Dagstuhl Seminar 10042 Demetris Zeinalipour University of Cyprus

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Crowdsourcing Urban Data with Smartphones Demetrios Zeinalipour-Yazti Data Management Systems Laboratory Department of Computer Science University of Cyprus http: //www. cs. ucy. ac. cy/~dzeina/ Invited Talk at the Mining Urban Data (MUD) Workshop (with EDBT/ICDT), Athens, Greece, March 28, 2014 www. insight-ict. eu/mud

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Talk Objective • • • To review some primitive web crowdsourcing concepts and challenges. To show these challenges emerge and evolve in Urban Data Collection spaces. To present some of our own developments related to: i) Location Data; ii) Data Collection Testbeds, and iii) Social Data and discuss particular challenges and future work. – – Much of the discussion is work in progress (how we plan to apply the ideas in urban spaces). IEEE MDM’ 13 Tutorial: "Crowdsourcing for Mobile Data Management", G. Chatzimilioudis and D. Zeinalipour-Yazti, "Proceedings of the 14 th International Conference on Mobile Data Management" (MDM '13), Milan Italy, Volume 2, Pages: 3 -4, 2013. 2/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Crowdsourcing Definitions • Crowdsourcing = Crowd + Outsourcing – Jeff Howe (2006). "The Rise of Crowdsourcing". Wired. • From our recent work: – Crowdsourcing refers to a distributed problemsolving model in which a crowd of undefined size is engaged in the task of solving a complex problem through an open call for monetary or ethical benefit. “Crowdsourcing with Smartphones”, Georgios Chatzimiloudis, Andreas Konstantinidis, Christos Laoudias, Demetrios Zeinalipour. Yazti, IEEE Internet Computing, Special Issue: Sep/Oct 2012 Crowdsourcing, May 2012. IEEE Press, Volume 16, Pages: 36 -44, 2012. 3/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Web Crowdsourcing Open Call (Task) Solutions Requester (Crowdsourcer) Rewards Workers Platform (Solvers) 4/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Web Crowdsourcing Microtasking Platform: Qualifications b) Redundancy: Each worker solves a Hit once (3 -5 assignment per hit) to enable majority voting a) Reward 5/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Web Crowdsourcing: Incentives • Tangible (Monetary) Incentives – Cash, Credit or Gifts (MTurk, Kickstarter) – Unintended or as-a-by-product (re. Captchas) • Ethical Incentives – Socialize & Fun – Earn Prestige – Altruism – Learn something New • Usually a combination of several incentives © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece 6/50

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Web Crowdsourcing: Challenges • • How to Recruit Contributors (randomly, marketplaces? ) / What the Contributors Can Do (qualifications, tests)? How to Combine their Contributions? How to Manage Abuse? How To Scale/Manage Complex/Larger Tasks? Openness / Quality? Disclosure Issues (Privacy related to Tasks, NDAs? ) Minimum Wages & Social Contributions? Anhai Doan, Raghu Ramakrishnan, and Alon Y. Halevy. 2011. Crowdsourcing systems on the World-Wide Web. Commun. ACM 54, 4 (April 2011), 86 -96. 7/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Declarative Crowdsourcing • Crowd. DB, Qurk, Deco, Mo. Da. S, Crowdforge. SELECT abstract FROM talk WHERE title = "Crowd. DB Crowd Extensions Crowd. DB: Answering Queries with Crowdsourcing, M. J. Franklin, D. Kossmann , T. Kraska, S. Ramesh, R. Xin, SIGMOD‘ 11 & VLDB'11 Demo 8/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Mobile Crowdsourcing • txt. Eagle (now JANA) founded by Nathan Eagle (Ph. D, MIT, 2005) a first-of-a-kind mobile CS system: – – Requesters: can assign small tasks (translation, transcription and surveys) on their mobile phones. Workers (today 3. 48 Billion Workers in 102 countries!): : rewarded with airtime on their mobile subscriber accounts or MPESA (mobile money described next). txteagle: Mobile Crowdsourcing, Internationalization, Design and Global Development, LNCS Volume 5623, pp 447 -456, 2009. © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece 9/50

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Mobile Crowdsourcing • Another app txt. Eagle SMS Bloodbank : – Idea: to report blood levels of local hospitals centrally by nurses. – Initially, in the absence of an incentive, the system was a complete failure. – In summer 2007, automatic airtime credit was incorporated to award nurses for their contribution => then a huge success! • Other txt. Eagle SMS applications: – – – Transcription mentioned previously (global market $18 B in 2010) Software Localization (60 local languages in Kenya, txt. Eagle generated a cookbook Citizen Journalism, Sentiment Analysis, Surveys 10/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 The Smartphone Era April 2013: Beginning of Smartphone Era! • In April, 2013, for the first time in history the number of Worldwide Smartphone sales exceeded that of feature phones (according to IDC) – – • 51. 6% were Smartphones (216 M units) 48. 4% were Feature Phones (186 M units) The bulk of mobile phones are acquired in the developing world (e. g. , China, India, Africa etc. ) – Chinese manufactures (ZTE, Huawei) started building smartphones for the wide markets. More Smartphones Were Shipped in Q 1 2013 Than Feature Phones, An Industry First According to IDC, 25 Apr 2013, http: //www. idc. com/getdoc. jsp? container. Id=pr. US 24085413 11/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Crowdsourcing with Smartphones • A smartphone crowd is constantly moving and sensing providing large amounts of opportunistic data enabling new applications 12/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Smartphone Crowdsourcing: Challenges (Beyond Web Crowdsourcing) 1. Big Data – Velocity by sensor data generates Volume 2. Typing and User Interfaces – – Participatory typing is cumbersome due to small form factor / display keyboard. Scrolling & Crowded GUIs. Attention issues due to possible mobility. Opportunistic Solutions? 3. (Location) Privacy – Coarse-grain (cell, wifi) vs. fine (gps) 4. Energy Consumption – Power Hungry (GPS, Brightness, etc. ) © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece 13/50

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Smartphone Crowdsourcing: Challenges (Beyond Web Crowdsourcing) 5. Calibration and Multi-device Issues – – Different readings by different sensors (e. g. , Wifi RSS, magnetic field, etc. ) Incomplete Data & Quality Issues. 6. Connectivity Issues – Workforce might have intermittent connectivity (e. g. , while travelling) thus can’t provide online readings. 7. Heterogeneous Clients hinders deployment – – Different OSes, sensor, features, APIs, etc. One supports active background tasks another OS doesn’t, etc. © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece 14/50

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Talk Outline • Introduction & Challenges • Urban Location Data – Anyplace Indoor Information System • Urban Sensing Testbeds – Smart. Lab Smartphone Programming Cloud • Urban Trajectory Search – Smart. Trace Query Processing Framework • Urban Social Networks – Rayzit Crowd Messaging Service 15/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Location Data • • People spend 80 -90% of their time inside buildings, while 70% of cellular calls and 80% of data connections originate from indoors. GPS has low availability indoors due to the blockage or attenuation of the satellite signals but it is also very power hungry. Smartphones can nowadays localize off-the-shelf with onboard sensors and Wi. Fi signal fingerprints (coined Hybrid Localization) New Applications: – – In-building Navigation (Malls, Airports, Museums, Schools, etc. ) Asset Tracking and Inventory Management (Hospitals, etc) Elderly support for Ambient and Assisted Living (AAL) Augmented Reality (Firefighters), Social Networking, etc. 16/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Location Data • Indoor Localization using proprietary infrastructure: Infrared, Bluetooth, Visual or Acoustic Analysis, RFID, Ultra-Wide-Band, Wireless Sensor Network, Inertial Measurement Units (IMU), Wireless LAN. Smartphone Localization: • • Hybrid Localization: Combination of more than 1 techniques such as IMU+Wi. Fi (acceler. , gyro, digital compass) Map. Matching, Magnetic Data, pedometer 17/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Location Data I can see these Reference Points, where am I? Cellular Wi. Fi (x, y)! . . . Cellular Radio. Map Service User u 18/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Location Data • References – – [Airplace] "The Airplace Indoor Positioning Platform for Android Smartphones", C. Laoudias et. al. , Best Demo Award at IEEE MDM'12. (Open Source!) [Hybrid. Cywee] "Demo: the airplace indoor positioning platform", C. -L. Li, C. Laoudias, G. Larkou, Y. -K. Tsai, D. Zeinalipour-Yazti and C. G. Panayiotou, in ACM Mobisys'13. Video at: http: //youtu. be/Dyv. QLSu. I 00 I [Ucy. Cywee] IPSN’ 14 Indoor Localization Competition (Microsoft Research), Berlin, Germany, April 13 -14, 2014. [Anyplace] Crowdsourced Indoor Localization and Navigation with Anyplace, In ACM/IEEE IPSN’ 14. http: //anyplace. cs. ucy. ac. cy/ Cywee / Airplace 19/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Location Data Anyplace Architecture Navigator Viewer, Widget 20/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Location Data • Anyplace Indoor Information Service (IIS) http: //anyplace. cs. ucy. ac. cy/ Live Demo! 21/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Anyplace Crowdsourcing Challenges • A) Big Data – Massively process RSS log traces to generate a valuable Radiomap • Utilized for KNN positioning – Processing current logs in Anyplace for a single building might take several minutes! – Challenges in Map. Reduce: • Spatio-temporal Analysis • Missing Values / Outliers / Quality / Multi-device Issues (see next) 22/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Anyplace Crowdsourcing Challenges • B) Quality: Unreliable Crowdsourcers, Multidevice Issues, Hardwar Outliers, Temporal Decay, etc. – Remark: There is a Linear Relation between RSS values of devices. – Challenge: Can we exploit this to align reported RSS values? "Crowdsourced Indoor Localization for Diverse Devices through Radiomap Fusion", C. Laoudias, D. Zeinalipour-Yazti and C. G. Panayiotou, "Proceedings of the 4 th Intl. Conference on Indoor Positioning and Indoor Navigation" (IPIN '13), Montbeliard-Belfort France, 2013. 23/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Anyplace Crowdsourcing Challenges • C) Privacy – Challenge: How to localize using a Radiomap Service, without revealing my location to the service? – Solution (ongoing): We developed a spatio-temporal privacy scheme using bloom filters coined Temporal Vector Map (TVM). • Provides k-anonymity guarantees • Enables both snapshot and continuous localization. – – "Towards planet-scale localization on smartphones with a partial radiomap", A. Konstantinidis, G. Chatzimilioudis, C. Laoudias, S. Nicolaou and D. Zeinalipour-Yazti, ACM Hot. Planet '12. “Privacy-Preserving Indoor Localization on Smartphones with Vector. Map”, A. Konstantinidis, P. Mpeis, N. Pelekis, D. Zeinalipour-Yazti and Y. Theodoridis, under submission. 24/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Anyplace Crowdsourcing Challenges TVM Outline: Bloom Filter (u's APs) Wi. Fi K=3 Positions . . . Wi. Fi Radio. Map (server-side) User u 25/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Anyplace Crowdsourcing Challenges 26/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Talk Outline • Introduction & Challenges • Urban Location Data – Anyplace Indoor Information System • Urban Sensing Testbeds – Smart. Lab Smartphone Programming Cloud • Urban Trajectory Search – Smart. Trace Query Processing Framework • Urban Social Networks – Rayzit Crowd Messaging Service 27/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Sensing • • • Use sensors in urban environments in support of more classic environmental sensing applications. "People sense and contribute data about their surroundings using mobile devices" (Kanhere) Example Projects: – – Dartmouth | Metrosense: Sound. Sense, Cence. Me, Sensor Sharing, Bike. Net, Anony. Sense, and Second Life Sensor. MIT | Cartel: VTrack/CTrack, Pot. Hole Harvard : Citysense (grew out of Mote. Lab) UNSW: Noise (Earphone) & Air pollution 28/50 (Haze. Watch, Common. Sense), © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Sensing • Monitoring Urban Spaces Noise. Map "Ear-Phone: An End-to-End Participatory Urban Noise Mapping System " Rajib Rana, Chun Tung Chou, Salil Kanhere, Nirupama Bulusu, and Wen Hu. In ACM/IEEE IPSN 10, SPOTS Track, Stockholm, Sweden, April 2010. 29/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Sensing • This kind of a paradigm has nowadays an industrial success. • Crowd. Sensing app by Waze (Israel) now Google! • Waze: Free GPS Navigation with Turn by Turn – Workers report their GPS location and events (gas prices, traffic jams, etc. ) – Real-time updates to users 31/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Testbeds • Smartphone Testbeds: Allow the requestor to deploy a task (app, data collection, remote terminal etc. ) directly on the end smartphone devices. – – – [PRISM] T. Das, P. Mohan, V. N. Padmanabhan, R. Ramjee, and A. Sharma, “PRISM: Platform for Remote Sensing using Smartphones”, In ACM Mobi. Sys’ 10. [Crowd. Lab] E. Cuervo, P. Gilbert, B. Wu, and L. P. Cox, “Crowd. Lab: An Architecture for Volunteer Mobile Testbeds”, In COMSNETS’ 11. [Phone. Lab] G. Challen et. al. “Phone. Lab: A Large-Scale Participatory Smartphone Testbed”, In USENIX NSDI’ 12 (poster). [Smart. Lab. Demo] "Demo: a programming cloud of smartphones", A. Konstantinidis, C. Costa, G. Larkou, D. Zeinalipour-Yazti, In ACM Mobisys '12. [Smart. Lab] "Managing Smartphone Testbeds with Smart. Lab", G. Larkou, C. Costa, P. Andreou, A. Konstantinidis, D. Zeinalipour-Yazti, In 27 th USENIX LISA '13, Washington D. C. USA, 115 -132, 2013. 32/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Sensing Test. Beds • Phone. Lab: a Participatory Smart. Phone Sensing Testbed (People-Centric Testbed) 200 Nexus S 4 G phones used by Students and Faculty Members at the Univ. of Buffalo • • • Incentive: Free Sprint Phone for 1 st year. After that, only $44. 23/month for an unlimited plan (claimed to be better than competition) Targeted for Data Collection Scenarios (not fine-grain access like Smart. Lab) – – Each Data Collection task need to undergo an Institutional Review Board process (similar to other projects touching ethical issues) Data Collection: Workers (Students) have to bring in their smartphones to have the app installed + data collected. [Phone. Lab] G. Challen et. al. “Phone. Lab: A Large-Scale Participatory Smartphone Testbed”, In USENIX NSDI’ 12 (poster). © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece 33/50

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Sensing Testbeds • We developed a comprehensive architecture for managing smartphones through a web browser. • Smart. Lab (http: //smartlab. cs. ucy. ac. cy/): – 40+ Android Devices, Real Sensors, Real Computing Stack – Different Connection Modalities: 3 G (unlimited 3 G bancwidth by MTN Telecom), Wifi, Wired, Remote  Static Androids Mobile Androids 34/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Sensing Testbeds Smart. Lab (http: //smartlab. cs. ucy. ac. cy/) Rent Manage See/Click Shell File Sys. Automation Debug Data Live Demo! © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece 35/50

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Sensing Testbeds Scenario I: Data Collection in Smart Cities – How to handle a fleet of Android-powered entertainment equipment installed on 1000 buses? – How to manage a city-scale infrastructure comprising of low-power, low-value Androidoriented devices (installed on traffic lights, etc. ) – How to manage a city-scale SETI-like computational cluster comprising of Smartphones. • We tend to change smartphones faster than PCs … 36/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Sensing Testbeds Scenario II: Application Testing (Mockup Studies) – How to test my app automatically on N different smartphones scattered around in a city? Mockup Sensors • GPS mockup • Accelerometer sensor • Compass sensor • Orientation sensor • Temperature sensor • Light sensor • Proximity sensor • Pressure sensor • Gravity sensor 37/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Sensing Testbeds Scenario III: Personal Gadget Management – How to manage my personal gadgets at a finegrain (i. e. , clicks, file-transfer, update, etc. ) Smart Watches Tablets Smart Glasses e. Readers Smart. Books Smart Home Phones Smart TVs Rasperry PI 38/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Talk Outline • Introduction & Challenges • Urban Location Data – Anyplace Indoor Information System • Urban Sensing Testbeds – Smart. Lab Smartphone Programming Cloud • Urban Trajectory Search – Smart. Trace Query Processing Framework • Urban Social Networks – Rayzit Crowd Messaging Service 39/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Trajectory Search Fact: Smartphones can collect positional (x, y) in a power efficient manner (e. g. , i. Phone triangulated log file, Android Geolocation wardriving). Crowdsourcing Incentive: Contribute to the resolution of queries for Social Benefit (without revealing traces). Applications: • • • Intelligent Transportation Systems: “Find whether a new bus route is similar to the trajectories of K other users. ” Social Networks: “Find if there is an evening cycling route from MOMA to the Julliard” Geo. Life, GPS-Waypoints, Sharemyroutes, etc. offer centralized counterparts. 40/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Trajectory Search • Problem: Compare a query with all distributed trajectories and return the k most similar trajectories to the query. • Similarity between two objects A, B is associated with a distance function. Distance D = 7. 3 ? D = 10. 2 K D = 11. 8 Query D = 17 D = 22 41/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Trajectory Search • • • An intelligent top-K processing algorithm for identifying the K most similar trajectories to Q in a distributed environment. Step A: Conduct an inexpensive lineartime LCSS(MBEQ, Ai) computation on the smartphones to approximate the answer. Step B: Exploit the approximation to identify the correct answer by iteratively asking specific nodes to conduct LCSS(Q, Ai). • "Crowdsourced Trace Similarity with Smartphones", Demetrios Zeinalipour-Yazti and Christos Laoudias and Constandinos Costa and Michail Vlachos and Maria I. Andreou and Dimitrios Gunopulos, IEEE TKDE, Vol. 25, 1240 -1253, 2013. • "Smart. Trace: Finding similar trajectories in smartphone networks without disclosing the traces", Costa et al. , IEEE ICDE'11. 42/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Trajectory Search Smart. Trace for Android (open source)! http: //smarttrace. cs. ucy. ac. cy/ Query Q Device B Device C 43/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Trajectory Search Answer Privacy Setting Answer With Trace © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece 44/50

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Talk Outline • Introduction & Challenges • Urban Location Data – Anyplace Indoor Information System • Urban Sensing Testbeds – Smart. Lab Smartphone Programming Cloud • Urban Trajectory Search – Smart. Trace Query Processing Framework • Urban Social Networks – Rayzit Crowd Messaging Service 45/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Social (Crowd) Networks • Social Media (Facebook, Linked-in, … ) utilize a Social Graph (friendship, follower, followee) to map the relationships between users. • Social Media in Urban Settings: Issues – Urban Applications many times require locationbased rather than social-based interactions, e. g. , • Inform my neighboring drivers about an accident (e. g. , in Waze). • Inform people in a city about an event. – Location-based services suffer from bootstrapping • e. g. , Check in to Foursquare and find nobody else there – Interacting with the Crowd, calls for stronger Privacy! 46/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Social (Crowd) Networks • We developed Rayzit for Windows Phone after receiving an Industrial Award by the Appcampus Program (Microsoft, Nokia & Aalto, Finland). – Ranked among the 5 best apps of the given program among 3500 submissions. – A few thousand downloads and active users on our big-data backend. 47/50 © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Urban Social (Crowd) Networks Find 2 Closest Neighbors for ALL User "Continuous all k-nearest neighbor querying in smartphone networks", Georgios Chatzimilioudis, Demetrios Zeinalipour-Yazti, Wang-Chien Lee, Marios D. Dikaiakos, In IEEE MDM'12. © Demetrios Zeinalipour-Yazti, Mining Urban Data, EDBT’ 14, Athens, Greece 48/50

Dagstuhl Seminar 10042, Demetris Zeinalipour, University of Cyprus, 26/1/2010 Crowdsourcing Urban Data with Smartphones Demetrios Zeinalipour-Yazti Data Management Systems Laboratory Department of Computer Science University of Cyprus Thanks! Questions? http: //www. cs. ucy. ac. cy/~dzeina/ Invited Talk at the Mining Urban Data (MUD) Workshop (with EDBT/ICDT), Athens, Greece, March 28, 2014 www. insight-ict. eu/mud