Transforming big data into smart data involves deriving value from harnessing the volume, variety, and velocity of big data using semantics and the semantic web. This allows making sense of big data by providing actionable information that improves decision making. Examples discussed include a healthcare application called kHealth that uses personal sensor data along with population level data to provide personalized and timely health recommendations and interventions for conditions like asthma.
Philosophy of Big Data: Big Data, the Individual, and Society
Similar to TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, Variety, and Velocity using Semantic Techniques and Technologies
Similar to TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, Variety, and Velocity using Semantic Techniques and Technologies (20)
TRANSFORMING BIG DATA INTO SMART DATA: Deriving Value via Harnessing Volume, Variety, and Velocity using Semantic Techniques and Technologies
1. Transforming Big Data into Smart Data:
Deriving Value via harnessing Volume, Variety and Velocity
using semantics and Semantic Web
Put Knoesis Banner
Keynote at 30th IEEE International Conference on Data Engineering (ICDE) 2014
Amit Sheth
LexisNexis Ohio Eminent Scholar & Exec. Director,
The Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)
Wright State, USA
5. Only 0.5% to 1% of
the data is used for
analysis.
5
http://www.csc.com/insights/flxwd/78931-big_data_growth_just_beginning_to_explode
http://www.guardian.co.uk/news/datablog/2012/dec/19/big-data-study-digital-universe-global-volume
6. Variety – not just structure but modality: multimodal, multisensory
Semi structured
6
8. • 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?
• What is coverage, skew, quality?
How can you find out which data points are
really important?
• How can you use it to your best advantage?
9
Questions typically asked on Big Data
http://www.sas.com/big-data/
10. • 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 that provided 45 important
parameters
– 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]
11
Illustrative Big Data Applications
11. 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, personalized smart energy)
12
What is missing?
12. highly personalized/individualized/contextualized
Incorporate real-world complexity:
- multi-modal and multi-sensory nature of
physical-world and human perception
Can More Data beat better algorithms?
Can Big Data replace human judgment?
13
Many opportunities, many challenges, lessons to apply
13. • Not just data to information, not just analysis, but actionable
information, delivering insight and support better decision
making right in the context of human activities
15
What is needed?
Data Information
Actionable: An apple a day
keeps the doctor away
14. 16
What is needed? Taking inspiration from cognitive models
• Bottom up and top down cognitive
processes:
– Bottom up: find patterns, mine (ML, …)
– Top down: Infusion of models and background
knowledge (data + knowledge + reasoning)
Left(plans)/Right(perceives) Brain
Top(plans)/Bottom(perceives) Brain
http://online.wsj.com/news/articles/SB10001424052702304410204579139423079198270
15. • Ambient processing as much as possible while enabling
natural human involvement to guide the system
17
What is needed?
Smart Refrigerator: Low on Apples
Adapting the Plan:
shopping for apples
16. Makes Sense to a human
Is actionable –
timely and better decisions/outcomes
18
17. 20
My 2004-2005 formulation of SMART DATA - Semagix
Formulation of Smart Data
strategy providing services
for Search, Explore, Notify.
“Use of Ontologies and
Data repositories to gain
relevant insights”
18. Smart Data (2013 retake)
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.
21
19. OF human, BY human 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
22
20. OF human, BY human FOR human
Another perspective on Smart Data
23
21. Petabytes of Physical(sensory)-Cyber-Social Data everyday!
More on PCS Computing: http://wiki.knoesis.org/index.php/PCS 24
„OF human‟ : Relevant Real-time Data
Streams for Human Experience
22. OF human, BY human FOR human
25
Another perspective on Smart Data
23. Use of Prior Human-created Knowledge Models
26
„BY human‟: Involving
Crowd Intelligence in data processing workflows
Crowdsourcing and Domain-expert guided
Machine Learning Modeling
24. OF human, BY human FOR human
Another perspective on Smart Data
27
25. Detection of events, such as wheezing
sound, indoor temperature, humidity,
dust, and CO level
Weather Application
Asthma Healthcare
Application
Close the window at home
during day to avoid CO in
gush, to avoid asthma attacks
at night
28
„FOR human‟ :
Improving Human Experience
Population Level
Personal
Public Health
Action in the Physical World
Luminosity
CO level
CO in gush
during day time
26. 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
29
„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.
27. 30
Every one and everything has Big Data –
It is Smart Data that matter!
28. • Healthcare:
ADFH, Asthma, GI
– Using kHealth system
• Social Media Analysis:
Crisis coordination
– Using Twitris platform
• Smart Cities:
Traffic management
31
I will use applications in 3 domains to demonstrate
29. • Healthcare:
ADFH, Asthma, GI
– Using kHealth system
• Social Media Analysis:
Crisis coordination
– Using Twitris platform
• Smart Cities:
Traffic management
43
Smart Data Applications
30. 44
A Historical Perspective on Collecting Health Observations
Diseases treated only
by external observations
First peek beyond just
external observations
Information overload!
Doctors relied only on
external observations
Stethoscope was the
first instrument to go
beyond just external
observations
Though the stethoscope
has survived, it is only one
among many observations
in modern medicine
http://en.wikipedia.org/wiki/Timeline_of_medicine_and_medical_technology
2600 BC ~1815 Today
Imhotep
Laennec’s stethoscope
Image Credit: British Museum
31. The Patient of the Future
MIT Technology Review, 2012
http://www.technologyreview.com/featuredstory/426968/the-patient-of-the-future/ 45
32. 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
Empowering Individuals (who are not Larry Smarr!) for their own health
kHealth: knowledge-enabled healthcare
46
35. Sensordrone
(Carbon monoxide,
temperature, humidity)
Node Sensor
(exhaled Nitric
Oxide)
49
Sensors
Android Device
(w/ kHealth App)
Total cost: ~ $500
kHealth Kit for the application for Asthma management
*Along with two sensors in the kit, the application uses a variety of population level signals from the web:
Pollen level Air Quality
Temperature & Humidity
36. 51
Data Overload for Patients/health aficionados
Providing actionable information in a timely manner is
crucial to avoid information overload or fatigue
Personal level
Signals
Public level
Signals
Population level
Signals
37. 52
Data Overload Spanning Physical-Cyber-Social Modalities
Increasingly, real-world events are:
(a) Continuous: Observations are fine grained over time
(b) Multimodal, multisensory: Observations span PCS modalities
38. what can we do to avoid asthma episode?
54
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
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: Asthma example
39. kHealth: Health Signal Processing Architecture
Personal level
Signals
Public level
Signals
Population level
Signals
Domain
Knowledge
Risk Model
Events from
Social Streams
Take Medication before
going to work
Avoid going out in the
evening due to high pollen
levels
Contact doctor
Analysis
Personalized
Actionable
Information
Data Acquisition &
aggregation
55
40. 57
Asthma Domain Knowledge
Domain
Knowledge
Asthma Control
à
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
41. 58
Patient Health Score (diagnostic)
Risk assessment
model
Semantic
Perception
Personal level
Signals
Public level
Signals
Domain
Knowledge
Population level
Signals
GREEN -- Well Controlled
YELLOW – Not well controlled
Red -- poor controlled
How controlled is my asthma?
42. 59
Patient Vulnerability Score (prognostic)
Risk assessment
model
Semantic
Perception
Personal level
Signals
Public level
Signals
Domain
Knowledge
Population level
Signals
Patient health
Score
How vulnerable* is my control level today?
*considering changing environmental conditions and current control level
43. 60
3.4 billion people will have smartphones or tablets by 2017
-- Research2Guidance
“Intelligence at the Edges” for Digital Health
http://www.digikey.com/us/en/techzone/energy-harvesting/resources/articles/zigbees-smart-energy-20-profile.html
m-health app market is predicted to reach $26 billion in 2017
-- Research2Guidance
44. 63
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?
Commute to Work
Asthma: Actionable Information for Asthma Patients
Luminosity
CO level
CO in gush
during day time
Actionable
Information
Personal level
Signals
Public level
Signals
Population level
Signals
What is the air quality indoors?
45. 64
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
46. 70
RDF OWL
How are machines supposed to integrate and interpret sensor data?
Semantic Sensor Networks (SSN)
47. 71
W3C Semantic Sensor Network Ontology
Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K.,
Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
48. 73
W3C Semantic Sensor Network Ontology
Lefort, L., Henson, C., Taylor, K., Barnaghi, P., Compton, M., Corcho, O., Garcia-Castro, R., Graybeal, J., Herzog, A., Janowicz, K.,
Neuhaus, H., Nikolov, A., and Page, K.: Semantic Sensor Network XG Final Report, W3C Incubator Group Report (2011).
49. SSN
Ontology
2 Interpreted data
(deductive)
[in OWL]
e.g., threshold
1 Annotated Data
[in RDF]
e.g., label
0 Raw Data
[in TEXT]
e.g., number
Levels of Abstraction
3 Interpreted data
(abductive)
[in OWL]
e.g., diagnosis
Intellego
“150”
Systolic blood pressure of 150 mmHg
Elevated
Blood
Pressure
Hyperthyroidism
……
75
51. People are good at making sense of sensory input
What can we learn from cognitive models of perception?
• The key ingredient is prior knowledge
77
52. * 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
78
53. To enable machine perception,
Semantic Web technology is used to integrate
sensor data with prior knowledge on the Web
79
54. Prior knowledge on the Web
W3C Semantic Sensor
Network (SSN) Ontology Bi-partite Graph
80
55. Prior knowledge on the Web
W3C Semantic Sensor
Network (SSN) Ontology Bi-partite Graph
81
57. 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
85
59. Semantic scalability: Resource savings of abstracting sensor data
90
Orders of magnitude resource savings for generating and storing relevant
abstractions vs. raw observations.
Relevant abstractions
Raw observations
60. 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)
92
61. 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
93
Henson et al. 'An Efficient Bit Vector Approach to Semantics-based Machine Perception in Resource-Constrained Devices,
ISWC 2012.
62. 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
94
63. 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
95
64. 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
96
65. • Healthcare:
ADFH, Asthma, GI
– Using kHealth system
• Social Media Analysis:
Crisis coordination
– Using Twitris platform
• Smart Cities:
Traffic management
98
Smart Data Applications
66. 99
Smart Data for Social Good
Mining human behavior to help
societal and humanitarian
development
• crisis response coordination,
harassment, gender-based
violence, …
67. 20 million tweets with “sandy, hurricane”
keywords between Oct 27th and Nov 1st
2nd most popular topic on Facebook during 2012
Social (Big) Data during Crisis- Example of Hurricane Sandy
100
• http://www.guardian.co.uk/news/datablog/2
012/oct/31/twitter-sandy-flooding
• http://www.huffingtonpost.com/2012/11/02
/twitter-hurricane-sandy_n_2066281.html
• http://mashable.com/2012/10/31/hurricane-
sandy-facebook/
69. Twitris‟ Dimensions of Integrated Semantic Analysis
104Sheth et al. Twitris- a System for Collective Social Intelligence, ESNAM-2014
70. 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!
113
Because everyone wants to Help, but DON’T KNOW HOW!
71. Really sparse Signal to Noise:
• 2M tweets during the first 48 hrs. of #Oklahoma-tornado-2013
- 1.3% as the precise resource donation requests to help
- 0.02% as the precise resource donation offers to help
114
• 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
Blog by our colleague Patrick Meier on this analysis: http://irevolution.net/2013/05/29/analyzing-tweets-tornado/
72. 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
needs and offers
Using Social Media
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/knoesis/iccm-2013ignitetalkhemantpurohitunnairobi
115
Purohit et al. Emergency-relief coordination on social media: Automatically matching resource requests and offers, 2014. With Int’l collaborator
75. • Healthcare:
ADFH, Asthma, GI
– Using kHealth system
• Social Media Analysis:
Crisis coordination
– Using Twitris platform
• Smart Cities:
Traffic management
130
Smart Data Applications
78. Vehicular traffic data from San Francisco Bay Area aggregated from on-road
sensors (numerical) and incident reports (textual)
133
http://511.org/
Every minute update of speed, volume, travel time, and occupancy resulting in
178 million link status observations, 738 active events, and 146 scheduled
events with many unevenly sampled observations collected over 3 months.
Variety Volume
VeracityVelocity
Value
Can we detect the onset of traffic congestion?
Can we characterize traffic congestion based on events?
Can we estimate traffic delays in a road network?
semantics
Representing prior knowledge of
traffic lead to a focused exploration
of this massive dataset
Big Data to Smart Data: Traffic Management example
81. City Infrastructure
Tweets from a city
POS
Tagging
Hybrid NER+
Event term
extraction
Geohashing
Temporal
Estimation
Impact
Assessment
Event
Aggregation
OSM
Locations
SCRIBE
ontology
511.org hierarchy
City Event Extraction
City Event Extraction Solution Architecture
City Event Annotation
OSM – Google Open Street Maps
NER – Named Entity Recognition 136
82. City Event Annotation – CRF Annotation Examples
Last O night O in O CA... O (@ O Half B-LOCATION Moon I-LOCATION Bay B-LOCATION
Brewing I-LOCATION Company O w/ O 8 O others) O http://t.co/w0eGEJjApY O
B-LOCATION
I-LOCATION
B-EVENT
I-EVENT
O
Tags used in our approach:
These are the annotations provided
by a Conditional Random Field model
trained on tweet corpus to spot
city related events and location
BIO – Beginning, Intermediate, and Other is a notation used in multi-phrase entity spotting 138
83. City Events from Sensor and Social Streams can be…
• Complementary
• Additional information
• e.g., slow traffic from sensor data and accident from textual data
• Corroborative
• Additional confidence
• e.g., accident event supporting a accident report from ground truth
• Timely
• Additional insight
• e.g., knowing poor visibility before formal report from ground truth
143
84. Events from Social Streams and City Department*
Corroborative EventsComplementary Events
Event Sources
City events extracted from tweets
511.org, Active events e.g., accidents, breakdowns
511.org, Scheduled events e.g., football game, parade
City event from twitter providing complementary and
corroborative evidence for fog reported by 511.org
*511.org
146
85. 147
Actionable Information in City Management
Tweets from a CityTraffic Sensor Data
OSM
Locations
SCRIBE
ontology
511.org hierarchy
Web of Data
How issues in a city can be resolved?
e.g., what should I do when I have fog condition?
86. • Big Data is every where
– at individual level and not just limited to
corporation
– with growing complexity: multimodal, Physical-
Cyber-Social
• Analysis is not sufficient
• Bottom up techniques is not sufficient, need
top down processing, need background
knowledge
149
Take Away
87. Take Away
• Focus on Humans and Improve human life and
experience with SMART Data.
– Data to Information to Contextually Relevant
Abstractions
– Actionable Information (Value from data) to assist
and support Human in decision making.
• Focus on Value -- SMART Data
– Big Data Challenges without the intention of deriving
Value is a “Journey without GOAL”.
150
88. 153
thank you, and please visit us at
http://knoesis.org/vision
Kno.e.sis – Ohio Center of Excellence in Knowledge-enabled Computing
Wright State University, Dayton, Ohio, USA
Smart Data
89. Ohio Center of Excellence in Knowledge-enabled
Computing
• Among top universities in the world in World Wide Web (cf: 5-yr
impact, Microsoft Academic Search: shared 2nd place in Mar13)
• Largest academic group in the US in Semantic Web + Social/Sensor
Webs, Mobile/Cloud/Cognitive Computing, Big Data, IoT,
Health/Clinical & Biomedicine Applications
• Exceptional student success: internships and jobs at top salary (IBM
Research, MSR, Amazon, CISCO, Oracle, Yahoo!, Samsung, research
universities, NLM, startups )
• 100 researchers including 15 World Class faculty (>3K
citations/faculty) and 45+ PhD students- practically all funded
• $2M+/yr research for largely multidisciplinary projects; world class
resources; industry sponsorships/collaborations (Google, IBM, …)
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.
Note:For images and sources, if not on slides, please see slide notesSome 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.
Types of DataFormats of DataAlso talk about the increase in the platforms that helps generating these data
Example high velocity Big Data applications at work:financial services, stock brokerage, weather tracking, movies/entertainment and online retail.Fast data (rate at which data is coming: esp from mobile, social and sensor sources), Rapid changes – in the data content, Stream analysis – to cope with the incoming data for real-time online analytics
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
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
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
Top and bottom part of the brain -- http://online.wsj.com/news/articles/SB10001424052702304410204579139423079198270 Top part of the brain is known for generating plansBottom part of the brain deals with current situational awarenessPerception through senses happens in the primitive part of the brain (mostly subconsciously)Machine perception allows us to transform low level sensor observations to higher level abstractions that are directly communicable to the upper part of the brain (non-subconscious)Thus, people can understand/adapt their plan quickly with abstractionsThe left brain here is generating plan of having an apple a day to make a healthy living The right part of the brain identifies an apple through senses
Communicating the “abstraction” of less apples at home through “Ambient processing/intelligence”The left/top part of the brain will adapt the plan to shopping for apple soon so that the overall plan of having an apple a day can be achieved
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!!
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
Actionable information example:In Asthma use case we have a sensor – sensordrone which records luminosity and CO levelsA high correlation between CO level and luminosity is foundThis is an actionable information to the user interpreting it as CO in gush during day time=> Mitigating action can be “closing the window” during day
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..
http://www.huffingtonpost.com/2012/10/30/hurricane-sandy-power-outage-map-infographic_n_2044411.htmlI would like to start with a motivational example here.
Fraustino, Julia Daisy, Brooke Liu and Yan Jin. “Social Media Use during Disasters: A Review of the Knowledge Base and Gaps,” Final Report to Human Factors/Behavioral Sciences Division, Science and Technology Directorate, U.S. Department of Homeland Security. College Park, MD: START, 2012. Disaster communication deals with disaster information disseminated to the public by governments, emergency management organizations, and disaster responders as well as disaster information created and shared by journalists and the public. Disaster communication increasingly occurs via social media in addition to more conventional communication modes such as traditional media (e.g., newspaper, TV, radio) and word-of-mouth (e.g., phone call, face-to-face, group). Timely, interactive communication and user-generated content are hallmarks of social media, which include a diverse array of web- and mobile-based tools Disaster communication deals with (1) disaster information disseminated to the public by governments, emergency management organizations, and disaster responders often via traditional and social media; as well as (2) disaster information created and shared by journalists and affected members of the public often through word-of-mouth communication and social media. For information seeking. Disasters often breed high levels of uncertainty among the public (Mitroff, 2004), which prompts them to engage in heightened information seeking, (Boyle, Schmierbach, Armstrong, & McLeod, 2004; Procopio & Procopio, 2007). As expected, information seeking is a primary driver of social media use during routine times and during disasters (Liu et al., in press; PEW Internet, 2011). For timely information. Social media provide real-time disaster information, which no other media can provide (Kavanaugh et al., 2011; Kodrich & Laituri, 2011). Social media can become the primary source of time-sensitive disaster information, especially when official sources provide information too slowly or are unavailable (Spiro et al., 2012). For example, during the 2007 California wildfires, the public turned to social media because they thought journalists and public officials were too slow to provide relevant information about their communities (Sutton, Palen, & Shklovski, 2008). Time-sensitive information provided by social media during disasters is also useful for officials. For example, in an analysis of more than 500 million tweets, Culotta (2010) found Twitter data forecasted future influenza rates with high accuracy during the 2009 pandemic, obtaining a 95% correlation with national health statistics. Notably, the national statistics came from hospital survey reports, which typically had a lag time of one to two weeks for influenza reporting. For unique information. One of the primary reasons the public uses social media during disaster is to obtain unique information (Caplan, Perse, & Gennaria, 2007). Applied to a disaster setting, which is inherently unpredictable and evolving, it follows that individuals turn to whatever source will provide the newest details. Oftentimes, individuals experiencing the event first-hand are on the scene of the disaster and can provide updates more quickly than traditional news sources and disaster response organization. For instance, in the Mumbai terrorist attacks that included multiple coordinated shootings and bombings across two days, laypersons were first to break the news on Twitter (Merrifield & Palenchar, 2012). Research participants report using social media to satisfy their need to have the latest information available during disasters and for information gathering and sharing during disasters (Palen, Starbird, Vieweg, & Hughes, 2010; Vieweg, Hughes, Starbird, & Palen, 2010). For unfiltered information. To obtain crisis information, individuals often communicate with one another via social media rather than seeking a traditional news source or organizational website (Stephens & Malone, 2009). The public check in with social media not only to obtain up-to-date, timely information unavailable elsewhere, but also because they appreciate that information may be unfiltered by traditional media, organizations, or politicians (Liu et al., in press). To determine disaster magnitude. The public uses social media to stay apprised of the extent of a disaster (Liu et al., in press). They may turn to governmental or organizational sources for this information, but research has shown that if the public do not receive the information they desire when they desire it, they, along with others, will fill in the blanks (Stephens & Malone, 2009), which can create rumors and misinformation. On the flipside, when the public believed that officials were not disseminating enough information regarding the size and trajectory of the 2007 California wildfires, they took matters into their own hands, using social media to track fire locations in real-time and notify residents who were potentially in danger (Sutton, Palen, & Shklovski, 2008). To check in with family and friends. While Americans predominately use social media to connect with family and friends (PEW Internet, 2011), during disasters those connections may shift. For those with family or friends directly involved with the disaster, social media can provide a way to ensure safety, offer support, and receive timely status updates (Procopio & Procopio, 2007; Stephens & Malone, 2009). In a survey of 1,058 Americans, the American Red Cross (2010) found that nearly half of their respondents would use social media to let loved ones know they are safe during disasters. After the 2011 earthquake and tsunami in Japan, the public turned to Twitter, Facebook, Skype, and local Japanese social networks to keep in touch with loved ones while mobile networks were down (Gao, Barbier, & Goolsby, 2011). Researchers also note that disasters may enhance feelings of affection toward family members, and indeed survey participants reported expressing more positive emotions toward their loved ones than usual as a result of the September 11 terrorist attacks, even if they were not directly impacted by the disaster (Fredrickson et al., 2003). Finally, disasters can motivate the public to reconnect with family and friends via social media (Procopio & Procopio, 2009; Semaan & Mark, 2012). To self-mobilize. During disasters, the public may use social media to organize emergency relief and ongoing assistance efforts from both near and afar. In fact, one research group dubbed those who surge to the forefront of digital and in-person disaster relief efforts as “voluntweeters” (Starbird & Palen, 2011). Other research documents the role of Facebook and Twitter in disaster relief fundraising (Horrigan & Morris, 2005; PEJ, 2010). Research also reveals how social media can help identify and respond to urgent needs after disasters. For example, just two hours after the 2010 Haitian earthquake Tufts University volunteers created Ushahidi-Haiti, a crisis map where disaster survivors and volunteers could send incident reports via text messages and tweets. In less than two weeks, 2,500 incident reports were sent to the map (Gao, Barbier, & Gollsby, 2011). To maintain a sense of community. During disasters the media in general and social media in particular may provide a unique gratification: sense of community. That is, as the public logs in online to share their feelings and thoughts, they assist each other in creating a sense of security and community, even when scattered across a vast geographical area (Lev-On, 2011; Procopio & Procopio, 2007). As Reynolds and Seeger (2012) observed, social media create communities during disasters that may be temporary or may continue well into the future. To seek emotional support and healing. Finally, disasters are often inherently tragic, prompting individuals to seek not only information but also human contact, conversation, and emotional care (Sutton et al., 2008). Social media are positioned to facilitate emotional support, allowing individuals to foster virtual communities and relationships, share information and feelings, and even demand resolution (Choi & Lin, 2009; Stephens & Malone, 2009). Indeed, social media in general and blogs in particular are instrumental for providing emotional support during and after disasters (Macias, Hilyard, & Freimuth, 2009; PEJ New Media Index, 2011). Additionally, social media in general and Twitter in particular can aid healing, as research finds during both natural disasters, such as Hurricane Katrina (Procopio & Procopio, 2007), and man-made disasters, such as the July 2011 attacks in Oslo, Norway (Perng et al., 2012).
http://www.buzzfeed.com/annanorth/how-social-media-is-aiding-the-hurricane-sandy-rec -- Facebook help during Hurricane Sandyhttp://blog.twitter.com/2012/10/hurricane-sandy-resources-on-twitter.html – Twitter page for Hurricane Sandyhttp://www.treehugger.com/culture/12-ways-help-hurricane-sandy-relief-efforts.htmlCategorization of severity based on weather conditions. Actionable information is contextually dependent.
http://news.cnet.com/8301-1023_3-57541566-93/report-twitter-hits-half-a-billion-tweets-a-day/http://semiocast.com/en/publications/2012_07_30_Twitter_reaches_half_a_billion_accounts_140m_in_the_USLet me consider one small example of how social data (in turn data) can help people during disasters. Data becomes smart data if it takes recipient into account - context.Sensor data for emergency responders. Who in the population needs immediate attention (1) Location (2) Severity (3) Health Condition Need for abstraction. – Semantic Perception needs abstraction. 90 + Heart Problem Don’t run out23 Run out
http://news.cnet.com/8301-1023_3-57541566-93/report-twitter-hits-half-a-billion-tweets-a-day/http://semiocast.com/en/publications/2012_07_30_Twitter_reaches_half_a_billion_accounts_140m_in_the_UShttp://www.internews.org/sites/default/files/resources/InternewsEurope_Report_Japan_Connecting%20the%20last%20mile%20Japan_2013.pdfLet me consider one small example of how social data (inturn data) can help people during disasters. Data becomes smart data if it takes recipient into account and changes contact accordingly.Sensor data for emergency responders. Who in the population needs immidiate attention (1) Location (2) Severity (3) Health Condition Need for abstraction. – Semantic Perception needs abstraction. 90 + Heart Problem Don’t run out23 Run out
http://news.cnet.com/8301-1023_3-57541566-93/report-twitter-hits-half-a-billion-tweets-a-day/http://semiocast.com/en/publications/2012_07_30_Twitter_reaches_half_a_billion_accounts_140m_in_the_USLet me consider one small example of how social data (inturn data) can help people during disasters. Data becomes smart data if it takes recipient into account and changes contxt accordingly.Sensor data for emergency responders. Who in the population needs immediate attention (1) Location (2) Severity (3) Health Condition Need for abstraction. – Semantic Perception needs abstraction. 90 + Heart Problem Don’t run out23 Run out
http://www.buzzfeed.com/jackstuef/the-man-behind-comfortablysmug-hurricane-sandysDuring the storm last night, user @comfortablysmug was the source of a load of frightening but false information about conditions in New York City that spread wildly on Twitter and onto news broadcasts before Con Ed, the MTA, and Wall Street sources had to take time out of the crisis situation to refute them.
Although we face challenges like these with data everytime. The most important thing is what you aim to do with the data. I mean what value do you intend to provide from the data
-- Contextual Questioning – Potential Information needed from Humans
"2600 BC – Imhotep wrote texts on ancient Egyptian medicine describing diagnosis and treatment of 200 diseases in 3rd dynasty Egypt.”Sir William Osler, 1st Baronet, was a Canadian physician and one of the four founding professors of Johns Hopkins Hospital. He was called the father of modern medicine. Sir William Osler called Imhotep as the true father of medicine.Observations related to human body was quite limited Initially, doctors communicated with patients asking for their symptoms (subjective)Laennec’s [Rene TheophileHyacintheLaënnec, a French Physician] stethoscope was the fist peek into the observations of human body (objective)Now, there are petabytes of data being generated for observations of human body
Larry Smarr is a professor at the University of California, San DiegoAnd he was diagnosed with crohn's 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
- 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
ADHF – Acute Decompensated Heart Failure
1)www.pollen.com(For pollen levels)2)http://www.airnow.gov/(For air quality levels)3)http://www.weatherforyou.com/(For temperature and humidity)
Data overload in the context of asthma
“
AmitSheth, Pramod Anantharam, Cory Henson, 'Physical-Cyber-Social Computing: An Early 21st Century Approach,' IEEE Intelligent Systems, vol. 28, no. 1, pp. 78-82, Jan.-Feb., 2013.
Research on Asthma has three phases Data collection: what signals to collect?Analysis: what analysis to be done?Actionable information: what action to recommend?In the next slide, we take a peek into the analysis that we do for Asthma
What is the current state of a person/patient? => Summarizing all the observations (sensor and personal) into a single score indicating health of a personInstead of presenting all the raw data (often to much e.g., Asthma application we have developed collects CO, temperature, and humidity every 10 seconds resulting in 8,640 observations/day) which may not be comprehensible to the patient, we empower them by providing actionable summaries.
What is the likely state of the person in future? => Given the current state and the changing environmental conditions, estimate the state of the person by summarizing it into a number which is actionable. For example, vulnerability score for a person with Asthma is computed with environmental factors (pollen, air quality, external temperature and humidity) and current state of the patient. Intuitively, a person with well controlled asthma should have a lower vulnerability score than a person with poorly controlled asthma both being in a poor environmental state.
In the absence of declarative knowledge in a domain, we resort to statistical approaches to glean insights from dataEven if there is declarative knowledge of a domain, it may have to be personalizedThe CO level may be related to the luminosity as observed by the sensordrone – as it gets brighter the CO level also increases => high CO level in daytime If such an insight is provided to a person, the interpretation can be:Some activity inside the house leads to high CO levelsOutside activity leads to high CO levels inside the houseSince the person knows that he/she is absent in the house during mornings, it has to be something from outside.- Person narrows down to a possible opened window at home (forgot to close more often)
There are two components in making sense of Health Signals:Health signal extraction – processing, aggregating, and abstracting from raw sensor/textual data to create human intelligible abstractionsHealth signal understanding – derive (1) connections between abstractions and (2) Action recommendation:ContinueContact nurseContact doctor
Only score based structure extraction is presented here. Other popular structure extraction techniques include constraint based approaches which finds independences between random variables X1, …, XnI-Map => different structures result in the same loglikelihood score. Thus recovering the original structure of the graph generating data using data alone is considered impossible! We go the the rescue of declarative knowledge to: (1) choose promising structures and (2) to break ties when two structure results in the same score
Massive amount of data is 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
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.Parsimony criteria is single feature assumption to choose from among multiple explanationsNot true: if multiple disease account for single property…Rewrite with more relaxed parcimony criteria (complex, cannot be modeled in OWL)Make KB more intelligent: create an individual that represents the two disease which together explain a symptom
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)
So check galvanic skin response sensor
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)
http://www.guardian.co.uk/news/datablog/2012/oct/31/twitter-sandy-floodinghttp://www.huffingtonpost.com/2012/11/02/twitter-hurricane-sandy_n_2066281.htmlhttp://mashable.com/2012/10/31/hurricane-sandy-facebook/We in our lab have quite a bit of Social Data Research going on. So I would like to focus on the use of social networks during these disasters/crisis.Twitter and Facebook are massively used during disasters. During Hurricane Sandy there were …Not only this a major outbreak of tweets were during Japan earthquake which crossed more that 2000 tweets/sec.So why do people intend to use social networks to this extent during disasters.
Categorization of severity based on weather conditions. Actionable information is contextually dependent.
- 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 help
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
Definition of the event US Elections and some changes/subevents --- Primaries --- Debates -- People/Places/Organizations involved in the eventArab Spring -- Subevents during those -- Egypt protests
Explain about continuous semantics
Pucher, J., Korattyswaroopam, N., & Ittyerah, N. (2004). The crisis of public transport in India: Overwhelming needs but limited resources. Journal of Public Transportation, 7(4), 1-30.
Twitter as a source of real-time informationThere are over 200 million users generating 500 million tweets / dayTwitter as a source of events in a cityCitizens use twitter to express their concerns of city infrastructure that impacts their life
The red-tweets are the tweets that are related to city infrastructure e.g., trafficThere are two steps in converting raw tweets from a city to city related events:City event annotation: sequence labeling technique to spot location and event termsCity event extraction: aggregating all the location + event terms to derive eventsTo do this aggregation, we follow some principles that characterize city events
CRF assigns a tag to each tokenGlobal normalization is the argmax termRHS is just a regression based implementation of linear chain (potentials defined only over adjacent tags) CRFLingPipe implementation of CRF is used in our experiments
There principles characterize city events
localized event detection strategy, city a composition of smaller geographical unitsWe call these geographical units as grids Geohash provides us a way of compartmentalizing a city into uniquely addressable gridsDistance computed using the formula:dlon = lon2 - lon1 dlat = lat2 - lat1 a = (sin(dlat/2))^2 + cos(lat1) * cos(lat2) * (sin(dlon/2))^2 c = 2 * atan2( sqrt(a), sqrt(1-a) ) d = R * c (where R is the radius of the Earth)Found the box for the tweet!37.7545166015625, -122.42065429687537.7545166015625, -122.4096679687537.7490234375, -122.4096679687537.7490234375, -122.420654296875
These algorithms take the annotated tweets as input and then emit events with their metadata
Now that we have presented the (1) event extraction and (2) event aggregation algorithms, how well are we doing?We evaluate both the componentsThe ground truth are the events reported on 511.orgWe compare the events we extract from tweets with the 511.org events
We evaluate the extracted events based on there orthogonal metrics.We compare (1) events extracted from tweets using our algorithms and (2) 511.org events Complementary events – the events from (1) and (2) may complement each other i.e., one providing a different view from the otherCorroborative events – the events from (1) and (2) may support each other i.e., redundant eventsTimeliness – the events were reported on (1) before it was reported on (2)
Next few slides give examples of the evaluation metric
The record of 511.org may have its own timestamp which may be before tweets
More at: http://wiki.knoesis.org/index.php/PCSAnd http://knoesis.org/projects/ssw/