Social media has played a significant role in disaster management, as it enables the general public to contribute to the monitoring of disasters by reporting incidents related to disaster events. However, the vast volume and wide variety of generated social media data create an obstacle in disaster management by limiting the availability of actionable information from social media. Several approaches have therefore been proposed in the literature to cope with the challenges of social media data for disaster management. To the best of our knowledge, there is no published literature on social media data management and analysis that identifies the research problems and provides a research taxonomy for the classification of the common research issues. In this paper, we provide a survey of how social media data contribute to disaster management and the methodologies for social media data management and analysis in disaster management. This survey includes the methodologies for social media data classification and event detection as well as spatial and temporal information extraction. Furthermore, a taxonomy of the research dimensions of social media data management and analysis for disaster management is also proposed, which is then applied to a survey of existing literature and to discuss the core advantages and disadvantages of the various methodologies.
Modern early warning system (EWS) requires sophisticated knowledge of the natural hazards, the urban context and underlying risk factors to enable dynamic and timely decision making (e.g., hazard detection, hazard preparedness). Landslides are a common form of natural hazard with a global impact and closely linked to a variety of other hazards. EWS for landslides prediction and detection relies on scientific methods and models which requires input from the time series data, such as the earth observation (EO) and urban environment data. Such data sets are produced by a variety of remote sensing satellites and Internet of things sensors which are deployed in the landslide prone areas. To this end, the automatic discovery of potential time series data sources has become a challenge due to the complexity and high variety of data sources. To solve this hard research problem, in this paper, we propose a novel ontology, namely Landslip Ontology, to provide the knowledge base that establishes relationship between landslide hazard and EO and urban data sources. The purpose of Landslip Ontology is to facilitate time series data source discovery for the verification and prediction of landslide hazards. The ontology is evaluated based on scenarios and competency questions to verify the coverage and consistency. Moreover, the ontology can also be used to realize the implementation of data sources discovery system which is an essential component in EWS that needs to manage (store, search, process) rich information from heterogeneous data sources.
Early warning systems (EWS) for electrical grid infrastructure have played a significant role in the efficient management of electricity supply in natural hazard prone areas. Modern EWS rely on scientific methods to analyze a variety of Earth Observation and ancillary data provided by multiple and heterogeneous data sources for the monitoring of electrical grid infrastructure. Furthermore, through cooperation, EWS for natural hazards contribute to monitoring by reporting hazard events that are associated with a particular electrical grid network. Additionally, sophisticated domain knowledge of natural hazards and electrical grid is also required to enable dynamic and timely decision-making about the management of electrical grid infrastructure in serious hazards. In this paper, we propose a data integration and analytics system that enables an interaction between natural hazard EWS and electrical grid EWS to contribute to electrical grid network monitoring and support decision-making for electrical grid infrastructure management. We prototype the system using landslides as an example natural hazard for the grid infrastructure monitoring. Essentially, the system consists of background knowledge about landslides as well as information about data sources to facilitate the process of data integration and analysis.Using the knowledge modeled, the prototype system can report the occurrence of landslides and suggest potential data sources for the electrical grid network monitoring.
R apid population growth in cities demands effective plans to protect people from vulnerabilities, for example, natural disasters. Urban risk analytics can play a significant role in enabling dynamic and timely decision-making for risk management in cities. Urban risk analytics is the process of analyzing huge amounts of urban data to understand and model city vulnerability in a holistic way. Due to the complexity of risk management for cities, this process requires sophisticated techniques such as data integration, pattern detection, and data mining to manage and process big city data from different sources using both real-time and batchprocessing models. Here, we propose a cloud-based general framework for facilitating effective urban risk analytics over big city data. We also discuss research challenges in developing cloud-based data integration and analytics algorithms for urban risk management. Urban Risk Analytics Scenario Urban data collection systems derived from pervasive sensors are being deployed in many cities across
Rich information from various data sources has been recognized for enabling people to perform an efficient decision-making and problem-solving. According to thousands of data sources and numerous information types available at the edge of the Internet, the discovery of all potential data sources are needed to facilitate the procurement of rich information. However, current P2P approaches are limited to the discovery of potential data sources in which the number of data sources does not cause tremendous consequences for decision-making and problem-solving. In this paper, we thus propose WindChimer, a partially centralized and controlled P2P system that hybridly combines network topology of structured and unstructured P2P systems. Our experiments showed that the partially centralized and controlled topology enables an efficient discovery of all potential peers as per a given information type. This was accomplished via (i) acceptable number of messages sent throughout the network; (ii) high messaging accuracy for propagating the message to potential peers; and (iii) effective scalability when adding more peers. However, it is currently applicable for low degree of transient peer population, and sustainable data sources of organizations only.
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