Download
1 / 52
520 likes | 617 Views
SenMinCom: Pervasive Distributed Dynamic Sensor Data Mining for Effective Commerce. Outline. What is SenMinCom ? Past Works & Why SenMinCom ? How SenMinCom ? SenMinCom’s Contributions Current Methods v/s SenMinCom SenMinCom’s Simulations Shopping Model Mobile Device Usage Model
E N D
SenMinCom: Pervasive Distributed Dynamic Sensor Data Mining for Effective Commerce
Outline • What is SenMinCom ? • Past Works & Why SenMinCom ? • How SenMinCom ? • SenMinCom’s Contributions • Current Methods v/s SenMinCom • SenMinCom’s Simulations • Shopping Model • Mobile Device Usage Model • Conclusion • References • Acknowledgements
What is SenMinCom [24] ? • Independent units that receive and respond to signals • Unobtrusive • Cheaply available computer • Sensing
contd… • Process of sorting through heap of data and picking out relevant gems • Mostly on data that have not been previously discovered • Mining
contd… • mobile Commerce • Mobile commerce or U commerce is the ability to conduct commerce using a cellular device • U-commerce because of its Ubiquitous-ness
contd… Shopping Scenario
contd… Mobile Usage Scenario
contd… Centralized static data mining
contd… Aggregator nodes Sensor-ized area
contd… • A DDMS is a set of transactions <T, t> where 'T' is a purchase or product information event and 't' represents the time and date of the occurrence of T. • DDMSM = { <T1 ,t1>...<Tn, tn>} where M is the recorded Mac Id of the customer's cell phone. • System will have pre-defined rule base from which the distinction of customers is achieved.
contd… • Define Rules and corresponding parameters for each Rule • for shopper (Mac Id) m=1 to M • Identify DDMSm from set of DDMS • for segment r=1 to R • Select Ruler from the set of Rules, R • If(Ruler ⊆DDMSm) add shopper m to group r • The R groups of shoppers are the segments
contd… If(thisNode = = firstAggregator) MA migrates toward firstAggregator Else if( (thisNode = = nextAggregator) && (nextAggregator != lastAggregator) ) MA collects sensed raw data and does local mining Set nextAggregator in the MA packet MA migrates towards next aggregator Else if(thisNode = = lastAggregator) MA collects sensed data MA migrates back to sink
Shopping Model [24] • Random shoppers have no strong intention to purchase something, and just wander among aisles a.k.a. window shoppers • Rational shoppers visiting a store, know clearly what they need a.k.a prompt shoppers • Recurrent or regular customers are customers who visit the store often. They can be further divided into • Customers with higher purchasing power • Customers with lower purchasing power
contd… • Example • Book store company e.g. Barnes & Nobles • Store modeled on SenMinCom architecture • Result • Customers shopping & checkout patterns dynamically tracked
contd… Features Aisle wise real time products distribution Reveals aisle popularity Consequences Restacking products Maximize selling
contd… Features Aisle wise real time products distribution at separate time intervals Aisle popularity Consequences Restacking products according to different hours of a day, days in a week, etc.
contd… Features Reveals customers purchasing power Categorize customers Consequences Directed products promotion
contd… Feature Products lifted to checked out Consequences With shopping history product promotion offers Customer
contd… Feature Products lifted to checked out customer level Consequences Shopping history leads product promotion offers Products picked to checked out share Aisle movement pattern
Mobile Device Usage Model • Popular cellular phone cravings • Brand popularity where the people are attracted or loyal towards a company • For a cell phone company, popularity of a given model or total volume of their models • Cellular phone usage among an age group • Educational period is a stage among the age group of 18-28, generally students attending schools, colleges, and universities. • Working period, among the age group 28-60
contd… • Example • Georgia State University Campus • Area modeled on SenMinCom architecture • Result • Students real time device usage scenario • Manual device survey avoided
contd… Mobile Devices GSU Plaza
contd… Mobile Devices GSU Student Center
contd… Popular Mobile Devices @ GSU
contd… Features Area wide popular mobile models Total mobile device usage scenario Consequences Real time mobile popularity Brand consideration leads to streamlining promotions
contd… Feature Various mobile models of a brand Consequences Popularity of models Reasons like cost, intriguing features, etc. revealed
contd… Motorola Volume Usage Popular Mobile Devices
contd… Feature Market share of cell phone models Consequences Timeline based share of model Provide insight for a newly released model
contd… Feature Mobile usage of new cell phone models Consequences Crosscheck their marketing campaign Peoples’ current mobile preferences
References • Mainwaring, A., Polastre, J., Szewczyk, R., Culler, D., Anderson, J.,“Wireless Sensor Networks for Habitat Monitoring”, Proceedings of the 1st ACM International Workshop on Wireless Sensor Networks and Applications, 2002, pp.88-97. • Warrior, J., “Smart Sensor Networks of the Future”, Sensors Magazine, March 1997. • Pottie, G.J., Kaiser, W.J., “Wireless Integrated Network Sensors”, Communications of the ACM, vol. 43, no. 5, pp.551-55 8, May 2000. • Cerpa, A., Elson, J., Estrin, D., Girod, L., Hamilton M., Zhao, J., “Habitat monitoring: Application driver for wireless communications technology”, 2001 ACM SIGCOMM Workshop on data Communications in Latin America and the Caribbean, Costa Rica, April 2001. • Werner-Allen, G., Johnson, J., Ruiz, M., Lees, J., Welsh, M., “Monitoring volcanic eruptions with a wireless sensor network”, Wireless Sensor Networks, 2005. Proceedings of the second European Workshop, 2005, pp.108-120. • Intel Research Sensor Network Operation, http://intel.com/research/exploratory/wireless_sensors.htm. • Shih, E., Cho, S., Ickes, N., Min, R., Sinha, A., Wang, A., Chandrakasan, A., “Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks”, Proceedings of ACM MobiCom’01, Rome, Italy, July 2001, pp.271-286.
contd… • Herring, C., Kaplan, S., “Component-based software systems for smart environments, IEEE Personal Communications, October 2000, pp. 60-61. • Varshney, U., Vetter, R., “Framework, Applications, and Networking Support for M-commerce”, ACM/Kluwer Journal on Mobile Network and Applications (MONET), June 2002. • Varshney, U., Vetter, R., Kalakota, R.,”Mobile Commerce: A New Frontier”, IEEE Computer, 2000, 22(10), pp.32-38. • GyPSiiWebtop, http://www.gypsii.com/ • Social Networking moves to the cell phone, http://www.nytimes.com/2008/03/06/technology/06wireless.html?_r=1&oref=slogin • Social Network Zingku, http://www.infoworld.com/article/07/09/28/Google-buys-Zingku-mobile-social-networking-service_1.html • NTT DoCoMo Newsletter, Mobility, Adding the Human Touch to Communication, http://www.nttdocomo.com/binary/about/mobility_doc_15.pdf • New Cell phone doubles as personal trainer and shrink, http://tech.yahoo.com/blogs/null/50133
