Identity Matching and Geographical Movement of Open-Source Software Mailing List Participants

Transcription

1 and Geographical Movement of Open-Source Software Mailing List Participants Midterm presentation Erik Kouters Eindhoven University of Technology June 19, /20 Erik Kouters and Geographical Movement of Open-Sourc

2 Geographical Movement Case Study Introduction Geographical Movement Skilled workers typically travel more than average person. Open-source software (OSS) contributors Mailing list participants New technique to uncover migration flows of OSS mailing list participants. represents person being at a location at a certain point in time. Movement/migration flows of OSS mailing list participants compared to the average person. Mobility of OSS mailing list participants. 2/20 Erik Kouters and Geographical Movement of Open-Sourc

3 Introduction Geographical Movement Case Study OSS mailing list participants( 12.7%) often use multiple addresses. 27.1% of the addresses have an owner who uses multiple addresses. Data aggregated on address by default Needs to be aggregated on person (). 3/20 Erik Kouters and Geographical Movement of Open-Sourc

4 Introduction Case Study Geographical Movement Case Study Case study: GNOME Well-known amongst researchers GNOME s mailing list archives extracted on April 11, Mailing list archives include 2,202,746 s 73, 920 addresses 4/20 Erik Kouters and Geographical Movement of Open-Sourc

5 Process Overview Introduction Parsing Archives IP Resolution extract parse Person1 Address1 - IP1, Timestamp1 - IP2, Timestamp2 Person2 Address2 - IP3, Timestamp3 - IP4, Timestamp4 IP Resolution Person1 Address1 - Loc1, Timestamp1 - Loc2, Timestamp2 Person2 Address2 - Loc3, Timestamp3 - Loc4, Timestamp4 Person1 Address1 - Loc1, Timestamp1 - Loc2, Timestamp2 Address2 - Loc3, Timestamp3 - Loc4, Timestamp4 5/20 Erik Kouters and Geographical Movement of Open-Sourc

6 Raw s Introduction Parsing Archives IP Resolution Raw s contain headers: From, To, Subject, Date, Received xena storm 2 3 localhost gnome 4 6/20 Erik Kouters and Geographical Movement of Open-Sourc

7 Parsed s Introduction Parsing Archives IP Resolution Each yields name address IP address timestamp Filter automated s sent: Announcements (gnome-announce-list) Request Tracker ( via RT ) Git repository (commits-list) New tarballs on FTP site (ftp-release-list) Aggregate parsed data on address. 7/20 Erik Kouters and Geographical Movement of Open-Sourc

8 IP Resolution Introduction Parsing Archives IP Resolution Resolve IP address to location at city level using multiple (commercial and non-commercial) solutions. IP address to location at city level is not 100% accurate: IP2Location Country level: > 99.5% City level: 77% MaxMind Country level: 99.8% City level: 64.33% IPInfoDB Country level: 99.5% City level: 60% HostIP Country level: 100% City level: unknown 8/20 Erik Kouters and Geographical Movement of Open-Sourc

9 IP Resolution (Contd.) Parsing Archives IP Resolution We use the location which most solutions agree on. Our data set includes 227, 751 unique IP addresses IP addresses resolved to country: 226, 005( 99.23%) IP addresses resolved to city: 189, 879( 83.37%) High number of resolved locations, precision unknown. 9/20 Erik Kouters and Geographical Movement of Open-Sourc

10 Introduction Other Algorithms New Algorithm Alias: name, address : identifying which aliases belong to same individual. Existing algorithms: Simple algorithm Bird et al. s algorithm Developed a new algorithm which is More customisable (i.e. more parameters/thresholds to fit algorithm to data set) More robust w.r.t. noisy and inconsistent data 10/20 Erik Kouters and Geographical Movement of Open-Sou

11 Other Algorithms New Algorithm Simple & Bird et al. s Algorithms Simple Algorithm Bird et al. s Algorithm Our data set contains 146 david@... addresses. 11/20 Erik Kouters and Geographical Movement of Open-Sou

12 New Algorithm Overview Other Algorithms New Algorithm Aliases Convert to vector space model Document-term matrix Add terms with levenshtein similarity Levenshteindocument-term matrix Apply TF IDF model Tfidfdocument-term matrix Apply SVD Decomposed matrices Cosine similarity Matched aliases 12/20 Erik Kouters and Geographical Movement of Open-Sou

13 Other Algorithms New Algorithm New Algorithm Memory Independent Previous version (ICSM ERA 12) depended on memory: 4GB RAM supported data set up to 10,000 aliases. Current version is Memory-independent Parallelised Optimised for speed (using profiler) 13/20 Erik Kouters and Geographical Movement of Open-Sou

14 Other Algorithms New Algorithm New Algorithm Vector Space Model John Travolta, John Joseph Travolta, A = johnt@doma j.travolta@domb john 1... johnt 1... joseph 1... jtravolta 0... travolta /20 Erik Kouters and Geographical Movement of Open-Sou

15 Other Algorithms New Algorithm New Algorithm Levenshtein Similarity John Travolta, John Joseph Travolta, A = johnt@doma j.travolta@domb john 1... johnt 1... joseph 1... jtravolta 8/9... travolta /20 Erik Kouters and Geographical Movement of Open-Sou

16 New Algorithm TF IDF Other Algorithms New Algorithm Reduces value of common terms. Increases value of rare terms. Original scales to number of documents m: m idf (term i ) = log m j=1 a ij New model scales to most frequent term t max : idf (term i ) = log df (t max) m j=1 a ij df = document frequency; computes #documents in which a term occurs 16/20 Erik Kouters and Geographical Movement of Open-Sou

17 New Algorithm SVD Other Algorithms New Algorithm Singular Value Decomposition mathematically uncovers topics. Rank reduction removes the least important topics, theoretically removing noise. Memory independent version was challenging; Will take 8 days on our data set. Slight difference in results without SVD: Skip computing SVD. 17/20 Erik Kouters and Geographical Movement of Open-Sou

18 New Algorithm Overview Other Algorithms New Algorithm Aliases Convert to vector space model Document-term matrix Add terms with levenshtein similarity Levenshteindocument-term matrix Apply TF IDF model Tfidfdocument-term matrix Apply SVD Decomposed matrices Cosine similarity Matched aliases 18/20 Erik Kouters and Geographical Movement of Open-Sou

19 Introduction Recently performed manual identity matching on mailing list data. Compare our algorithm to Simple and Bird et al. s with new, bigger data set. Will send questionnaire to confirm Identity matching Migrations uncovered from s Analyse confirmed migration flows of mailing list participants. 19/20 Erik Kouters and Geographical Movement of Open-Sou

20 Questions Introduction Thank you for listening! Questions? 20/20 Erik Kouters and Geographical Movement of Open-Sou