ELEC6910Q Analytics and Systems for Social Media and Big Data Applications Lecture 9. Prof. James She

Transcription

1 ELEC6910Q Analytics and Systems for Social Media and Big Data Applications Lecture 9 Prof. James She james.she@ust.hk 1

2 Announcements 1. Tutorial tomorrow: quick review (socket programming + VMs), parrallel computing get ready the image links from T5, as well as your result matrix from T6 (the one with size 5k*2K) 2. Final Project Guideline released on the course webiste 3. Lecture arrangement: next lecture: (50% a Guest Speaker, 50% a site visit), wait for /facebook annoucement lecture on Nov. 26: Student Presentation (15mins including Q&A) 2

3 Selected Works from T6 3

4 Selected Works from T6 4

5 Last lecture 5

6 Outcome of this lecture Multimedia Big Data - 2 Multimedia Big Data: Analytics of Connection Discovery 6

7 Multimedia Big Data 7

8 Multimedia Big Data High-dimensional and universal signals + Social signals (by social network analytics) Content signals (by multimedia signal processing) Combined signals (by multimedia big data analytics) Cross-cultural/language information The data is unstructured 8

9 Multimedia Big Data Biggest Big Data Share same challenges in regular Big Data Can process, compute and store analytics as below? Anything missing here? Big Data Multimedia Big Data 9

10 Multimedia Big Data Image Processing Similar colour (RGB, HSV) RGB Sample set 1 Sample Set 2 Similar objects (SIFT) HSV Similar texture (GIST) SIFT And many more GIST 10

11 Multimedia Big Data Audio Processing Similar volume Similar content (natural language processing) Similar pitch (Fourier transform) And many more I like mountains natural language processing Fourier transform male 11

12 Multimedia Big Data Multimedia Data Processing People share text, audio, image and video (image + audio) cross disciplines techniques needed to understanding the data extract information that are not available or obvious before Text: natural language processing cultures, location, etc. Image: image processing techniques colour, texture Audio: signal processing tones, backgrounds Video: any suggestions? 12

13 Multimedia Big Data Analytics involves content signals multimedia signal processing analysis modelling and compute collected data vector forms measurements and visualizations analytics 13

14 In-class Activity 2 (Individual, 1mins, 1-page): Imagine you are sharing these 2 images in a social network, and only provide 3 user tags for each image Picture 1 Picture 2 14

15 How do you tag this image? Tags from previous class Picture Picture black car sportscar BMW Tiger windsor 15

16 An Example Work in Multimedia Big Data Connection Discovery Using Big Data of User-Shared Images in Social Media, IEEE Trans. on Multimedia, Sep by M. Cheung, J. She., Z. Jie. 16

17 B Introduction Applications of SG social graph C 0.6 item/ info. recommendation (e.g., games, product, location, etc.) A? B friendship C recommendation? 0.1 D B 0.9 C A A and more However, SG is only available to giant companies like Facebook/ Google 17

18 Introduction - Motivations Observation: Friends share visually similar images and tagged user A user B user C user D Can we obtain recommend friends from their shared images with tags? 18

19 Introduction - Motivations However, user tags are not always reliable tie calculation user A user C user B user D missing connection wrong connection How can we understand the shared images without using unreliable user tags? Understand images visually! 19

20 What is a feature? Features are distinct points on images Similar objects share similar features users A B C user shared images features 20

21 Proposed Methods: BoFT Extract features, and assign a non-user generated label to each image to represent its features 21

22 Proposed Methods: BoFT Obtain the user profiles (label distribution) that represent the characteristics of users User connections can be obtained from the profiles 22

23 Datasets Two general social networks, Skyrock and 163 Weibo users can share images, videos and more # of user shared images: 360,000+ # of users follower/followee (a) shared content 23 (b )

24 Datasets Measurements on histograms about # of follower/followee, and # of shared image: They proved that the users are good representation skyrock Weibo

25 Data measurements User profile of user i: L i = (l i,1, l i,k l i,k ), l i,k is the number of occurrence of label k for user i Similarity calculation: S i,j = S L i, L j = L i L j L i L j Recall what you have learned for friendship similarity? Share image of related pairs (friends, C=1) are more similar 25

26 Data measurements The histogram of S i,j (number of users given a S i,j ): skyrock 163 weibo skyrock 163 weibo 26

27 A set diagram of data measured Most pairs have low S i,j, while a few have high S i,j 27

28 Measurements Based on the histograms of S i,j, we can estimate P(S i,j C = 1): probability density function of S i,j for a related pair P S i,j : probability density function of S i,j for all pairs They can be estimated by: b n Si,j P b S i,j a = a ds, P(b S (b a)n i,j a C = 1)= a p b n Si,j,C=1 ds (p) (b a)n C=1 where a = BS i,j B, b = BS i,j B with B=10 What distribution are they? => f S i,j = γe λs i,j 28

29 Data measurements skyrock 163 weibo skyrock 163 weibo 29

30 Recalled: observations Most pairs have low S i,j, while a few have high S i,j Related pairs have higher S i,j than unrelated at high S i,j Can we make use of this observation for recommendation? 30

31 Can we utilize user shared images for follower/followee recommendations 31

32 Problem Formulation Given S i,j, how like users i and j are related? Mathematically: P(C = 1 S i,j ) Using Bayes theorem, it becomes: P(C = 1 S i,j )= P(S i,j C=1)P C=1 P S i,j where: P(S i,j C = 1): PDF of S i,j for a related pair P S i,j : PDF of S i,j for all pairs P C = 1 : Prob. of a related pair, or the network density 32

33 Problem Formulation How can we calculate P(S i,j C = 1) and P S i,j? Recall: P S i,j = γ a e λ as i,j, and P S i,j C = 1 = γ f e λ fs i,j 33

34 Problem Formulation How can we calculate P C = 1? P C = 1 is the network density P C = 1 = N (p) C=1 (p), where N (p) N C=1 is the number of related pairs N (p) is the number of all pairs N (p) can be calculated as N (p) = N(N 1) 2 N is the number of users in the system 34

35 Problem Formulation Combing all the equations, we have: P C = 1 S i,j = γ fe λ fs i,j (p) N C=1 γ a e λ as i,j N (p) = γ f γ a (p) 2N C=1 N(N 1) e (λ a λ f )S i,j In another form: P C = 1 S i,j = γ t e λ ts i,j where: γ t = γ f γ a 2N C=1 (p) N u (N u 1), λ t = λ a λ f 35

36 Problem Formulation γ t = γ f γ a (p) 2N C=1 N u (N u 1) not affect the trend of P C = 1 S i,j is always a positive number which does By measurement, λ t = 8.09 and λ t = 2.36 on Skyrock and 163 Weibo, respectively. It can be concluded that a higher S i,j always implies a higher chance the user i and j to be a related pair But how this helps follower/followee recommendation? 36

37 Follower/followee recommendation Obtain a list of J users, U i,j, is recommended to user i, given S i,j, that are most likely to be related pairs with i Mathematic formulation: U i,j = arg max P(U i,j S i,1, S U i,n (u)) i,j where U i,j is the set of J users recommended to user i By using an naïve Bayes: U i,j = arg max U i,j J j=1 P C = 1 S i,j 37

38 Follower/followee recommendation U i,j = arg max U i,j J j=1 P C = 1 S i,j As P C = 1 S i,j is a strictly increasing function, we have: U i,j = arg max U i,j J j=1 S i,j which is the set of users with the highest S i,j with i Let s see how good it is compare to 2 approaches: friendship similarity random 38

39 Results 39

40 Conclusion Related pairs likely to share visually similar images (detectable by some multimedia signal processing techniques) Discovered useful formulations of user image sharing that is critical for analytics development Follower/followee recommendation can be based on similarity in images features 40

41 Future works How s about other social networks? are they following the same distribution and trend? What s about other image processing techniques? How to make a scalable big data system (as well as storage)? To be discussed in the next topic 41

42 An Example Work in Multimedia Big Data System A Cloud-assisted Framework for Bag-of-Features Tagging in Social Networks, IEEE 4th Symposium on Network Cloud Computing and Applications (NCCA), Jun by Z. Jie, M. Cheung and J. She 42

43 Recalled Proposed Methods: BoFT Extract features, and assign a non-user generated label to each image to represent its features which part is the most computationally intensives? 43

44 Proposed Methods: BoFT k-means: computationally expensive! Can we use multiple machines (e.g., VMs on Amazon) to speed up? 44

45 Cloud-Assisted Framework for BoFT Using MapReduce to handle the k-means Map: classification step; data parallel over data point Reduce: recomputed means; data parallel over centers 45

46 Experimental Results Speedup θ: execution time on stand-alone machine(ts) divided by the one on cloud (Tc). θ=ts/tc if θ=1: no difference using cloud or stand-alone machine. if θ>1: cloud is faster if θ<1: stand-alone machine is faster 3 dimensions of the study: no. of VMs k (no. of clusters in k-means) no. of images involved 46

47 Experimental Results Dataset: Skyrock (same as the previous paper) User can share images and other types of content follower/followee shared content 47

48 Experimental Results # of images has to be large to make cloud faster It is common for a social network to have >120k images 48

49 Experimental Results # of VMs has to be large to make cloud faster 49

50 Experimental Results Cloud is better when k (# of unique label) is larger 50

51 Experimental Results Scaleup defines the ability of an m-times larger system to perform on m-times larger datasets Tm: the execution time for m VMs to perform on m- times larger datasets. Tm # of images # of VMs 1 10k k k k k k 6 51

52 Experimental Results Scaleup drops slowly with m, which means that more data and VMs only reduce the efficiency a little A good system should have a Tm closes to 1, means that increase the data and VMs will not increase much overhead (e.g., communication among VMs) 52

53 Conclusion A cloud-assisted framework improves the efficiency of BoFT for computing the analytics However, cloud-assisted frameworks only helps with large # of images, VMs. The system design and resources optimization of the cloud platform is driven by the mechanism of the multimedia big data analytics. 53

54 -End of Lecture 9 Questions / Comments? 54