ISSN 2186-7437

NII Shonan Meeting Report

No. 2016-6

Big Data: Challenges and Opportunities
for Disaster Recovery
Takahiro Hara
Sanjay Madria
Cyrus Shahabi
Calton Pu
March 28–31, 2016

National Institute of Informatics
2-1-2 Hitotsubashi, Chiyoda-Ku, Tokyo, Japan

Big Data: Challenges and Opportunities for
Disaster Recovery
Organizers:
Takahiro Hara (Osaka University)
Sanjay Madria (Missouri University of Science and Technology)
Cyrus Shahabi (University of Southern California)
Calton Pu (Georgia Tech)
March 28–31, 2016

1

Introduction

1.1

Motivation

Tsunamis in Japan, earthquakes in Haiti, ﬂoods in Colorado, and many suchother disasters pose a signiﬁcant threat to people and a nation’s critical infrastructures. To minimize human loss, and protect critical infrastructures more
eﬀectively, we need a well-coordinated emergency management and response
system that involves situation-awareness, timely information dissemination, coordination to move people from aﬀected areas, guiding the medical team to themost appropriate locations, and real-time planning and decision-making. Thisis possible due to large amount of Big data is constantly generated by millions of smart phones and heterogeneous sensors in various formats, granularity,
dynamicity, and quality. Thus, the challenge is to overcome the problem ofinteroperability, and support for the extraction of knowledge from large-scale
dynamic data which can be used. This will help in providing more and more
services for city oﬃcials, utility services, and citizens, a necessary requirementfor disaster management.

1.2

Goal

The main purpose of this NII Shonan meeting was to bring together researchers
from the multidisciplinary ﬁelds of data management and analytics; mobiles,
sensors and pervasive computing; geography and urban-planning; and disaster
response and recovery with public agencies and commercial entities towards
using big data for better decision-making and problem solving in the eventof a disaster. To do so, we need to close the gaps between those who collect the data (data providers), those who could beneﬁt from using the data(domain experts), and those who are capable of developing the methods forstoring/managing/processing the data (technology enablers).

1

1.3

Big Data

So-called “Big Data” began when the Enterprise era generated the ﬁrst waveof data through various software applications such as inventory management
or human resource applications. Soon the ﬁeld of computer science realizedthat there were commonalities in how the data was being stored and accessed,
which led to the development of databases. As the size of data grew due tobroad adoption by many enterprises (Volume), new research ﬁelds emerged todeal with eﬃcient access (parallel databases), integration (data warehouses)
and analysis (data mining) of large datasets. However, the second wave of data,
Human-generated data (the Web), exposed the fundamental challenges resulting
from data heterogeneity (Variety); this data is semi-structured (text documents)
or non-structured (pictures and videos) and is growing at a much higher rate.
The rapid growth of web applications left academics with little opportunity toidentify commonalities of data usage, leading to many independent tools that
focus on a narrow aspect of data preparation for a given application type andrequiring human in-the-loop data extraction and preparation. This worked tosome extent, as human data creation processes led to a natural gap between
data generation and data consumption. Machine-generated data represents thenewest wave as they are generated continuously at a high rate (Velocity) fromvarious sensors in the physical world, starting with sensor instrumentation, e.g.,pavement traﬃc loop detectors, SCADA industrial automation sensors, CCTV
cameras, satellite- or plane-based LIDAR sensors, and continuing with inexpensive sensors in our mobile phones, refrigerators, watches, and soon, everything
we wear. These three waves of data gave rise to numerous approaches beneﬁting
from data use in critical decision-making (Big Data).

1.4

Challenges

The time is ripe to embark on a fundamental approach to Big Data challenges byassembling stakeholders to review case studies, design and develop several prototypical end-to-end systems, identify the commonalities, and develop lessons
learned stories. This was exactly the goal of our proposed meeting with afocus application of disaster response and recovery. This is because eﬃcientand thorough data collection and its timely analysis are critical to any disaster response and recovery system in order to save people’s lives during disasters. However, access to comprehensive data in disaster areas and their quick
analysis to transform the data to actionable knowledge are major data sciencechallenges. Moreover, the eﬀective presentation of the collected knowledge tohuman decision-makers is an open problem. Therefore, the proposed meeting is
to study and share experiences in Big Data research, Education and Training aswell as discuss challenges and disseminate solutions, blueprints, and prototypes
focusing on the disaster recovery application domain.

1.5

Results

Twenty researchers attended the meeting. They came from various disciplines(e.g., Computer Science, Geography and Engineering) and organizations (e.g.,
academic, industry and government). During the four-day meeting, various
research challenges and potential solutions were discussed. The summary of the2

Big Data Collection

Big Data Processing

Big Data Presentation

CrowdsSocial Media

Analytics: Deep learning, Spatiotemporal analysis, Time-series analysis,
Uncertainty data analysis
Tools: Multi-dimensional data indexing,
Adaptive networking, GeoQ

Situational
Awareness

Cloud: Service composition

Sensor/IoT/CPS

Figure 1: Summary of Discussed Topics
topics discussed is presented in Figure 1. Basically, the discussion topics can begrouped into three categories: Data Collection, Processing and Presentation.
Under data-collection, we discussed various sources of data available before,
during and after disaster. The data can be obtained from all sorts of devices
instrumented in the physical world, such as from sensors, Internet-of-Things
and Cyber-Physical systems, and also from various kinds of social media suchas Twitter and ﬂickr. The data would then need to be stored and managed(discussed under “tools”), processed (e.g., in “cloud”) and analyzed (under “analytics”) using various techniques. Finally, the results should be made available
to decision makers to become aware of the situation (i.e., situational awareness)and perhaps ask for more data to be collected, stored, processed and analyzed.
In the remainder of this report, we discuss these topics in more details.

2

Overview of Talks

2.1

Big Data Collection based on Crowdsourcing

Crowd Sensing from Heterogeneous Sensors for Disaster
Mitigation
Teruo Higashino, Osaka University, Japan
Recently, sensing technology and IoT (Internet of Things) have much attention for designing and developing aﬄuent and smart social systems. Althoughhuge sensing data are collected in cloud, generally cloud systems are facing
poor scalability and diﬃculty of real-time feedback. Here, we focus on geospatial sensing data welled out continuously everywhere and consider how we cantreat such huge sensing data. We introduce the notion of “Edge Computing”
for this purpose, and discuss how we can apply this notion for designing scalablesocial systems.
We also introduce our recent research work about crowd sensing from heterogeneous sensors for disaster mitigation. In some areas, cameras cannot be used3

for privacy problems. Smartphones have potentially powerful sensing abilities
for crowd sensing Thus, ﬁrst, we introduce Laser Range Scanner (LRS) based
crowd sensing technique. We have realized the technique at Grand Front Osaka,
which is a commercial complex near JR Osaka station, Japan.
We also introduce smartphone-based crowd sensing techniques using accelerometers, microphones and Bluetooth signals. Then, we apply our humanmobility generation technique to create a realistic human mobility called Urban
Pedestrian Flow (UPF) Mobility using sensing data from heterogeneous sensors. In order to create UPF mobility, we observe node densities at multiple
observable points, enumerate pedestrians’ moving routes for the target area suchas routes from a station to a department store, and use a linear programming
technique. Then, we can estimate the number of pedestrians for each routeand create UPF mobility so that the observable errors become minimum. We
create techniques to reproduce passages, add normal and emergency pedestrianﬂows and check eﬃciency of evacuation plans on 3D map so that local governments can make eﬃcient evacuation plans (e.g., evacuation planning for ﬁre andﬂooding of target underground malls). Depending on date/time and real-timesituations of disaster victims, we provide suitable evacuation routes and disaster
information to their smartphones.

Enabling the Crowd for Emergency Crisis Management
Stephen Jones, The MITRE Corporation, USA
On the afternoon of May 20, 2013, an EF5 tornado struck Moore, Oklahoma
and adjacent areas with peak winds estimated at 210 mph (340 km/h), killing 24
people (plus one indirect fatality) and injuring 212 others. In order to coordinate
an immediate response, analysts used a new application called GeoQ to sort
through the open source video, pictures, social media, and overhead imagery
that was available immediately after the event.
As an open source project sponsored by the National Geospatial Intelligence
Agency (NGA), GeoQ’s capabilities include visualizing disparate sources of data,
incorporating a crowd-sourcing workﬂow for analysis, and providing a standardsbased method of sharing this information. This presentation will describe how
GeoQ provides these capabilities and lessons we learned using the tool duringcrisis events.
As described within the talk, GeoQ’s main emphasis was the introduction of
a common workﬂow for analysts and supervisors tasked to respond to a crisis.
Data in the form of imagery, social media, and videos is often extensive during
these situations which leads to analysts performing work on individual data,
leading to an extensive amount of redundancy. GeoQ addresses this ineﬃciency
by geospatially segmenting an impacted area into individual work cells. This
work cell is drawn onto the user’s map, with the size and shape scaled so thearea covered can be analyzed in a few hours. This concept allows users to
discover and overlay appropriate data into that area, building a situational
picture from which they can draw appropriate conclusions. In discussions withanalysts using GeoQ, it was reported that they saw a 20% increase in eﬃciency
using this technique.
In addition to the geospatial segmentation, it was noted in discussions withanalysts that there was no structure in tracking and completing work. Some4

information was collected on spreadsheets, however most was done ad hoc, withlittle rigor in the process. GeoQ changes that approach by implementing a
6-step process (Unassigned, Assigned, In Work, Awaiting Review, In Review,
Complete) that not only provided a framework for doing the work, but also
provided a means for collecting metrics on the time to complete each step,
providing valuable information to those managing the resources required torespond within a short time frame.
As seen through numerous sites online, valuable information can be found
not only from commercially available imagery and systems, but also picturesand videos shared across the Internet. In fact, it has been noticed in our events
that data mined from social media can provide a clearer picture than othersources, especially if weather conditions are severe. It has been said that every
disaster event is a local event, so we want to be able to leverage the informationfrom these sources. Naturally, the amount of information from these sources
can be overwhelming, and often irrelevant to the task at hand. We don’t oﬀer
a solution to this particular problem, however we want to maintain an open
standards interface to allow us to integrate with systems that can mine important information from social media and allow GeoQ to display these results inconjunction with other data sources. Additionally, after analysts have created
a common operating picture in near real-time, this information can be sharedthrough a number of diﬀerent API interfaces to other systems participating in
the event. We have not designed GeoQ to be the required interface to any organization; instead our aim was to be a facilitator between a wide variety of data
sources and emergency management systems already in place.
A signiﬁcant advantage to structuring the workﬂow process when analyzing
a crisis situation is that the process can be parallelized signiﬁcantly. Within
GeoQ, we combined this process with a web interface to allow a large number ofusers to work on a crisis event simultaneously. Within our environment, while
we have tested with a relatively small number of users, we did receive positive
feedback on this form of crowd-sourcing for doing analysis over a large area.
We believe a couple of factors will need to be considered when assemblinga larger crowd to perform these tasks. Users will need an incentive in order toparticipate. Although it is likely that a number of people want to help during acrisis event, they may quickly become demotivated if the event does not impact
them directly. We have experimented with the notion of gamiﬁcation, awarding
points and badges for those performing this work. We have seen positive eﬀects,
such as users competing with each other to accrue the most points, and wehave also seen negative reactions to gamiﬁcation as some analysts believe thismakes the application ’childish’. However, a strong motivation for staying with
gamiﬁcation was found in rewarding users for achievable skills. By presenting
users with badges that represent a skill gained, such as a Residential Damage
Assessment Expert, they could not only be working towards a tangible goal, but
it is something they can further advertise as a skill for other events requiring
assistance. Likewise, groups organizing a crowd to provide help on an eventof their own will have an indication of individuals qualiﬁed to perform criticaltasks by assessing their users’ skills. We have been experimenting with The
Mozilla Foundation’s Open Badges API as a way to provide this capability.
The decision to release GeoQ as an Open Source project had a numberof motivations. First, we were looking to provide transparency for the work
being done by our organization and allow outside organizations to freely use
5the software and the techniques used by our analysts. The hope was that wewould help to promote a community of crisis analysts, from local and stateorganizations through the national level. To this end, we have ﬁelded GeoQ tosome partner organizations during crisis event training exercises and have founda strong acceptance of the system.

Eﬀectively Crowdsourcing the Acquisition and Analysis of
Visual Data for Disaster Response
Seon Ho Kim, University of Southern California, USAEﬃcient and thorough data collection and its timely analysis are critical fordisaster response and recovery in order to save peoples lives during disasters.
However, access to comprehensive data in disaster areas and their quick analysis to transform the data to actionable knowledge are challenging. With the
popularity and pervasiveness of mobile devices, crowdsourcing data collection
and analysis has emerged as an eﬀective and scalable solution.
This talk introduces a new way of data collection (speciﬁcally mobile images
and videos) with spatiotemporal metadata which can be used for collecting,organizing, sharing, and crowdsourcing data in disasters. On top of an existing
mobile media management platform named MediaQ developed by USC IMSC
(Integrated Media systems Center), this study addresses the problem of, andprovides a solution for crowdsourcing mobile videos for disasters by identifying
two unique challenges of 1) prioritizing visual- data collection and transmission
under bandwidth scarcity caused by damaged communication networks and 2)
analyzing the acquired data using a space decomposition technique which canevenly distribute human analyst’s load.
MediaQ system has been described as the base for acquiring and analyzing
the mobile videos utilizing ﬁne granularity spatial metadata of videos including
Field of View model in utilizing spatial metadata (i.e., camera location fromGPS receiver and viewing direction from digital compass on a smartphone),novel queries such as directional query, and spatial crowdsourcing. An analytical model has been also explained to quantify the visual awareness of a video
based on its metadata, speciﬁcally proposed the visual awareness maximization
problem for acquiring the most relevant data under bandwidth constraints. So,
recorded videos are ranked based on the amount of visual awareness and transmitted to server to maximize the visual awareness for a given network bandwidth. Then, the collected videos are evenly distributed to oﬀ-site analysts by
space decomposition by kd-tree to collectively minimize their eﬀorts for analysis. A cyclic data collection using urgency map (cycle of data crowdsourcing,human analysis, updated urgency map from analysis result, new crowdsourcing)
can be applied during disaster time. Simulation results have been presented todemonstrate the eﬀectiveness and feasibility of the proposed framework.
As a part of real application, USC IMSC’s collection with US NGA’s (National Geospatial-intelligence Agency) GeoQ has been presented. MeidaQ system is linked to GeoQ platform by a layer named MediaQ layer so that GeoQ
users can access and search mobile videos collected by MediaQ system. Also,a new way of mobile video collection using drones has been introduced by NII,USC IMSC’s Japanese partner in a joint international project between US NSF

6

and JST. A collaboration is going on between IMSC and NII to collect geotagged videos from multiple drones in disasters and utilize MediaQ for eﬀectively
archiving and eﬃciently searching them with geospatial metadata.
Privacy issue of the people in the collected images was questioned and discussed. We believe that people understand and sacriﬁce some privacy in disastersituation to help others. And at the same time a new privacy policy mechanism
needs to be studied for such applications. Visual analysis of collected media canbe considered to enhance situation awareness. And this approach is orthogonal
to the presented spatiotemporal solution for big visual data.

2.2

Big Data Processing

2.2.1

Analytics

Spatio-Temporal Data Retrieval for Disaster Estimation
Hideki Hayashi, Hitachi, Ltd., Japan
Presentation:
Japan is located in the so-called “Circum-Paciﬁc Mobile Belt,” where seismic
and volcanic activities occur constantly. Moreover, owing to its geographical, topographical, and meteorological conditions, Japan is subject to frequent natural
hazards such as typhoons, torrential rains, and heavy snow. Consequently, suchnatural disasters can cause a great loss of life and signiﬁcantly damage property.
Accordingly, it is a national priority to protect citizens’ lives, livelihoods, andproperty from large-scale natural disasters. When a large-scale natural disaster occurs, damage information is collected in the “ﬁrst action” period. After
that, disaster-relief operations and support over a wide area (depending on thescale of the disaster) are requested. At present, to collect damage information,municipalities, cities, districts, towns, and villages report damage information
to prefectural governments, which then report to the national government by email or fax. Disaster-related organizations need a few days to grasp the damage
situation of a large-scale natural disaster because they collect the damage information manually. Thus, the time taken to grasp damage situations of large-scale
natural disasters needs to be reduced.
At the time of a large-scale natural disaster, disaster-related organizations
obtain insuﬃcient information because the infrastructure such as the power
networks and the communications network is damaged. Even so, they still haveto estimate the damage situation concerning a large-scale natural disaster fromthat insuﬃcient information. Disaster-related organizations especially need toquickly obtain the coverage of the natural disaster. So, they can plan whereto dispatch rescue teams and disaster relief. Recently developed simulation
technologies for simulating tsunamis, spreading of ﬁres, heavy rain, and so on
have made it possible to estimate the coverage of a natural disaster.
Here, after a large-scale natural disaster occurs, a natural disaster simulationis considered to be run to estimate the disaster’s eﬀect. An input condition isnecessary to run the natural disaster simulation. However, there are some cases
where the input condition is not collected after a large-scale natural disasteroccurs. For example, a ﬁre spread simulation is run for estimating the damage
caused by ﬁre after a large earthquake right under an urban area. Information
on ﬁre outbreak spots and directions and velocities of the wind is necessary as
7the input condition, but the information on ﬁre outbreaks spots is diﬃcult tocollect. Even if an input condition is collected, there are some cases where theprocessing time for simulating a natural disaster is long. For example, tsunami
simulators take much time to simulate tsunami coverage because they calculate
complicated tidal wave propagation.
To solve these problems, we proposed a fast spatio-temporal similarity searchmethod that searches a database storing many scenarios of disaster simulation
results represented by time-series grid data for some scenarios similar to insufﬁcient observed data sent from sensors. The proposed method eﬃciently processes spatio-temporal intersection by using a spatio-temporal index to reduce
the processing time for the spatio-temporal similarity search. The results of theperformance evaluation showed that the proposed method achieves a shorter
response time for the spatio-temporal similarity search than two conventional
methods that use a temporal index and a spatial index.
Discussion:
Applications of spatio-temporal similarity search were discussed. As applications, we gave a disaster estimation system and a disaster warning systemat the time when a large-scale disaster occurs. Especially, in a case where an
urgent response of a disaster such as tsunami is necessary, these systems allow
disaster-related organizations and residents to do quick actions. In such a situation, we recognized issues of improving estimation accuracy and translating of
the probabilities into information that people can understand. Additionally,
spatio-temporal indexing of spatio-temporal similarity search was discussed.
The reduction of simulation result data and the the shortening of response time
are important challenges.
Acknowledgement:This work is supported by consignment research and development of techniques about use and application of real-time information in G-space platformfrom the Ministry of Internal Aﬀairs and Communications, Japan.

Traﬃc Flow Analysis for Urban Railway Networks using
Self-learnt Cellular Handoﬀ Patterns
Hiroki Ishizuka, KDDI R&D Laboratories, Inc., Japan
Increasing traﬃc congestion at commute times has signiﬁcantly degraded
the eﬃciency of the transportation infrastructure and increased travel time, air
pollution, and fuel consumption. As a result, urban areas are faced with thenecessity of improving public transport to reduce car use. Urban areas (NewYork, London, Paris, Beijing, and Tokyo) have multimodal public transportsystems consisting of railways and buses or light rail. In particular, railway
services can carry massive amount of people at once are expected to be eﬀective
during commute times.
The Tokyo metropolitan area experienced a shaker in the upper 5 during
the Great East Japan Earthquake. As a result, almost all railways temporarily
stopped their service. Since the earthquake occurred on a weekday afternoon,a large number of commuting workers and students were in their oﬃces andschools in Tokyo. The stoppage of railways made it impossible for such people to return home. During the Great East Japan Earthquake, there were 3.5

8

million as commuters stranded in the Tokyo metropolitan area. For the temporary evacuation of such stranded commuters, the government should estimate anumber of potential candidates for the stranded commuters by analyzing railway
commuters in daily life.
In order to roughly understand the behavior of railway commuters, we canuse the results of a metropolitan transportation census provided by Japan’s
Ministry of Land, Infrastructure, Transport, and Tourism’s (MLIT) and local
governments. However, the census has been conducted every ﬁve years., e.g.,
the latest result was summarized in 2010. Because the environment of a transport infrastructure changes every year, understanding the behavior of railway
commuters in detail is diﬃcult using such old data. As another approach toﬁnd the behavior of railway commuters, we may use ticket sales from railway
operators; however, railway operators have not provided such data to the public
because of privacy issues.
On the other hand, mobile phones have become a key device for pervasive
computing with users carrying them at almost all times. The ubiquity of theseplatforms has transformed mobile phones into one of the main surveyors ofhuman behavior. However, conventional work using sensors on a mobile phonemight not be suitable for large-scale, sustainable data collection in the analysis
of human behavior. For example, participants (users) must install to analyze
user activities. Moreover, the application wastes energy from the mobile phonebattery with the unintended sensing operations. Though participatory sensing
is one of the attractive solutions, collaborative users have been limited.
In order to cope with the problem, understanding human behaviors using calldetail records (CDRs) that automatically recorded the location of the user andwithout the user’s intentional operation have attracted attention. In fact, every
time a subscriber makes or receives a phone call, a short message service (SMS),
a multimedia messaging service (MMS), or data communication regarding theinteraction and the location of the user (in the location of the base station
used for the communication) is logged as CDR for billing purposes. CDRs
have automatically and routinely been recorded without the user’s intentional
operation. Even if the user doesn’t operate the mobile phone intentionally, somesocial networking applications on the mobile phone periodically are sending keep
alive messages in several minutes. Although large-scale data are accumulated
from CDRs through everyday use of mobile phones, the spatial and temporal
resolution of CDRs is lower than that of existing positioning technologies.
To overcome the weak points of CDRs, some existing works have focused on
the cellular handoﬀ patterns as a substitute for the sparse location of CDRs.
Characteristics of the handoﬀ pattern when people get on the same rail line arelikely to be similar. Thus, the handoﬀ pattern of base stations can be useful data
to analyze railway commuters. To manage a large numbers of rail lines, however,the conventional works are not suitable at the point of scalability, because theworks have to learn the handoﬀ patterns for each route manually. Moreover, theworks don’t adapt to be rebuilt the correct cellular handoﬀ pattern including
the modiﬁcation when a new rail line or a new base station has been laid after
once building the pattern.
To solve the issue, we propose the system that enables to maintain a large
number of the cellular handoﬀ patterns automatically. Our proposed systemself-learns all cellular handoﬀ patterns in urban complex railway networks using
a huge amount of anonymous sparse location data of CDRs. In this talk, we
9evaluate the three kinds of self-learning methods that include the Voronoi diagram based approach, the mahalanobis distance based approach and the static
range based approach using CDRs from a millions of subscribers.
As the result, the accuracy of classifying the rail lines from the individual
cellular handoﬀ pattern achieved 81% against accurate 7600 GPS trajectories.
In addition, the correlation about the traﬃc ﬂow of urban railway networks
between our output and the latest census data was 0.768. As the contribution of this talk, our proposed system indicates the possibility of understanding
the traﬃc ﬂow of the urban railway networks more often using only CDRs as
substitute for the census.

A Deep Learning-Based Forecasting Tool for Big Data
Kuo-yi Lin, Asia University, Taiwan
Jeﬀrey J.P. Tsai, Asia University, Taiwan
The disaster response provides immediate assistance in several areas suchas healthcare, operation and manufacturing to manage the occurring suddenly
to avoid the loss of life and resources damage. Therefore, the forecasting tool
should be developed to plan in advance to minimize the negative eﬀects of adisaster and allow the organizations to recovery the critical functions. In the big
data era, the real time collection and analysis of data help managers to search,deﬁne, evaluate and rescue resources and facilitates immediately. However, toforecast situation and support decision for disaster response in real setting arechallenging.
We developed a deep learning-based forecasting tool to extract the relationship between the inputs and outputs factors via historical data for capturing disaster response insights. The convolutional neuron networks (CNN) andconvolutional deep belief networks (CDBN) were proposed. We validated the
proposed tool with realistic data collected from a leading medical informationcompany, and perform scenario analysis to evaluate its performance under different conditions. The results have shown the practical viability of this tool toallow the decision makers to identify the solution among a set of non-dominatedsolutions within a reasonable time. Due to the high competitively among diﬀerent types of ﬁelds in the artiﬁcial intelligence, these trends are forcing them toconstantly improve their strategy to enhance the eﬃciency for disaster response.
While this research are focused on the accuracy of the predicting model thatwas built based on data extracted from the environment data and life style data.
Indeed, disaster response decisions are a nonlinear assignment problem withthe two objectives for minimizing the negative eﬀects and minimizing the gap
between management preferences including the allocation ratios among the resources. The conducted leading factors provide insights to adjust the manage
settings based on the strategies. In particular, the environment data and life
style data were collected. Deep learning utilized to execute signiﬁcant analysis
and produce reliable predictions to build forecasting model based on environment data as the prediction parameter. As a result of the information obtainedby this research, recommendations concerning the management insight weremade based on the domain knowledge.
Deep Learning played a fundamental role been crucial in the building of theprediction model through the artiﬁcial neural network based on environment
10

parameters to disaster forecasting. The information obtained through the research beneﬁts the administrative policies bases, in aspects such as, allocationsof resources and enhances the rescue eﬃciency. Although this tool is validated
towards medical information industry, it is expected for it to be applicable on
diﬀerent area. The research sets the bases in an administrative medical information matter to continue developing diﬀerent models for them to provide
more accurate actual and future information that can enlighten the managers.
Further research should be done to classify the factors regarding diﬀerent scenario such as earthquake and typhoon via rolling prediction method to forecast
further information.

Reliable Spatial and Spatio-Temporal Pattern Analysis to
Support Decision Making in Disaster Management Applications
Matthias Renz, George Mason University, USA
Geo-spatial- related data has a tremendous impact in disaster recovery applications and spatial applications are critical in pre-, during, and in post-disastermanagement and response. An inherent property of any spatial and spatiotemporal dataset is uncertainty due to various sources of imprecision. In addition to the fact that measurements are naturally imprecise, uncertainty isalso a consequence of insuﬃcient sensor coverage and the quest for energy- and(network-) communication savings. All of these issues introduce the notion of
uncertainty in the context of spatial and spatiotemporal data management - an
aspect raising an imminent need for its scalable and ﬂexible management.
In my talk, i discussed two ways of coping with uncertain data for data query
processing and analysis aiming with focus on achieving reliable results: 1) data
cleaning and 2) probabilistic data processing. Data cleaning is the most common approach. The idea is to estimate the true data values by applying data
cleaning methods (e.g. expected value, most probable value, or other statistic
methods) and taking the estimated (“non-uncertain”) values as true data basis
which allows to apply standard query processing and data analysis methods.
Since the data cleaning approach goes hand in hand with information loss andmissing information about the trustability of the query and data analysis resultsthat are returned to the users, the probabilistic data processing is presented as
better alternative. The idea of the probabilistic data processing approach is
to incorporate the potentially available knowledge about the uncertainty of the
input data into the entire query process yielding probabilistic query and data
analysis results. The advantage of the probabilistic query processing strategy
is that the user gets a more reliable query and analysis output in from of aset of possible true result alternatives associated with conﬁdences that might
help to make better decisions. On the other hand, probabilistic data processing
is much more expensive and requires special techniques and methods that arescalable and meet the performance requirements of disaster management andrecovery applications. Two generic approaches are introduced that addresses
the aforementioned problems, (1) the paradigm of equivalent worlds and (2)the representative result oriented approach based on Monte Carlo sampling.
Example applications are sketched for both approaches, including probabilistic11

co-location mining that follows the paradigm of equivalent worlds and representative clustering as example of the second generic approach.
One important and critical issue that has been discussed most is that all approaches addressed in this talk make the assumption that the knowledge about
the uncertainty, i.e. the uncertainty model, of the data is available. The derivation of the uncertainty model is a huge research ﬁeld by itself. One possibility
to get the information is to learn the model based on multiple observations (ifavailable) or based on historic data, or using approaches from statistics, e.g.
Kalman ﬁlter or Kriging, the latter is an approach used in geographics andmakes estimations based on distance to the origin of a measurement. The question here is how accurate these models have to be to get suﬃcient reliable queryand analysis results. Correlations between the (input) data quality and the corresponding result quality for speciﬁc query and data mining tasks is an open
and quite interesting future research topic. Another question that has been
discussed was the applicability of probabilistic query or data analysis results inthe context of disaster recovery. I belief that the diversity of possible results
is a very important parameter for decision making. However, the applicability
highly depends on how the probabilistic results are represented, reported to theuser, and how they are interpreted, which is also another huge research ﬁeld forthe future on itself.

Mining and Forecasting of Big Time-series Data
Yasushi Sakurai, Kumamoto University, Japan
The increasing volume of online, time-stamped activity represents a vital newopportunity for data scientists and analysts to measure the collective behaviorof social, economic, and other important evolutions in the real world. Timeseries data occur naturally in many online applications, and the logging rate has
increased greatly with the progress made on hardware and storage technology.
One big challenge for time-series data mining is to handle and analyze such large
volumes of data (i.e., “big” time-series data) at a very high logging rate. Timeseries data comes in various types of formats including co-evolving numericalsequences (e.g., IoT device data, video, audio), complex time-stamped events(e.g., web-click logs of the form ¡user-ID, URL, time¿), and time-evolving graph(e.g., social networks over time). Data variety imposes new requirements to data
mining, therefore recent studies has revealed some new directions for researchon time-series analysis, which include:• Large-scale tensor analysis:
Given huge collections of time-evolving online activities such as Google
search queries, which consist of multiple attributes (e.g., keywords, locations, time), how can we analyze temporal patterns and relationships
among all these activities and ﬁnd location-speciﬁc trends? Time-evolving
online activities and many other time-series data and can be modeled as
tensors, and tensor analysis is an important data mining tool that hasvarious applications including web-click logs for multiple users/URLs, IoT
data streams, hyperlinks and social networks over time.
• Non-linear modeling:12Non-linear models are widely used in a variety of areas, such as epidemiology, biology, physics and economics, since the nature of real data sets suggests that non-linear models are appropriate for describing their dynamics.
In the data mining ﬁeld, analyses of social media and online user activities have attracted considerable interest, and recent studies have focusedon non-linear time-series analysis to understand the dynamic behavior ofsocial networks (e.g., information diﬀusion, inﬂuence propagation).
• Automatic mining:
We also emphasize the importance of fully-automatic mining. Most of
the existing time-series tools basically require parameter settings and ﬁnetuning, such as the number of coeﬃcients, and the reconstruction error
thresholds, and they are very sensitive to these parameters. So, what if wehave to handle very large datasets with millions, billions (or even trillionsof time-series? In fact, faced with “big data”, fully automatic mining iseven more important: otherwise, the data scientists and analysts would
have to try several parameter tuning steps, each of which would take toolong (e.g., hours, or days). Namely, as regards real big data analysis, wecannot aﬀord human intervention.
Recent work has revealed a huge demand for research on natural problems,
including natural disasters. Since the environmental IoT data is insuﬃcient
to discover all important properties of natural issues. One of our future goals
is to combine environmental IoT and social sensing data and examine the inﬂuence of natural phenomena (e.g., earthquakes in Kumamoto) to the society,along with the data of the number of patients/victims. We will integrate theabove approaches (i.e., large-scale tensor analysis, non-linear modeling, automatic mining), which helps us to achieve this goal.

Estimation of People Movement from Mobile Phone Data
using Data Assimilation Technology
Yoshihide Sekimoto, University of Tokyo, Japan
Recently, an understanding of mass movement in urban areas immediately
after large disasters, such as the Great East Japan Earthquake (GEJE), hasbeen needed. In particular, mobile phone data is available as time-varying
data. However, much more detailed movement that is based on network ﬂow
instead of aggregated data is needed for appropriate rescue on a real-time basis.
Hence, our research aims to estimate real-time human movement during large
disasters from several kinds of mobile phone data. In this paper, we simulate themovement of people in the Tokyo metropolitan area in a large disaster situationand obtain several kinds of fragmentary movement observation data from mobile
phones. Our approach is to use data assimilation techniques combining withsimulation of population movement and observation data. The experimental
results conﬁrm that the improvement in accuracy depends on the observation
data quality using sensitivity analysis and data processing speed to satisfy eachcondition for real-time estimation.
First, the system starts to collect mobile phone CDR data when it detectsthat a large-scale disaster has occurred. At the same time, it starts to collect13

disaster information from mobile phones owned by some people. Fifteen minutes
after the disaster occurs, it displays fragmentary disaster information collectedfrom mobile phones. Thirty minutes after the disaster occurs, it displays theestimated movement of people and disaster status. After that, it updates theestimated results every 30 minutes. As a result, local governments can thenexamine the situation to ensure the safety and security of residents based on
fragmentary disaster information 15 min after the disaster occurs. After 30
min, they start to set up their emergency headquarters and request support
from neighboring prefectures based on the estimated results of the movement ofpeople and the disaster situation. At the same time, the national government
establishes the Headquarters for Major Disaster Management and consults withthe local government to allocate resources such as relief supplies and rescue
teams to the prefectures based on the estimated results.
Our approach utilizes two kinds of data obtained from mobile phones. For
the ﬁrst type, we utilize aggregated mesh-based population data from the CDR
data of a mobile telecommunication company. These CDR data are automatically and routinely recorded without the user’s intention. In fact, every timea user makes or receives a phone call, short message service (SMS), multimedia messaging service (MMS), or data communication regarding the interaction
and location of the user (to locate a base station for communication), this data
is logged as CDR for billing purposes. Even if the user does not operate themobile phone intentionally, some applications on the mobile phone periodicallysend “keep-alive” messages every few minutes. Using these data, we are able todetermine an individual user’s location every several minutes using triangulation over the round trip delays of each base station to which the mobile phoneof the user is connected. The provider of this data, as a mobile operator, has
obtained a clear opt-in permission for the use of CDRs from each user for thisresearch.
In this work, we generated an aggregated mesh-based population for privacy
issues. The spatio-temporal resolution of each mesh is 500 m2 and 5 min Whena large-scale disaster occurs, some base stations will immediately stop service
because of damages caused by the disaster. Further, other base stations will
gradually stop because of power supply loss within three to six hours after thedisaster occurs. Therefore, the population data from the CDR will also decreaseover time. We created the observation population data of our experiment toconsider the shutdown rate of base stations using an actual base station spatial
distribution according to the disaster level.
In this experiment, we showed that the sensitivity of the results depends
on the observation data quality and quantity in the Tokyo metropolitan area.
Moreover, the processing time was well within the 30 min assimilation step
duration. However, some problems still remain. The accuracy of the estimatednetwork-based population movement volume is still not suﬃcient, although weestimate population movement. Additional improvement will be needed using
network-based volume observation data.

14

Mining Social Media to Support Disaster Management
Kyoungsook Kim, National Institute of Advanced Industrial Science and Technology, Japan
Social media data provide insight into people’s opinions, thoughts, and reactions about real-world events such as hurricanes, infectious diseases, or urban
crimes. In particular, the role of location-embedded social media (for short, geosocial media) for innovative disaster management systems is being emphasized
to monitor surrounding situations and predict future eﬀects by the geographyof data shadows, as evidenced by experience from recent natural disasters suchas the Tsunami and earthquake in Japan and Hurricanes in the USA (Sandy
and Katrina). For example, tweets mentioning ﬂooding from Oct. 29th to 30th,
2012 concentrated in the path of Hurricane Sandy and reﬂected experiences ofthe storm. Even though, there is high potential of geo-social media for disastermanagement, it brings big challenges to ﬁnd meaningful information about dynamic social phenomena from the mountains of fragmented, noisy data ﬂooding.
In this talk, I introduce Sophy framework to create a spatiotemporal knowledge in real time and RendezView system as an interactive visual data mining
tool. People in disaster areas need to continuously receive disaster data thatare related to local areas (such as ﬂooded areas nearby and collapse of the nearest freeway). The spatiotemporal knowledge about proximity and relationships
plays a basic role in localizing the information during the disaster. The Sophy
framework constructs spatiotemporal relations among morphological features(i.e., geometric shape and size) of the distribution of geo-tagged Twitter messages (for short, geo-tweets) regardless of the identiﬁcation of users in the realtime. Then, it estimates basic topological relationships between features byusing diﬀerential measurements in spatial, temporal, and semantic dimensions
and stores the extracted features and relations into a graph-based database.
Through the demonstration, I show how to discover an interesting pattern of
topic ﬂocks through our framework by using a typhoon case of HALONG in2014.
Also I demonstrate another visualization tool, called RendezView, composedof a three-dimensional (3D) map, word cloud, and Sankey ﬂow diagram. RendezView allows a user to interactively and intuitively discern spatiotemporaland semantic contexts of local social ﬂock phenomena and their co-occurrence
relationships. In the 3D map represented in the X-Y dimensions as a geospatial
space and the Z dimension as a time line, cubes indicate local social ﬂock phenomena (spatiotemporal clusters) and their color represent the measure of data
concertation. When the user selects boxes in the map, the word cloud appears
and displays a group of keywords as semantic contexts. Moreover, it represents
the most common keyword co-occurrences through the Sankey diagram. I expect RendezView can be used by researchers and scientists to investigate social
phenomena over a geographic region, such as information ﬂow during disasters,
patterns in work and hiring, or trends in political discourse. As an example, asocial scientist can use the 3D map to compare the ﬂock pattern of job postings between the east and west coasts in the US by searching on the keyword“work.” The researcher then identiﬁes keywords in the word cloud with a similar
pattern, such as “apply” and “hire.” The Sankey diagram informs the next keyword search based on strong spatiotemporal co-occurrence frequency with the15

original keyword, and the user iterates with this process to thoroughly analyze
nationwide work and hiring patterns.
2.2.2

Tools

Big Data Framework for Integrating Sensor Data and Information Extracted from Social Media
Takahiro Hara, Osaka University, Japan
Recent studies have revealed that messages posted on SNSs (Social Network
Services) such as twitter and Facebook can be used for detecting various kindsof facts in the real world such as events, trends, and people’s sentiment, which
can be considered as kinds of social sensor data. Social sensor data are very
useful for Big Data analysis because these tell many things representing thereal world, which cannot be known by only analyzing traditional Big Data using general sensor data such as GPS (location), temperature, seismometer, andwater-level gauge data. In particular, in disaster situations, real-time messages
from ordinary people are quite useful for disaster situation awareness and decision making. However, in the current situations such as the cases of Hurricane
Sandy and in Haiti earthquake, over 20M messages posted, only 100,000 messages were actually processed by the government agencies mainly by manual.
On the other hand, while many research projects have proposed various techniques for extracting information from SNSs, most of them do not assume toreuse the output results for other applications as social sensor data, i.e., theseare sole applications.
To tackle these problems and encourage reuse of SNS analytical results associal sensor data, we have just started a new project to develop a framework
to share social sensor data, which can handle both (real) sensor data and social
sensor data. In this position talk, we brieﬂy present this on-going study. Our
framework aims to share not only social sensor data (i.e., analytical result of
SNS data) but also deﬁnitions of social sensor data and procedures to generate(analyze) them, which, we hope, are useful for application developers to reuse
the existing deﬁnitions and procedures of social sensor data for developing new
applications or generating new types of sensor data. To this end, we havedeveloped a prototype where the social sensor deﬁnition, procedures to generatesocial sensor data, and database conﬁguration are separately written in three
diﬀerent ﬁles, and the framework converts these ﬁles into a runtime programand executes it to generate social sensor data. Then, other users can search anddownload both the generated sensor data and its source ﬁles.
During the discussion in the meeting, some open issues were discussed including how to share SNS data stored by diﬀerent groups/organizations because
making stored SNS data is basically illegal in many SNS applications. The issues
discussed also include the ownership problem for social sensor data generated
from SNS data whose owner is not the one made analytics.

16

M-Grid : A Distributed Framework for Multidimensional
Indexing and Querying
Sanjay Madria, Missouri University of Science and Technology, USA
The widespread use of mobile devices and the real time availability of userlocation information is facilitating the development of new personalized, locationbased applications and services (LBSs). Such applications require multi attribute query processing, scalability for supporting millions of users, real time
querying capability and analyzing large volumes of data. Cloud computing
aided a new generation of distributed databases commonly known as key-value
stores. Key-value stores were designed to extract value from very large volumesof data while being highly available, fault-tolerant and scalable, hence providing
much needed features to support LBSs. However, complex queries on multidimensional data cannot be processed eﬃciently as they do not provide means toaccess multiple attributes.
In this talk, I will present MGrid, a unifying indexing framework which enables key-value stores to support multidimensional queries. We organize a set ofnodes in a modiﬁed P-Grid overlay network which provides fault-tolerance andeﬃcient query processing. We use Hilbert Space Filling Curve based linearization technique which preserves the data locality to eﬃciently manage multidimensional data in a key-value store. We propose algorithms to dynamicallyprocess range and k nearest neighbor (kNN) queries on linearized values. This
removes the overhead of maintaining a separate index table. The approach iscompletely independent from the underlying storage layer and can be implemented on any cloud infrastructure. Experiments on Amazon EC2 show that
MGrid achieves a performance improvement of three orders of magnitude incomparison to MapReduce and four times to that of MDHBase scheme.
Research Questions and Comments:
Q. improve multiple queries or single query?
A: multiple queries, with load balancing.
Q: does Hilbert ordering preserve locality?
A: table per share and table per node preserve data locality, not Hilbert encoding.
Q: Do you handle updates?
A: update is an insertion.
Q: indexing updates may cause data movements.
A: we are not considering updates, but only insertions (append).
Addition comments: No concern for consistency problems.

Big-Data-Driven Research on Disaster Response and Management
Calton Pu, Georgia Tech, USA
Critical infrastructures from the backbone of modern civilization, and they
can be damaged or stressed when disasters strike. As a representative example,
transportation networks such as roads are often aﬀected by earthquakes andﬂoods, causing stress in traﬃc due to reduced transportation capacity. Similar situation happens with healthcare. When hurricane Katrina ﬂooded part of17

New Orleans, medical facilities and personnel became stressed and the quality ofhealthcare in some hospitals became sub-optimal. Our hypothesis is that better
information (both from the past and on the present) will enable better scheduling of limited resources. A successful example in transportation that supports
our hypothesis is the Waze mobile app, which uses real-time crowdsourcing tohelp drivers get around traﬃc congestion.
Although the actual tasks to repair physical infrastructures are engineering in nature, optimized use of limited resources will depend fundamentally on
improved information on the events and the current state of infrastructures affected. We will integrate two complementary kinds of sensor information. The
ﬁrst kind of information come from physical sensors, which supply precise information on a narrow area (or electromagnetic band), but they are often limitedby the small scale of sensor network deployment. The second kind of information
come from social media, which provide a large amount of data on a wide rangeof topics, but their value is often limited by noise, where real information ismixed with misinformation and disinformation. The strengths and weaknessesof physical sensors and social media complement each other, providing more
information on current events and up-to-date state of critical infrastructures.
Within the general areas of resilient infrastructures and big data, we will focus on the integration of heterogeneous big data and real-time analytics that will
improve the adaptive management of resources when critical infrastructures areunder stress. The integration of heterogeneous data sources is needed because
we are working with many kinds of physical sensors and social media when trying
to determine the status of infrastructures and demands under stress. Real-time
analytics is needed when we apply the algorithms and software tools to manage
the resources smartly and adaptively in a stressed environment.
We outline an illustrative example, the LITMUS project that demonstrates
the feasibility and usefulness of integrating heterogeneous data sources for realtime big data analytics to collect and ﬁlter data on landslides, an example ofsmall and medium disasters that do not have reliable physical sensor detection
networks). Reliable and timely detection of disasters such as landslides is an
important building block towards resilient infrastructures in smart cities. Good
information on disasters can help focus the eﬀorts to delimit the damages causedby disasters, and improve our knowledge of transportation networks, which inturn can lead to faster and more precise emergency response and recovery.
LITMUS is a landslide information service based on the integration of multiple, independent sensor information providing high quality information on
landslides by integrating data from several physical sensors (rain and earthquakes, the two major causes of landslides) and social media such as Tweeter,
Instagram, YouTube, and Facebook. LITMUS uses several ﬁltering techniques
to make social media data useful for landslide information, starting from simple and eﬀective positive and negative keyword ﬁlters, geo-location information
synthesis, machine learning algorithms, and reduced explicit semantic analysis(ESA).
The current version of LITMUS consists of a front-end implemented as a
Web application and a back-end, which is the core of the system. The front-end
is a live demonstration that runs on Apache web server. It uses Google Maps
JavaScript API to render all feeds, including detected landslides, and PHP toaccess LITMUS’ back-end. The back-end is developed in Python, except forbinary classiﬁcation for which we used Weka’s library implemented in Java. All
18

data from social and physical sensors are currently stored and the social media
data from the 4 sources have been collected since August 2013 and it takes up
about 2GB every 6 months. The LITMUS software tools have been released on
github.
Information services such as LITMUS provide the situational awareness information needed for adaptive resource scheduling during disasters and large
scale events. Although the current implementation of LITMUS is focused primarily on the disaster aspects, we will start a parallel eﬀort to collect andﬁlter situational awareness information on critical infrastructures, by applying
techniques similar to the outlined above for LITMUS. Achieving high quality
real-time situational awareness information will enable eﬀective adaptive resource allocation algorithms to manage limited infrastructures in an eﬀective
and resilient way.

Resilience and Dependability in Challenged Environments– A Middleware Approach
Nalini Venkatasubramanian, University of California, Irvine, USA
Advances in technology mobile computing, wireless communications, cyberphysical systems, Internet-of-Things (IoT) and cloud computing technologies aremaking available new modalities of information and new channels of communication. It has enabled the interconnection of objects and data to provide novel
services and applications that will improve and enrich our lives. During large
scale disasters and unexpected events, such technologies can be brought to bear
to gain improved situational awareness and better decision support for response
personnel and agencies. Mechanisms to integrate any and all available technologies and information in real-time to support reliable and timely sensemaking inresource challenged situations where the computing and communication infrastructure may be limited, unavailable and/or partially damaged (e.g. in humanor manmade disasters). Dependability needs vary across applications; and thatchallenges and solutions exist at multiple system layers.
We discuss challenges in leveraging heterogeneous and emerging networking
technologies to create a resilient and ﬂexible infrastructure for both informationcollection and information dissemination. Middleware technologies play and important role in supporting cross-layer interactions amongst (a) the devices thatcollect and receive information, (b) multiple networks that communicate thecontent, (c) the platforms that process and store the information and (d) applications that use the information for diverse purposes. Drawing on recent IoT
deployments, we will show how IoT and big data can drive semantic middleware
to incorporate diverse sensors and inputs in a structured manner to generatesituational awareness. The ability to combine novel technologies at multiple
layers will open up new possibilities for resilient, adaptive and scalable societal
scale systems of the future.
2.2.3

Cloud

Great Service: Beneﬁts for Disasters Big Data?
Jianwei Yin, Zhejiang University, China
Yan Tang, Zhejiang University, China
19

Calton Pu, Georgia Tech, USA
Through the applications of Big Data software techniques and tools that
can handle the “4V” challenges (Volume, Velocity, Variety and Veracity), manyﬁelds have reaped the beneﬁts of Big Data, e.g., healthcare, manufacturing, anddisasters preparation. One of the earliest and best known successes of Big Data
was in service industry: through integrating data from diﬀerent areas enabled
Wal-mart to increase the inventory of lumber and other disaster preparationmaterials in Florida stores several days before a hurricane arrives. Customers
beneﬁt from higher quality of service enabled by Big Data. Service providers gethigher proﬁts from more precise control of costs and accurate knowledge of customer needs. We deﬁne the next generation high quality services as Great Services, characterized by “4P” Quality-of-Service (QoS) dimensions: Panorama,
Penetration, Prediction and Personalization.
Panorama QoS indicates such an essence that Great Services is built onmuch more comprehensive observations and descriptions towards objective substances. As a product atop Big Data, Great Services inherit the ability ofgathering diversiﬁed data and sources from multi-domains and focus on synthetic and integrated views rather than isolated facts. Panorama QoS represents
the integration of knowledge and facts from multi-domains. Panorama proﬁlesGreat Services from the dimension of horizontal space. As an orthogonal viewto Panorama, Penetration stands for the evolution and innovation that services can achieve in the context of Big Data. Great Services with Penetration
QoS achieves deeper and more accurate insights towards objective things, aswell as innovative experience and services that do not exist before. Penetration
proﬁles Great Services from the dimension of vertical space. Prediction is themost perceptual QoS of Great Services. Based on the rules and facts derived
from massive volumes of historic information, service providers are capable ofpredicting the future and establishing more targeted services — knowing past
well to predict the future better. Prediction proﬁles Great Services from thedimension of Time. Personalization is the embodiment of the diversity thatBig Data introduces to services industry. Compared to general-purpose services,
Great Services personalize speciﬁc business logic to speciﬁc customers, making
diﬀerent users feel that such services are designed exclusively for themselves.
Personalization proﬁles Great Services from the dimension of User.
Nevertheless, challenges exist in the process of achieving and measuring “4P”
QoS. Data heterogeneity is the biggest challenge for Panorama. The eﬃciency
of processing various types of data from totally diﬀerent backgrounds directly
determines “how panoramic can Great Services be.” Challenges for Penetration
QoS is establishing an uniﬁed evaluation system and a set of speciﬁed standards to access the “Level of Integration” among services. Major challenges of
evaluating Prediction QoS are the diversity among diﬀerent services and theinstability of some services, which makes it sophisticated to estimate how close
to the optimal predictability can a service achieve. For Personalization QoS, themajor focus is about how to avoid or alleviate the over-enlargement of single individual behavior, and how to control and detect positive and negative changesof behavior that go beyond historical prediction.

20

2.3

Data Presentation

Situational Awareness by Social Solutions: Textual GeoTagging and Photographic Crowd-Reporting
Asanobu Kitamoto, National Institute of Informatics, Japan
In the case of disasters, we need to know what is happening where andwhat time. This requires massive data collection schemes which cannot be
achieved by traditional schemes such as hierarchical governmental information
network or mass media with limited number of reporters. Our challenge is toestablish an alternative data collection scheme, namely social solutions, such associal network and crowd-sourcing. Hence we propose two methods, textual geotagging and photographic crowd-reporting, to solve the problem of information
communication and sharing during disasters using social solutions. The former
is a solution for information overload, in which too much information should be
divided into spatio-temporal units, while the latter is a solution for information
scarcity, in which too little information should be enhanced by crowd reporting
to ﬁll spatio-temporal gaps.
First, to solve the problem of information overload, we developed software
called GeoNLP, which aims at integrating geographic information processing(Geo) and natural language processing (NLP). GeoNLP has three components,
namely GeoNLP data, GeoNLP software and GeoNLP application. GeoNLP
data is the collections of place names (toponyms) with attributes that help
GeoNLP software perform disambiguation. GeoNLP software deals with thetask of extracting and disambiguating place names from natural language text.
Finally GeoNLP application provides infrastructure to build geo-tagging application on Japanese natural language text such as news articles and social media
like Twitter. One example of the application is social weather monitoring to
improve situation awareness on weather during snow. We showed that the collection of tweets can estimate the boundary between snow and rain by counting
the number of tweet mentions at each place.
For GeoNLP, the feasibility of using machine translation services was suggested. Geo-tagging software is normally designed for a speciﬁc language because linguistic diﬀerence much more signiﬁcant than commonality across languages. A machine translation service may solve the problem because geotagging software can be designed for one language accepting translated text
from other languages. This idea, however, has a number of problems. It may
work on famous place names, but local names cannot be treated properly due
to the lack of gazetteer in translated languages and the insuﬃcient performanceof machine translation services. Second, to solve the problem of information
scarcity, we developed a mobile app called “Snowlog,” which is based on theidea of “active viewﬁnder” to help users take photographs of the same scene
in diﬀerent times. The idea is to use a mobile phone as a ﬁxed-point camera
for everywhere to observe the change of scenes over time. Change detection is
the fundamental task in situation awareness, because it diﬀerentiates abnormal
from normal. In fact, the idea of ﬁxed point observation was studied in several
recent papers, such as time-lapse mining, and re-photography, but our app focuses more on the idea of spatial crowd sourcing, or crowd reporting that asks
people report the situation using a smartphone camera with annotated text.

21

This app was applied to snow monitoring at Sapporo city, where snow monitoring and removal is a critical societal problem for all citizens. Our app was
experimentally used by several Sapporo city citizens during the winter season of
2015, and collected time-series photographs of several points over several days.
The result shows that our app is helpful to collect the situation of snow at multiple points. We also found, however, that the smart phone app was not easy
to manipulate due to the complexity of ﬁnding the best matching under the six
degree of freedom.
We explained a typical scenario to use this app during disasters. We ﬁrst
prepare the list of images as crowd reporting tasks, and ask people to takepictures from the same point with the same scene. This method is good forcontrolling the behavior of crowd workers because photographs can be chosen to
avoid dangerous places for monitoring. It was also suggested that a requirement
for taking the same scene may be overly restrictive. We emphasize, however,that this requirement may contribute to the gamiﬁcation of the app, because adiﬃcult task may be perceived as a challenging task to achieve the goal. Thishypothesis was empirically proved by observing the behavior of children. Someof them were strongly attracted by the task and highly motivated so that theyspent much more time than adults to ﬁnd the same scene and improve the qualityof matching. It was also suggested that the network of smartphone-based ﬁxed
point cameras may become more powerful by integrating it with other ﬁxed
point cameras, such as cameras on the road, or other network of sensors suchas weather monitoring. Enhancement by other types of information is always
promising, but this is left for future work, and our immediate goal is to improve
the system of crowd reporting to assess the eﬀectiveness of this idea during
disasters.

A Situational Awareness Platform for Disaster Response
based on the Supervision of Multiple Unmanned Aerial Vehicles
Helmut Prendinger, National Institute of Informatics, JapanThe ultimate goal of our work is to create a dynamic digital map that provides real-time or near real-time sharable information of all objects in some relevant geographical areas, such as large-scale event or disaster site. Our approachis to use multiple Unmanned Aerial Vehicles (UAVs) under the supervisory control of an oﬀsite operator. This involves important challenges such as (1) the
shared control of the UAVs by the operator and an algorithm that allocates the
UAVs to waypoints, (2) estimation of the operator’s attentional state, and (3)scene understanding by the UAVs using Deep Learning methods. As part of
the JST-NSF project, we focus on the eﬀectiveness of diﬀerent interfaces for theoﬀsite commander in Search and Rescue operations.

22

2.4

Visionary Talks

Perspectives on Smart and Connected Communities andCyber-Physical Systems
Gurdip Singh, Kansas State Univ. and NSF, USA
Development of Smart and Connected Communities (SCC) will require novel
approaches to design reliable and robust infrastructure systems. In addition, toprovide resilient services, the interactions and interdependence of infrastructure
systems in diﬀerent domains (e.g., energy, transportation, and public health)must be addressed. This is also resulting in accumulation of large amounts ofdata, which can be analyzed, interpreted, and appropriately leveraged. Whenmultiple systems are interacting with each other, and closed-loop control is implemented, real-time analysis of the large amount of cross-device data becomes a
critical requirement. A number of programs at NSF such as the Cyber-Physical
Systems program, the Critical Resilient Interdependent Infrastructure Systems
and Processes program, and the Partnership for Innovation: Broadening Innovation Capacity program are supporting the development of technologies tosupport SCC and big-data analytics in real-time. In this presentation, we provide an overview of these programs, and focus on their multidisciplinary nature.
We will discuss synergies between these programs, and provide perspectives ontechniques for reliable and robust software infrastructure systems. Some of the
key challenges faced in advancing resilience research include:• Development of Test-beds: Conducting experiments to study interdependence of infrastructure systems require availability of test-beds thathave two or more infrastructure systems, which are currently lacking.
Cities as living labs to conduct such evaluation appears to be a promisingsolution.
• Availability of datasets: This is a challenge faced by researchers inacademia. Datasets to drive experimental evaluations must be made available to academic researchers.
• Academia-Industry-Government partnerships: Partnership among
representatives from these three sectors is important to advance researchin smart cities, disaster response and infrastructure resilience. Each brings
unique resources and perspectives that are essential for advancing researchin the areas discussed above.

The Challenges and Opportunities Accompanying Geospatial Big DataJohn P. Wilson, University of Southern California, USA
The traditional geospatial workﬂow incorporates ﬁve sets of tasks: (1) thecollection and/or acquisition of data; (2) the preparation, reconciliation, andintegration of data; (3) the completion of a series of spatial analysis and/or
modeling tasks; (4) the interpretation of the results; and (5) the transformationof these results into “actionable” information. The volume, velocity, variety,23and veracity of “big” data provides new opportunities for knowledge discovery
and modeling for disaster response and recovery so long as we can solve threechallenges: (1) the need to work with populations in place of samples; (2) theneed to work with messy as opposed to clean data; (3) the need to work withcorrelations as opposed to causality; and (4) the need to fuse the information
gathered with these new methods and data sources with the theory and empiricalknowledge of existing domains (Miller and Goodchild 2014).
The talk next used two examples to illustrate the production and use of“actionable” information. The ﬁrst example described how spatial modelinghas been used with a variety of inputs to delineate critical habitat areas forthreatened & endangered species. The veriﬁcation of the model outputs constitutes the greatest challenge with this kind of application and several examples(i.e. the Audubon Christmas Bird Count and the eBird Program) were used
to illustrate how volunteered geographic information (VGI) might be used togather ground-truth data and help with model calibration and/or veriﬁcation.
The second example described how spatial analysis was used to support Operation Smile, a volunteer organization that provides free medical services forchildren around the world with facial deformities. Geographic data were usedwith a series of spatial analysis tools to ﬁrst identify the number of children
by geographic unit in Vietnam and second, provide a series of travel itineraries
to maximize the opportunities to locate and identify children in need in theﬁeld. There are numerous opportunities to add additional geospatial variables
to reﬁne these searches and provide better guidance in future campaigns.
The third and ﬁnal part of the talk brieﬂy described three disaster response
and recovery applications that were focused on one or more of the preparation,
response, recovery, and mitigation elements that comprise the disaster life cycle.
The ﬁrst consisted of an interactive seismicity and building response map of Los
Angeles (Carlson et al. 2015). This map looks at the past, present, and futureand includes speciﬁc building characteristics, instrumentally recorded and interpolated past ground motions, and earthquake scenarios. The second example
recommends evacuation routes using a new and novel method to perform urban routing eﬃciently under capacity constraints (Shahabi and Wilson 2014).
The third example collected spatial video following a tornado and used FEMA’s
damage assessment protocols to assess damage to individual residences and document the speed, accuracy and reliability of the damage estimates provided fromthis new data source (Lue et al. 2014). These three examples, taken as a whole,were used to show the kinds of work that will be needed to take advantage ofthe new data sources discussed in this meeting.

References[1] Carlson, A., Moﬀett, B., Longcore, T., McPherson, K. 2015. Interactive
Seismic Map of Los Angeles. Paper presented at the Second ATC and SEI
Conference on Improving the Seismic Performance of Existing Buildings,
San Francisco, California.
[2] Lue, E., Wilson, J.P., Curtis, A. 2014. Conducting disaster damage assessments with spatial video, experts and the inexperienced public. Applied
Geography, 52, 46-54.

24

[3] Miller, H.J., Goodchild, M.F. 2014. Data-driven geography. GeoJournal,
80, 449-461.
[4] Shahabi, K., Wilson, J.P. 2014. CASPER: Intelligent capacity-aware urban
routing. Computers, Environment and Urban Systems, 46, 12-24.

3Open Issues

During the closing session, the participants have discussed various open issues
which are not covered by the topics presented by the speakers. Some typicaland signiﬁcant issues are summarized as follows.
• How to get data at disaster sites
In many disaster situations, it is diﬃcult to get enough and right datafor big data analysis. Crowdsourcing and Crowdsensing can be promising approaches to this end. However, more eﬀorts are needed from bothtechnical and practical perspectives to use such approaches in real disastersituations.
• How to get right samples
Even if enough amount of data are available for big data analysis during
disaster situations, the data generally include many messy (noisy) data orthat are not suitable for the target analysis. So, we need some techniques
to ﬁlter out unnecessary data (samples) and get only right samples.
• Big data directory
Recently, many open data repositories which are mainly provided by governments and public organizations are available. However, it is not easy
for big data application developers to fully make use of them because (i)it is diﬃcult to horizontally search these repositories to ﬁnd data of interest, and (ii) most of open data are provided in a format (e.g., PDF) thatcannot be easily processed by machines for big data analysis. Therefore,
we need a new big data directory to easily ﬁnd data of interest from a big
volume of available open data.
• Privacy preserving data analysis
It has been known that someone’s private information may be revealed byanalyzing a big volume of data. Therefore, preserving privacy has been
considered as one of the most signiﬁcant issues in big data analysis. Here,
since various data come from diﬀerent data sources in disaster situations,
it becomes more diﬃcult to conduct data analysis while preserving privacy.
• How to integrate functionalities developed by diﬀerent organizations andgroups
While diﬀerent organizations and groups have been developing various
analytical techniques, systems and tools for disaster big data management,these should be integrated into a single system to fully make use of them.
In doing so, there must be many new technical challenges.

25• How to integrate simulations and big data
In disaster situations, it often happens that some data necessary for analysis are missing or not available. Data interpolation helps in such a case.
To this end, simulations based on models developed by using historical
data are useful. Therefore, some mechanisms for integrating simulations
and big data are needed.

26

Participants• Takahiro Hara, Osaka University, Japan• Hideki Hayashi, Hitachi, Ltd., Japan• Teruo Higashino, Osaka University, Japan• Hiroki Ishizuka, KDDI R&D Labora-tories, Inc., Japan• Stephen Jones, The MITRE Corporation, USA• Kyoungsook Kim, National Institute of Advanced Industrial Science andTechnology, Japan• Seon Ho Kim, University of Southern California, USA• Asanobu Kitamoto, National Institute of Informatics, Japan• Kuo-yi Lin, Asia University, Taiwan• Sanjay Madria, Missouri University of Science and Technology, USA• Helmut Prendinger, National Institute of Informatics, Japan• Calton Pu, Georgia Tech, USA• Matthias Renz, George Mason University, USA• Yasushi Sakurai, Kumamoto University, Japan• Yoshihide Sekimoto, University of Tokyo, Japan• Cyrus Shahabi, University of Southern California, USA• Gurdip Singh, Kansas State University and NSF, USA• Nalini Venkatasubramanian, University of California, Irvine, USA• John P. Wilson, University of Southern California, USA• Jianwei Yin, Zhejiang University, China

27

Meeting Schedule
Check-in Day: March 27 (Sun)• Welcome Banquet
Day1: March 28 (Mon)• Talks and Discussions
Session 1: Tools and Frameworks for Big Data Analysis
Chair: Sanjay Madria, Missouri University of Science and Technology,
USA– Kuo-yi Lin, Asia University, Taiwan, A Deep Learning-Based Forecasting Tool for Big Data
– Matthias Renz, George Mason University, USA, Reliable Spatial andSpatio-Temporal Pattern Analysis to Support Decision Making in
Disaster Management Applications– Hideki Hayashi, Hitachi, Ltd., Japan, Spatio-Temporal Data Retrieval for Disaster Estimation– Yasushi Sakurai, Kumamoto University, Japan, Mining and Forecasting of Big Time-series Data
– Sanjay Madria, Missouri University of Science and Technology, USA,
M-Grid : A Distributed Framework for Multidimensional Indexing
and Querying
Session 2: Social Media Analysis for Disaster Management
Chair: Takahiro Hara, Osaka University, Japan– Takahiro Hara, Osaka University, Japan, Big Data Framework for Integrating Sensor Data and Information Extracted from Social Media– Calton Pu, Georgia Tech, USA, Big-Data-Driven Research on Disaster Response and Management– Kyoungsook Kim, National Institute of Advanced Industrial Scienceand Technology, Japan, Mining Social Media to Support Disaster
Management
Day2: March 29 (Tue)• Talks and Discussions
Session 3: Disaster Data Collection Using Crowdsourcing
Chair: Cyrus Shahabi, University of Southern California, USA– Stephen Jones, The MITRE Corporation, USA, Enabling the Crowdfor Emergency Crisis Management– Seon Ho Kim, University of Southern California, USA, Eﬀectively
Crowdsourcing the Acquisition and Analysis of Visual Data for Disaster Response– Asanobu Kitamoto, National Institute of Informatics, Japan, Situational Awareness by Social Solutions: Textual Geo-Tagging and
Photographic Crowd-Reporting28

– Nalini Venkatasubramanian, University of California, Irvine, USA,
Resilience and Dependability in Challenged Environments – A Middleware Approach
Session 4: Big Data Analysis and Platform for Situational Awareness
Chair: Takahiro Hara, Osaka University, Japan– Yoshihide Sekimoto, University of Tokyo, Japan, Estimation of People Movement from Mobile Phone Data using Data Assimilation
Technology– Hiroki Ishizuka, KDDI R&D Laboratories, Inc., Japan, Traﬃc Flow
Analysis for Urban Railway Networks using Self-learnt Cellular Handoﬀ Patterns– Helmut Prendinger, National Institute of Informatics, Japan, A Situational Awareness Platform for Disaster Response based on the Supervision of Multiple Unmanned Aerial Vehicles
Group Photo Shooting
Day3: March 30 (Wed)• Talks and Discussions
Session 5: Challenges on Big Data Research for Disaster Management
Chair: Calton Pu (Georgia Tech, USA)
– John P. Wilson, University of Southern California, USA, The Challenges and Opportunities Accompanying Geospatial Big Data
– Gurdip Singh, Kansas State Univ. and NSF, USA, Perspectives on
Smart and Connected Communities and Cyber-Physical Systems– Jianwei Yin, Zhejiang University, China, Great Service: Beneﬁts for
Disasters Big Data?
– Teruo Higashino, Osaka University, Japan, Crowd Sensing from Heterogeneous Sensors for Disaster Mitigation
Excursion and Main Banquet
Day4: March 31 (Thu)• Discussions
Closing Session
Wrap up29