Knowledge Discovery from Mining Big Data

Knowledge Discovery from Mining Big Data
(the Borne Identity) Data Literacy for all ! (the Borne ultimatum) Knowledge Discovery from Mining Big Data Kirk Borne @KirkDBorne George Mason University School of Physics, Astronomy, & Computational Sciences

The Big Data Manifesto (the Borne Ultimatum) Data Literacy for all !
More data is not just more data … more is different! Discover the unknown unknowns. Address massive Data-to-Knowledge (D2K) challenge. Data Literacy for all !

Ever since we first began to explore our world…

… humans have asked questions and …
We have collected evidence (data) to help answer those questions.

… humans have asked questions and …
We have collected evidence (data) to help answer those questions. The journey from traditional science to …

… Data-intensive Science is a Big Challenge
6

http://www. economist. com/specialreports/displaystory. cfm

Scary News: Big Data is taking us to a Tipping Point
8

Promising News: Big Data leads to Big Insights and New Discoveries
9

Good News: Big Data is Sexy
10

Characteristics of Big Data – 1234
Computing power doubles every 18 months (Moore’s Law) ... 100x improvement in 10 years I/O bandwidth increases ~10% / year <3x improvement in 10 years. The amount of data doubles every year ... 1000x in 10 years, and 1,000,000x in 20 yrs. How much data are there in the world? From the beginning of recorded time until 2003, we created 5 billion gigabytes (exabytes) of data. In 2011 the same amount was created every two days. In 2013, the same amount is created every 10 minutes.

Computing power doubles every 18 months (Moore’s Law) ... 100x improvement in 10 years The amount of data doubles every year (or faster!) ... 1000x in 10 years, and 1,000,000x in 20 yrs. I/O bandwidth increases ~10% / year <3x improvement in 10 years.

Computing power doubles every 18 months (Moore’s Law) ... 100x improvement in 10 years The amount of data doubles every year ... 1000x in 10 years, and 1,000,000x in 20 yrs. I/O bandwidth increases ~10% / year <3x improvement in 10 years. Moore’s Law of Slacking will not help !

Big quantities of data are acquired everywhere. It is now a big issue in all aspects of life: science, business, healthcare, gov, social networks, national security, media, etc.

Big quantities of data are acquired everywhere. It is now a big issue in all aspects of life: science, business, healthcare, gov, social networks, national security, media, etc. LSST project ( : 20 Terabytes of astronomical imaging every night Petabyte image archive after 10 years 20-40 Petabyte database 2-10 million new sky events nightly that need to be characterized and classified – potential new discoveries!

Job opportunities are sky-rocketing Extremely high demand for Data Science skills Demand will continue to increase Old: “100 applicants per job”. New: “100 jobs per applicant”

Job opportunities are sky-rocketing Extremely high demand for Data Science skills Demand will continue to increase Old: “100 applicants per job”. New: “100 jobs per applicant” McKinsey Report (2011**) : Big Data is the new “gold rush” , the “new oil” 1.5 million skilled data scientist shortage within 5 years **

Data Sciences: A National Imperative
1. National Academies report: Bits of Power: Issues in Global Access to Scientific Data, (1997) 2. NSF (National Science Foundation) report: Knowledge Lost in Information: Research Directions for Digital Libraries, (2003) downloaded from 3. NSF report: Cyberinfrastructure for Environmental Research and Education, (2003) downloaded from 4. NSB (National Science Board) report: Long-lived Digital Data Collections: Enabling Research and Education in the 21st Century, (2005) downloaded from 5. NSF report with the Computing Research Association: Cyberinfrastructure for Education and Learning for the Future: A Vision and Research Agenda, (2005) downloaded from 6. NSF Atkins Report: Revolutionizing Science & Engineering Through Cyberinfrastructure: Report of the NSF Blue-Ribbon Advisory Panel on Cyberinfrastructure, (2005) downloaded from 7. NSF report: The Role of Academic Libraries in the Digital Data Universe, (2006) downloaded from 8. NSF report: Cyberinfrastructure Vision for 21st Century Discovery, (2007) downloaded from 9. JISC/NSF Workshop report on Data-Driven Science & Repositories, (2007) downloaded from 10. DOE report: Visualization and Knowledge Discovery: Report from the DOE/ASCR Workshop on Visual Analysis and Data Exploration at Extreme Scale, (2007) downloaded from 11. DOE report: Mathematics for Analysis of Petascale Data Workshop Report, (2008) downloaded from 12. NSTC Interagency Working Group on Digital Data report: Harnessing the Power of Digital Data for Science and Society, (2009) downloaded from 13. National Academies report: Ensuring the Integrity, Accessibility, and Stewardship of Research Data in the Digital Age, (2009) downloaded from 14. NSF report: Data-Enabled Science in the Mathematical and Physical Sciences, (2010) downloaded from 15. National Big Data Research and Development Initiative, (2012) downloaded from

The 4 Scientific Paradigms:
The Fourth Paradigm: Data-Intensive Scientific Discovery The 4 Scientific Paradigms: Experiment (sensors) Theory (modeling) Simulation (HPC) Data Exploration (KDD)

The emergence of Data Science and Data-Oriented Science (the 4th paradigm of science). “Computational literacy and data literacy are critical for all.” - Kirk Borne

The emergence of Data Science and Data-Oriented Science (the 4th paradigm of science). “Computational literacy and data literacy are critical for all.” - Kirk Borne A complete data collection on any complex domain (e.g., Earth, or the Universe, or the Human Body) has the potential to encode the knowledge of that domain, waiting to be mined and discovered. “Somewhere, something incredible is waiting to be known.” - Carl Sagan

The emergence of Data Science and Data-Oriented Science (the 4th paradigm of science). “Computational literacy and data literacy are critical for all.” - Kirk Borne A complete data collection on any complex domain (e.g., Earth, or the Universe, or the Human Body) has the potential to encode the knowledge of that domain, waiting to be mined and discovered. “Somewhere, something incredible is waiting to be known.” - Carl Sagan We call this “X-Informatics”: addressing the D2K (Data-to-Knowledge) Challenge in any discipline X using Data Science. Examples: Astroinformatics, Bioinformatics, Geoinformatics, Climate Informatics, Ecological Informatics, Biodiversity Informatics, Environmental Informatics, Health Informatics, Medical Informatics, Neuroinformatics, Crystal Informatics, Cheminformatics, Discovery Informatics, and more.

Characterizing the Big Data Hype
If the only distinguishing characteristic was that we have lots of data, we would call it “Lots of Data”.

If the only distinguishing characteristic was that we have lots of data, we would call it “Lots of Data”. Big Data characteristics: the 3+n V’s = Volume (lots of data = “Tonnabytes”) Variety (complexity, curse of dimensionality) Velocity (rate of data and information flow) V

If the only distinguishing characteristic was that we have lots of data, we would call it “Lots of Data”. Big Data characteristics: the 3+n V’s = Volume (lots of data = “Tonnabytes”) Variety (complexity, curse of dimensionality) Velocity (rate of data and information flow) Veracity Variability Venue Vocabulary Value

If the only distinguishing characteristic was that we have lots of data, we would call it “Lots of Data”. Big Data characteristics: the 3+n V’s = Volume (lots of data = “Tonnabytes”) Variety (complexity, curse of dimensionality) Velocity (rate of data and information flow) Veracity (verifying inference-based models from comprehensive data collections) Variability Venue Vocabulary Value

If the only distinguishing characteristic was that we have lots of data, we would call it “Lots of Data”. Big Data characteristics: the 3+n V’s = Volume (lots of data = “Tonnabytes”) Variety (complexity, curse of dimensionality) Velocity (rate of data and information flow) Veracity (verifying inference-based models from comprehensive data collections) … as I said earlier: Variability Venue Vocabulary Value A complete data collection on any complex domain (e.g., Earth, or the Universe, or the Human Body) has the potential to encode the knowledge of that domain, waiting to be mined and discovered.

If the only distinguishing characteristic was that we have lots of data, we would call it “Lots of Data”. Big Data characteristics: the 3+n V’s = Volume Variety : this one helps us to discriminate subtle new classes (= Class Discovery) Velocity Veracity Variability Venue Vocabulary Value Big Data Example :

Insufficient Variety: stars & galaxies are not separated in this parameter

Sufficient Variety: stars & galaxies are separated in this parameter

4 Categories of Scientific KDD (Knowledge Discovery in Databases)
Class Discovery Finding new classes of objects and behaviors Learning the rules that constrain the class boundaries Novelty Discovery Finding new, rare, one-in-a-million(billion)(trillion) objects and events Correlation Discovery Finding new patterns and dependencies, which reveal new natural laws or new scientific principles Association Discovery Finding unusual (improbable) co-occurring associations

This graphic says it all …
Clustering – examine the data and find the data clusters (clouds), without considering what the items are = Characterization ! Classification – for each new data item, try to place it within a known class (i.e., a known category or cluster) = Classify ! Outlier Detection – identify those data items that don’t fit into the known classes or clusters = Surprise ! Graphic provided by Professor S. G. Djorgovski, Caltech

Scientists have been doing Data Mining for centuries
“The data are mine, and you can’t have them!” Seriously ... Scientists love to classify things ... (Supervised Learning. e.g., classification) Scientists love to characterize things ... (Unsupervised Learning. e.g., clustering) And we love to discover new things (Semi-supervised Learning. e.g., outlier detection) 33 33

Data-Driven Discovery: Scientific KDD (Knowledge Discovery from Data)
Class Discovery Novelty Discovery Correlation Discovery Association Discovery Graphic from S. G. Djorgovski Graphic from S. G. Djorgovski Benefits of very large datasets: best statistical analysis of “typical” events automated search for “rare” events 34

Scientific Data-to-Knowledge Problem 1-a
The Class Discovery Problem : (clustering) Find distinct clusters of multivariate scientific parameters that separate objects within a data set. Find new classes of objects or new behaviors. What is the significance of the clusters (statistically and scientifically)? What is the optimal algorithm for finding friends-of-friends or nearest neighbors in very high dimensions (complex data with Variety)? N is >1010, so what is the most efficient way to sort? Number of dimensions > 1000 – therefore, we have an enormous subspace search problem

Scientific Data-to-Knowledge Problem 1-b
The superposition / decomposition problem: Finding the parameters or combinations of parameters (out of 100’s or 1000’s) that most cleanly and optimally (parsimoniously) distinguish different object classes What if there are 1010 objects that overlap in a 103-D parameter space? What is the optimal way to separate and accurately classify the different unique classes of objects?

Class Discovery: feature separation and discrimination of classes
Reference: The separation of classes improves when the “correct” features are chosen for investigation, as in the following star-galaxy discrimination test: the “Star-Galaxy Separation” Problem Not good Good 37

Scientific Data-to-Knowledge Problem 1234
The Novelty Discovery Problem : Anomaly Detection, Deviation Detection, Surprise Discovery, Novelty Discovery: Finding objects and events that are outside the bounds of our expectations (outside known clusters) Finding new, rare, one-in-a-million(billion)(trillion) objects and events – Finding the Unknown Unknowns These may be real scientific discoveries or garbage Outlier detection is therefore useful for: Anomaly Detection – is the detector system working? Data Quality Assurance – is the data pipeline working? Novelty Discovery – is my Nobel prize waiting? How does one optimally find outliers in 103-D parameter space? or in interesting subspaces (in lower dimensions)? How do we measure their “interestingness”?

Novelty Discovery: Improved Discovery of Rare Objects or Events across Multiple Data Sources

The Correlation Discovery Problem = Dimension Reduction Problem: Finding new correlations and “fundamental planes” of parameters. Such correlations, patterns, and dependencies may reveal new physics or new scientific relations. Number of attributes can be hundreds or thousands = The Curse of High Dimensionality ! Are there eigenvectors or condensed representations (e.g., basis sets) that represent the full set of properties?

Fundamental Plane for 156,000 Elliptical Galaxies:
plot shows variance captured by first 2 Principal Components as a function of local galaxy density. Reference: Borne, Dutta, Giannella, Kargupta, & Griffin 2008 Slide Content Slide content % of variance captured by PC1+PC2 low (Local Galaxy Density) high 41

The Association Discovery Problem : Link Analysis – Network Analysis – Graph Mining Identify connections between different events (or objects) Find unusual (improbable) co-occurring combinations of data attribute values Find data items that have much fewer than “6 degrees of separation” Identifying such connectivity in our scientific databases and knowledge repositories can lead to new insights, new knowledge, new discoveries.

There are many technologies associated with Big Data
43

One approach to Big Data: Computational Science (Hadoop,Map/Reduce)
44

Another approach to Big Data: Data Science (Informatics)
45

A third approach to Big Data: Citizen Science (crowdsourcing)
46

Galaxy Zoo: example of Citizen Science (crowdsourcing)
47

Astroinformatics Research paper available !
Addresses the data science challenges, research agenda, application areas, use cases, and recommendations for the new science of Astroinformatics. Borne (2010): “Astroinformatics: Data-Oriented Astronomy Research and Education”, Journal of Earth Science Informatics, vol. 3, pp See also

LSST = Large Synoptic Survey Telescope
8.4-meter diameter primary mirror = 10 square degrees! Hello ! Petabyte image archive 20-40 Petabyte database catalog 49

Ten-year time series imaging of the night sky – mapping the Universe !
Observing Strategy: One pair of images every 40 seconds for each spot on the sky, then continue across the sky continuously every night for 10 years (~ ), with time domain sampling in log(time) intervals (to capture dynamic range of transients). LSST (Large Synoptic Survey Telescope): Ten-year time series imaging of the night sky – mapping the Universe ! ~2,000,000 events each night – anything that goes bump in the night ! Cosmic Cinematography! The New 50

The LSST Informatics and Statistics Science Collaboration (ISSC) Research Team
The ISSC team focuses on several research areas: Statistics Data & Information Visualization Data mining (machine learning) Data-intensive computing & analysis Large-scale scientific data management These areas represent Statistics and the science of Informatics (Astroinformatics) = Data-intensive Science = the 4th Paradigm of Scientific Research Addressing the LSST “Data to Knowledge” challenge Helping to discover the unknown unknowns Informatics

The LSST ISSC Research Team
Chairperson: K.Borne, GMU Core team: 3 astronomers + 2 = K.Borne (astroinformatics) Eric Feigelsen, Tom Loredo (astrostatistics) Jogesh Babu (statistics) Alex Gray (computer science, data mining) Full team: 41 scientists ~60% astronomers ~30% statisticians ~10% data mining, machine learning computer scientists

Some key astronomy problems that require informatics and statistical techniques … Astroinformatics & Astrostatistics! Probabilistic Cross-Matching of objects from different catalogues The distance problem (e.g., Photometric Redshift estimators) Star-Galaxy separation ; QSO-Star separation Cosmic-Ray Detection in images Supernova Detection and Classification Morphological Classification (galaxies, AGN, gravitational lenses, ...) Class and Subclass Discovery (brown dwarfs, methane dwarfs, ...) Dimension Reduction = Correlation Discovery Learning Rules for improved classifiers Classification of massive data streams Real-time Classification of Astronomical Events Clustering of massive data collections Novelty, Anomaly, Outlier Detection in massive databases

ISSC “current topics” Advancing the field = Community-building:
Astroinformatics + Astrostatistics (several workshops this year!!) Education, education, education! (Citizen Science, undergrad+grad ed…) LSST Event Characterization vs. Classification Sparse time series and the LSST observing cadence Challenge Problems, such as the Photo-z challenge and the Supernova Photometric Classification challenge Testing algorithms on the LSST simulations: images/catalogs PLUS observing cadence – can we recover known classes of variability? Generating and/or accumulating training samples of numerous classes (especially variables and transients) Proposing a mini-survey during the science verification year (Science Commissioning): e.g., high-density and evenly-spaced observations of extragalactic and Galactic test fields are obtained, to generate training sets for variability classification and assessment thereof Science Data Quality Assessment (SDQA): R&D efforts to support LSST Data Management team

Agenda of ISSC Workshop at LSST Project Meeting, August 2012
Brief talks by team members: Kirk Borne: Outlier Detection for Surprise Discovery in Big Data Jogesh Babu: Statistical Resources Nathan De Lee: The VIDA Astroinformatics Portal Matthew Graham: Characterizing and Classifying CRTS Joseph Richards: Time-Domain Discovery and Classification Sam Schmidt: Upcoming Challenges for Photometric Redshifts Lior Shamir: Automatic Analysis of Galaxy Morphology John Wallin: Citizen Science and Machine Learning Jake Vanderplas: AstroML – Machine Learning for Astronomy

Why do all of this? … for 4 very simple reasons:
(1) Any real data collection may consist of millions, or billions, or trillions of sampled data points. (2) Any real data set will probably have many hundreds (or thousands) of measured attributes (features, dimensions). (3) Humans can make mistakes when staring for hours at long lists of numbers, especially in a dynamic data stream. (4) The use of a data-driven model provides an objective, scientific, rational, and justifiable test of a hypothesis.

Why do all of this? Variety Volume Velocity Veracity
… for 4 very simple reasons: (1) Any real data collection may consist of millions, or billions, or trillions of sampled data points. (2) Any real data set will probably have many hundreds (or thousands) of measured attributes (features, dimensions). (3) Humans can make mistakes when staring for hours at long lists of numbers, especially in a dynamic data stream. (4) The use of a data-driven model provides an objective, scientific, rational, and justifiable test of a hypothesis. Volume Variety Velocity Veracity

Why do all of this? Variety Volume Velocity Veracity
… for 4 very simple reasons: (1) Any real data collection may consist of millions, or billions, or trillions of sampled data points. (2) Any real data set will probably have many hundreds (or thousands) of measured attributes (features, dimensions). (3) Humans can make mistakes when staring for hours at long lists of numbers, especially in a dynamic data stream. (4) The use of a data-driven model provides an objective, scientific, rational, and justifiable test of a hypothesis. Volume It is too much ! Variety It is too complex ! Velocity It keeps on coming ! Veracity Can you prove your results ?

Knowledge Discovery from Mining Big Data – 12
By collecting a thorough set of parameters (high-dimensional data) for a complete set of items within our domain of study, we should be able to build a “perfect” statistical model for that domain. In other words, Big Data becomes the model for a domain X = we call this X-informatics. Anything we want to know about that domain is specified and encoded within the data. The goal of Big Data Science is to find those encodings, patterns, and knowledge nuggets. Example : Big-Data Vision? Whole-population analytics

By collecting a thorough set of parameters (high-dimensional data) for a complete set of items within our domain of study, we should be able to build a “perfect” statistical model for that domain. In other words, Big Data becomes the model for a domain X = we call this X-informatics. Anything we want to know about that domain is specified and encoded within the data. The goal of Big Data Science is to find those encodings, patterns, and knowledge nuggets. Recall what we said before …

… one of the two major benefits of BIG DATA is to provide the best statistical analysis ever(!) for the domain of study. Benefits of very large datasets: best statistical analysis of “typical” events automated search for “rare” events

Data Science: Resistance is futile, you will be assimilated

Knowledge Discovery from Mining Big Data

Similar presentations

Presentation on theme: "Knowledge Discovery from Mining Big Data"— Presentation transcript:

Similar presentations

About project

Feedback

Log in

Auth with social network:

Knowledge Discovery from Mining Big Data

Similar presentations

Presentation on theme: "Knowledge Discovery from Mining Big Data"— Presentation transcript:

Similar presentations

About project

Feedback