Keynote at the Workshop on Building Research Collaboration: Electricity Systems. Purdue University, West Lafayette, IN. Aug 28-29, 2013.
Abstract:
Big Data has captured much interest in research and industry, with anticipation of better decisions, efficient organizations, and many new jobs. Much of the emphasis is on technology that handles volume, including storage and computational techniques to support analysis (Hadoop, NoSQL, MapReduce, etc), and the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity. However, the most important feature of data, the raison d'etre, is neither volume, variety, velocity, nor veracity -- but value. In this talk, I will emphasize the significance of Smart Data, and discuss how it is can be realized by extracting value from Big Data. Accomplishing this task requires organized ways to harness and overcome the original four V-challenges; and while the technologies currently touted may provide some necessary infrastructure-- they are far from sufficient. In particular, we will need to utilize metadata, employ semantics and intelligent processing, and leverage some of the extensive work that predates Big Data.
For achieving energy sustainability, Smart Grids are known to transform the way we generate, distribute, and consume power. Unprecedented amount of data is being collected from smart meters, smart devices, and sensors all throughout the power grid. I will discuss the central question of deriving Value from the entire smart grid data deluge by discussing novel algorithms and techniques such as Semantic Perception for dealing with Velocity, use of ontologies and vocabularies for dealing with Variety, and Continuous Semantics for dealing with Velocity. I will discuss scenarios that exemplify the process of deriving Value from Big Data in the context of Smart Grid.
Additional background is at: http://wiki.knoesis.org/index.php/Smart_Data
A previous version of this talk with more technical details but not focused on energy: http://j.mp/SmatData
2. 2
Power Grids: A Historical Perspective on Complexity
Before Alternating Current (AC) After Alternating Current After/During Smart Grid
High System
Complexity!
Moderate System Complexity +
Low Data Complexity
High System + Data
Complexity!
Separate power lines
for different voltages.
AC as a boon for Electric
companies.
Smart Grid = high volume,
variety and velocity
http://en.wikipedia.org/wiki/Electric_power_transmission
Late 1800’s 1900’s Today
3. 3
Big Data in Smart Grid
One data point per month 96 million data points / day / million
consumers
Low instrumentation of the
power grid with sensors High instrumentation of the power
grid with sensors
Low number of energy sources
High proliferation of cleaner energy
sources like renewable energy
http://www.smartgridupdate.com/dataforutilities/pdf/DataManagementWhitePaper.pdf
4. 4
Sources of Big Data in Smart Grid
Velocity
Volume
Variety
Veracity
Original 3Vs: Doug Laney: http://goo.gl/wH3qG
From: http://www.smartgridupdate.com/dataforutilities/pdf/DataManagementWhitePaper.pdf
6. • What if your data volume gets so large and
varied you don't know how to deal with it?
• Do you store all your data?
• Do you analyze it all?
• How can you find out which data points are
really important?
• How can you use it to your best advantage?
6
Questions typically asked on Big Data
http://www.sas.com/big-data/
8. • Prediction of the spread of flu in real time during H1N1 2009
– Google tested a mammoth of 450 million different mathematical
models to test the search terms, comparing their predictions against
the actual flu cases; 45 important parameters were founds
– Model was tested when H1N1 crisis struck in 2009 and gave more
meaningful and valuable real time information than any public health
official system [Big Data, Viktor Mayer-Schonberger and Kenneth Cukier, 2013]
• FareCast: predict the direction of air fares over different
routes [Big Data, Viktor Mayer-Schonberger and Kenneth Cukier, 2013]
• NY city manholes problem [ICML Discussion, 2012]
8
Illustrative Big Data Applications
9. • Current focus mainly to serve business intelligence and targeted analytics
needs, not to serve complex individual and collective human needs (e.g.,
empower human in health, fitness and well-being; better disaster
coordination, smart energy consumption) that is highly
personalized/individualized/contextualized
– Incorporate real-world complexity: multi-modal and multi-sensory nature of real-
world and human perception
– Need deeper understanding of data and its role to information (e.g., skew,
coverage)
– Beyond correlation -> causation :: actionable info, decisions grounded on insights
• Human involvement and guidance: Leading to actionable information,
understanding and insight right in the context of human activities
– Bottom-up & Top-down processing: Infusion of models and background knowledge
(data + knowledge + reasoning)
9
What is missing?
11. Smart Data
Smart data makes sense out of Big data
It provides value from harnessing the
challenges posed by volume, velocity,
variety and veracity of big data, in-turn
providing actionable information and
improve decision making.
11
12. “OF human, BY human and FOR human”
Smart data is focused on the actionable
value achieved by human involvement in
data creation, processing and consumption
phases for improving
the human experience.
Another perspective on Smart Data
12
13. • Focus on verticals: advertising‚ social media‚ retail‚
financial services‚ telecom‚ and healthcare
– Aggregate data, focused on transactions, limited
integration (limited complexity), analytics to find
(simple) patterns
– Emphasis on technologies to handle volume/scale,
and to lesser extent velocity: Hadoop, NoSQL,MPP
warehouse ….
– Full faith in the power of data (no hypothesis),
bottom up analysis
13
Current Focus on Big Data
15. “OF human, BY human and FOR human”
Another perspective on Smart Data
15
16. Petabytes of Physical(sensory)-Cyber-Social Data everyday!
More on PCS Computing: http://wiki.knoesis.org/index.php/PCS 16
„OF human‟ : Relevant Real-time Data
Streams for Human Experience
17. “OF human, BY human and FOR human”
17
Another perspective on Smart Data
18. Use of Prior Human-created Knowledge Models
18
„BY human‟: Involving
Crowd Intelligence in data processing workflows
Crowdsourcing and Domain-expert guided
Machine Learning Modeling
19. “OF human, BY human and FOR human”
Another perspective on Smart Data
19
20. Electricity usage over a day, device at
work, power consumption, cost/kWh,
heat index, relative humidity, and public
events from social stream
Weather Application
Power Monitoring
Application
20
„FOR human‟ :
Improving Human Experience
Population Level Observations
Personal Level Observations
Action in the Physical World
Washing and drying has
resulted in significant cost
since it was done during peak
load period. Consider
changing this time to night.
21. 21
What matters?
Personal and Population
Level Observations
Actionable information for
optimized resource utilization
“The challenge for utilities in maximizing the benefits from smart grid data
analytics is the ability to turn the huge volume of smart grid data into value”
- Marianne Hedin, Senior Research Analyst,
Navigant Research
22. 22
Why do we care about Smart Data
rather than Big Data?
23. Transforming Big Data into Smart Data for Smart Energy:
Deriving Value via harnessing Volume, Variety and Velocity
using semantics and Semantic Web
Put Knoesis Banner
Keynote at Building Research Collaborations: Electricity Systems @ Purdue, August 28-29, 2013
Pavan
Kapanipathi
Pramod
Anantharam
Amit Sheth
Cory
Henson
Dr. T.K.
Prasad
Maryam
Panahiazar
Contributions by many, but Special Thanks to:
Hemant
Purohit
Special Thanks
The Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State, USA
24. 24
10 Years Ago, August 14, 2003 Blackout!
http://www.npr.org/2013/08/14/210620446/10-years-after-the-blackout-how-has-the-power-grid-changed
Robert Giroux/Getty Images
25. 25
50 Million People without Power in 5 Northeastern States of US
http://www.npr.org/2013/08/14/210620446/10-years-after-the-blackout-how-has-the-power-grid-changed
Jonathan Fickies/Getty Images
26. 26
$6 Billion Lost Revenue
http://www.scientificamerican.com/article.cfm?id=2003-blackout-five-years-later
http://www.npr.org/2013/08/14/210620446/10-years-after-the-blackout-how-has-the-power-grid-changed
Julie Jacobson/AP
Julie Jacobson/AP
Utilities are hit with millions of dollars of fine when such blackouts
happen costing them on an average 1 million dollars a day!
27. 27
Cause of the Problem: Informal Investigation
Excessive summer heat (31° C or 88° F) caused consumers to draw excess
power for running air conditioners. Heating of power lines led to sagged
cables touching vegetation creating a fault.
FirstEnergy (FE) Corporation’s control room had a failed alarm system
further propagating the fault (cascading effect).
Lack of situational awareness by the control room is only one aspect of the
problem. The problem is deeply rooted in consumer awareness for making
informed decisions
http://www.npr.org/2013/08/14/210620446/10-years-after-the-blackout-how-has-the-power-grid-changed
28. 28
Cause of the Problem: Official Investigation
The U.S.-Canada Power System Outage Task Force reported four major causes
leading to the blackout:
1) "failed to assess and understand the inadequacies of FE's system, particularly with
respect to voltage instability and the vulnerability of the Cleveland-Akron area, and FE
did not operate its system with appropriate voltage criteria."
2) "did not recognize or understand the deteriorating condition of its system."
3) "failed to manage adequately tree growth in its transmission rights-of-way."
4) "failure of the interconnected grid's reliability organizations to provide effective real-time
diagnostic support."
http://en.wikipedia.org/wiki/Northeast_blackout_of_2003
29. 29
"We've done some things that will reduce the risks of the blackouts that happened last
time, but haven't done things that would prevent the next blackout”
-- Paul Hines, University of Vermont
Can we Prevent such Blackouts?
“we have new sensors installed in the grid, but utilities don't totally understand what to do
with all the data”
-- Paul Hines, University of Vermont
http://epaabuse.com/5159/news/after-coal-plants-close-where-does-america-get-cheap-electricity/
31. 31
Derive Insights from Smart Grid Data
"Big data .. for utility companies.. can turn the information from smart meter and smart grid
projects into meaningful operational insights and insights about their customer’s behavior."
- Big Data in Action, IBM
http://www.ecomagination.com/portfolio/ges-grid-iq-advanced-metering-infrastructureami-point-to-multipoint-p2mp-solution
http://gkenergyproject.blogspot.com/2010/07/smart-meter-diagram.html
32. 32
Power Grid Control Rooms are Complex!
Pacific Gas and Electric Company in California has collected over 70 terabytes of AMI
(Advanced Metering Infrastructure) data and this volume is increasing by 3 terabytes a
month
- Data Management And Analytics for Utilities, Smart Grid Update, 2013
http://www.rugeleypower.com/electricity-generation/producing-electricity.php
33. 33
Multimodal, Multisensory, and Real-time Observations
Synchrophaso
r data
Heat index,
relative humidity
Current Grid
Conditions
Renewable energy
generation forecast
What is the overall health of the Grid?
What are the vulnerabilities for today?
Power consumption
by consumers
http://www.rugeleypower.com/electricity-generation/producing-electricity.php
34. 34
Grid Health Score (diagnostic)
Semantic Perception and risk assessment algorithms can transform raw data (hard to
comprehend) to abstractions (e.g., Grid Health is 3 on a scale of 5) that is intuitively
understandable and valuable for decision makers.
Having health score for various parts of a grid will allow efficient utilization of
a decision maker’s precious attention
Risk assessment
model
Semantic
Perception
Synchrophaso
r data
Heat index,
relative humidity
Current Grid
Conditions
Renewable energy
generation forecast
Power consumption
by consumers
35. 35
Vulnerability Score (prognostic)
Vulnerability score (e.g., Today’s vulnerability score 4 on a scale of 5) is an abstraction
that uses current state of the grid (health score), power demand forecast, availability of
alternative energy sources, and historical consumer behavior
Vulnerability score will alleviate the data deluge problem of decision makers by
leveraging prior knowledge of the domain for creating risk assessment models
Risk assessment
model
Semantic
Perception
Synchrophaso
r data
Heat index,
relative humidity
Current Grid
Conditions
Renewable energy
generation forecast
Power consumption
by consumers
37. 37
“To make good on the promise of a truly “smart” grid, the industry must continue to
implement equipment that employs distributed intelligence, out to the edges of the
distribution system.“
-- Layered Intelligence Smart Grid Solutions, S&C Electric Company
“Intelligence at the Edges” of a Smart Grid
http://www.digikey.com/us/en/techzone/energy-harvesting/resources/articles/zigbees-smart-energy-20-profile.html
38. 38
Data Overload for Consumers
“They respond well to suggestions to do something.”
- Alex Laskey, President
and Founder of Opower
Personal
Schedule Smart Meters Power Consumption
Temperature,
relative humidity
Dynamic pricing
information
http://www.identika.com/2012/02/every-movie-made/
39. 39
Optimizing Cost, Benefit, and Preferences
Algorithms on the consumer side of the Smart Grid should should consider cost, benefit, and
preference of the user to devise an optimal strategy for power consumption
Which devices are contributing to higher power bill?
When should I operate the washer/dryer?
How much convenience I am willing to forego?
Semantic
Perception
Personalized
optimization
Personalized
recommendation
Img: http://marloncarvallovillae.blogspot.com/2011_02_01_archive.html
http://www.1800timeclocks.com/icon-time-systems/icon-time-upgrades/icon-time-advanced-pack-upgrade-sb100-pro/
Personal
Schedule
Smart Meters
Power Consumption
Temperature,
relative humidity
Dynamic pricing
information
40. 41
Big Data to Smart Data: A peek at some domains
Healthcare
Social Media &
Disaster Response
http://theshannoncompany.com.au/blog/?p=504
41. Sensing is a key enabler of the Internet of Things
BUT, how do we make sense of the resulting avalanche
of sensor data?
50 Billion Things by 2020 (Cisco)
44
42. … and do it efficiently and at scale
What if we could automate this
sense making ability?
45
44. People are good at making sense of sensory input
What can we learn from cognitive models of perception?
• The key ingredient is prior knowledge
47
45. * based on Neisser’s cognitive model of perception
Observe
Property
Perceive
Feature
Explanation
Discrimination
1
2
Perception Cycle*
Translating low-level signals
into high-level knowledge
Focusing attention on those
aspects of the environment that
provide useful information
Prior Knowledge
48
46. To enable machine perception,
Semantic Web technology is used to integrate
sensor data with prior knowledge on the Web
49
47. Prior knowledge on the Web
W3C Semantic Sensor
Network (SSN) Ontology Bi-partite Graph
50
48. Prior knowledge on the Web
W3C Semantic Sensor
Network (SSN) Ontology Bi-partite Graph
51
50. Explanation
Inference to the best explanation
• In general, explanation is an abductive problem; and
hard to compute
Finding the sweet spot between abduction and OWL
• Single-feature assumption* enables use of OWL-DL
deductive reasoner
* An explanation must be a single feature which accounts for
all observed properties
Explanation is the act of choosing the objects or events that best account for a set of
observations; often referred to as hypothesis building
53
51. Explanation
Explanatory Feature: a feature that explains the set of observed properties
ExplanatoryFeature ≡ ∃ssn:isPropertyOf—.{p1} ⊓ … ⊓ ∃ssn:isPropertyOf—.{pn}
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Observed Property Explanatory Feature
54
52. Discrimination is the act of finding those properties that, if observed, would help distinguish
between multiple explanatory features
Observe
Property
Perceive
Feature
Explanation
Discrimination
2
Focusing attention on those
aspects of the environment that
provide useful information
Discrimination
55
53. Discrimination
Expected Property: would be explained by every explanatory feature
ExpectedProperty ≡ ∃ssn:isPropertyOf.{f1} ⊓ … ⊓ ∃ssn:isPropertyOf.{fn}
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Expected Property Explanatory Feature
56
54. Discrimination
Not Applicable Property: would not be explained by any explanatory feature
NotApplicableProperty ≡ ¬∃ssn:isPropertyOf.{f1} ⊓ … ⊓ ¬∃ssn:isPropertyOf.{fn}
elevated blood pressure
clammy skin
palpitations
Hypertension
Hyperthyroidism
Pulmonary Edema
Not Applicable Property Explanatory Feature
57
56. Through physical monitoring and
analysis, our cellphones could act as
an early warning system to detect
serious health conditions, and
provide actionable information
canary in a coal mine
Our Motivation
kHealth: knowledge-enabled healthcare
59
57. How do we implement machine perception efficiently on a
resource-constrained device?
Use of OWL reasoner is resource intensive
(especially on resource-constrained devices),
in terms of both memory and time
• Runs out of resources with prior knowledge >> 15 nodes
• Asymptotic complexity: O(n3)
60
58. intelligence at the edge
Approach 1: Send all sensor observations
to the cloud for processing
Approach 2: downscale semantic
processing so that each device is capable
of machine perception
61
Henson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices,
ISWC 2012.
59. Efficient execution of machine perception
Use bit vector encodings and their operations to encode prior knowledge and
execute semantic reasoning
010110001101
0011110010101
1000110110110
101100011010
0111100101011
000110101100
0110100111
62
60. O(n3) < x < O(n4) O(n)
Efficiency Improvement
• Problem size increased from 10’s to 1000’s of nodes
• Time reduced from minutes to milliseconds
• Complexity growth reduced from polynomial to linear
Evaluation on a mobile device
63
61. 2 Prior knowledge is the key to perception
Using SW technologies, machine perception can be formalized and
integrated with prior knowledge on the Web
3 Intelligence at the edge
By downscaling semantic inference, machine perception can
execute efficiently on resource-constrained devices
Semantic Perception for smarter analytics: 3 ideas to takeaway
1 Translate low-level data to high-level knowledge
Machine perception can be used to convert low-level sensory
signals into high-level knowledge useful for decision making
64
62. Qualities
-High BP
-Increased Weight
Entities
-Hypertension
-Hypothyroidism
kHealth
Machine Sensors
Personal Input
EMR/PHR
Comorbidity risk score
e.g., Charlson Index
Longitudinal studies of
cardiovascular risks
- Find correlations
- Validation
- domain knowledge
- domain expert
Parameterize the
model
Risk Assessment Model
Current Observations
-Physical
-Physiological
-History
Risk Score
(Actionable Information)
Model CreationValidate correlations
Historical observations
of each patient
Risk Score: from Data to Abstraction and Actionable Information
65
63. 66
1 http://www.pdf.org/en/parkinson_statistics
10
million 60,000
$25
billion
$100,00
0
1 million
People worldwide are
living with Parkinson's
disease1.
Americans are
diagnosed with
Parkinson's disease
each year1.
Spent on Parkinson’s
alone in a year in the
US1
Therapeutic surgery
can cost up to $100,000
dollars per patient1.
Americans live with
Parkinson’s Disease1
Parkinson‟s Disease (PD)
64. Parkinson’s disease (PD) data from The Michael J. Fox Foundation
for Parkinson’s Research.
67
1https://www.kaggle.com/c/predicting-parkinson-s-disease-progression-with-smartphone-data
8 weeks of data from 5 sensors on a smart phone, collected for 16 patients
resulting in ~12 GB (with lot of missing data).
Variety Volume
VeracityVelocity
Value
Can we detect the onset of Parkinson’s disease?
Can we characterize the disease progression?
Can we provide actionable information to the patient?
semantics
Representing prior knowledge of PD
led to a focused exploration of this
massive dataset
WHY Big Data to Smart Data: Healthcare example
65. 68
Big Data to Smart Data Using a Knowledge Based Approach
ParkinsonMild(person) = Tremor(person) ∧ PoorBalance(person)
ParkinsonModerate(person) = MoveSlow(person) ∧ PoorSleep(person) ∧ MonotoneSpeech(person)
ParkinsonAdvanced(person) = Fall(person)
Control Group PD Patients
Movements of an active
person has a good
distribution over X, Y, and
Z axis
Restricted movements by
a PD patient can be seen
in the acceleration
readings
Audio is well modulated
with good variations in
the energy of the voice
Audio is not well
modulated represented a
monotone speech
Declarative Knowledge of
Parkinson’s Disease used to focus
our attention on symptom
manifestations in sensor
observations
67. Asthma is a multifactorial disease with health signals spanning personal,
public health, and population levels.
70
Real-time health signals from personal level (e.g., Wheezometer, NO in breath,
accelerometer, microphone), public health (e.g., CDC, Hospital EMR), and
population level (e.g., pollen level, CO2) arriving continuously in fine grained
samples potentially with missing information and uneven sampling frequencies.
Variety Volume
VeracityVelocity
Value
Can we detect the asthma severity level?
Can we characterize asthma control level?
What risk factors influence asthma control?
What is the contribution of each risk factor?semantics
Understanding relationships between
health signals and asthma attacks
for providing actionable information
WHY Big Data to Smart Data: Healthcare example
68. 71
Population Level
Personal
Public Health
Variety: Health signals span heterogeneous sources
Volume: Health signals are fine grained
Velocity: Real-time change in situations
Veracity: Reliability of health signals may be compromised
Value: Can I reduce my asthma attacks at night?
Decision support to doctors
by providing them with
deeper insights into patient
asthma care
Asthma: Demonstration of Value
69. 72
Sensordrone – for monitoring
environmental air quality
Wheezometer – for monitoring
wheezing sounds
Can I reduce my asthma attacks at night?
What are the triggers?
What is the wheezing level?
What is the propensity toward asthma?
What is the exposure level over a day?
What is the air quality indoors?
Commute to Work
Personal
Public Health
Population Level
Closing the window at home
in the morning and taking an
alternate route to office may
lead to reduced asthma attacks
Actionable
Information
Asthma: Actionable Information for Asthma Patients
70. Personal, Public Health, and Population Level Signals for Monitoring Asthma
Asthma Control => Daily Medication
Choices for starting
therapy
Not Well Controlled Poor Controlled
Severity Level of
Asthma
(Recommended Action) (Recommended Action) (Recommended Action)
Intermittent Asthma SABA prn - -
Mild Persistent Asthma Low dose ICS Medium ICS Medium ICS
Moderate Persistent
Asthma
Medium dose ICS alone
Or with
LABA/montelukast
Medium ICS +
LABA/Montelukast
Or High dose ICS
Medium ICS +
LABA/Montelukast
Or High dose ICS*
Severe Persistent Asthma High dose ICS with
LABA/montelukast
Needs specialist care Needs specialist care
ICS= inhaled corticosteroid, LABA = inhaled long-acting beta2-agonist, SABA= inhaled short-acting beta2-agonist ;
*consider referral to specialist
Asthma Control
and Actionable Information
Sensors and their observations
for understanding asthma
73
71. 74
Personal
Level Signals
Societal Level
Signals
(Personal Level Signals)
(Personalized
Societal Level Signal)
(Societal Level Signals)
Societal Level Signals
Relevant to the
Personal Level
Personal Level Sensors
(kHealth**) (EventShop*)
Qualify Quantify
Action
Recommendation
What are the features influencing my asthma?
What is the contribution of each of these features?
How controlled is my asthma? (risk score)
What will be my action plan to manage asthma?
Storage
Societal Level Sensors
Asthma Early Warning Model (AEWM)
Query AEWM
Verify & augment
domain knowledge
Recommended
Action
Action
Justification
Asthma Early Warning Model
*http://www.slideshare.net/jain49/eventshop-120721, ** http://www.youtube.com/watch?v=btnRi64hJp4
72. 75
Population Level
Personal
Wheeze – Yes
Do you have tightness of chest? –Yes
ObservationsPhysical-Cyber-Social System Health Signal Extraction Health Signal Understanding
<Wheezing=Yes, time, location>
<ChectTightness=Yes, time, location>
<PollenLevel=Medium, time, location>
<Pollution=Yes, time, location>
<Activity=High, time, location>
Wheezing
ChectTightness
PollenLevel
Pollution
Activity
Wheezing
ChectTightness
PollenLevel
Pollution
Activity
RiskCategory
<PollenLevel, ChectTightness, Pollution,
Activity, Wheezing, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
<2, 1, 1,3, 1, RiskCategory>
.
.
.
Expert
Knowledge
Background
Knowledge
tweet reporting pollution level
and asthma attacks
Acceleration readings from
on-phone sensors
Sensor and personal
observations
Signals from personal, personal
spaces, and community spaces
Risk Category assigned by
doctors
Qualify
Quantify
Enrich
Outdoor pollen and pollution
Public Health
Health Signal Extraction to Understanding
Well Controlled - continue
Not Well Controlled – contact nurse
Poor Controlled – contact doctor
73. • Real Time Feature Streams:
http://www.youtube.com/watch?v=_ews4w_eCpg
• kHealth: http://www.youtube.com/watch?v=btnRi64hJp4
76
Demos
74. 77
Smart Data in Social Media & Disaster Response
To Understand
critical information
dynamics in real
world events
75. Twitris‟ Dimensions of Integrated Semantic Analysis
78Sheth et al. Twitris- a System for Collective Social Intelligence, ESNAM-2013
76. What is Smart Data in the context of
Disaster Management
ACTIONABLE: Timely delivery of
right resources and information to
the right people at right location!
79
77. Join us for the Social
Good!
http://twitris.knoesis.org
RT @OpOKRelief:
Southgate Baptist Church
on 4th Street in Moore
has food, water, clothes,
diapers, toys, and more.
If you can't go,call 794
Text "FOOD" to
32333, REDCROSS to
90999, or STORM to
80888 to donate $10
in storm relief.
#moore #oklahoma
#disasterrelief
#donate
Want to help animals in
#Oklahoma? @ASPCA tells
how you can help:
http://t.co/mt8l9PwzmO
CITIZEN SENSORS
RESPONSE TEAMS
(including humanitarian
org. and ‘pseudo’ responders)
VICTIM SITE
Coordination of
emerging needs
after a disaster
Does anyone
know where to
send a check to
donate to the
tornado
victims?
Where do I go
to help out for
volunteer work
around Moore?
Anyone know?
Anyone know
where to donate
to help the
animals from the
Oklahoma
disaster? #oklah
oma #dogs
Matched
Matched
Matched
Serving the need!
If you would like to volunteer
today, help is desperately
needed in Shawnee. Call
273-5331 for more info
http://www.slideshare.net/hemant_knoesis/cscw-2012-hemantpurohit-11531612
80
Purohit et al. Framework to Analyze Coordination in Crisis Response, 2012. Int’l Collaboration in-progress:
78. Smart Data from Twitris system for
Disaster Response Coordination
Which are the primary locations of
power failure?
Who are all the people to engage
with for better information
diffusion?Where are the charging stations to
sustain communication?
Smart data provides actionable information and improve decision making through
semantic analysis of Big Data.
Who are the resource seekers and
suppliers?
81
79. Disaster Response Coordination:
Twitris Summary for Actionable Nuggets
83
Important tags to
summarize Big Data flow
Related to Oklahoma
tornado
Images and Videos Related
to Oklahoma tornado
80. 84
Disaster Response Coordination:
Twitris Real-time information for needs
Incoming Tweets with need
types to give quick idea of
what is needed and where
currently #OKC
Legends for Different
needs #OKC
(It is real-time widget for monitoring of needs, so will not be active after the event has passed)
http://twitris.knoesis.org/oklahomatornado
82. Really sparse Signal to Noise:
• 2M tweets during the first week after #Oklahoma-tornado-2013
- 1.3% as the highly precise donation requests to help
- 0.02% as the highly precise donation offers to help
86
• Anyone know how to get involved to
help the tornado victims in
Oklahoma??#tornado #oklahomacity
(OFFER)
• I want to donate to the Oklahoma cause
shoes clothes even food if I can (OFFER)
Disaster Response Coordination:
Finding Actionable Nuggets for Responders to act
• Text REDCROSS to 909-99 to donate to
those impacted by the Moore tornado!
http://t.co/oQMljkicPs (REQUEST)
• Please donate to Oklahoma disaster
relief efforts.: http://t.co/crRvLAaHtk
(REQUEST)
For responders, most important information is the scarcity and
availability of resources, can we mine it via Social Media?
83. 87
Disaster Response Coordination:
Engagement Interface for responders
What-Where-How-Who-Why
Coordination
Influential users to engage
with and resources for
seekers/supplies at a location,
at a timestamp
Contextual
Information for a
chosen topical tags
84. • Illustrious scenario: #Oklahoma-tornado 2013
88
Disaster Response Coordination:
Anecdote for the value of Smart Data
FEMA asked us to quickly filter
out gas-leak related data
Mining the data for smart nuggets
to inform FEMA (Timely needs)
Engaged with the author of this
information to confirm (Veracity)
e.g., All gas leaks in #moore were capped and stopped by
11:30 last night (at 5/22/2013 1:41:37)
Lot of tweets for ‘how to/where to’ assist (‘pseudo’ responders)
e.g., I want to go to Oklahoma this weekend & do what i can to help those people with
food,cloths & supplies,im in the feel of wanting to help ! :)
85. 89
Current Grid Conditions
Renewable energy
generation forecast
Synchrophasor data
Heat index,
relative humidity
Power consumption
by consumers
Big Data from Smart Grid Smart Data from Smart Grid
What is the overall health of the Grid?
What are the vulnerabilities for today?
Red, yellow, and green indicate high,
medium, and low risk allowing decision
makers to focus on red & yellow lines
Big Data vs. Smart Data in Smart Grids (Utilities perspective)
86. 90
Personal Schedule
Big Data from Smart Grid
& Smart Meters
Smart Data from Smart
Grid & Smart Meters
Smart Meters
Power Consumption
Temperature, relative humidity
Dynamic pricing information
http://www.digikey.com/us/en/techzone/energy-harvesting/resources/articles/zigbees-smart-energy-20-profile.html
Which devices are contributing to higher power bill?
When should I operate the washer/dryer?
Red, yellow, and green
indicating high, medium, and
low power consumption
Recommendation algorithms
will analyze these abstractions
with domain knowledge
Actions to optimize power bill
will be recommended
Big Data vs. Smart Data in Smart Grids (Consumer perspective)
87. Take Away
• Data processing for Smart Grids/Utilities and Consumers is
lot more than a Big Data processing problem
• It is all about the human – not computing, not device: help
them make better decisions, give actionable information
– Computing for human experience
• Whatever we do in Smart Data, focus on human-in-the-loop
(empowering machine computing!):
– Of Human, By Human, For Human
– But in serving human needs, there is a lot more than what
current big data analytics handle – variety, contextual,
personalized, subjective, spanning data and knowledge across P-
C-S dimensions
91
88. Acknowledgements
• Kno.e.sis team
• Funds: NSF, NIH, AFRL, Industry…
• Note:
• For images and sources, if not on slides, please see slide notes
• Some images were taken from the Web Search results and all such images belong
to their respective owners, we are grateful to the owners for usefulness of these
images in our context.
92
89. • OpenSource: http://knoesis.org/opensource
• Showcase: http://knoesis.org/showcase
• Vision: http://knoesis.org/node/266
• Publications: http://knoesis.org/library
93
References and Further Readings
90. Amit Sheth’s
PHD students
Ashutosh Jadhav
Hemant
Purohit
Vinh
Nguyen
Lu Chen
Pavan
Kapanipathi
Pramod
Anantharam
Sujan
Perera
Alan Smith
Pramod Koneru
Maryam Panahiazar
Sarasi Lalithsena
Cory Henson
Kalpa
Gunaratna
Delroy
Cameron
Sanjaya
Wijeratne
Wenbo
Wang
Kno.e.sis in 2012 = ~100 researchers (15 faculty, ~50 PhD students)
91. 95
thank you, and please visit us at
http://knoesis.org
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio, USA
Smart Data
Editor's Notes
Major historical events are outlined here.Before Alternating Current (AC) was discovered, each device operating at different voltage required a separate power line! Also, the power generation has to be very close to the load. The power grid was visualized more like a distributed generators all throughout the grid.After AC, the power could be transmitted over long distances and a single voltage could be adapted to various devices operating at different voltages.The power grids are getting complex with many alternate sources of energy, varying consumer demand, increasing area of coverage, and increased loads due to electric vehicles. Smart Grid will generate lot of data and interpretation of this data is a challenging.
http://radhakrishna.typepad.com/rks_musings/2013/04/big-data-review.htmlGoogle predicted the spread of flu in real time - after analyzing two datasets, a.) 50 million most common terms that Americans type, b.) data on the spread of seasonal flu from public health agency- tested a mammoth of 450 million different mathematical models to test the search terms, comparing their predictions against the actual flu cases- model was tested when H1N1 crisis struck in 2009 and gave more meaningful and valuable real time information than any public health official system (Big Data, Viktor Mayer-Schonberger and Kenneth Cukier, 2013)
Better Algorithms Beat More Data — And Here’s Whyhttp://allthingsd.com/20121128/better-algorithms-beat-more-data-and-heres-why/Big Data Cannot Replace Human Judgmenthttp://www.matchcite.com/blog/blog/2012/july/big-data-cannot-replace-human-judgment.aspx**Comments about the articles
Smart data makes sense out of big data – it provides value from harnessing the challenges posed by volume, velocity, variety and veracity of big data, to provide actionable information and improve decision making.
- HUMAN CENTRIC!!
Information is CREATED by human with the Machinery available – Wikipedia tool, sensors and social networksInformation is STORED in Man+Machine readable format, LODInformation is PROCESSED using the LOD and Human assisted Knowledge-basedHigher level abstraction on info is now consumed in many mechanistic ways (including GIS) to provide EXPERIENCE for humans
All the data related to human activity, existence and experiencesMore on PCS Computing: http://wiki.knoesis.org/index.php/PCS
Information is CREATED by human with the Machinery available – Wikipedia tool, sensors and social networksInformation is STORED in Man+Machine readable format, LODInformation is PROCESSED using the LOD and Human assisted Knowledge-basedHigher level abstraction on info is now consumed in many mechanistic ways (including GIS) to provide EXPERIENCE for humans Example of a human guided modeling and improved performancehttp://research.microsoft.com/en-us/um/people/akapoor/papers/IJCAI%202011a.pdf
Also, we have weather application which performs abstraction on weather sensory observations to identify blizzard conditions (food for actions!!) :--20,000 weather stations (with ~5 sensors per station)-- Real-Time Feature Streams - live demo: http://knoesis1.wright.edu/EventStreams/ - video demo: https://skydrive.live.com/?cid=77950e284187e848&sc=photos&id=77950E284187E848%21276
Lets find it..
Starting slide Various Big data problems – Traditional examples vs what we are doing examples. Variety and Velocity than Volume. kHealth problem. People will be interested in Smart Data.Traditional ML techniques, High Performance Computing, Statistics. Human level of Abstraction is Smart data.
Such a blackout would cause billions of dollars in lost revenue. This particular blackout resulted in 6 billion dollar loss.Not only the consumers lose revenue even the power utility companies are fined almost a million dollars a day.
Two of the problems are problems in “understanding” the system and the available data/observations. Lack of experience/training in deciphering this data had serious implications.The items 1 and 2 related to understanding real-world complexity by incorporating multi-modal and multi-sensory observations. Algorithms that can provide abstractions to decision makers for better comprehension of the situation.Providing abstractions in the context of the grid state continuously would lead to actionable information to decision makers.
“Syncrophasors are like traffic cameras on a road traffic monitoring system”It provides high frequency (30 samples per second) updates on voltage, power flow in an electric network, phase difference, and many more electric quantities.https://www.selinc.com/SELUniversity/Courses/SYS/310/ http://green.blogs.nytimes.com/2010/04/01/for-the-smart-grid-a-synchophasor/?_r=0
All the dollar amount here is per year.On an average, each household can save $369 / year which is $4 billion / year of reduced energy bills in the US=> Not all this is by doing nothing but just monitoring the usage and provide near real-time power consumption and electricity bill using smart meters
Larry Smarr is a professor at the University of California, San DiegoAnd he was diagnosed with Crones DiseaseWhat’s interesting about this case is that Larry diagnosed himselfHe is a pioneer in the area of Quantified-Self, which uses sensors to monitor physiological symptomsThrough this process he discovered inflammation, which led him to discovery of Crones DiseaseThis type of self-tracking is becoming more and more common
- what if we could automate this sense making ability?- and what if we could do this at scale?
sense making based on human cognitive models
perception cycle contains two primary phasesexplanationtranslating low-level signals into high-level abstractions inference to the best explanationdiscriminationfocusing attention on those properties that will help distinguish between multiple possible explanationsused to intelligently task sensors and collect additional observations (rather than brute force approach of blindly collecting all observations)
perception cycle contains two primary phasesexplanationtranslating low-level signals into high-level abstractions inference to the best explanationdiscriminationfocusing attention on those properties that will help distinguish between multiple possible explanationsused to intelligently task sensors and collect additional observations (rather than brute force approach of blindly collecting all observations)
A single-feature (disease) assumption means that all the observed properties (symptoms) must be explained by a single feature.i.e., this framework is not expressive enough to model comorbidity where there may be more than one feature (disease) co-existing For example, if there are two diseases causing disjoint symptoms, and all the symptoms of both the diseases are observed, then this framework will not be able to find the coverage and returns no diseases.
perception cycle contains two primary phasesexplanationtranslating low-level signals into high-level abstractions inference to the best explanationdiscriminationfocusing attention on those properties that will help distinguish between multiple possible explanationsused to intelligently task sensors and collect additional observations (rather than brute force approach of blindly collecting all observations)
- With this ability,many problems could be solved- For example: we could help solve health problems (before they become serious health problems) through monitoring symptoms and real-time sense making, acting as an early warning system to detect problematic health conditions
Intelligence distributed at the edge of the networkRequires resource-constrained devices (mobile phones, gateway notes, etc.) to be able to utilize SW technologies
Intelligence distributed at the edge of the networkRequires resource-constrained devices (mobile phones, gateway notes, etc.) to be able to utilize SW technologiesHenson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices, ISWC 2012.
compute machine perception inferences -- i.e., explanation and discrimination -- of high-complexity on a resource-constrained devices in milisecondsDifference between the other systems and what this system provides
Intelligence at the age. Shipping computation and domain models to the edge (Distributed)
Massive amount of data will be collected by sensors and mobile devices yet patients and doctors care about “actionable” information.This data has all the four Vs of big data and we used knowledge enabled techniques to transform it into valueIn the context of PD, we analyzed massive amount of sensor data collected by sensors on a smartphones to understand detection and characterization of PD severity.
Main idea: Prior knowledge of PD was used to facilitate its detection from massive sensor data by reducing the search spaceDetails:Declarative knowledge of PD includes PD severity and their symptoms as shown in the logical rule aboveEach PD severity level is a conjunction of a set of PD symptomsEach symptom was mapped to its manifestation in sensor observationsThe availability of declarative knowledge significantly improved the analytics by aiding feature selection processThe graphs above contrasts the physical movements and voice of two control group members and two PD patients
Much of the early work in Big data is being done with focusing on uni-directional among XYZ.
Categorization of severity based on weather conditions. Actionable information is contextually dependent.
Power Grid Context:Power blackout will result in critical needs such as food, water, charging stations, fuel, etc. which need to be addressed by the on-ground responders and remote responders. - 1 (+half) minuteAlright, so let’s motivate by this situation during emergency - Various actors: resource seekers, responder teams, resource providers at remote siteAnd - each of these actor groups have questions --- - needs - providers - responders: wondering!Here we have social network to connect these actors and bridge the gap for communication platformBut it’s potential use is yet to be realized for effective helpBecause.. (next slide)
Talk about what kind of smart data we provide that helps the actions of crisis response coordination.
Source: Purohit et. al 2013 (https://docs.google.com/a/knoesis.org/document/d/1aBJ2egHICUwaWxR8jOoTIUfEYj1QAnUt0q7haIKoYGY/edit# , http://www.knoesis.org/library/resource.php?id=1865)
http://twitris.knoesis.org/oklahomatornado
(It is real-time widget for monitoring of needs, so will not be active after the event has passed) http://twitris.knoesis.org/oklahomatornado
Highly rich interface for response team
Imagine if this is a power failure, we can get actionable insights in a timely manner
More at: http://wiki.knoesis.org/index.php/PCSAnd http://knoesis.org/projects/ssw/