Oberseminar Convergence Mechanisms for a Smart Space App Store - PowerPoint PPT Presentation

Lehrstuhl für Netzarchitekturen und Netzdienste Institut für Informatik Technische Universität München Oberseminar Convergence Mechanisms for a Smart Space App Store Bibek Shrestha bibek.shrestha@tum.de Under supervision of Marc-Oliver Pahl and Benjamin Hof 27.10.2014

Presentation Overview 1. Objective for thesis 2. Convergence 3. S2Store Simulations 4. Evaluation 5. Questions and Answers 2

Objective For Smart Space AppStore study how using Crowdsourcing Mechanisms bring Convergence Services 3

Objective For Smart Space AppStore study how using Crowdsourcing Mechanisms bring Convergence Services S2Store 3

Objective For Smart Space AppStore study how using Crowdsourcing Mechanisms bring Convergence Services S2Store 3

Objective For Smart Space AppStore study how using Crowdsourcing Mechanisms bring Convergence Services S2Store 3

Objective For Smart Space AppStore study how using Crowdsourcing Mechanisms bring Convergence Services Large and global user contributions cause: S2Store a) duplication b) good, average and low quality 3

Objective For Smart Space AppStore study how using Crowdsourcing Mechanisms bring Convergence Services Large and global user contributions cause: S2Store a) duplication b) good, average and low quality 3

S2Store Entities a) Services b) Context Models c) Access Groups a) Service Lamp Device Driver (Java) b) Context Model (XML) 4

S2Store Entities a) Services b) Context Models c) Access Groups a) Service Lamp Device Driver (Java) (c) b) Context Model (XML) 4

S2Store activities Developer and User interaction with the S2Store and its Entities 5

S2Store Simulation Developer agents build Initialize User agents browse service, context model Update S2Store Ecosystem and download services and access groups loop for N timesteps User provides Increase agent System calculates Exit feedback of the population rankings for all entities services Inspired by AppEco Simulation from [LP12] 6

Service Convergence with Reputation System a) Services 1) Explicit Reputation Systems [FG10] b) Context Models c) Access Groups 2) Implicit Reputation Systems [GM10] * Error reports * User action - install, update, uninstall 7

Service Convergence with Reputation System a) Services 1) Explicit Reputation Systems [FG10] b) Context Models c) Access Groups 2) Implicit Reputation Systems [GM10] * Error reports * User action - install, update, uninstall N times Pick random Explicit and Implicit service Reputation Feedback Observe Convergence Service Simulation 7

Context Model Convergence with Graph Simulation a) Services b) Context Models c) Access Groups Context Model (XML) 8

Context Model Convergence with Graph Simulation a) Services b) Context Models c) Access Groups Context Model (XML) Create or choose node dependencies Update S2Store Create context N times Context Model Repository model Update node ranking Context Model Simulation 8

Context Model Convergence with Graph Simulation a) Services b) Context Models c) Access Groups Node Ranking Algorithms 1. PageRank (Eigenvector Centrality) 2. In-Degree Centrality Graph Properties for Convergence 1. Small World and Scale Free [LW04] 2. Disassortative [LW04] 3. Hierarchical distribution [Hal03] 9

Access Groups Convergence a) Services b) Context Models c) Access Groups Access Groups Create or choose existing Access Groups Create context Update S2Store N times model Calculate ranking of Access Groups Access Groups Simulation 10

Evaluation: Input 11

Evaluation: Expected Output Legend End of Simulation 3/4th of the Simulation 2/4th of the Simulation Beginning of Simulation Node Importance Node Distribution 12

Questions? 13

References [FG10] Randy Farmer and Bryce Glass. Building web reputation systems. " O’Reilly Media, Inc.", 2010. [GM10] A. Girardello and F. Michahelles. Explicit and implicit ratings for mobile applications. In Workshop “Digitale Soziale Netze” and der , volume 40, 2010. [Hal03] R. Hall. Software systems as complex networks: structure, function, and evolvability of software collaboration graphs, 2003. [LW04] N. LaBelle and E. Wallingford. Inter-package dependency networks in open-source software, 2004. 14