Transforming Big Data into Smart Data: Deriving Value via harnessing Volume, Variety and Velocity using semantics and Semantic Web

Editor's Notes

• #3: http://www.knowledgeinfusion.com/blog/2011/11/get-your-head-out-of-the-clouds-and-into-big-data/
• #4: http://www.csc.com/insights/flxwd/78931-big_data_growth_just_beginning_to_explodehttp://www.guardian.co.uk/news/datablog/2012/dec/19/big-data-study-digital-universe-global-volume
• #5: Types of DataFormats of DataAlso talk about the increase in the platforms that helps generating these data
• #6: 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
• #9: Source: http://techcrunch.com/2012/10/27/big-data-right-now-five-trendy-open-source-technologies
• #10: 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)
• #11: 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
• #12: 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.
• #13: - HUMAN CENTRIC!!
• #15: 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
• #17: All the data related to human activity, existence and experiencesMore on PCS Computing: http://wiki.knoesis.org/index.php/PCS
• #19: 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
• #21: 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
• #22: Lets find it..
• #23: 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.
• #24: 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.
• #25: 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.
• #26: 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).
• #27: 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.
• #28: 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
• #29: 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
• #30: 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
• #31: 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.
• #32: 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
• #33: http://www.wired.com/insights/2013/04/big-data-fast-data-smart-data/
• #34: http://www.wired.com/insights/2013/04/big-data-fast-data-smart-data/
• #35: -- Contextual Questioning – Potential Information needed from Humans
• #37: 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
• #40: 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.
• #41: 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
• #47: kHealth:http://www.youtube.com/watch?v=btnRi64hJp4EventShop:*http://www.slideshare.net/jain49/eventshop-120721, http://dl.acm.org/citation.cfm?id=2488175
• #49: - what if we could automate this sense making ability?- and what if we could do this at scale?
• #50: sense making based on human cognitive models
• #51: 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)
• #55: 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)
• #56: 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.
• #58: 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)
• #62: - 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
• #64: Intelligence distributed at the edge of the networkRequires resource-constrained devices (mobile phones, gateway notes, etc.) to be able to utilize SW technologies
• #65: 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.
• #66: 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
• #67: Intelligence at the age. Shipping computation and domain models to the edge (Distributed)
• #70: “to help software reusability in order to allow new applications to be built faster and to share innovations (software components, novel approaches) amongst software developers” “to standardize and commoditize back-end data stores so client software may access any Open mHealth-compliant data store in a uniform way (interoperability)” “to produce examples and documentation of these concepts meaningfully and simply”
• #71: Observe data from different sensors at the same time.
• #72: System Architecture Fig. shows an overview of the SemHealth architecture. SensorsAll are bluetooth sensors already utilized by the current k-Health application to measure weight, heart rate, and blood pressureAndroid applicationReads sensor observations through bluetoothPerforms annotation on observations and generates percepts from those observationsUploads annotated observations and percepts to the server-side data storeRetrieves data using DSU API and feeds data to DPU and/or DVU APIsVisualizes data through DVU APIConsidered a “nice to have” as existing visualization may be used as-isWill utilize existing graphing library for Android with Open mHealth-style API that may be translated to browser at a later timeServer-sideOpen mHealth compliant DSU and DPU APIsTriple data storage replaces existing SQLite database in k-Health applicationExisting k-Health reasoner now the brains behind DPU
• #75: http://www.flickr.com/photos/twitteroffice/5897088517/sizes/o/in/photostream/http://bayarea.sbnation.com/49ers/2013/2/3/3947738/super-bowl-prop-bets-2013-twitterhttp://bayarea.sbnation.com/49ers/2013/2/3/3947738/super-bowl-prop-bets-2013-twitterhttp://expandedramblings.com/index.php/march-2013-by-the-numbers-a-few-amazing-twitter-stats/
• #78: Much of the early work in Big data is being done with focusing on uni-directional among XYZ.
• #79: http://semanticweb.com/picking-the-president-twindex-twitris-track-social-media-electorate_b31249http://semanticweb.com/election-2012-the-semantic-recap_b33278
• #82: http://knoesis.wright.edu/library/resource.php?id=1787
• #87: Categorization of severity based on weather conditions. Actionable information is contextually dependent.
• #88: - 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)
• #89: Talk about what kind of smart data we provide that helps the actions of crisis response coordination.
• #90: 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)
• #91: http://twitris.knoesis.org/oklahomatornado
• #92: (It is real-time widget for monitoring of needs, so will not be active after the event has passed) http://twitris.knoesis.org/oklahomatornado
• #97: Highly rich interface for response team
• #99: 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
• #100: Explain about continuous semantics
• #105: 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.
• #107: Point of this slide: correlations
• #108: Point of this slide: heterogeneity and uncertainty
• #109: A single observation of slow moving traffic may have multiple explanations.
• #110: Internal observations are limited to whatever the on-road sensors can observe. In the 511.org data we have analyzed, the internal observations are mentioned above.External events are obtained from sources beyond the on-road sensors e.g., some agency like 511.org which reports traffic incidents.Note that: Internal observations are mostly machine sensors External events are mostly textual observationsThe analogy in healthcare will be:Internal observations: on body sensors such as heart rate, temperatureExternal events: jogging, walking, taking stairs
• #112: e.g. equation for projectile motion may not precisely compute the actual projectile. Air resistance may have been ignored
• #114: Used of open data for parameters is promising and can be explored as future research.
• #115: Some facts about the domain of traffic got from Conceptnet5The types of events are obtained by using the comprehensive subsumption relationship from 511.orgWe propose to use such a knowledge in complementing the PGM structure learning algorithmsCapableOf(traffic jam, occur twice each day)CapableOf(traffic jam, slow traffic)RelatedTo(accident, car crash)Causes(road ice, accident)CapableOf(bad weather, slow traffic)HasSubevent(go to concert, car crash)Causes(go to baseball game, traffic jam)Causes(traffic accident, traffic jam)BadWeather(road ice)BadWeather(bad weather)ScheduledEvent(go to concert)ScheduledEvent(go to baseball game)ActiveEvent(traffic accident)Delay(slow traffic)Delay(traffic jam)TimeOfDay(occur twice each day)
• #116: Declarative knowledge + statistical correlationThis slide illustrates the three operations to enrich the correlation structure extracted using statistical methods These operations utilize declarative knowledge form ConceptNet5 as shown in each step
• #117: Statistical correlation structure shown aboveThe enriched structure is shown belowThe enrichment of the graphical model will potentially allow us to capture the domain precisely and also improve our prediction as the model would get closer to the underlying probabilistic distribution in the real-worldLog-Likelihood score is one way of quantifying how good a structure is based on the observed data There may be many candidate structures extracted from data which result in the log likelihood scoreDeclarative knowledge will help us ground statistical models to reality which will allow us to pick one structure over the other Pramod Anantharam, KrishnaprasadThirunarayan and AmitSheth, 'Traffic Analytics using Probabilistic Graphical Models Enhanced with Knowledge Bases,' 2nd International Workshop on Analytics for Cyber-Physical Systems (ACS-2013) at SIAM International Conference on Data Mining (SDM13), pp. 13--20, Texas, USA, May 2-4, 2013.We stopped at structure extraction for our workshop paper (SIAM ACS workshop) since the declarative knowledge we used (ConceptNet5) and statistical model (nodes and edges) are at the same level of abstraction
• #125: More at: http://wiki.knoesis.org/index.php/PCSAnd http://knoesis.org/projects/ssw/