Transcript Collaborative Acquisition, Querying, and Spatiotemporal Analysis of

Session-2 Participants
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Cyrus Shahabi
Raju Vatsavai
Mohamed Mokbel
Shashi Shekhar
Mubarak Shah
Jans Aasman
Monika Sester
Wei Ding
Phil Hwang
Anthony Stefanidis
(Matt Duckham)
(Angelos Stavrou)
Making sense of spatiotemporal
sensor data
(GeoSensor Nexus)
Collaborative Acquisition, Querying, and
Spatiotemporal Analysis of distributed
sensor data in geographic spaces
Defining Characteristics
• Cover all locations, all the time (with resource constraints) in 3D
– Various resolution, quality/uncertainty, sparsity in space and
time
• Support all types of sensors (Multi-modal, multi-source)
– Remote sensors, moving sensors, humans as sensors, web as
sensors, cameras, camcorders, cell-phones, microphones,
RFID, human body sensors, chemical sensors
• Large scale of sensors (and hence data)
• Spatiotemporal analysis to identify, model and understand highlevel events and processes; and then react to them
– Timely spatiotemporal analysis of sensor data (present)
– Archival and predictive spatiotemporal analysis of sensor data (past &
future)
Sample Challenges
• Analysis
• On GPS data: noisy and uncertain data; clustering trajectories,
classification (e.g., indoor/outdoor, walking/running)
• On Traffic sensors: bulky data; traffic prediction
• On remote sensors: not enough training data ; extracting associating
rules
• On special-purpose sensors (e.g., radioactive detection): signature not
unique, multi-feature; outlier detection
• On video/image sensors: non-structured data; track moving objects,
geo-registering through video cameras
• Performance (e.g., response-time)
• On remote sensor and traffic sensor data: Multi-resolution
aggregation and indexing
• Optimization
• On moving sensors (e.g., humans w/ cell phones, trucks): Planning for
efficient and optimal acquisition of data at the right time and space
Sample Challenges …
• In-network processing
– Add geo to sensor networks; adapt query processing techniques to
restrictions of geo-sensors (e.g., power, bandwidth)
– The local+distributed computation, e.g., improve the accuracy of GPS
data by using multiple sensors (with differential GPS)
– Distributed in-field computation of geo-sensors (e.g., monitoring
propagation of a contaminant, tracking)
– Track moving objects from a network of still and moving cameras
• Fusion of sensors (integration)
– For forensic (identify when a video/image is taken, what was
happening there, …)
– Using geospatial sources to improve image/video analysis
– With other traditional sources (different reliability and granularity)
• Privacy, Security & Trust
– When people data are involved: Trajectory anonymization, Private LBS
– Share sensor & resources without sharing data (network)
– Dealing with adversarial data & un-trusted sources for analysis
A Different Perspective: Spatio-Temporal Scale
(not necessarily in hierarchical order)
• Level 1: Process local raw data measured by a sensor, e.g.
thresholding
• Level 2: Multi-sensor correlation for focal or teleconnection
– Triangulation to position a moving object
– Identify anomalies across sensors (e.g. discontinuity)
• Level 3: Aggregate common global operation picture
– Extract events/processes, Interpret events in context, Develop
hypothesis about current events
– Knowledge Discovery - maps, descriptive models, visualization
– Data Mining - Descriptive models: clusters, trends, associations, ...
• Level 4: Prediction (e.g. via forward/inverse models) of
process
– Predict future states, e.g. final states (goals) and
– Explain cause (intent/drivers/phase changes)
• Level 5 – Action, System Optimization:
– How to redirect intelligence, surveillance, and reconnaissance (ISR)
to improve performance (e.g. get better sensor utilization)
Backup Slides
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Topics
Raju: Sensor Net, ORNL government program
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Data Processing: sensors measuring radioactive material (at truck weight station) – Challenges: signature not unique,
outlier detection, multi-feature
Wei: Remote sensing dataset: multispectral , analyze images to detect shapes (crater), integrating multilayers (land-use, vegetation, temperatures) – Challenges: noisy data, too many false positives, not enough
training data; complex structures
Mubarak: image/video sensor; track moving objects from a network of still and moving cameras, fusion of
videos; geo-registering through video cameras – challenges: using geospatial sources to improve
image/video analysis
Mohamed: add geo to sensor networks; adapt query processing techniques to restrictions of geo-sensors
(e.g., power, bandwidth)
Monika: specifics of sensor network is the local computation (local+distributed processing); where are the
applications?; improve the accuracy of GPS data by using multiple sensors (with differential sensors)
Jans: graph database (social networks): GPS sensors on moving objects and dealing with moving objects
(historic and real-time decisions and planning)
Tony: distributed in-field computation of geo-sensors (e.g., propagation of a contaminant, tracking) –
Challenges: missing data, mobile sensors (actuate sensors); both real-time and historical, prediction.
Human as sensors (text, speech) , web sensors, surveillance (integrate multi-model and multi-source
information to detect events) ; interacting/navigating the network
Phil: sensors: traffic cameras, cell-phones; back-end: forensic (identified when is taken, what was
happening there, …)
Shashi: Analyzing/mining the measurements of sensors – several levels of processing: raw data analysis
(denoising), simple aggregate queries (average, standard), data-mining (patterns), decision making (human
analysis), power restriction is not an issue all the time
Topics ….
Cyrus:
• Traffic sensors (mining; large size/responsetime; compression)
• Humans as sensors (planning)
• GPS sensors (privacy LBS; mining: trajectory
clusters, classification, e.g., indoor/outdoor,
walking/running)
• Remote sensors (access to raw data at
multiple resolutions, e.g., for visualization)