Steven Luis

Survey of data management and analysis in disaster situations

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.

Data Mining Meets the Needs of Disaster Information Management

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.

Applying data mining techniques to address disaster information management challenges on mobile devices

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 Apples 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 users 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.

Using data mining techniques to address critical information exchange needs in disaster affected public-private networks

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 companys 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.

Hierarchical disaster image classification for situation report enhancement

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.

A Visual Analytics Multimedia Mobile System for Emergency Response

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.

Disaster SitRep - A vertical search engine and information analysis tool in disaster management domain

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.

DI-DAP: An Efficient Disaster Information Delivery and Analysis Platform in Disaster Management

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.

Constraint driven model using correlation and collaborative filtering for sustainable building

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 buildings 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.

PIRE: a global living laboratory for cyberinfrastructure application enablement

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.