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1 Web ( )
2 Web Web
3 Web Web Web Web Web incoming outgoing i
4 pages category ii
5 Web A controller action pages category iii
6 1 1.1 Web 100 Web Web Web Web Web Web Web PV 1
7 1.2 Web Web Web Web Web Web [18] Web Web Web Web 1.3 Web Web Web 1.4 Web Web Web A
8 1.1: V F D h D h = (V, F ) V u v e = (u, v) F u v u v u Ch(u) u pa(v) v root v root u V dep(v) v V v Web Ch(v) = ϕ v Ch(v) ϕ v V w(v) v w(v) V E D b D b = (V, E) 3
9 1.2: 4
10 V u v e = (u, v) E u v u v e E w(e) Web Web D = (V, E, F ) 1.3 V F E 1.3: Web 1.5 Web Web Web Web Web 5
11 Web Web 2. Web 3. Web 6
12 2 2.1 Herman [8] Sugiyama [23] Frishman [6] Sugiyama Frishman Holten [9] 2.2 Web Web Turetken Web [12] Pitknow [20] Pitknow Web Web Pitknow Web WWW Munzner 3 [16] Munzner Web WWW Freire [5] Lai WWW [15] Durand [2] Durand Web 7
![2 Web Web Turetken Web [12] Pitknow [20] Pitknow Web Web](/docs-images/53/20123946/images/page_12.jpg "Pitknow Web WWW Munzner 3 [16] Munzner Web WWW Freire")
13 Fluit Web [4] Fluit Web Web Nation [17] Nation Web Web Web [25] 2.3 Web Google Analytics 1 Google Analytics Bar chart Sankey diagram Sankey diagram Sankey diagram Riehmann [21] Kawamoto Web [13] Kawamoto Kawamoto Kawamoto Zaiane [26] Labroche [14] Web 1 8
![Sankey diagram Sankey diagram Riehmann [21] Kawamoto Web [13] Kawamoto](/docs-images/53/20123946/images/page_13.jpg "Kawamoto Kawamoto Zaiane [26] Labroche [14] Web 1 https://www.google.")
14 Phan [19] : 2.1 Web Kawamoto Kawamoto 9
15 3 3.1 Web Web Web Web Web 10
16 B C Web
17 4 4.1 Tree-map[11] Tree-map [7] [25] Tree-map
![Tree-map [7]](/docs-images/53/20123946/images/page_17.jpg "[25] Tree-map")
18 4.1: 13
19 4.2 u V Ch(u) ϕ u D u b Db u = (V u, E u ) (4.1) V u = Ch(u) {voutgoing, u vincoming} u (4.2) E u = {(u, w) E u V u, w V u } {(vincoming, u x), (x, voutgoing) x u V u } (4.3) voutgoing u vu incoming outgoing incoming Db u A V a 1 Ch(A) a 2 Ch(A) : A 14
20 4.2.1 u V Db u 4.2 v V u w(v) D u b W u vertex W u vertex = {w(v) v V u } max(w u vertex) v V u l(v) l(v) = α u w(v) max(wvertex u ) (4.4) α u = l(u) count(ch(u)) count(ch(u)) Ch(u) u u v V u 4.5 α u 4.4 (4.5) incoming outgoing incoming outgoing vincoming u vu outgoing vu incoming 4.2 (1) (5) v u outgoing v u incoming voutgoing u 4.2 (2)(3) (1) (2) (3) (5) u V D u b e Eu 4.2 (1) (2) (3) (4) e = (v, w) E u v w 4.2 (3) (4) (3) (4) (1) (2) (1) (2) 15
21 Holten [10] 1px w(e) D u b W u edge W u edge = {w(e) e Eu } max(w u edge ) min(w u edge ) e E u d(e) d(e) = (d max d min ) w(e) max(w u edge ) min(w u edge ) d max d min d min > 0 d max Web D u b [24] F spring F charge F gravity Dwyer [3] 16
22 F spring = k L k L L k e E u k e k e = w(e) max(w u edge ) min(w u edge ) max(wedge u ) min(w edge u ) F charge v, w V u F charge = w(v) w(w) distance(v, w) distance(v, w) F gravity F gravity = Const F = F spring + F charge + F gravity 4.3 F spring F charge F gravity 4.4 (1) (2) incoming (3) outgoing F spring outgoing (1) F charge (2)
23 4.3: 4.4: 18
24 4.5: = D h D b D = (D h, D b ) = (V, E, F )
25 4.6: 20
26 4.7:
27 Web 1.2 Web Web 5.1 time (controller, action, resourceid) Web Ruby on Rails 3 controller action Ruby on Rails MVC Controller Ruby on Rails REST REST ID resourceid referer Web Web 5.1: time controller action resource ID referer :14:08 pro recipe service/pro recipes show :14:12 pro recipe service/search show nil :14:20 pro recipe service/pages top nil :14:25 pro recipe service/pro recipes show controller action controller controller action
28 5.1: controller action 23
29 Web Fluentd 4 SaaS TreasureData 5 HQL SQL TreasureData Hive 6 Hive Map/Reduce HQL TreasureData HQL MySQL 7 referer controller action resourceid : 5.1 referer controller action resource ID pro recipe service/pro recipes show pro recipe service/pages top nil pro recipe service/pages top nil category show SQL Ruby 8 JSON Ruby Web Web Web
30 5.2: Sinatra 9 O/R ActiveRecord Web CoffeeScript 11 JavaScript Web GoogleChrome 12 D3.js 13 D3.js [1] 5.3 Web
31 (F spring, F charge, F gravity ) D3.js GoogleMap
32 5.3:
33 28
34 6 5.1 Web Web 386, PV 6.1 pro recipes pro recipes (1) (2) search (3) recipe sets pages pages (4) pages top pages 29
35 図 6.1: 手作業で 1 階層目を配置した全体図 30
36 pages 6.1 pages 6.2 (1) top PV popular top top popular (3) (4) top popular PV popular pages 6.2: 6.1 pages category 6.1 (6) category 31
37 6.1 category category 6.3 category incoming outgoing category category 6.3: 6.1 category Web
38 top popular pages category 33
39 7 Web Web Web Web Web Web Web Web 34
40 Simona Vasilache NAIS Web 35
41 [1] Bostock, M., Ogievetsky, V., and Heer, J., D 3 data-driven documents. In IEEE Transactions on Visualization and Computer Graphics, Vol. 17, No. 12, pp , [2] Durand, D., and Kahn, P., MAPA: a system for inducing and visualizing hierarchy in Websites In Proceedings of the 9th ACM Conference on Hypertext and Hypermedia, pp , [3] Dwyer, T., Scalable, versatile and simple constrained graph layout. In Proceedings of the IEEE-VGTC Symposium on Visualization 2009, Vol. 28, No. 3, pp , [4] Fluit, C., Sabou, M., and Harmelen, F. V., Ontology-based Information Visualisation In Visualising the Semantic Web, Springer, pp , [5] Freire, M., and Rodrguez, P., A graph-based interface to complex hypermedia structure visualization. In Proceedings of the Working Conference on Advanced Visual Interfaces, pp , [6] Frishman, Y., and Tal, A., Dynamic Drawing of Clustered Graphs In Proceedings of IEEE Symposium on Information Visualization 2004, pp , [7],, 9, [8] Herman, I., Melanon, G., and Marshall, M. S., Graph Visualization and Navigation in Information Visualization: a Survey In IEEE Transactions on Visualization and Computer Graphics, Vol. 6, No. 1, pp , [9] Holten, D., Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data In IEEE Transactions on Visualization and Computer Graphics, Vol. 12, No. 5, pp , [10] Holten, D., and Wijk, J. J. V., A user study on visualizing directed edges in graphs In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp , [11] Johnson, B., and Shneiderman, B., Tree-maps: A Space-filling Approach to the Visualization of Hierarchical Information Structures. In Proceedings of the IEEE Visualization 1991, pp ,
42 [12] Turetken, O., and Sharda, R., Visualization of web spaces: state of the art and future directions In Newsletter of ACM SIGMIS Database, Vol. 38, Issue 3, pp , [13] Kawamoto, M., and Itoh, T., A visualization technique for access patterns and link structures of web sites. In Proceedings of 14th International Conference on Information Visualisation 2010, pp , [14] Labroche, N., Lesot, M. J., and Yaffi, L., A new web usage mining and visualization tool In Proceedings of 19th IEEE International Conference on Tools with Artificial Intelligence, Vol. 1, pp , [15] Lai, W., Huang, X., Wibowo, R., and Tanaka, J. An On-Line Web Visualization System with Filtering and Clustering Graph Layout In The IEEE Intelligent Informatics Bulletin, Vol. 5, No. 1, pp , [16] Munzner, T., and Burchard, P., Visualizing the Structure of the World Wide Web in 3D Hyperbolic Space In Proceedings of the First Symposium on Virtual Reality Modeling Language, pp , [17] Nation, D. A., Plaisant, C., Marchionini, G., and Komlodi, A., Visualizing Websites Using a Hierarchical Table of Contents Browser: WebTOC In Proceedings of 3rd Conference on Human Factors and the Web, [18],, Web ( Web ), Vol. 21, No. 4, pp , [19] Phan, D., Xiao, L., Yeh, R., and Hanrahan, P., Flow map layout. In Proceedings of IEEE Symposium on Information Visualization 2005, pp , [20] Pitknow, J. E., and Bharat, K. A., Webviz: A tool for world wide web access log analysis In Proceedings of the First International World Wide Web Conference, [21] Riehmann, P., Hanfler, M., and Froehlich, B., Interactive sankey diagrams. In Proceedings of IEEE Symposium on Information Visualization 2005, pp , [22] Shneiderman, B., The eyes have it: A task by data type taxonomy for information visualizations. In Proceedings of IEEE Symposium on Visual Languages, pp , [23] Sugiyama, K., and Misue, K., Visualization of structural information: automatic drawing of compound digraphs In IEEE Transactions on Systems, Man and Cybernetics, Vol. 21, Issue 4, pp , [24] Tamassia, R., Constraints in Graph Drawing Algorithms In Constraints, Springer, Vol. 3, Issue 1, pp ,
43 [25],,,,, Vol. 32, No. 4, pp , [26] Zaiane, O.R., Xin, M., and Han, J., Discovering Web access patterns and trends by applying OLAP and data mining technology on Web logs In Proceedings of IEEE International Forum on Research and Technology Advances in Digital Libraries, pp ,
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36 知能と情報知能と情報 ( 日本知能情報ファジィ学会誌 ( ))Vol.29, No.6, pp.226-230(2017) 会告 Zadeh( ザデー ) 先生を偲ぶ会 のご案内 Zadeh( ) とと と 日 2018 1 20 日 ( ) 15:00 17:30(14:30 18:00 ) 2F ( ) 530-8310 1-1-35 TEL: 06-6372-5101 https://www.hankyu-hotel.com/hotel/osakashh/index.html
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