Analytic innovation transforming instagram data into predicitive analytics with references
Download as PPTX, PDF2 likes3,297 views
This document discusses plans to transform Instagram data into predictive analytics. It provides references on predicting human mobility and regularity in activities. Topic areas covered include pattern mining trajectories from Instagram data, NoSQL databases like MongoDB, and a datafication backend. The motivations cited for the Instagram project include using trajectory data for authentication, predictive services for phones/wearables, and location-based applications. Analytics tools for Instagram data and storing Instagram posts efficiently in MongoDB are also mentioned. The roadmap outlines collecting Instagram data, analyzing individual/group patterns, and developing recommendations based on preferences, behavior and trajectory patterns.
Analytic innovation transforming instagram data into predicitive analytics with references
- 1. Analytic Innovation: Transforming Instagram Data Into Predictive Analytics [email protected] or linkedin.com/in/sureshsood [email protected] or linkedin.com/pub/xinhua-zhu/27/448/621
- 2. Useful References Informing our Thinking (Silva et al (2013) A comparison of Foursquare and Instagram to the study of city dynamics and urban social behavior, Proceedings of the 2nd ACM SIGKDD International Workshop on Urban Computing Instagram and Foursquare datasets might be compatible in finding popular regions of city Chaoming Song, et al. (2010), Limits of Predictability in Human Mobility, Science There is a potential 93% average predictability in user mobility, an exceptionally high value rooted in the inherent regularity of human behavior. Yet it is not the 93% predictability that we find the most surprising. Rather, it is the lack of variability in predictability across the population. Scellato et al. (2011), NextPlace: A Spatio-temporal Prediction Framework for Pervasive Systems. Proceedings of the 9th International Conference on Pervasive Computing (Pervasive'11) Daily and weekly routines => Few significant places every day => Regularity in human activities => Regularity leads to predictability
- 3. Useful References Informing our Thinking Domenico, A. Lima, Musolesi.M. (2012) Interdependence and Predictability of Human Mobility and Social Interactions. Proceedings of the Nokia Mobile Data Challenge Workshop. we have shown that it is possible to exploit the correlation between movement data and social interactions in order to improve the accuracy of forecasting of the future geographic position of a user. In particular, mobility correlation, measured by means of mutual information, and the presence of social ties can be used to improve movement forecasting by exploiting mobility data of friends. Moreover, this correlation can be used as indicator of potential existence of physical or distant social interactions and vice versa. Sadilek, A and Krumm, J. (2012) Far Out: Predicting Long-Term Human Mobility Where are you going to be 285 days from now at 2pm …we show that it is possible to predict location of a wide variety of hundreds of subjects even years into the future and with high accuracy.
- 4. Topic Areas 1. 2. 3. 4. 5. 6. 7. Analytic innovation and exploratory analysis Motivations for Instagram project Pattern mining trajectories Instagram analytics tools NoSQL- MongoDB Datafication 3 back end (walk thru) Q&A
- 5. Analytic Innovation “Let’s define analytic innovation as any type of analytical approach that is new and unique. It is something a given organization has not done before, and perhaps something nobody anywhere has done before…An analytic innovation should be focused on analyzing a new data source, solving a new problem…” Franks, B. (2012) Taming the Big Data Tidal Wave, p. 255, John Wiley & Son
- 6. Discovery (Exploratory) Analytics Exploratory – – – – – Unstructured Machine learning Data mining Complex analysis Data diversity Richness X Business Intelligence – Dashboard – Real time decisioning – Alerts – Fresh data – Response time Speed of Query
- 7. Smartphone, Google Glass or Apple Watchwill Know What you Want before you do “…from 2014 your phone *glasses or watch+ will anticipate your needs, do the research, tell you what what you want to know – sometimes before the question even occurs to you…” Chapman, Jake (2013), The Wired World in 2014
- 8. Push Notification Providers 1. Appboy 2. Urban Airship 3. StackMob 4. Parse 5. https://notifica.re 6. http://www.xtify.com 7. http://push.io 8. http://streamin.io 9. https://pushbots.com 10.http://appsfire.com 11.mBlox 12.http://quickblox.com/ 13.https://www.mobdb.net 14.http://www.elementwave.com 15.Kahuna - http://www.usekahuna.com/ http://www.quora.com/What-are-some-alternatives-to-Urban-Airship-for-mobile-push
- 9. Mobile Relationship Management Workflow (Urban Airship) What/When?/Where?
- 10. Apple Passbook Styles Urban Airship
- 11. Motivations for Instagram Project • Trajectory data (not i.i.d. – independent and identically distributed) • A new authentication approach based on trajectory • Predictive capability phones, glasses and watches • Internet of Things (Sensors, RFID and Drones) • Indoor GPS • Car parking “anywhere” • Location based services e.g. advertising • Tourist recommender system • Food analytics and traceability (farm fork) • Mobile apps with trajectory data e.g. Foursquare, Instagram, Nike+ EveryTrial • Insurance “pay as you drive”– telematics black box based insurance policy
- 12. Black Box Insurance • Telematics technology (black box) helps assess the driving behavior and deliver true driver centric premiums by capturing: – – – – – – – Number of journeys Distances travelled Types of roads Speed Time of travel Acceleration and braking Any accidents • Benefits low mileage, smooth and safe drivers • Privacy vs. Saving monies on insurance (Canada) – http://bit.ly/Black_box
- 13. Pattern Mining Trajectories Trajectory Patterns: Group of Trajectories 1. Hot regions (basic unit) 2. Trajectory pattern is relationships amongst regions Opportunities : Location based networks Destination prediction Car-pooling Personal route planning Group buying Loyalty Credit card data Adapted from: Chang, Wei, Yeh and Peng, “Discovering Personalised Routes from Trajectories” ACM, LBSN’11, Chicago,illinois,USA, 1 November 2011
- 14. Instagram Analytics Tools (off the shelf) • Statigram – – – – • Simply Measured – – – – – – – • Lifetime likes Total comments New followers/last 7 days Most liked photos Total engagement Instagram, Facebook and Twitter Engaging photo/filter/location Top photos by date Active commenters Best time for engagement Best day for engagement Top filters Nitrogram – – – – Countries of followers Most engaging Most commented Likes and comments on a photo
- 15. First Australian Instagram Study Conducted by UTS:AAI
- 16. Why is Instagram Popular ? • Mobile photo sharing app + social network • Mobile first Workflow: – take picture or select => crop/filter => geo-tag/hashtag/description/share • • • • Instagram is “Twitter but with photo updates” Status updates are transformed photos Default is pictures and accounts are public Pictures include: – Geolocation, hashtags, comments and likes • Mobile app friendly vs. desktop
- 17. MongoDB - An Innovation in Databases? “MongoDB gets the job done” “document-oriented NoSQL database” “MongoDB is natural choice when dealing with JSON” “Same data model in code = same model in database” “Data structure store to model applications” “In MongoDB Instagram post can be stored in single collection and stored exactly as represented in the program as one object. In a relational database an Instagram post would occupy multiple tables.” “MongoDB understands geo-spatial co-ordinates and supports geo-spatial indexing” “Initial MongoDB prototype RedHat OpenShift (Public/Private or Community “Platform as a Service”) Recommendation engine integrating Mahout libraries and MongoDB (see Roadmap) As discussed @ Journey to MongoDB:Trajectory Pattern Mining in Australian Instagram By Suresh Sood and Xinhua Zhu **Sydney MongoDB Meetup 30 April 2013
- 18. Timeline based Trajectory Analysis
- 19. Google Map based Trajectory Analysis
- 24. Peak Usage Time Analysis
- 26. Roadmap Data collection Individual(Group) Analysis Find Preference and Behavior pattern(including Trajectory pattern) Manually Recommendation Recommend right product (or service) to right person ( or group) at right time and place Automatically
- 27. Thank you! Q/A