A Homespun Decentralised DIY Data Science Research Pipeline for the Internet of *Your* Things

A very brief intro to data science from a computer scientist's point of view. Slides for a talk at DevDay Poland 2016 #abbdevday

Transcript

1. JOSE DOMINGUEZ @JOSMASFLORES A HOMESPUN DECENTRALISED DIY DATA SCIENCE RESEARCH PIPELINE FOR THE INTERNET OF *YOUR* THINGS
2. This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 JOSE DOMINGUEZ 2 @JOSMASFLORES
3. This publication has emanated from research supported in part by a research grant from Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289 CONTRIBUTIONS 3
4. CO-ORGANISER 4
5. AGENDA ▸ Homespun ▸ *Your* things in IoT ▸ *Your* Data ▸ Data Science ▸ Tools: End User Development ▸ Data Collection and Communication ▸ Backend and Storage ▸ Analysis and Visualisation 5
6. HOMESPUN
8. SIMPLE AND UNSOPHISTICATED keep it simple! 8
9. *YOUR* THINGS
13. PLENTY OF COMPLICATED STUFF OUT THERE… IF YOU NEED IT! Keep it simple, if you can! 13
17. BORIS ADRYAN ON DATA STORAGE AND VISUALISATION PLATFORMS HTTP://IOT.GHOST.IO/NODE-RED-INTEROPERABILITY-TEST/ 17
18. HARDER THAN YOU MAY THINK LEARNING MODE
22. WHAT KIND OF STUFF CAN YOU DO WITH *YOUR* THINGS? ▸ Grab raw data from the accelerometer ▸ Zero cross analysis can give you a rough amount of steps. 22
25. BUT… BUT… BUT… WHY DO ALL THESE THINGS IF COMMERCIAL SOLUTIONS DO IT OUT OF THE BOX? 25
26. *YOUR* DATA
27. GOOGLE APIS. SECTION 5: CONTENT b. Submission of Content Some of our APIs allow the submission of content. Google does not acquire any ownership of any intellectual property rights in the content that you submit to our APIs through your API Client, except as expressly provided in the Terms. For the sole purpose of enabling Google to provide, secure, and improve the APIs (and the related service(s)) and only in accordance with the applicable Google privacy policies, you give Google a perpetual, irrevocable, worldwide, sublicensable, royalty-free, and non-exclusive license to Use content submitted, posted, or displayed to or from the APIs through your API Client. ”Use” means use, host, store, modify, communicate, and publish. Before you submit content to our APIs through your API Client, you will ensure that you have the necessary rights (including the necessary rights from your end users) to grant us the license.
28. GOOGLE APIS. SECTION 5: CONTENT b. Submission of Content Some of our APIs allow the submission of content. Google does not acquire any ownership of any intellectual property rights in the content that you submit to our APIs through your API Client, except as expressly provided in the Terms. For the sole purpose of enabling Google to provide, secure, and improve the APIs (and the related service(s)) and only in accordance with the applicable Google privacy policies, you give Google a perpetual, irrevocable, worldwide, sublicensable, royalty-free, and non-exclusive license to Use content submitted, posted, or displayed to or from the APIs through your API Client. ”Use” means use, host, store, modify, communicate, and publish. Before you submit content to our APIs through your API Client, you will ensure that you have the necessary rights (including the necessary rights from your end users) to grant us the license.
30. POSTING YOUR CONTENT ON THE FITBIT SERVICE ▸ You may post photos, exercise regimens, food logs, recipes, comments, and other content (“Your Content”) to the Fitbit Service. You retain all rights to Your Content that you post to the Fitbit Service. By making Your Content available on or through the Fitbit Service you grant to Fitbit a non- exclusive, transferable, sublicensable, worldwide, royalty-free license to use, copy, modify, publicly display, publicly perform and distribute Your Content only in connection with operating and providing the Fitbit Service. 30
31. WITH A SMALL NUMBER OF GEOLOCATION DATA POINTS (1 DAYS WORTH), LOCATION CAN BE INFERRED, POTENTIALLY LEADING TO PRIVACY DISCLOSURES Liccardi et al, 2016. I Know Where You Live: Inferring Details of People's Lives by Visualizing Publicly Shared Location Data (CHI '16).
33. AN ANONYMISED MEDICAL DATABASE WAS SUCCESSFULLY COMBINED WITH A VOTERS LIST TO EXTRACT THE HEALTH RECORD OF THE GOVERNOR OF MASSACHUSETTS Sweeney, L. k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness and Knowledge-Based Systems 10, 557–570 (2002) 33
34. USING PUBLICLY AVAILABLE DATA, THE TYPES OF LOCATIONS CAN BE USED TO ESTIMATE SOMEONE’S AVERAGE INCOME […], AVERAGE HOUSING COST, DEBT, […] POLITICAL VIEWS ETC. Liccardi et al, 2016. I Know Where You Live: Inferring Details of People's Lives by Visualizing Publicly Shared Location Data (CHI '16). 34
35. PRIVACY CONCERNS ▸ IoT and fitness devices especially. 35
36. HEALS AND GEORGATOS (2014), PRIVACY AND HEALTH: HOW MOBILE HEALTH ‘APPS’ FIT INTO A PRIVACY FRAMEWORK NOT LIMITED TO HIPPA ▸ Surveillance (unauthorised collection) ▸ Identification ▸ Insecurity (lack of encryption) ▸ Disclosure (of sensitive data to third parties) ▸ Aggregation (consumer profiles) 36
37. A FEW MORE REASONS IN CASE YOU STILL DON’T CARE Steven Spann (2016). Wearable Fitness Devices: Health Data Privacy in Washington State
38. DATA COULD BE USED TO LEGALLY OR ILLEGALLY RESTRICT AN INDIVIDUAL’S ABILITY TO ACCESS CERTAIN MARKETS Steven Spann
39. STEVEN SPANN (2016) THROUGH THE USE OF PERSONAL DATA, ENTITIES COULD DISCRIMINATE AGAINST AND INDIVIDUAL IN: ▸Employment ▸Health Care and Insurance ▸Credit-based Lending and other life necessities and options
40. IT’S NOT JUST ADS 40 http://www.newyorker.com/images/2014/07/21/cartoons/140721_cartoon_047_a18282_p465.jpg
41. TERMS AND CONDITIONS ▸ T&C concerns 41
42. TERMS AND CONDITIONS IN RESEARCH CONTEXTS ▸ T&C concerns can inadvertently break Ethics agreements. 42
43. TERMS AND CONDITIONS IN RESEARCH CONTEXTS ▸ T&C concerns can inadvertently break Ethics agreements. 43
44. ▸ http://motherboard.vice.com/read/free-isnt-freedom-epstein-essay 44
45. DECENTRALISED BLOCKCHAIN
47. THE REVOLUTION WILL (NOT) BE DECENTRALISED ▸http://commonstransition.org/the- revolution-will-not-be- decentralised-blockchains/ 47
50. DATA SCIENCE
51. DATA SCIENCE WHAT IS DATA SCIENCE 51
52. DATA SCIENCE WHAT IS DATA SCIENCE 52
53. DATA SCIENCE WHAT IS DATA SCIENCE 53
54. DATA SCIENCE WHAT IS DATA SCIENCE 54
55. DATA SCIENCE WHAT IS DATA SCIENCE 55
56. DATA SCIENCE HOW TO READ THE DATA SCIENCE BENN DIAGRAM 56 http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram
57. RESOURCES DATA SCIENCE
58. DATA SCIENCE 58
59. CS109 - HARVARD ▸ The prerequisite for this class is programming knowledge at the level of CS 50 (or above), and statistics knowledge at the level of Stat 100 (or above). ▸ Stats 110: sample spaces, naive definition of probability, counting, sampling, random variables, CDFs, PMFs, discrete vs. continuous, Hypergeometric, Poisson distribution, Poisson approximation, standard Normal, Normal normalizing constant, Markov chains, transition matrix, stationary distribution, Chi-Square, Student-t, Multivariate Normal 59
60. CAN YOU HACK YOUR WAY THROUGH IT?
61. DATA SCIENCE TETIANA IVANOVA: DATA SCIENCE IN 6 MONTHS
62. STATISTICS JAKE VANDERPLAS: STATISTICS FOR HACKERS
63. DATA SCIENCE BE WEARY OF TERMINOLOGY ▸ Is it Machine Learning or Data Mining? Or is it Statistical Learning? 63
64. DATA SCIENCE WHAT ARE YOU TRYING TO DO? ▸ Figure something out ▸ Predict something 64
65. DATA SCIENCE TYPES OF LEARNING ▸ Supervised: dataset with tags ▸ Unsupervised: dataset with no tags 65
66. DATA SCIENCE TYPES OF LEARNING ▸ Supervised ▸ Unsupervised ▸ Reinforcement 66
67. DATA SCIENCE TYPES OF ACTIVITY ▸ Classification (supervised) ▸ Regression (supervised) ▸ Clustering (unsupervised) 67
69. DIY
71. END USER DEVELOPMENT
72. A SET OF METHODS, TECHNIQUES AND TOOLS THAT ALLOW USERS OF SOFTWARE SYSTEMS, WHO ARE ACTING AS NON-PROFESSIONAL SOFTWARE DEVELOPERS, AT SOME POINT TO CREATE, MODIFY, OR EXTEND A SOFTWARE ARTIFACT. Lieberman et al. 2006
73. A SET OF METHODS, TECHNIQUES AND TOOLS THAT ALLOW USERS OF SOFTWARE SYSTEMS, WHO ARE ACTING AS NON-PROFESSIONAL SOFTWARE DEVELOPERS, AT SOME POINT TO CREATE, MODIFY, OR EXTEND A SOFTWARE ARTIFACT. Lieberman et al. 2006
76. https://www.youtube.com/watch?v=0nbkaYsR94c
80. TOOLS
81. TOOLS DATA COLLECTION AND COMMUNICATION ▸ MIT App Inventor ▸ Node-RED 81
82. DATA COLLECTION APP INVENTOR 82 ‣ DEMO
83. ACTIVITY DETECTION: JUMPING
84. TEXT DATA ABSTRACTION ▸ Data used at different levels of abstraction in App Inventor
85. COMMUNICATION DATA COMMUNICATION 85 ‣ Use HTTP ‣ Save data to a file and send it to a server ‣ Do analysis directly on the phone; necessary if you need to do real time: see Jan Machacek's Exercise Analysis talk. ‣ You probably want to use MQTT
86. COMMUNICATION MQTT PROTOCOL 86
90. DATA COLLECTION AND COMMUNICATION NODE-RED 90
91. COMMUNICATION AND STORAGE APP INVENTOR AND NODE-RED 91 ‣ Collect accelerometer data in App Inventor ‣ MQTT to a broker ‣ Subscribe in Node-RED: ‣ send to storage ‣ analyse ‣ visualise
92. COMMUNICATION AND INTERACTION APP INVENTOR AND NODE-RED 92 ‣ Send a notification from Node-RED with AeroGear Push Server ‣ Get it on the phone with App Inventor Work In Progress! Node-RED node for Notifications: https://github.com/CLDTio/node-red-contrib-aerogear-notifications App Inventor Component for AeroGear: https://github.com/josmas/app-inventor/tree/aerogear
93. TOOLS BACKEND AND STORAGE ▸ Use a commercial solution: ▸ there are plenty, and now you know better so you can make an informed decision if you want to use it or not! ▸ iSense (read T&Cs first) ▸ Your own backend with Parse, MIT Solid, or any other solution you like (including rolling your own) 93
94. BACKEND ISENSE 94
95. STORAGE DATABASES 95 Time Series Real Time
96. GRAPHING AND MONITORING
97. DIY BACKEND 97
98. BACKEND SERVICES MANY SOLUTIONS… REMEMBER TO READ THE T&CS!!! 98
99. CONTAINERS DOCKER FOR EVERYTHING 99 ‣ https://github.com/CLDTio/docker-influxdb ‣ https://github.com/CLDTio/appinventor-env-docker ‣ https://github.com/tensorflow/tensorflow/tree/master/ tensorflow/tools/docker ‣ Browse the Docker Hub and Github for many more!
100. TOOLS DATA ANALYSIS AND VISUALISATION ▸ iSense ▸ Weka ▸ Machine learning ▸ Activity recognition ▸ Visualisation tools ▸ Python notebooks, R, Julia, spider, and so forth. 100
101. VISUALISATION ISENSE 101
102. ACTIVITY RECOGNITION JUPYTER NOTEBOOKS 102
104. MACHINE LEARNING AND VISUALISATION WEKA AND THE IRIS DATASET 104
108. PICTURE CREDITS ▸ Tablet: http://siliconangle.com/files/2012/02/Android_Portrait_Overview.jpg ▸ Smart Watch: https://lh3.ggpht.com/ElwMg8bubiVB33euotYaD_mpKxSrr7SXTsrMwamk3_SRZx1VYkqVT8-HvkQDqXvLWw=h900 ▸ HeartRate monitor http://i00.i.aliimg.com/img/pb/355/386/400/400386355_864.jpg ▸ Arduino: https://cdn.instructables.com/FDW/WCKV/HKBG733D/FDWWCKVHKBG733D.MEDIUM.jpg ▸ Core motion axes: http://blog.denivip.ru/wp-content/uploads/2013/07/CoreMotionAxes.png ▸ Zero cross: https://c2.staticflickr.com/8/7269/7866678792_b375ae7b26.jpg ▸ Step counter: https://www.mathworks.com/matlabcentral/mlc-downloads/downloads/submissions/40876/versions/8/previews/sensorgroup/Examples/html/StepCounter_03.png ▸ Rewriting: http://www.boyter.org/wp-content/uploads/2016/04/Ce5nYp0W4AAxp5Z.jpg ▸ Machine learning NG: http://cdn.usefulstuff.io/2016/01/machine-learning-ng.jpg ▸ Researcher: http://images.clipartpanda.com/researcher-clipart-cartoons_laboratory_201533_tnb.png ▸ Ethics: http://tcrc.eu/en/wp-content/uploads/2013/06/ethics.png ▸ Developer: http://www.grapessoftware.com/wp-content/uploads/2014/02/Hire-Developer-Sprite.png ▸ Señor developer: http://startupmyway.com/wp-content/uploads/2016/02/senordeveloper.gif ▸ API integrations: http://www.surf2host.com/images/api_integrations.png ▸ Data Science: http://static1.squarespace.com/static/5150aec6e4b0e340ec52710a/t/51525c33e4b0b3e0d10f77ab/1364352052403/Data_Science_VD.png ▸ DreamWeaver: http://getintopc.com/wp-content/uploads/2014/02/dreamweaver-cs6-jquery-mobile.png ▸ Spreadsheet: http://www.openoffice.us.com/cmsimages/software/calc2.gif ▸ Advanced spreadsheet: http://www.comfsm.fm/~dleeling/statistics/sc3/cover.png ▸ LabView: http://sql-lv.sourceforge.net/new_sql_LV.png ▸ E-prime2: http://www.pstnet.com/internal/kbimage/1801-1.gif ▸ Blockchain: http://www.cbronline.com/Uploads/NewsArticle/4978988/main.jpg ▸ Ethereum: https://ethereum.org/images/wallpaper-homestead.jpg ▸ Types of networks: https://upload.wikimedia.org/wikipedia/en/b/ba/Centralised-decentralised-distributed.png ▸ AeroGear logo: https://yt3.ggpht.com/-9bdWbHB80Og/AAAAAAAAAAI/AAAAAAAAAAA/2VbWVkS5CmY/s88-c-k-no-mo-rj-c0xffffff/photo.jpg ▸ Iris dataset: http://5047-presscdn.pagely.netdna-cdn.com/wp-content/uploads/2015/04/iris_petal_sepal.png ▸ Jupyter: http://jupyter.org/assets/main-logo.svg ▸ Pandas: http://pandas.pydata.org/_static/pandas_logo.png 108
109. QUESTIONS? 109 @JOSMASFLORES