargon, slang, and contractions with more common wordings. The result is stored in a MongoDB dataset [
137].
The tweet processing component aims at detecting stranded individuals and determine the rescue needs and priority. For this purpose, the system extracts locations. Multi-label multi-class classification is then performed based on a taxonomy provided by the Federal Emergency Management Agency (FEMA) for rescuing stranded people. The categories are: rescue needed, DECW (diseased, elderly, children, and pregnant women), water needed, injured, sick, and flood. Then, rescue priority is calculated based on the aggregation of different factors, such as weather conditions obtained using Open Weather API (Open Weather Service available at
https://openweathermap.org/api). The tweet classifier uses a deep neural network that uses Keras [
138] and Tensorflow [
139] libraries and has been trained with Harvey and Irma datasets, and evaluated in 15 public disaster datasets.
The rescue scheduling component provides tools for managing the rescue operation. It provides a web interface so that rescue teams can manage their tasks, and an administrator can monitor task progress. A scheduling algorithm assigns tasks to rescue teams based on the tweet processing component’s priority and based on their capacity.
According to the eNRF core capabilities taxonomy, this system is used during the response phase in mass search and rescue operations.
Figure 3 describes the mapping of the use case to the reference architecture. The system uses two data providers, Twitter and Open Weather API, that expose a collection of interfaces. Data consumers are government institutions and NGOs since the system aims at coordinating institutional rescue efforts and volunteers. The big data framework provider provides data facilities (MongoDB) and task distribution (Hadoop). The case study uses neither an orchestrator component nor a management fabric.
The core of the STIMULATE system is mapped onto the big data application provider. The collection component consists of a web server that processes data requests and interacts with the data provider.
Data consumers carry out these requests through the collection interface within the access component, which is implemented as a web application The collection component stores the information in the data facilities of the big data application framework, in this case, the database MongoDB. The preparation component pre-processes incoming tweets chaining geocoding and transformation tasks (i.e., management of slang, emojis, and contractions). Then the analytics component performs the tweet classification activity to determine rescue priority that feeds the scheduling task. Results from the analytics component are shown in the visualization component, which provides two interfaces, for administrators and rescue teams. The interface for rescue teams shows a route map for visiting each task location in order. Access to visualization is controlled by an authorization and authentication policy defined in the security and privacy fabric. The access component enables communication with data consumers. In this case, data consumers can configure and interact with the collection component and visualization component.
From this simple case study, some advantages of using a reference architecture can be pointed out. The proposed reference architecture can help us to evaluate the architecture, propose enhancements, and improve reusability. First, the system could benefit from the usage management fabric for automating configuration, resource management, and monitoring. Second, the security and privacy fabric is only used for controlling access in the visualization component, which can be an issue since the system should preserve confidentiality, privacy, and security. Since the collection component’s functionality is not specific to this problem, the system could reuse available collection components designed with security and privacy in mind. Similarly, the preparation component is generic, and the system could benefit from a library of pre-processing multi-lingual components. Finally, the developed analytic component could be reused for other purposes. The use of well-defined interfaces would enable its reuse and improvement.
5. Discussion
This article proposes a reference architecture for big data processing in disaster management. The reference architecture has been designed inductively, based on an extensive review of the literature and the published implementation architectures in the domain. As a framework for its definition, we have chosen NBDRA, since it provides a general framework defined in a public working group, with participants from industry, academia, and government. As a result, NBDRA provides a vendor-neutral, technology-agnostic, and infrastructure-independent ecosystem.
The proposed reference architecture has identified the key components that are relevant for disaster management, and has categorized them based on NRF core capabilities [
19] and the CommonKADS task hierarchy [
88]. The combination of both taxonomies provides an explicit schema of knowledge reusability, and shows big data technologies’ applications for every single core capability for managing disasters. Given that many stakeholders participate in emergency management, the definition of standardized interfaces is essential for effective coordination of the efforts, the provision of access to data sources taking into account privacy and security concerns, and the customization of data consumer and data provider access. NBDRA defines functional components, and an actor can play several roles (i.e., data consumer and data provider). Since NBDRA supports the representation of stacking and chaining of big data systems [
34], the cooperation of the big data systems participating in disaster management can also be represented in the proposed reference architecture. The need for cooperation is widely recognized in emergency management [
140], since responses require a great diversity of skills and resources. Big data integration and Extract, Transform, and Load (ETL) technologies can be crucial for breaking down and bridging data silos [
141]. Moreover, the proposed reference architecture can help in organizing and classifying existing experiences and sharing best practices.
We have detected that the component “security and privacy fabric” should receive more attention in this domain since most works do not mention how they address these concerns. As discussed in some reviews about big data technologies for disaster management [
3,
54,
65], security and privacy issues are still a big challenge. Nevertheless, this problem is not specific to disaster management since big data introduces many privacy preservation challenges [
142]. Thus, adopting a reference architecture can provide a good starting point for fostering the sharing and adoption of best practices.
A limitation of this work is that the reference architecture has been based on published research and should be complemented by consultation with domain stakeholders. Besides, NIST Big Data Working Group has defined interfaces between the NBDRA components [
36]. Nevertheless, there is not an available reference implementation of NBDRA, which could foster its adoption. Another limitation of this work is that we have focused on big data architectural aspects, but other aspects should be addressed. In particular, big data potential can only be achieved if legal, organizational, semantic, and technical interoperability is reached [
143]. In particular, some researchers report [
144] that while technical interoperability has reached a high level of maturity, semantic and legal interoperability remains a significant barrier for the sector. Future work should be carried out to address semantic interoperability, taking into account existing standards, such as OASIS Emergency Data Exchange Language (EDXL) Emergency Standards [
145], and semantic interoperability based on ontologies [
146,
147] to exploit the potential of disaster knowledge graphs [
148].
Author Contributions
Conceptualization, C.A.I., A.F., and Á.C.; methodology, C.A.I. and A.F.; investigation, C.A.I., A.F., and Á.C.; resources, C.A.I., A.F., and Á.C.; data curation, C.A.I., A.F., and Á.C.; writing—original draft preparation, C.A.I.; writing—review and editing, C.A.I., A.F., and Á.C. All authors have read and agreed to the published version of the manuscript.
Funding
This research has been partially funded by the Spanish Ministry of Science and Innovation (Ministerio de Ciencia e Innovación) under the R&D project COGNOS (PID2019-105484RB-I00) and by the Spanish Ministry of Education, Culture, and Sport (Ministerio de Educación, Cultura y Deporte) through the mobility research stay grant PRX16/00515.
Conflicts of Interest
The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
CEOS | Committee on Earth Observation Satellites |
CSP | Crowdsourced Stream Processing |
CRG | Community Response Grids |
ECN | Emergency Communication Network |
EDXL | EDXL |
ETL | Extract, Transform, and Load |
FEMA | Federal Emergency Management Agency |
GIS | Geographical Information System |
ICT | Information and Communication Technologies |
NBDRA | NIST Big Data Reference Architecture |
NGO | Non-Governmental Organization |
NRF | National Response Framework |
OSM | OpenStreetMap |
SM | Social Media |
SNS | Social Network Sites |
USGS | US Geological Survey |
VGI | Volunteer Geographic Information |
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