⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩9,,ィ⟬᝟ሗᏛ◊✲㒊㛛
9,, ࢹ࣮ࢱᇶ┙ศ㔝 ࣓ࣥࣂ࣮
ᩍᤵ໭ᕝ ༤அ෸ᩍᤵኳ➟ ಇஅ

Ꮫ⏕    ኱Ꮫ㝔⏕ 32 ྡࠊᏛ㢮⏕ 7 ྡ
 ᴫせe ࢧ࢖࢚ࣥࢫ࡟࠾࠸࡚㸪኱つᶍࢹ࣮ࢱࡢ⟶⌮ࡸά⏝ࡣᴟࡵ࡚㔜せ࡞ㄢ㢟࡜࡞ࡗ࡚࠸ࡿ㸬ィ⟬᝟ሗᏛ◊✲㒊㛛ࢹ࣮ࢱᇶ┙ศ㔝ࡣ㸪ࢹ࣮ࢱᕤᏛ㛵㐃ศ㔝ࡢ◊✲㛤Ⓨࢆᢸᙜࡋ࡚࠸ࡿ㸬ලయⓗ࡟ࡣ㸪␗✀ࢹ࣮ࢱ࣮࣋ࢫࡸከᵝ࡞᝟ሗ※ࢆ⤫ྜⓗ࡟ᢅ࠺ࡓࡵࡢ᝟ሗ⤫ྜᇶ┙ᢏ⾡㸪ࢹ࣮ࢱ୰࡟ᇙࡶࢀࡓ▱㆑ࡸつ๎ࢆⓎぢࡍࡿࡓࡵࡢࢹ࣮ࢱ࣐࢖ࢽࣥࢢ࣭▱㆑Ⓨぢᢏ⾡㸪࢖ࣥࢱ࣮ࢿࢵࢺ⎔ቃ࡟࠾࠸࡚ᵝࠎ࡞ࢹ࣮ࢱࢆ⤫୍ⓗ࡟ᢅ࠺ࡓࡵࡢ XML 㛵㐃ᢏ⾡ࡢ◊✲ࢆ⥅⥆ࡋ࡚⾜ࡗࡓ㸬ࡲࡓ㸪ࢭࣥࢱ࣮ෆࡢᆅ⌫⎔ቃ◊✲㒊㛛ࡸ⣲⢏Ꮚ≀⌮◊✲㒊㛛㸪⏘ᴗᢏ⾡⥲ྜ◊✲ᡤ㸪JAXA ࡜㐃ᦠࡋ࡚㸪ィ⟬⛉Ꮫࡢྛศ㔝࡟࠾ࡅࡿᛂ⏝ⓗ࡞◊✲ࢆ᥎㐍ࡋࡓ㸬 ◊✲ᡂᯝ࠙㸯ࠚ ᝟ሗ⤫ྜᇶ┙ᢏ⾡㸦㛵㐃◊✲㈝㸸ᩥ㒊⛉Ꮫ┬ཷク◊✲㸪኱ᕝ᝟ሗ㏻ಙᇶ㔠㸪୕⳻㟁ᶵཷク◊✲㸧㸦㸯㸧ᣦᐃ࢖࣋ࣥࢺ㥑ືᆺࢫࢺ࣮࣒ࣜฎ⌮ ㏆ᖺ㸪ࢿࢵࢺ࣮࣡ࢡࣃࢣࢵࢺࡸࣟࢢࢹ࣮ࢱࡸࢭࣥࢧࢹ࣮ࢱ➼㸪Ọ⥆ⓗ࡟⏕ᡂࡉࢀࡿࢫࢺࣜ
࣮࣒ࢹ࣮ࢱࡀቑຍࡋ࡚ࡁ࡚࠸ࡿ㸬ࡇࡢࢫࢺ࣮࣒ࣜࢹ࣮ࢱࢆᢅ࠺ࡓࡵ࡟㸪ࢫࢺ࣮࣒ࣜฎ⌮࢚ࣥࢪࣥࡀ㛤Ⓨࡉࢀ࡚࠸ࡿ㸬ᚑ᮶ࡢࢫࢺ࣮࣒ࣜฎ⌮࢚ࣥࢪࣥࡣ㸪࡝ࡢࢫࢺ࣮࣒ࣜࢯ࣮ࢫ࠿ࡽ᮶ࡓ
ࢹ࣮ࢱ࡟ᑐࡋ࡚ࡶၥྜࡏࡀᐇ⾜ࡉࢀ㸪⤖ᯝࡀ⏕ᡂࡉࢀࡿ㸬ࡋ࠿ࡋ㸪ᛂ⏝ฎ⌮࡟ࡼࡗ࡚ࡣࡇࢀ
ࡣᚲࡎࡋࡶᮃࡲࡋ࠸࡜ゝ࠼࡞࠸ࡇ࡜ࡶ࠶ࡿ㸬౛࠼ࡤ㸪」ᩘࡢࢭࣥࢧࢫࢺ࣮࣒ࣜࢆ⤫ྜฎ⌮ࡋ࡚࠸ࡿሙྜ㸪࠶ࡿࢭࣥࢧ್࡟ࡼࡾ␗ᖖࡀほ ࡉࢀࡓሙྜ࡟௚ࡢ㛵㐃ࢭࣥࢧࢹ࣮ࢱࢆ㞟⣙ศᯒࡋࡓ࠸࡜࠸࠺ࡼ࠺࡞ሙྜ㸪඲࡚ࡢࢭࣥࢧ್࡟㐃ືࡋ࡚⤫ྜฎ⌮ࢆ⾜࠺ࡇ࡜ࡣ↓㥏࡛࠶ࡿ㸬ࡲࡓ㸪⤫ྜฎ⌮࡟࠾࠸࡚ࡣࢱ࢖࣐࣮➼࡟㐃ືࡋ࡚࿘ᮇⓗ࡟ࢹ࣮ࢱࡢศᯒࡸ㞟⣙ࢆ⾜࠸ࡓ࠸࡜࠸
࠺ሙྜࡶከ࠸㸬ࡇࡢࡼ࠺࡞せồ࡟ᑐᛂࡍࡿࡓࡵ࡟ࡣ㸪ᚑ᮶ࡢࡼ࠺࡞࠶ࡽࡺࡿ✀㢮ࡢ᪂つࢹ࣮ࢱࡢ฿╔࡟ฎ⌮ࡀ㐃ືࡍࡿࡢ࡛ࡣ࡞ࡃ㸪≉ᐃࡢᣦᐃ࢖࣋ࣥࢺࡢࡳ࡟㐃ືࡋࡓᣦᐃ࢖࣋ࣥࢺ㥑ືᆺࡢࢫࢺ࣮࣒ࣜฎ⌮ࡀᚲせ࡛࠶ࡿ㸬ࡲࡓ㸪ᣦᐃ࢖࣋ࣥࢺ㥑ືᆺࡢࢫࢺ࣮࣒ࣜฎ⌮ࡣ㸪ࢫࢺ࣮࣒ࣜᆺࢹ࣮ࢱศᯒฎ⌮࡜ࣂࢵࢳᆺࢹ࣮ࢱศᯒฎ⌮ࢆ⮬↛࡟⼥ྜࡍࡿୖ࡛ࡶ㔜せ࡛࠶ࡿ㸬 ᮏ◊✲࡛ࡣ㸪ᣦᐃ࢖࣋ࣥࢺ㥑ືᆺࢫࢺ࣮࣒ࣜฎ⌮ࢆᐇ⌧ࡋࡓࢫࢺ࣮࣒ࣜ⤫ྜᇶ┙ࢩࢫࢸ࣒
JsSpinner ࡢࣉࣟࢺࢱ࢖ࣉࢆ㛤Ⓨࡋࡓ㸬JsSpinner ࡣ㸪ከᵝ࡞୙ᆒ㉁ࢹ࣮ࢱࡢ⤫ྜฎ⌮࡟ᑐᛂࡍࡿࡓࡵ㸪ከࡃࡢࢫࢺ࣮࣒ࣜฎ⌮࢚ࣥࢪࣥࡀ᥇⏝ࡋ࡚࠸ࡿ࣮ࣜࣞࢩࣙࢼࣝࢫࢺ࣮࣒࡛ࣜࡣ
࡞ࡃ㸪༙ᵓ㐀ࢹ࣮ࢱࡢ JSON ᙧᘧࡢࢫࢺ࣮࣒ࣜࡸ᝟ሗ※ࢆᑐ㇟࡜ࡍࡿࡼ࠺࡟タィࡉࢀ࡚࠸ࡿ㸬ᣦᐃ࢖࣋ࣥࢺ㥑ືᆺࢫࢺ࣮࣒ࣜฎ⌮࡛ࡣ㸪ၥྜࡏ࡟࠾࠸࡚࣮ࣘࢨ࡟ࡼࡗ࡚࣐ࢫࢱࢫࢺࣜ
࣮࣒࡜ࡋ࡚ᣦᐃࡉࢀࡓࢫࢺ࣮࣒ࣜࢯ࣮ࢫ࠿ࡽ᪂つࡢࢹ࣮ࢱࡀ฿╔ࡋࡓ࡜ࡁࡢࡳ㸪ၥྜࡏࡀホ
౯ࡉࢀࡿ㸬

-148-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩ ᐇ㝿ࡢࢫࢺ࣮࣒ࣜฎ⌮࢚ࣥࢪࣥ࡟
࠾ࡅࡿၥྜࡏฎ⌮࡛ࡣ㸪෕㛗࡞ฎ⌮ࢆ㑊ࡅࡿࡓࡵᕪศィ⟬ࡀ⾜ࢃࢀࡿ㸬ᣦᐃ࢖࣋ࣥࢺ㥑ືᆺࢫࢺ࣮࣒ࣜฎ⌮ࢆᕪศィ⟬ฎ⌮ࡢᯟ⤌ࡳࡢ୰࡛ᐇ⌧ࡍࡿ᭱ࡶࢼ࢖࣮ࣈ࡞᪉ἲࡣ㸪࣐ࢫࢱ࣮ࢫࢺ
࣮࣒ࣜ࠿ࡽࡢ᪂つࢱࣉࣝ࡟㉳ᅉࡍࡿ⤖ᯝ࠿㸪ࡑࢀ௨እࡢ᪂つࢱࣉࣝ࡟㉳ᅉࡍࡿ⤖ᯝ࠿࡟࣐࣮࢟ࣥࢢࢆ⾜࠸㸪࣐ࢫࢱ࣐࣮ࢡࡢ࠶ࡿࢱࣉࣝࡀ฿╔ࡋࡓ᫬
Ⅼ࡛ࡢࡳ㸪᭱⤊ⓗ࡞ၥྜࡏ⤖ᯝࡢฟຊࢆ⾜࠺࡜࠸࠺ࡶࡢ࡛࠶ࡿ㸬ࡋ࠿ࡋ㸪ࢼ࢖ᅗ 1 ࢫ࣐࣮ࢺ᪉ᘧ࡜ࢼ࢖࣮ࣈ᪉ᘧࡢẚ㍑
࣮ࣈ࡞᪉ᘧ࡛ࡣ㸪᭱⤊ⓗ࡞ၥྜࡏ⤖ᯝ࡟ᐤ୚ࡋ࡞࠸ከࡃࡢ↓㥏࡞ฎ⌮ࡀⓎ⏕ࡋᚓࡿⅬࡀၥ㢟࡜ࡋ࡚࠶ࡿ㸬ࡑࡇ࡛ᮏ◊✲࡛ࡣ㸪࣐ࢫࢱࢫࢺ࣮࣒ࣜࢹ࣮ࢱࢆฎ⌮ࡍࡿࡓࡵࡢ࣐ࢫࢱ࢘࢕ࣥࢻ࢘₇⟬Ꮚ࡜㠀࣐ࢫࢱࢫࢺ࣮࣒ࣜࢹ࣮ࢱ
ࢆฎ⌮ࡍࡿࡓࡵࡢࢫ࣐࣮ࢺ࢘࢕ࣥࢻ࢘₇⟬Ꮚࢆᑟධࡍࡿࢫ࣐࣮ࢺ᪉ᘧࢆᥦ᱌ࡋࡓ㸬ணഛⓗ࡞ẚ㍑ᐇ㦂ࡢ⤖ᯝࡣࢫ࣐࣮ࢺ᪉ᘧࡢ᭷ຠᛶࢆ☜ㄆࡋࡓ㸬㸦㸰㸧ࢺࣛࣥࢨࢡࢩࣙࢼࣝࢫࢺ࣮࣒ࣜฎ⌮ ࢹ࣮ࢱࢫࢺ࣮࣒ࣜࡢศᯒฎ⌮ࡢࡓࡵ࡟ࢹ࣮ࢱࢫࢺ࣮࣒ࣜฎ⌮࢚ࣥࢪࣥࡀ⏝࠸ࡽࢀ࡚࠸ࡿ㸬ࢹ࣮ࢱࢫࢺ࣮࣒ࣜࡢࡼࡾヲ⣽࡞ゎᯒฎ⌮ࡢࡓࡵ࡟ࡣ㸪ࢹ࣮ࢱࢫࢺ࣮࣒࡛ࣜ࠶ࡿࣟࢢࢹ࣮ࢱ
࡜࣮ࣜࣞࢩࣙࢼࣝࢹ࣮ࢱ࣮࣋ࢫ࡞࡝ࡢእ㒊ࣜࢯ࣮ࢫ୰ࡢ࣮ࣘࢨࢹ࣮ࢱࢆ⤖ྜࡍࡿฎ⌮࡞࡝ࡀᚲせ࡟࡞ࡿሙྜࡀ࠶ࡿ㸬ࡋ࠿ࡋࡇࡢࡼ࠺࡞ฎ⌮ࢆᐇ⾜ࡍࡿ㝿࡟እ㒊ࣜࢯ࣮ࢫ࡟ᑐࡋ࡚᭦
᪂ฎ⌮ࡀ᪋ࡉࢀࡿሙྜࢆ⪃࠼ࡿ࡜㸪㐃⥆ⓗၥྜࡏࡢ୍ᅇࡢฎ⌮⤖ᯝࡢ୰࡛㸪୍㈏ࡋ࡚ྠࡌ
እ㒊ࣜࢯ࣮ࢫࡢ≧ែࢆཧ↷ࡋ࡞ࡃ࡞ࡿ࡜࠸࠺୍㈏ᛶࡢၥ㢟ࡀⓎ⏕ࡋ࠺ࡿ㸬ࡑࡇ࡛㸪ᡃࠎࡣ㐃⥆ⓗၥྜࡏࡢ୍ᅇࡢฎ⌮⤖ᯝ࡟࠾ࡅࡿእ㒊ࣜࢯ࣮ࢫࡢཧ↷ࡀ୍㈏ᛶࢆ᭷ࡋ࡚࠸ࡿࡇ࡜ࢆಖドࡍࡿ㸪ࢺࣛࣥࢨࢡࢩࣙࢼࣝࢫࢺ࣮࣒ࣜฎ⌮ࡢᴫᛕࢆ᪤࡟ᥦ᱌ࡋ࡚࠸ࡿ㸬ᮏ◊✲࡛ࡣ㸪ᕪศィ⟬ฎ⌮ࡢᯟ⤌ࡳ࡟࠾࠸࡚ࢺࣛࣥࢨࢡࢩࣙࢼࣝࢫࢺ࣮࣒ࣜฎ⌮ࢆᐇ⌧ࡍࡿࡓࡵࡢ᪉ᘧ
ࢆᥦ᱌ࡋ㸪ࡑࡢ᭷ຠᛶࢆ᳨ドࡋࡓ㸬ලయⓗ࡟ࡣ㸪እ㒊ࣜࢯ࣮ࢫࡢ᭦᪂ࢆ┘どࡍࡿࣔࢽࢱ₇⟬Ꮚࢆᑟධࡋ㸪᭦᪂࢖࣋ࣥࢺࢆࢫࢺ࣮࣒ࣜ࡜ࡋ࡚ୗὶ࡟ఏᦙࡍࡿࡇ࡜࡛ཧ↷ࡢ୍㈏ᛶࢆಖ

ドࡍࡿࡇ࡜ࡀྍ⬟࡛࠶ࡿ㸬
ᅗ 2 ୍㈏ᛶࡢ࡞࠸እ㒊ࣜࢯ࣮ࢫཧ↷

ᅗ 3 ୍㈏ᛶࡢ࠶ࡿእ㒊ࣜࢯ࣮ࢫཧ↷㸦㸱㸧ࢫࢺ࣮࣒ࣜ OLAP ㏆ᖺ㸪ࢭࣥࢧࢹ࣮ࢱࡸ࣐࢖ࢡࣟࣈࣟࢢ➼㸪㐃⥆ⓗ࡟⏕ᡂࡉࢀ㸪㓄ಙࡉࢀࡿࡼ࠺࡞ࢫࢺࣜ
࣮࣒ࢹ࣮ࢱࡢቑຍ࡟క࠸㸪ࢫࢺ࣮࣒ࣜฎ⌮࢚ࣥࢪࣥࡀከᩘ㛤Ⓨࡉࢀ࡚ࡁ࡚࠸ࡿ㸬୍᪉㸪ࢫࢺ࣮࣒ࣜࢹ࣮ࢱ࡟ᑐࡋ࡚ࡼࡾ㧗࠸ࣞ࣋ࣝࡢศᯒࢆ⾜࠸ࡓ࠸࡜࠸࠺ࢽ࣮ࢬࡀቑຍࡋ࡚࠸ࡿ㸬ࡇࡢ௦⾲౛࡜ࡋ࡚㸪ከḟඖࢹ࣮ࢱศᯒ(OLAP ฎ⌮)ࡀ࠶ࡿ㸬ࢫࢺ࣮࣒ࣜࢹ࣮ࢱ࡟ᑐࡋ࡚
-149-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩

OLAP ฎ⌮ࢆ⾜࠺◊✲࡜ࡋ࡚㸪Jiawei Han ࡽࡣࢫࢺ࣮࣒ࣜࢹ࣮ࢱ࡟ᑐࡍࡿ OLAP ฎ⌮ࢆᐜ᫆࡟ࡍࡿ Stream Cube ࢆᥦ᱌ࡋ࡚࠸ࡿ㸬ࡓࡔࡋ㸪ࢫࢺ࣮࣒ࣜฎ⌮࢚ࣥࢪࣥࢆά࠿ࡋࡓ OLAPฎ⌮࡟ࡘ࠸࡚ࡣ༑ศ᳨ウࡀ⾜ࢃࢀ࡚࠸࡞࠸㸬 ࡑࡇ࡛ᮏ◊✲࡛ࡣ㸪ࢫࢺ࣮࣒ࣜฎ⌮࢚ࣥࢪࣥࢆ⏝࠸ࡓ OLAP ฎ⌮ࢆᐇ⌧ࡍࡿࡓࡵࡢᡭἲࢆ᳨ウࡋࡓ㸬ලయⓗ࡟ࡣ㸪࢚ࣥࢪࣥഃ࡟㐃⥆ⓗၥྜࡏࢆⓏ㘓ࡋ࡚࠾ࡁ㸪ࡇࢀࡽࡢⓏ㘓ࡋ࡚࠾࠸ࡓၥྜࡏ࠿ࡽ OLAP ฎ⌮ࡢ⤖ᯝ࡜ࡋ࡚ᚲせ࡞ࢹ࣮ࢱࢆᚓࡿ㸬OLAP ฎ⌮࡛ࡣ㸪ศᯒᑐ㇟ࡢࢹ࣮ࢱࡢḟඖ㸦ᒓᛶ㸧ࡸḟඖෆࡢ㝵ᒙࡢ඲࡚ࡢ⤌ྜࡏࢆ㡬Ⅼ࡜ࡍࡿ lattice ࢆ⪃࠼ࡿ㸬౛࠼ࡤ㸪ၟရ᝟ሗ㸦ၟရ ID, ၟရྡ, ࢪࣕࣥࣝ, 㢳ᐈID, 㢳ᐈྡ, ᆅᇦ, ኎ୖ㢠㸧ࡢࡼ࠺࡞ࢫ࣮࣐࢟ࢆ⪃࠼ࡿ࡜ᅗࡢࡼ࠺࡞ lattice ࢆᵓ⠏࡛ࡁࡿ㸬 lattice ࡢྛ㡬Ⅼࡣ㸪OLAP ฎ⌮࡟࠾࠸࡚㞟⣙ࡍࡿᑐ㇟ࡢḟඖࡢ⤌ྜࡏ࡟ᑐᛂࡍࡿ㸬᭱ࡶ༢⣧࡟ࡣ㸪
lattice ෆࡢ඲࡚ࡢ㡬Ⅼࡢḟඖࡢ⤌ྜࡏࡢ㞟⣙ࢆᐇ⾜ࡍࢀࡤࡼ࠸㸬ࡋ࠿ࡋ୍⯡࡟ࡣ㸪lattice ࡢ඲㡬Ⅼᩘࡣ⭾኱ᅗ 4 ከẁࡢ㞟⣙ࣞ࣋ࣝ࡞ᩘ࡟࡞ࡿࡇ࡜ࡀከࡃ㸪ࡇࢀࡽࡢ඲࡚ࢆ࢚ࣥࢪࣥ࡟Ⓩ㘓
ࡍࡿࡇ࡜ࡣ㸪ฎ⌮ᛶ⬟ୖ㞴ࡋ࠸ሙྜࡀከ࠸㸬ࡲࡓ㸪ᚲࡎࡋࡶ඲࡚ࡢ㡬Ⅼ࡟ᑐᛂࡍࡿ㞟⣙್ࢆᖖ᫬ྲྀᚓࡋ⥆ࡅ࡞ࡃ࡚ࡶ㸪࣮ࣘࢨせồ࡟ᛂࡌ࡚ᑟฟ࡛ࡁࢀࡤࡼ࠸࡜࠸࠺ሙྜࡶከ࠸࡜⪃࠼ࡽࢀࡿ㸬ࡑࡇ࡛ᮏ◊✲࡛ࡣ㸪lattice ෆࡢ୍㒊ࡢ㡬Ⅼࡢࡳࢆ㐺ษ࡟㑅ᢥࡋ㐃⥆ⓗၥྜࡏ࡜ࡋ࡚Ⓩ㘓ࡋ㸪ࡑࢀ௨እࡣ࣮ࣘࢨ࠿ࡽồࡵࡽࢀࡓ࡜ࡁࡢࡳ㞟⣙ฎ⌮ࢆࡋ⤖ᯝࢆ⏕ᡂࡍࡿၥྜ
ࡏ㸦࢜ࣥࢹ࣐ࣥࢻ࡟ホ౯ࡍࡿၥྜࡏ㸧࡜ࡍࡿࡇ࡜࡛㸪ຠ⋡ⓗ࡞ OLAP ฎ⌮ࢆᐇ⌧ࡍࡿ㸬 ࡝ࡢၥྜࡏࢆ࢚ࣥࢪࣥ࡟Ⓩ㘓ࡋ㸪࡝ࡢၥྜࡏࢆ࢜ࣥࢹ࣐ࣥࢻ࡟ホ౯ࡍࡿၥྜࡏ࡜ࡍࡿ࠿
ࡢ᭱㐺࡞⤌ࡳྜࢃࡏࡢ㑅ᢥࡣ୍⯡࡟⤌ྜࡏ᭱㐺໬ၥ㢟࡜࡞ࡿࡓࡵ㸪㈎ḧ࢔ࣝࢦࣜࢬ࣒ࢆᥦ
᱌ࡋࡓ㸬ࡇࡢ࢔ࣝࢦࣜࢬ࣒ࡣ㸪᭱ึ࡟඲࡚ࡢ㞟⣙ၥྜࡏࢆ࢜ࣥࢹ࣐ࣥࢻ࡟ホ౯ࡍࡿၥྜࡏ
࡜ࡋ࡚࠾ࡁ㸪ࡑࢀࡒࢀࢆ࢚ࣥࢪࣥ࡟Ⓩ㘓ࡋࡓ࡜ࡁࡢฎ⌮ࢥࢫࢺ࡜Ⓩ㘓ࡍࡿ๓ࡢฎ⌮ࢥࢫࢺࡢᕪศࡀ᭱ࡶ኱ࡁࡃ࡞ࡿࡶࡢࢆ㡰࡟㑅ࢇ࡛࠸ࡃ㸬ࡲࡓ㸪✵㛫ࢥࢫࢺ࡜ࡋ࡚㸪࢚ࣥࢪࣥ࡟Ⓩ
㘓ࡋࡓ㞟⣙ၥྜࡏࡀಖᣢࡍࡿ₇⟬ᑐ㇟ࡢࢱࣉࣝᩘ࡜₇⟬⤖ᯝࡢࢱࣉࣝᩘࡢ⥲ᩘ࡜ࡍࡿ㸬࠙㸰ࠚ ࢹ࣮ࢱ࣐࢖ࢽࣥࢢ࣭▱㆑Ⓨぢᢏ⾡㸦㛵㐃◊✲㈝㸸ᩥ㒊⛉Ꮫ┬ཷク◊✲㸪⏘⥲◊ᇶ┙◊✲(A)㸪
ᐩኈ㏻◊✲ᡤཷク◊✲㸧㸦㸯㸧୙☜ᐇࢹ࣮ࢱ࡟ᑐࡍࡿእࢀ᳨್ฟ ࢹ࣮ࢱࡸᛂ⏝ࡢከᵝ໬ࡸ㸪ྛ✀ࢭࣥࢧࢹࣂ࢖ࢫࡢⓎ㐩࡟క࠸㸪୙☜ᐇᛶࢆక࠺ࢹ࣮ࢱฎ⌮࡟ᑐࡍࡿせồࡀ㧗ࡲࡗ࡚࠸ࡿ㸬౛࠼ࡤ㸪GPS ࡟ࡼࡿ఩⨨᝟ሗ࡟ࡣ㸪ᮏ㉁ⓗ࡟ㄗᕪࡀྵࡲࢀ࡚࠾ࡾ㸪ࡑࡢㄗᕪࢆ⪃៖ࡋࡓฎ⌮ࡀồࡵࡽࢀࡿ㸬୍᪉㸪㏻ᖖࡢࢹ࣮ࢱ࠿ࡽࡣⴭࡋࡃ␗࡞ࡿࢹ࣮ࢱ㸦እࢀ್㸧ࢆ᳨ฟࡍࡿእࢀ᳨್ฟࡀࡉࡲࡊࡲ࡞ᛂ⏝࡛฼⏝ࡉࢀ࡚࠸ࡿ㸬୙☜ᐇࢹ࣮ࢱ࡟ᑐࡋ࡚እࢀ᳨್ฟࢆ⾜࠺ሙྜ㸪ࢹ࣮ࢱࡢ୙☜ᐇᛶࢆ⪃៖ࡋࡓୖ᳨࡛ฟฎ⌮ࢆ⾜࠺ࡇ࡜ࡀᮃࡲࡋ࠸㸬 ᮏ◊✲࡛ࡣ㸪࢞࢘ࢫศᕸ࡟ᚑ࠺୙☜ᐇᛶࢆᣢࡘࢹ࣮ࢱ
࡟ᑐࡍࡿ㊥㞳࡟ᇶ࡙ࡃእࢀ᳨್ฟᡭἲ࡟ࡘ࠸᳨࡚ウࡋ ᅗ 5 ࢫࢺ࣮࣒ࣜ࡟࠾ࡅࡿእࢀ್
ࡓ㸬≉࡟㸪እࢀ್ࡢᗘྜ࠸ࡀ኱ࡁ࠸ࡶࡢ࠿ࡽ k ௳ࡢእࢀ್ࢆ᳨ฟࡍࡿࢺࢵࣉ k እࢀ᳨್ฟࡢ࢔ࣝࢦࣜࢬ࣒ࢆ⪃᱌ࡋࡑࡢ᭷ຠᛶࢆ♧ࡋࡓ㸬ཝᐦ࡞እࢀ್ᗘࢆィ⟬ࡍࡿୖ࡛ࡣ࢞࢘ࢫศᕸࢆ⪃៖ࡋࡓ㊥㞳ィ⟬ࡀᚲせ࡛࠶ࡿࡀ㸪ࡑࢀ࡟ࡣከ኱࡞ィ-150-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩

CDF⟬ࢥࢫࢺࡀక࠺㸬ࡑࡇ࡛㸪ᥦ᱌ᡭἲ࡛ࡣ㸪እࢀ್ೃ⿵࢜ࣈࢪ࢙ࢡࢺࡢእࢀ್ᗘࡢୖ㝈࡜ୗ㝈ࢆồࡵ㸪ࡑࢀ࡟ᇶ࡙ࡁࢺࢵࣉ k እࢀ್࡟ධࡾᚓࡿ࠿ࢆุᐃࡍࡿ㹩࡜࡛㸪ィ⟬ࢥࢫࢺࢆ኱ᖜ
࡟๐ῶࡍࡿ㸬 ࡲࡓࡉࡽ࡟㸪㟼ⓗ࡞ࢹ࣮ࢱ㞟ྜ࡛ࡣ࡞ࡃ㸪ࢹ࣮ࢱࡀࢫࢺ࣮࣒ࣜ࡜ࡋ࡚⣔ิⓗ࡟฿╔ࡍࡿሙྜࢆ᝿ᐃࡋ㸪ࡑࢀ࡟ᑐᛂࡋࡓᕪศィ⟬࡟ᇶ࡙ࡃຠ⋡ⓗ࡞㐃⥆ⓗእࢀ᳨್ฟࡢᡭἲ࡟ࡘ࠸࡚ࡶ᳨ウࢆ⾜ࡗࡓ㸬㸦㸰㸧Twitter ࣮ࣘࢨࡢ఩⨨᥎ᐃ ㏆ᖺ࡛ࡣ㸪ࢯ࣮ࢩ࣓ࣕࣝࢹ࢕࢔ࡢᬑཬ࡟ࡼࡾ㸪ࢯ࣮ࢩࣕࣝࢫࢺ࣮࣒ࣜࡢ᝟ሗࢆ฼ά⏝ࡋ࡚ᐇୡ⏺ࢆࣔࢽࢱࣜࣥࢢࡍࡿ◊✲ࡀከࡃ⾜ࢃࢀ࡚࠸ࡿ㸬ࢯ࣮ࢩࣕࣝࢫࢺ࣮࣒ࣜࡢ᝟ሗࢆ฼
⏝ࡍࡿୖ࡛㸪᫬㛫࡜఩⨨࡟㛵ࡍࡿ᝟ሗࡀ㔜せ࡛࠶ࡿ㸬᫬㛫᝟ሗࡣẚ㍑ⓗᐜ᫆࡟ᚓࡿࡇ࡜ࡀ
࡛ࡁࡿࡢ࡟ᑐࡋ㸪఩⨨᝟ሗࡢᢕᥱࡣ࠿࡞ࡽࡎࡋࡶᐜ࡛᫆ࡣ࡞࠸㸬௦⾲ⓗ࡞఩⨨᝟ሗ࡜ࡋ࡚
᝟ሗⓎಙ⪅ࡢᒃఫᆅ᝟ሗࡀ࠶ࡿ㸬ࡋ࠿ࡋ㸪ከࡃࡢ◊✲࡛ᣦ᦬ࡉࢀ࡚࠸ࡿࡼ࠺࡟㸪ࢯ࣮ࢩ࣓ࣕࣝࢹ࢕࢔࣮ࣘࢨࡣ⮬ࡽࡢᒃఫᆅࢆබ㛤ࡋ࡚࠸࡞࠸ࡇ࡜ࡀከ࠸㸬ᮏ◊✲࡛ࡣ㸪ࢫࢺ࣮࣒ࣜ
᝟ሗ※࡟ᑐࡍࡿ࣓ࢱࢹ࣮ࢱ᥎ᐃࡢලయⓗ࡞஦౛࡜ࡋ࡚㸪࣐࢖ࢡࣟࣈࣟࢢ࣮ࣘࢨࡢᒃఫᆅ᥎
ᐃᡭἲࢆ㛤Ⓨࡋࡓ㸬 ࣐࢖ࢡࣟࣈࣟࢢ࣮ࣘࢨࡢᒃఫᆅ᥎ᐃࢆ⾜࠺ᚑ᮶ࡢ◊✲ࡣ㸪኱ࡁࡃࢢࣛࣇ࣮࣋ࢫᡭἲ࡜ࢥ
ࣥࢸࣥࢶ࣮࣋ࢫᡭἲ࡟ศ㢮ࡉࢀࡿ㸬๓⪅ࡣ࣮ࣘࢨࡢ཭ே㛵ಀ➼ࢆ♧ࡋࡓࢢࣛࣇࡢศᯒࡀ࣋
࣮ࢫ࡜࡞ࡗ࡚࠾ࡾ㸪ᚋ⪅ࡣⓎಙࡉࢀࡓࢥࣥࢸࣥࢶࡢศᯒࡀ࣮࣋ࢫ࡜࡞ࡗ࡚࠸ࡿ㸬ᮏ◊✲࡟࠾࠸࡚ࡣ㸪ࢢࣛࣇ࣮࣋ࢫ࡜ࢥࣥࢸࣥࢶ࣮࣋ࢫࡢ୧ᡭἲ࡟㛵ࡋ࡚ᚑ᮶ࡼࡾࡶࡼࡾ᥎ᐃ⢭ᗘࡢ㧗࠸ᡭἲࢆ㛤Ⓨࡋࡓ㸬‫ࡓ࠸⏝ࢆࢡ࣮࣐ࢻࣥࣛࣇࣛࢢࡿࡅ࠾࡟ࣇࣛࢢࣝࣕࢩ࣮ࢯۑ‬ᡭἲ ᚑ᮶ࡢࢢࣛࣇ࣮࣋ࢫᡭἲࡢ኱㒊ศࡣ closeness assumption ࢆᇶ࡟ࡋ࡚࣮ࣘࢨࡢᒃఫᆅ
ࢆ᥎ᐃࡋ࡚࠸ࡿ㸬Closeness assumption ࡜ࡣ㸪ࢯ࣮ࢩࣕࣝࢢࣛࣇୖ࡛᥋⥆ࡉࢀ࡚࠸ࡿ࣮ࣘ
ࢨྠኈ㸦཭ே➼㸧ࡣࡑࡢᒃఫᆅࡀ஫࠸࡟㏆࠸࡜࠸࠺௬ᐃ࡛࠶ࡿ㸬ࡋ࠿ࡋ㸪ࢯ࣮ࢩࣕࣝࢢࣛ
ࣇࡢᛶ㉁࡟ࡼࡗ࡚ࡣ closeness assumption ࡣᚲࡎࡋࡶ᭷ຠ࡛ࡣ࡞࠸㸬౛࠼ࡤ࠶ࡿ୍⯡࣮ࣘࢨࡢ࢔࢝࢘ࣥࢺ࡜㸪ࡑࡢ࣮ࣘࢨࡀ⯆࿡ࢆᣢࡗ࡚࠸ࡿ௻ᴗ࡞࡝ࡢ࢔࢝࢘ࣥࢺࡀࢯ࣮ࢩࣕࣝࢢ
ࣛࣇୖ࡛᥋⥆ࡉࢀࡿࡇ࡜ࡀ࠶ࡿ㸬ࡇࡢࡼ࠺࡞⎔ቃ࡛ࡣ closeness assumption ࡣ࠶ࡲࡾ᭷ຠ
࡛࡞࠸ࡇ࡜ࡀከ࠸㸬 ࡑࡇ࡛ᮏ◊✲࡛ࡣ closeness assumption ࡜ࡣ␗࡞ࡿ concentration assumption ࢆᑟධࡍࡿ㸬Concentration assumption ࡜ࡣ㸪ࢯ࣮ࢩࣕࣝࢢࣛࣇୖ࡟ࡣ⮬ࡽࡢࣇ࢛࣮ࣟ࣡⩌ࡢᒃఫᆅࡀ࠶ࡿᆅᇦ࡟㞟୰ࡋ࡚࠸ࡿ࣮ࣘࢨࡀᏑᅾࡍࡿ࡜࠸࠺௬ᐃ࡛࠶ࡿ㸬ࡇࡢ࣮ࣘࢨࡢࡇ࡜ࢆ
ࢢࣛࣇࣛࣥࢻ࣐࣮ࢡ࡜࿧ࡪ㸬ࢢࣛࣇࣛࣥࢻ࣐࣮
1ࢡࢆ⏝࠸ࡿ࡜㸪ࠕࢢࣛࣇࣛࣥࢻ࣐࣮ࢡࡢࣇ࢛ࣟ࣡
0.9
࣮⩌ࡢᒃఫᆅࡣ஫࠸࡟㏆࠸ࠖ࡜࠸࠺᥎ᐃࡀྍ⬟
0.8
0.7
࡟࡞ࡿ㸬
0.6
 ᥦ᱌ᡭἲ㸦landmark mixture model; LMM㸧
0.5ࡣ㸪ࢢࣛࣇࣛࣥࢻ࣐࣮ࢡࢆࣇ࢛࣮ࣟࡍࡿ࣮ࣘࢨ
0.4⩌ࡢᒃఫᆅࡣ஫࠸࡟㏆࠸࡜࠸࠺௬ᐃ࡟ࡶ࡜࡙ࡁ㸪 0.3
LMM
0.2࣮ࣘࢨࡢᒃఫᆅࢆ☜⋡ศᕸ࡛ࣔࢹࣝ໬ࡍࡿᡭἲUDI
Backstrom
0.1
Jurgens࡛࠶ࡿ㸬ࡲࡎ඲࡚ࡢ࣮ࣘࢨ࡟ᑐࡋ࡚㸪ࡑࡢ࣮ࣘ
Naive0ࢨࡢࣇ࢛࣮ࣟ࣡⩌ࡢᒃఫᆅࡢศᕸ㸦dominance
1000
10000
1000001e+06
1e+07
Error distance (m)distribution㸧ࢆィ⟬ࡋ㸪๭ࡾᙜ࡚ࡿ㸬ࡑࡋ࡚㸪࠶ࡿ࣮ࣘࢨࡢᒃఫᆅࡢศᕸࢆࡑࡢ࣮ࣘࢨࡀࣇ࢛࣮ࣟࡍࡿ
ᅗ 6 ẚ㍑ᐇ㦂⤖ᯝ࣮ࣘࢨ⩌ࡢ dominance distribution ࢆΰྜࡍࡿࡇ࡜࡟ࡼ
ࡾᚓࡿ㸬ᚓࡽࢀࡓᒃఫᆅࡢศᕸ࡟࠾࠸࡚☜⋡ᐦᗘࡀ᭱኱࡟࡞ࡿⅬࢆ᥎ᐃࡋࡓᒃఫᆅ࡜ࡍࡿ㸬 ẚ㍑ᐇ㦂ࡢ⤖ᯝࢆᅗ 6 ࡟♧ࡍ㸬ᶓ㍈ࡣ᥎ᐃㄗᕪ㸦༢఩:m㸧ࡢ್ࢆ♧ࡋ㸪⦪㍈ࡣ᥎ᐃㄗᕪ
ࡀᑐᛂࡍࡿ್௨ୗ࡛࠶ࡿ࣮ࣘࢨࡢ๭ྜ㸪ࡍ࡞ࢃࡕ⢭ᗘࢆ♧ࡋ࡚࠸ࡿ㸬ᥦ᱌ᡭἲࡀ௚ࡢᡭἲ
ࢆᴫࡡୖᅇࡗ࡚࠸ࡿࡇ࡜ࡀศ࠿ࡿ㸬-151-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩‫ࣥ࢖ࣛࣥ࢜ࡓ࠸⏝ࢆ࣒࣮ࣜࢺࢫࣝࣕࢩ࣮ࢯۑ‬ᒃఫᆅ᥎ᐃ ࢯ࣮ࢩࣕࣝࢫࢺ࣮࣒࡛ࣜࡣ㸪࣮ࣘࢨࡀ᫬ࠎ้ࠎ࡜኱㔞ࡢᢞ✏ࢆࡋ࡚࠸ࡿ㸬ࡑࡢࡓࡵ㸪ᒃఫᆅ᥎ᐃࡢᡭࡀ࠿ࡾ࡜࡞ࡿ᝟ሗࡶࣜ࢔ࣝࢱ࢖࣒࡟ቑ࠼⥆ࡅ࡚࠾ࡾ㸪ࢥࣥࢸࣥࢶ࣮࣋ࢫᡭἲ
࡟࠾࠸࡚ࡶᮏ᮶࢖ࣥࢡ࣓ࣜࣥࢱࣝ࡟ᒃఫᆅࢆ᥎ᐃࡍࡿࡇ࡜ࡀྍ⬟࡛࠶ࡿ㸬ࡋ࠿ࡋ㸪᪤Ꮡࡢࢥࣥࢸࣥࢶ࣮࣋ࢫᡭἲ࡛ࡣ㸪ࡇࡢࡼ࠺࡞࢖ࣥࢡ࣓ࣜࣥࢱࣝ࡞ฎ⌮ࡣᐇ⌧࡛ࡁ࡚࠸࡞࠸㸬 ᮏ◊✲࡛ࡣ㸪ࢯ࣮ࢩࣕࣝࢫࢺ࣮࣒ࣜ࠿ࡽḟࠎ࡟ᚓࡽࢀࡿࢥࣥࢸࣥࢶࢆᇶ࡟ᒃఫᆅࢆ㏲ḟ
᥎ᐃࡍࡿࡇ࡜ࡢฟ᮶ࡿᡭἲ㸦Online Location Inference Method; OLIM㸧ࢆᥦ᱌ࡋࡓ㸬ࡲࡓ㸪ᮏᡭἲ࡛ࡣࢥࣥࢸࣥࢶࡢ᫬㛫ⓗ≉ᚩࢆ⪃៖ࡍࡿࡇ࡜࡟ࡼࡾ㸪temporally-local word ࡜࠸࠺᪂ࡋ࠸࣮࣮ࣟ࢝ࣝ࣡ࢻࢆᑟධࡍࡿ㸬Temporally-local word ࡜ࡣ㸪ᚑ᮶ࡢᐃᖖⓗ࡞ᒁᡤᛶࢆᣢࡘ࣮࣮ࣟ࢝ࣝ࣡ࢻ㸦statically-local word㸧࡜ࡣ␗࡞ࡾ㸪୍᫬ⓗ࡞ᒁᡤᛶࢆᣢࡘ༢ㄒ
ࡢࡇ࡜࡛࠶ࡿ㸬ᮏᡭἲ࡛ࡣࡇࢀࡽ㸰✀㢮ࡢ࣮࣮ࣟ࢝ࣝ࣡ࢻࢆే⏝ࡍࡿ㸬 ẚ㍑ࡢࡓࡵࡢホ౯ᐇ㦂࡛ࡣ㸪ᥦ᱌ᡭἲ࡜ᅄࡘࡢ᪤Ꮡᡭἲ㸦UDI㸪Cheng㸪Hecht㸪Kinsella㸧ཬࡧࢼ࢖࣮ࣈ࡞ᡭἲ㸦NaiveC㸧ࢆẚ㍑ࡋࡓ㸬ࢼ࢖࣮ࣈ࡞ᡭἲ࡜ࡣ㸪࣮ࣘࢨࡀᢞ✏ࡋࡓᆅྡࡢ࣓ࢻ࢖ࢻࢆィ⟬ࡍࡿᡭἲ࡛࠶ࡿ㸬ࡲࡓ㸪ᥦ᱌ᡭἲࡣ஧ࡘࡢ࣮࣮ࣟ࢝ࣝ࣡ࢻࢆే⏝ࡍࡿࡶࡢ࡜㸪statically-localwords ࡢࡳࢆ⏝࠸ࡿࡶࡢ࡜ࢆẚ㍑ࡋࡓ㸬 ᅗ 7 ࡣྛᡭἲࡢ⢭ᗘࡢẚ㍑⤖ᯝࢆ♧ࡋ࡚࠸ࡿ㸬⤖ᯝ࠿ࡽ㸪ᥦ᱌ᡭἲࡢ⢭ᗘࡀ᭱ࡶ㧗࠸ࡇ࡜ࡀศ࠿ࡿ㸬ᅗ 8 ࡣ᫬㛫ࢆ㏣ࡗ࡚ᥦ᱌ᡭἲࡀ᥎ᐃㄗᕪࢆῶᑡࡉࡏ࡚࠸ࡃ⤖ᯝࢆ♧ࡋ࡚࠸ࡿ㸬ᶓ㍈ࡣࢹ࣮ࢱࢭࢵࢺ࡟ྵࡲࢀࡿࢶ࢖࣮ࢺࢆ᫬⣔ิ㡰࡟ฎ⌮ࡋࡓ᫬ࡢฎ⌮ࡋࡓࢶ࢖࣮ࢺࡢ๭
ྜࢆ⾲ࡋ㸪⦪㍈ࡣࡑࡢ㎡඾࡛ࡢ᥎ᐃㄗᕪࡢ୰ኸ್ࢆ⾲ࡋ࡚࠸ࡿ㸬⤖ᯝ࠿ࡽ㸪᥎ᐃㄗᕪࡣ࢜
ࣥࣛ࢖ࣥ᥎ᐃ࡟ࡼࡾᚎࠎ࡟ῶᑡࡋ࡚࠸ࡃࡇ࡜ࡀศ࠿ࡿ㸬
1
0.9
0.8
0.7

250000O-LIM (TL+SL)
UDI
Cheng
Hecht
Kinsella
NaiveC

200000

ED@50

AP@d

0.6
0.5
0.4

150000100000

0.3
0.2

50000

0.1
0
1000

O-LIM (TL+SL)
O-LIM (SL)010000
100000Error distance (m)1e+06ᅗ 7 ᥎ᐃ⢭ᗘࡢẚ㍑

0

10

20

30
40
50
60
70
% of processed tweets

80

90

100

ᅗ 8 ᥎ᐃ⢭ᗘࡢ᫬㛫ኚ໬㸦㸱㸧GPU ࢆ⏝࠸ࡓ୙☜ᐇࢺࣛࣥࢨࢡࢩࣙࣥࢹ
࣮ࢱ࡟ᑐࡍࡿ☜⋡ⓗ㢖ฟ࢔࢖ࢸ࣒㞟ྜ࣐࢖ࢽࣥ
ࢢ୙☜ᐇᛶࢆྵࡴ኱㔞ࡢࢹ࣮ࢱࡢฎ⌮ࡢࡓࡵ࡟㸪୙☜ᐇࢹ࣮ࢱ࣮࣋ࢫࡢ◊✲ࡀᗈࡃ⾜࡞ࢃࢀ࡚࠸
ࡿ㸬୙☜ᐇࢹ࣮ࢱ࣮࣋ࢫ࡟ᑐࡋ࡚㸪㢖ฟ࢔࢖ࢸ࣒㞟ྜ࣐࢖ࢽࣥࢢࢆ⾜࡞࠺ᡭἲࡀ࠸ࡃࡘ࠿ᥦ᱌
ࡉࢀ࡚࠸ࡿࡀ㸪ฎ⌮㏿ᗘࡀ㐜࠸࡜࠸࠺ၥ㢟ࡀ࠶ࡿ㸬୍᪉㸪GPU (Graphics Processing Unit) ࢆ⏝ ࠸ ࡓ
GPGPU (General Purposeᅗ 9 ᛶ⬟ホ౯㸦Kosarak ࢹ࣮ࢱࢭࢵࢺ㸧
computation on GPU)࡜࠸࠺ᡭἲࡀ㸪㧗ᛶ⬟ィ⟬ࡢศ㔝࡛ὀ┠ࡉࢀ࡚࠸ࡿ㸬GPGPU ࡣ㸪ඖࠎࡣࢢࣛࣇ࢕ࢵࢡฎ⌮ࡢࡓࡵࡢ₇⟬⿦⨨࡛࠶ࡿ
-152-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩GPU ࢆ㸪ࡑࡢ㧗࠸୪ิᗘࢆ࠸࠿ࡋ࡚ỗ⏝ⓗ࡞ィ⟬࡟฼⏝ࡍࡿࡶࡢ࡛࠶ࡿ㸬ᡃࠎࡣࡇࢀࡲ࡛༢୍ GPU ࢆ⏝࠸ࡓ୙☜ᐇࢹ࣮ࢱ࣮࣋ࢫ࡟ᑐࡍࡿ㢖ฟ࢔࢖ࢸ࣒㞟ྜ࣐࢖ࢽࣥࢢࡢ㧗㏿໬ࡢࡓࡵࡢᡭἲࢆᥦ᱌ࡋ࡚ࡁࡓ㸬ᮏ◊✲࡛ࡣ༢୍ GPU ࡟ᑐࡍࡿᡭἲࢆ࣮࣋ࢫ
࡟㸪」ᩘࡢ GPU ࢆᦚ㍕ࡋࡓ」ᩘࡢࣀ࣮ࢻ࠿ࡽᵓᡂࡉࢀࡿࢡࣛࢫࢱ⎔ቃ࡟࠾࠸࡚㸪ྠᵝࡢฎ⌮ࢆ⾜࠺ᡭἲࢆᥦ᱌ࡍࡿ㸬」ᩘ GPU ࢆ⏝࠸ࡿ฼Ⅼ࡜ࡋ࡚㸪GPU ࡢቑຍ࡟ࡼࡿ୪ิᗘࡢྥ
ୖࡀ࠶ࡿ㸬ࡲࡓ㸪GPU ࡢ࣓ࣔࣜࡣࡑࢀ࡯࡝኱ࡁࡃ࡞࠸ࡀ㸪」ᩘ GPU ࡟ࡍࡿࡇ࡜࡛฼⏝ྍ⬟࡞࣓ࣔࣜ㡿ᇦࡀቑຍࡋ㸪ᕧ኱࡞ࢹ࣮ࢱࢆᢅ࠺ࡇ࡜ࡶྍ⬟࡜࡞ࡿ㸬୍᪉㸪」ᩘ GPU ࢆ฼⏝ࡍࡿୖ࡛ၥ㢟࡟࡞ࡿⅬ࡜ࡋ࡚㸪GPU 㛫࡛ࡢࢹ࣮ࢱ㏻ಙࡀ࠶ࡿ㸬㏻ᖖ㸪GPU ࡣ PCI-Express࡟ࡼࡗ࡚᥋⥆ࡉࢀ࡚࠸ࡿࡀ㸪ࡇࡇࡢ㏻ಙࡢ࣓ࣔࣜࣂࣥࢻᖜࡀᑠࡉ࠸ࡓࡵ㸪࣎ࢺࣝࢿࢵࢡ࡟࡞
ࡿྍ⬟ᛶࡀ㧗࠸㸬ࡑࡢࡓࡵ㸪GPU 㛫࡛ࡢࢹ࣮ࢱࡢ౫Ꮡࢆ࡛ࡁࡿࡔࡅ๐ῶࡍࡿࡇ࡜ࡀᮃࡲࡋ
࠸㸬ᥦ᱌ᡭἲ࡛ࡣ㸪GPU 㛫࡛ࡢࢹ࣮ࢱ㏻ಙࡢ๐ῶ࡟ຍ࠼㸪㈇Ⲵศᩓࢆ⾜࡞࠸ฎ⌮ࡢ㧗㏿໬ࢆᅗࡗࡓ㸬ࡉࡽ࡟㸪」ᩘ GPU ࢆᣢࡘࣀ࣮ࢻ࠿ࡽ࡞ࡿࢡࣛࢫࢱ࡟࠾ࡅࡿᡭἲࡶᥦ᱌ࡋࡓ㸬ࡲࡓ㸪ᐇ㦂࡟ࡼࡾ㸪ᥦ᱌ᡭἲࡢᛶ⬟ࢆホ౯ࡋࡓ㸬ᅗ 9 ࡀᐇ㦂⤖ᯝ࡛࠶ࡿ㸬NVIDIA Tesla
M2090 ࢆ 2 ྎᦚ㍕ࡋࡓ༢୍ࣀ࣮ࢻࡢᐇ㦂࡟࠾࠸࡚㸪༢୍ GPU ࢆ฼⏝ࡋࡓሙྜ࡟ẚ࡭࡚᭱኱ 2 ಸ⛬ᗘࡢ㧗㏿໬ࡀᐇ⌧࡛ࡁ࡚࠸ࡿ㸬ࡲࡓ㸪ࢫ࣮࣌ࢫࡢ㒔ྜ࡛๭ឡࡍࡿࡀ㸪」ᩘࣀ࣮ࢻࢆ฼⏝ࡋࡓ⎔ቃ࡛ࡶ㧗㏿໬ࡀ㐩ᡂࡉࢀࡿࡇ࡜ࢆ♧ࡋࡓ㸬࠙㸱ࠚ XML࣭Web ࣉࣟࢢ࣑ࣛࣥࢢ
㸦㸯㸧LINQ ࢆ⏝࠸ࡓ LOD ၥྜࡏ
ᨻᗓࡸ⾜ᨻ࡞࡝ࡢබඹᶵ㛵ࡀಖ᭷ࡍࡿᵝࠎ࡞ࢹ࣮ࢱࢆ㸪෌฼⏝ྍ⬟࡞ᙧᘧ࡛እ㒊࡬බ㛤ࡍࡿྲྀࡾ⤌ࡳࢆ࣮࢜ࣉࣥࢹ࣮ࢱ࡜࿧ࡧ㸪㏆ᖺඛ㐍ᅜࢆ୰ᚰ࡟✚ᴟⓗ࡟᥎㐍ࡉࢀ࡚࠸ࡿ㸬ࡇࡢࡼ࠺࡞ᵓ㐀໬ࡉࢀࡓࢹ࣮ࢱࢆ Web ୖ࡛බ㛤㸪ඹ᭷ࡍࡿ୍ࡘࡢ᪉ἲ࡜ࡋ࡚ Linked OpenData㸦LOD㸧ࡀὀ┠ࡉࢀ࡚࠸ࡿ㸬
LOD ࡛ࡣ㸪୍⯡࡟ RDF㸦Resource Description Framework㸧ࣇ࢛࣮࣐ࢵࢺࡀ⏝࠸ࡽࢀ㸪
RDF ࢹ࣮ࢱ࡟ᑐࡍࡿၥྜࡏ࡟ࡣ㸪ᑓ⏝ࡢၥྜࡏゝㄒ࡛࠶ࡿ SPARQL ࡀ⏝࠸ࡽࢀࡿ㸬ࡋ࠿ࡋ࡞ࡀࡽ SPARQL ࡢグ㏙࡟࠾࠸࡚ࡣ㸪ၥྜࡏゝㄒࡢ⩦ᚓ㸪LOD ࠾ࡼࡧ RDF ࡢ㛵㐃ᢏ⾡࡟ࡘ࠸࡚ࡢ▱㆑ࡀᚲせ࡟࡞ࡿ㸬ࡲࡓ㸪RDF ࡣᮏ㉁ⓗ࡟ࡣࢢࣛࣇᵓ㐀࡛࠶ࡾ㸪LOD ࡣ」㞧࠿ࡘ෕㛗࡞ᵓ㐀࡟࡞ࡿࡓࡵ㸪ࡇࢀࡽࡢ▱㆑ࢆᣢࡓ࡞࠸฼⏝⪅ࡀ㸪LOD ࡜ࡋ࡚බ㛤ࡉࢀࡓ࣮࢜
ࣉࣥࢹ࣮ࢱࢆ฼⏝ࡍࡿࡢࡣᐜ࡛᫆ࡣ࡞࠸㸬ࡑࡇ࡛㸪ᮏ◊✲࡛ࡣࣅ࣮ࣗࢆ⏝࠸ࡓ LINQ ࡟ࡼࡿ LOD ࡟ᑐࡍࡿၥྜࡏࢆᥦ᱌ࡋࡓ㸬ணࡵ LOD ࡸ RDF ࡟▱㆑ࡢ࠶ࡿࢹ࣮ࢱ࣮࣋ࢫタィ⪅ࡽࡀ LOD ࡟ᑐࡍࡿ JSON ࣅ࣮ࣗࢆᐃ⩏ࡍࡿ㸬ᐃ⩏ࡉࢀࡓࣅ࣮ࣗ࡟ᑐࡋ࡚㸪⡿࣐࢖ࢡࣟࢯࣇࢺ♫ࡀᥦ౪ࡍࡿ.NET Framework ࡢᶵ⬟ࡢ୍ࡘ࡛࠶ࡿ LINQ ࢆ⏝࠸࡚㸪C#➼ࡢࣉࣟࢢ࣑ࣛࣥࢢゝㄒ࠿ࡽ LOD ࡟ᑐࡍࡿၥྜࡏࢆ
グ㏙ࡍࡿ㸬ࡼࡾලయⓗ࡟ࡣ㸪ࡲࡎタィ⪅ࡀ㸪ᮏᡭἲ࡛ᥦ᱌ࡍࡿࣅ࣮ࣗᐃ⩏ゝㄒࢆ⏝࠸࡚㸪ࣅ࣮ࣗᐃ⩏ࢆ⾜࠺㸦ᅗ 10㸧㸬ࢹ࣮ࢱࡢ฼⏝⪅ࡣ㸪ࣅ࣮ࣗᐃ⩏࡟ᇶ࡙࠸࡚㸪LINQ ၥྜࡏࢆグ㏙ࡍࡿ࡜㸪ࢩࢫࢸ࣒ࡀ LINQ ၥྜࡏࢆࣅ࣮ࣗᐃ⩏࡟ᇶ࡙ࡁᑐᛂࡍࡿ᝟ሗ※࡟ᑐࡍࡿSPARQL ၥྜࡏ࡟᭩ࡁ᥮࠼ࡿ㸬SPARQL ၥྜࡏࡢ⤖ᯝࡣ㸪JSON ᙧᘧࡢࢹ࣮ࢱ࡟ኚ᥮ࡉࢀ㸪฼⏝⪅࡟㏉༷ࡉࢀࡿ㸬ࡇࡢࡼ࠺࡟㸪฼⏝⪅ࡣ SPARQL ࡸ LOD ࡢヲ⣽ࢆ▱ࡿࡇ࡜࡞ࡃ㸪LODࢆ฼⏝ࡍࡿࡇ࡜ࡀྍ⬟࡜࡞ࡿ㸬

-153-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩ᅗ 10 LOD ࡟ᑐࡍࡿ JSON ࣅ࣮ࣗࡢᐃ⩏␟㸦㸰㸧ᒁᡤィ⟬࡟ᇶ࡙ࡃ ObjectRank ᥎ᐃଂPageRank ࡣࣜࣥࢡᵓ㐀ゎᯒᡭἲࡢ୍ࡘ࡛࠶ࡾ㸪ࢢࣛࣇୖࡢࣀ࣮ࢻࡢ㔜せᗘࢆホ౯ࡍࡿᡭἲ࡛࠶ࡿ㸬 ਚ ॢWeb ᳨⣴ࡸࢯ࣮ࢩࣕࣝࢿࢵࢺ࣮࣡ࢡศᯒ㸪ࣂ࢖࢜࢖ ছࣥࣇ࢛࣐ࢸ࢕ࢡࢫ࡞࡝ࡢᵝࠎ࡞ศ㔝࡟⏝࠸ࡽࢀ࡚࠸ ই ࡿ㸬ࡋ࠿ࡋ㸪PageRank ࡣィ⟬ࢥࢫࢺ ࡀ኱ࡁ࠸࡜࠸ भ࠺ၥ㢟ࡀ࠶ࡿ㸬ࡲࡓ㸪ከࡃࡢᛂ⏝࡟࠾࠸࡚㸪඲࡚ࡢ ਴ ಑઀੧ু১ࣀ࣮ࢻ࡟࠾ࡅࡿ PageRank ࢫࢥ࢔ࡣᚲࡎࡋࡶᚲせ࡛ ੿&KHQैभু১
࡞ࡃ㸪ᑡᩘࡢࣀ࣮ࢻࡢࢫࢥ࢔ࡔࡅィ⟬࡛ࡁࢀࡤ༑ศ ਛ ৎ࡛࠶ࡿࢣ࣮ࢫࡀከ࠸㸬৑ ࡇࡢၥ㢟࡟ᑐࡋ࡚㸪ࢢࣛࣇ඲యࡢ᝟ሗࢆ⏝࠸ࡎ࡟≉ᐃࡢࣀ࣮ࢻ㸦ᑐ㇟ࣀ࣮ࢻ ࡢ PageRank ್ࢆィ⟬ ଧࡍࡿᡭἲࡀᥦ᱌ࡉࢀ࡚࠸ࡿ㸬ᮏ◊✲࡛ࡣ㸪Chen ࡽ




ࡢᡭἲࢆ࣮࣋ࢫ࡟㸪ࡼࡾຠ⋡ⓗ࡞ᡭἲࢆᥦ᱌ࡋࡓ㸬਴಑ଂਚॢছই१ॖ६
Chen ࡢᡭἲ࡛ࡣ㸪PageRank ࢫࢥ࢔ࢆィ⟬ࡋࡓ࠸
ᅗ 11 Web ࢢࣛࣇࢆ฼⏝ࡋࡓᐇ㦂⤖ᯝᑐ㇟ࣀ࣮ࢻ࡟ᑐࡋ࡚㸪ࡑࡢ࿘ᅖࡢᙳ㡪ຊࡢᙉ࠸㒊ศࢢࣛࣇࢆྠᐃࡍࡿ㸬࠶ࡿࣀ࣮ࢻࡢᙳ㡪ຊࡣࡑࡢࣀ࣮ࢻࡀ࢚ࢵࢪࢆᙇࡗ࡚࠸ࡿࣀ࣮ࢻࡢᙳ㡪ຊࢆ⏝࠸࡚෌ᖐⓗ࡟ィ⟬ࡍࡿࡇ࡜ࡀ࡛ࡁࡿ࡜࠸࠺≉ᚩࢆ⏝࠸࡚㸪཯᚟ィ⟬ࢆ⾜ࢃࡎ࡟㒊ศࢢࣛࣇࢆྠᐃࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㸬ࡇࢀ࡟ࡼࡾ㸪ᥦ᱌ᡭἲࡣ PageRank ್ࡢ᥎ᐃ⢭ᗘࢆ⥔ᣢࡋࡓࡲࡲ㸪ຠ⋡ⓗ࡟ᑐ㇟ࣀ࣮ࢻࡢ PageRank ್ ࢆ᥎ᐃࡍࡿࡇ࡜ࡀ࡛ࡁࡿ㸬ᥦ᱌ᡭἲࡢᛶ⬟ࢆホ౯ࡍࡿࡓࡵ࡟ Twitter ࡜ Web ࢢࣛࣇࡢࢹ࣮ࢱࢭࢵࢺࢆ⏝࠸࡚ホ౯ᐇ㦂ࢆ⾜ࡗࡓ㸬ᐇ㦂⤖ᯝࡼࡾ㸪ᥦ᱌ᡭἲࡣ᥎ᐃ⢭ᗘࢆ⥔ᣢࡋ࡞ࡀࡽ㸪㧗㏿࡟ᑐ㇟ࣀ࣮ࢻࡢ
PageRank ್ࢆ᥎ᐃ࡛ࡁࡿࡇ࡜ࡀศ࠿ࡗࡓ㸬ලయⓗ࡟ࡣ㸪Chen ࡽࡢᡭἲ࡜ẚ㍑ࡋ࡚㸪ᥦ᱌ᡭἲࡣ⢭ᗘࢆ⥔ᣢࡋࡓࡲࡲ㧗㏿࡟ PageRank ್ࢆ᥎ᐃࡍࡿࡇ࡜ࡀ࡛ࡁࡓ㸦ᅗ 11㸧㸬␠࠙㸲ࠚ ⛉Ꮫศ㔝࡟࠾ࡅࡿࢹ࣮ࢱ࣮࣋ࢫᛂ⏝㸦㸯㸧GPV/JMA ࢔࣮࢝࢖ࣈ
ᆅ⌫⎔ቃ◊✲㒊㛛࡜ඹྠ࡛㸪Ẽ㇟ᗇẼ㇟ணሗࢹ࣮ࢱ࣮࣋ࢫࠕGPV/JMA ࢔࣮࢝࢖ࣈࠖ
(http://gpvjma.ccs.hpcc.jp)ࡢ㛤Ⓨ㸪࠾ࡼࡧ⟶⌮㸪㐠⏝ࢆ⾜ࡗ࡚࠸ࡿ㸬GPV/JMA ࢔࣮࢝࢖ࣈ
ࡣ㸪Ẽ㇟ᗇࡀබ㛤ࡋ࡚࠸ࡿẼ㇟ணሗࢢࣜࢵࢻࢹ࣮ࢱ㸦GPV ࢹ࣮ࢱ㸧ࢆ⵳✚ࡍࡿ࡜࡜ࡶ࡟㸪-154-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩እ㒊Ⓩ㘓࣮ࣘࢨ࡬ࡢࢹ࣮ࢱࢆᥦ౪ࡍࡿࡇ࡜ࢆ
┠ⓗ࡜ࡋ࡚࠸ࡿ㸬GPV/JMA ࢔࣮࢝࢖ࣈ࡛ᥦ౪
ࡋ࡚࠸ࡿࢹ࣮ࢱࡣ㸪඲⌫ࣔࢹࣝ㸪࣓ࢯࢫࢣ࣮ࣝ
ࣔࢹࣝ㸪࣮ࣜࢪࣙࢼࣝࢫࢣ࣮ࣝࣔࢹࣝ㸪㐌㛫࢔ࣥࢧࣥࣈࣝ㸪᭶㛫࢔ࣥࢧࣥࣈࣝ㸪Ꮨ㛫࢔ࣥࢧࣥࣈࣝࡢ 6 ✀㢮࡛࠶ࡿ㸬㸦㸰㸧᱁Ꮚ QCD ࢹ࣮ࢱࢢࣜࢵࢻ
ILDG/JLDG
Japan Lattice Data Grid (JLDG),International Lattice Data Grid (ILDG)ࡣ㸪᱁Ꮚ QCD 㓄఩ࢹ࣮ࢱࢆඹ᭷ࡍࡿࡓࡵࡢࢹ࣮
ࢱࢢࣜࢵࢻ࡛࠶ࡿ㸬⣲⢏Ꮚ≀⌮◊✲㒊㛛࡜㐃ゎࡋ㸪JLDG/ILDG ࡢ㐠Ⴀ࡟⥅⥆ཧ⏬ࡋ࡚࠸ࡿ㸬
㸦㸱㸧X ⥺ኳయほࢹ࣮ࢱ࡟࠾ࡅࡿ࢔࢘ࢺࣂ࣮ࢫࢺࡢ㢮ఝ᳨⣴ ࣈࣛࢵࢡ࣮࣍ࣝ㸪୰ᛶᏊᫍ࡞࡝ࡣ X ⥺ࢆⓎࡍࡿኳయ࡜ࡋ࡚▱ࡽࢀ࡚࠾ࡾ㸪ࡑࢀࡽࡢኳయ
࡟ࡣ㸪▷ᮇ㛫࡟኱㔞ࡢ X ⥺ࢆᨺฟࡍࡿࠕ࢔࢘ࢺࣂ࣮ࢫࢺࠖ࡜࠸࠺⌧㇟ࡀᏑᅾࡍࡿࡇ࡜ࡀ▱ࡽࢀ࡚࠸ࡿ㸬ࡲࡓ㸪JAXA Ᏹᐂ◊ᾏ⪁ἑᩍᤵࡽࡢࢢ࣮ࣝࣉ࡟ࡼࡾ㸪␗࡞ࡿ X ⥺ኳయࡢ㛫࡛㸪࢔࢘ࢺࣂ࣮ࢫࢺࡢ X ⥺ᙉᗘኚ໬࡟㢮ఝᛶࡀぢࡽࢀࡿࡇ࡜ࡀ㏆ᖺ᫂ࡽ࠿࡟ࡉࢀࡓ㸬ࡇࢀࡣ㸪⫼ᚋ࡟࠶ࡿ≀⌮㐣⛬ࡢ㢮ఝᛶࢆ♧ࡍྍ⬟ᛶࡀ࠶ࡾ⯆࿡῝࠸㸬ࡇࡢࡓࡵ㸪ᾏ⪁ἑᩍᤵࡽࡢࢢ
࣮ࣝࣉ࡜ඹྠ࡛㸪X ⥺ኳయࡢほࢹ࣮ࢱࢆᑐ㇟࡟㸪㢮ఝࡋࡓ࢔࢘ࢺࣂ࣮ࢫࢺࣃࢱ࣮ࣥࢆ᳨⣴ࡍࡿᡭἲࢆ◊✲㛤Ⓨࡋ࡚࠸ࡿ㸬 ᩍ⫱Ꮫ⏕ࡢᣦᑟ≧ἣ࠙Ꮫ఩ㄽᩥࠚ
㸺༤ኈㄽᩥ㸼
1. Salman Ahmed SHAIKH 
A Study on Distance-based Outlier Detection on Uncertain Data
2. ᮧୖ ┤ᴫᛕࣔࢹࣜࣥࢢ࡟ᇶ࡙ࡃ O/R ࣐ࢵࣆࣥࢢᡭἲ࡟㛵ࡍࡿ◊✲ 
3. ᒣཱྀ ♸ே
A Study on User Location Inference in Social Media
4. 㧗ᶫ ⩼⣔ิࢹ࣮ࢱࡢ༏ྡ໬࡟㛵ࡍࡿ◊✲ 
5. ୸ᶫ ᘯ἞
A Study on Large Scale Graph Analysis Using Eigen Decomposition and Tensor
Decomposition㸺ಟኈㄽᩥ㸼
1. すᮧ ┤Ꮥ-155-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩୍ᣓホ౯࡟ࡼࡿ」ྜ࢖࣋ࣥࢺฎ⌮ࡢ㧗ࢫ࣮ࣝࣉࢵࢺ໬
2. ᆏ಴ ᝆኴࣜࣥࢡᵓ㐀ゎᯒ࡟࠾ࡅࡿ㒊ศࢢࣛࣇ࡟ᇶ࡙࠸ࡓຠ⋡ⓗ࡞ࣀ࣮ࢻホ౯್ࡢ᥎ᐃ
3. ஭ୖ ᐶஅ
Linked Open Data ࡟ᑐࡍࡿከᵝ࡞ၥྜࡏฎ⌮࡟㛵ࡍࡿ◊✲4. ୰ᮧ 㧗ኈ࣏ࣜࢪࢺࣜࢆ㊬࠸ࡔࢥ࣑ࢵࢺࢺࣛࣥࢨࢡࢩࣙࣥࡢ᥎ᐃ࡟ᇶ࡙ࡃࣟࢪ࢝ࣝ࢝ࢵࣉࣜ
ࣥࢢ᳨ฟᡭἲ
5. Bou Savong
A Study on Keyword Search over XML Streams
6. ▼ ๢ᓠModularity-based Clustering of Dynamic Graphs
7. ᯘ ྐᑛGPU ࢆ⏝࠸ࡓ Canopy ࢡࣛࢫࢱࣜࣥࢢࡢ㧗㏿໬㸺≉ᐃㄢ㢟◊✲ሗ࿌᭩㸼㈈๓ ᾴᏐᖥࢲ࣮࢘ࣥࣟࢻᶵ⬟௜ࡁ HDD ࣞࢥ࣮ࢲ㛤Ⓨ࡟ࡼࡿᏐᖥᚋ௜ࢩࢫࢸ࣒ࡢᐇ⌧-HDD ࣞࢥ࣮ࢲࡢ GUI ཬࡧࢧ࣮ࣂ࡜ࡢ㏻ಙᶵ⬟ࡢᐇ⿦- 㸺Ꮫኈㄽᩥ㸼
1. ᒸ⏣ Ⳁዉࢯ࣮ࢩࣕࣝࢿࢵࢺ࣮࣡ࢡࢹ࣮ࢱࡢ㊥㞳㛵ಀࡢኚ໬ࢆᢚไࡍࡿ k ༏ྡ໬࢔ࣝࢦࣜࢬ࣒
2. ᳃ ᬛᙪࢯ࣮ࢩ࣮ࣕࣝࣜࢹ࢕ࣥࢢࢩࢫࢸ࣒࡟࠾ࡅࡿࢹ࣮ࢱࡢ᱁⣡࡜᳨⣴࡟㛵ࡍࡿ◊✲3. ኱す ㄔ
Twitter ࡢ༶᫬ᛶ࡟╔┠ࡋࡓࢽ࣮ࣗࢫグ஦ࡢࣜ࢔ࣝࢱ࢖࣒ࢯ࣮ࢩࣕࣝ࢔ࣀࢸ࣮ࢩࣙ
ࣥ
4. ⇃ᮏ ࿴ṇࢸࣥࢯࣝศゎ࡟ࡼࡿ࣮ࣘࢨࣞࣅ࣮ࣗࡢศᯒ࡟㛵ࡍࡿ◊✲5. ᑠᰗ ᾴ௓
XML ࢹ࣮ࢱ࡟࠾ࡅࡿࢸ࢟ࢫࢺ࠾ࡼࡧᵓ㐀ࢆ⪃៖ࡋࡓຠ⋡ⓗ࡞㢮ఝ᳨⣴

5. ཷ㈹ࠊእ㒊㈨㔠ࠊ▱ⓗ㈈⏘ᶒ➼㸺ཷ㈹㸼
A1. Ꮫ⏕ࣉࣞࢮࣥࢸ࣮ࢩࣙࣥ㈹, ᆏ಴ ᝆኴ, ᒣཱྀ ♸ே, ኳ➟ ಇஅ, ໭ᕝ ༤அ, "㒊ศࢢࣛ-156-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩ࣇ࡟ᇶ࡙ࡃຠ⋡ⓗ࡞ PageRank ᥎ᐃ", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014), D6-4, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
A2. Ꮫ⏕ࣉࣞࢮࣥࢸ࣮ࢩࣙࣥ㈹, ᯘ ྐᑛ, ᑠ⃝ భ௓, ኳ➟ ಇஅ, ໭ᕝ ༤அ, "GPU ࢆ⏝࠸ࡓ Canopy ࢡࣛࢫࢱࣜࣥࢢࡢ㧗㏿໬", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014), D5-4, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
A3. Ꮫ⏕ࣉࣞࢮࣥࢸ࣮ࢩࣙࣥ㈹, ඃ⚽࢖ࣥࢱࣛࢡࢸ࢕ࣈ㈹, ᒸ⏣ Ⳁዉ, Ώ㎶ ▱ᜨ⨾, ໭ᕝ
༤அ, "ࢯ࣮ࢩࣕࣝࢿࢵࢺ࣮࣡ࢡࢹ࣮ࢱࡢ㊥㞳㛵ಀࡢኚ໬ࢆᢚไࡍࡿ k ༏ྡ໬࢔ࣝࢦࣜ
ࢬ࣒", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014), E5-4,
2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
A4. Ꮫ⏕ࣉࣞࢮࣥࢸ࣮ࢩࣙࣥ㈹, ᑠᰗ ᾴ௓, ኳ➟ ಇஅ, ໭ᕝ ༤அ, "ࢸ࢟ࢫࢺ࠾ࡼࡧᵓ㐀ࡢ㢮ఝᗘ࡟ᇶ࡙࠸ࡓ XML ࢹ࣮ࢱ࡟ᑐࡍࡿຠ⋡ⓗ࡞㢮ఝ᳨⣴", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ
࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014), D7-5, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
A5. Ꮫ⏕ዡບ㈹, ኱す ㄔ, ໭ᕝ ༤அ, "ࢽ࣮ࣗࢫグ஦ࡢຠ⋡ⓗ࡞ࣜ࢔ࣝࢱ࢖࣒ࢯ࣮ࢩࣕࣝ
࢔ࣀࢸ࣮ࢩࣙࣥᡭἲ", ᝟ሗฎ⌮Ꮫ఍➨ 76 ᅇ඲ᅜ኱఍ (IPSJ ඲ᅜ኱఍ 2014), 3M-8,
2014 ᖺ 3 ᭶ 11 ᪥㹼3 ᭶ 13 ᪥.
A6. Ꮫ⏕ዡບ㈹, ᑠᰗᾴ௓㸪ኳ➟ಇஅ㸪໭ᕝ༤அ, "኱つᶍ XML ࢹ࣮ࢱ࡟࠾ࡅࡿຠ⋡ⓗ࡞㔜」ࢹ࣮ࢱ᳨ฟ", ᝟ሗฎ⌮Ꮫ఍➨ 76 ᅇ඲ᅜ኱఍ (IPSJ ඲ᅜ኱఍ 2014), 3N-8, 2014 ᖺ
3 ᭶ 11 ᪥㹼3 ᭶ 13 ᪥.
A7. ᪥ᮏࢹ࣮ࢱ࣮࣋ࢫᏛ఍ㄽᩥ㈹㸪⚟ゅ㥴㸪᳃ᔱཌ⾜㸪ရᕝᚨ⚽㸪໭ᕝ༤அ㸪"DB ᢳ㇟໬࡜ࢤ࣮࣒⌮ㄽ࡟ᇶ࡙ࡃ࣐࢖ࢡࣟࣈࣟࢢ࠿ࡽࡢᵓ㐀ࢹ࣮ࢱᢳฟ GWAP ࡢ㛤Ⓨ"㸪᪥ᮏࢹ࣮ࢱ࣮࣋ࢫᏛ఍ㄽᩥㄅ, Vol. 11, No. 1, pp. 19-24, 2012.

㸺እ㒊㈨㔠㸼ཷク⤒㈝㸸ᩥ㒊⛉Ꮫ┬  㸦ᖹᡂ 25 ᖺᗘ㸧◊✲ㄢ㢟㸸ࣅࢵࢡࢹ࣮ࢱ฼ά⏝ࡢࡓࡵࡢࢹ࣮ࢱ㐃ᦠᢏ⾡࡟㛵ࡍࡿࣇ࢕࣮ࢪࣅࣜࢸ࢕ ࢫࢱࢹ࢕ཬࡧணഛ◊✲◊✲௦⾲⪅㸸໭ᕝ ༤அ㓄ศ㔠㢠㸸27,405,000 ෇㸦┤᥋⤒㈝ 21,080,770㸸㛫᥋⤒㈝ 6,324,230㸧
◊✲✀┠㸸ᇶ┙◊✲(㸿) 㸦ᖹᡂ 24 ᖺᗘ㹼ᖹᡂ 26 ᖺᗘ㸧◊✲ㄢ㢟㸸኱つᶍ࣭␗✀ࡢ᫬✵㛫ࢹ࣮ࢱ⤫ྜ࡛⏕ࡌࡿ▩┪ࢆチᐜࡍࡿࢧ࢖࢚ࣥࢫࢡࣛ࢘
ࢻᇶ┙㸦◊✲௦⾲⪅㸸ᑠᓥຌ㸦⏘⥲◊㸧㸧◊✲ศᢸ⪅㸸໭ᕝ ༤அ㓄ศ㔠㢠㸸1,300,000 ෇㸦┤᥋⤒㈝ 1,000,000㸸㛫᥋⤒㈝ 300,000㸧◊✲ศᢸ⪅㸸ኳ➟ ಇஅ-157-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩㓄ศ㔠㢠㸸2,210,000 ෇㸦┤᥋⤒㈝ 1,700,000㸸㛫᥋⤒㈝ 510,000㸧ᐤ௜㔠㸸኱ᕝ᝟ሗ㏻ಙᇶ㔠  㸦ᖹᡂ 25 ᖺᗘ㸧◊✲ㄢ㢟㸸኱つᶍᐇୡ⏺ᐇ᫬㛫᝟ሗᇶ┙ࡢࡓࡵࡢ㧗ᗘࢫࢺ࣮࣒ࣜฎ⌮◊✲௦⾲⪅㸸໭ᕝ ༤அ㓄ศ㔠㢠㸸1,000,000 ෇㸦┤᥋⤒㈝ 950,000㸸㛫᥋⤒㈝ 50,000㸧ཷク⤒㈝㸸୕⳻㟁ᶵᰴᘧ఍♫  㸦ᖹᡂ 25 ᖺᗘ㸧◊✲ㄢ㢟㸸᫬⣔ิࢹ࣮ࢱ࣮࣋ࢫ࣭ศᯒᢏ⾡ࡢ◊✲㛤Ⓨ◊✲௦⾲⪅㸸໭ᕝ ༤அ࣭ኳ➟ ಇஅ㓄ศ㔠㢠㸸500,000 ෇㸦┤᥋⤒㈝ 450,000㸸㛫᥋⤒㈝ 50,000㸧ཷク⤒㈝㸸ᰴᘧ఍♫ᐩኈ㏻◊✲ᡤ  㸦ᖹᡂ 25 ᖺᗘ㸧◊✲ㄢ㢟㸸᫬⣔ิࢹ࣮ࢱࡢศᯒᇶ┙ᢏ⾡ࡢ◊✲◊✲௦⾲⪅㸸໭ᕝ ༤அ࣭ኳ➟ ಇஅ㓄ศ㔠㢠㸸2,000,000 ෇㸦┤᥋⤒㈝ 1,800,000㸸㛫᥋⤒㈝ 200,000㸧◊✲✀┠㸸ᇶ┙◊✲(C) 㸦ᖹᡂ 25 ᖺᗘ㹼ᖹᡂ 27 ᖺᗘ㸧◊✲ㄢ㢟㸸㹃㹎㹓3.0 ࢆ᰾࡜ࡋࡓ▱㆑㞟✚ᆺࢯ࣮ࢩ࣮ࣕࣝࣜࢹ࢕ࣥࢢᇶ┙࡟㛵ࡍࡿ◊✲◊✲௦⾲⪅㸸ኳ➟ ಇஅ㓄ศ㔠㢠㸸1,690,000 ෇㸦┤᥋⤒㈝ 1,300,000㸸㛫᥋⤒㈝ 390,000㸧◊✲✀┠㸸ᇶ┙◊✲(㸿) 㸦ᖹᡂ 25 ᖺᗘ㸧◊✲ㄢ㢟㸸⅏ᐖᚋࡢ᚟ᪧ࣭᚟⯆࡟࠾ࡅࡿඹ᭷᝟ሗ⟶⌮◊✲ศᢸ⪅㸸ኳ➟ ಇஅ㓄ศ㔠㢠㸸1,300,000 ෇㸦┤᥋⤒㈝ 1,000,000㸸㛫᥋⤒㈝ 300,000㸧 ◊✲ᴗ⦼(1) ◊✲ㄽᩥA) ᰝㄞ௜ࡁㄽᩥ
㸺Ꮫ⾡㞧ㄅㄽᩥ㸼J1. Rong-Hua Li, Jianquan Liu, Jeffrey Xu Yu, Hanxiong Chen, and Hiroyuki
Kitagawa, "Co-occurrence

Prediction

in

a

Large

Location-based

SocialNetwork", Frontiers of Computer Science, Vol. 7, No. 2, pp. 185-194, April 2013.
J2. ᯘ ྐᑛ㸪ኳ➟ ಇஅ㸪໭ᕝ ༤அ㸪ᾏ⪁ἑ ◊㸪୰ᖹ ⪽ᚿ, "ືⓗࢱ࢖࣒࣮࣡ࣆࣥࢢ㊥㞳ࢆ⏝࠸ࡓ X ⥺ኳᩥࢹ࣮ࢱࡢ㢮ఝ᳨⣴", Ᏹᐂ⛉Ꮫ᝟ሗゎᯒㄽᩥㄅ ➨஧ྕ, pp. 19-27,-158-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩June 2013.
J3. Masafumi Oyamada, Hideyuki Kawashima, and Hiroyuki Kitagawa, "Data Stream
Processing with Concurrency Control", SIGAPP Appl. Comput. Rev., Vol. 13, No. 2,
pp. 54-65, June 2013.
J4. ᒣཱྀ♸ே㸪ఀᕝὒᖹ㸪ኳ➟ಇஅ㸪໭ᕝ༤அ, "ࢯ࣮ࢩ࣓ࣕࣝࢹ࢕࢔࡟࠾ࡅࡿ࣮ࣟ࢝ࣝ࢖࣋
ࣥࢺࢆ⏝࠸ࡓ࣮ࣘࢨ఩⨨᥎ᐃᡭἲ", ᝟ሗฎ⌮Ꮫ఍ㄽᩥㄅࢹ࣮ࢱ࣮࣋ࢫ (TOD60), Vol.
6, No. 5, pp. 23-37, December 2013.
J5. ୹἞ᐶె, ᳃ᔱཌ⾜, ஭ࣀཱྀ᐀ᡂ, ໭ᕝ༤அ, "Web ᝟ሗࢆ⏝࠸ࡓ❳ᕳ⤒㊰᥎ᐃᨭ᥼ࡢࡓ
ࡵࡢࢡࣛ࢘ࢻࢯ࣮ࢩࣥࢢᢏ⾡㛤Ⓨࡢヨࡳ", ᝟ሗฎ⌮Ꮫ఍ㄽᩥㄅࢹ࣮ࢱ࣮࣋ࢫ (TOD60),
Vol. 6, No. 5, pp. 95-106, December 2013.
㸺ᰝㄞ௜ࡁᅜ㝿఍㆟ㄽᩥ㸼C1.

Yutaka Kabutoya, Tomoharu Iwata, Hiroyuki Toda, and Hiroyuki Kitagawa, "A
Probabilistic

Model

for

Diversifying

Recommendation

Lists", Proc.

15th

Asia-Pacific Web Conference (APWeb 2013), Sydney, Australia, pp. 348-359, April
4-6, 2013.
C2.

Salman Shaikh and Hiroyuki Kitagawa, "Fast Top-k Distance-based Outlier
Detection on Uncertain Data", Proc. 14th International Conference on Web-Age
Information Management (WAIM 2013), Beidaihe, China, LNCS7923, pp. 301-313,
June 14-16, 2013.

C3.

Hiroyuki

Inoue,

Toshiyuki

Amagasa,andHiroyuki

Kitagawa, "AnETL

Framework for Online Analytical Processing of Linked Open Data", Proc. 14th
International Conference on Web-Age Information Management (WAIM 2013),
Beidaihe, China, LNCS7923, pp. 111-117, June 14-16, 2013.
C4.

Tadashi Murakami, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "DBPowder: A
Flexible

Object-Relational

Mapping

Framework

based

on

a

Conceptual

Model", Proc. 37th IEEE International Computer Software and Applications
Conference (COMPSAC 2013), Kyoto, Japan, pp. 589-598, July 22-26, 2013.
C5.

Yusuke Kozawa, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "Parallel andDistributed

MiningofProbabilistic

Frequent

Itemsets

Using

MultipleGPUs", Proc. 24th International Conference on Database and Expert Systems
Applications (DEXA 2013), Prague, Czech Republic, pp. 145-152, August 26-30,
2013.
C6.

Kenji

Gonnokami,

AtsuyukiMorishima,

ShigeoSugimoto,

Hiroyuki

Kitagawa, "Condition-Task-Store: A Declarative Abstraction for Microtask-based
Complex Crowdsourcing", Proc. First VLDB Workshop on Databases andCrowdsourcing (DBCrowd 2013), pp. 20-25, August 26-30, 2013.
-159-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩

C7.

Kousuke Nakabasami, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "Querying
MongoDB with LINQ in a Server-side JavaScript Environment", Proc. 2nd
International Workshop on Advances in Data Engineering and Mobile Computing(DEMoC 2013), Gwangju, Korea, pp. 344-349, September 4-6, 2013.

C8.

Yuto Yamaguchi, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "Landmark-Based
User Location Inference in Social Media", Proc. 1st ACM Conference on Online
Social Networks (COSN 2013), Boston, USA, pp. 223-234, October 7-8, 2013.

C9.

Atsuyuki Morishima, Erika Yumiya, Masami Takahashi, Shigeo Sugimoto,
Hiroyuki Kitagawa, "Efficient Filtering and Ranking Schemes for FindinInclusion Dependencies on the Web", Proc. 22nd International Conference on
Information and Knowledge Management (CIKM), pp. 763-768, October 29, 2013.

C10. Yuta Sakakura, Yuto Yamaguchi, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "A
Local Method for ObjectRank Estimation", Proc. 15th International Conference on
Information Integration and Web-based Applications & Services (iiWAS 2013),
Vienna, Austria, pp. 92-101, December 2-4, 2013.
C11. Eri Kataoka, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "A System for Social
Reading based on EPUB3", Proc. 15th International Conference on Information
Integration and Web-based Applications & Services (iiWAS 2013), Vienna, Austria,
pp. 72-76, December 2-4, 2013.
(2) ᅜ㝿఍㆟Ⓨ⾲A) ᣍᚅㅮ₇ヱᙜ࡞ࡋB) ୍⯡ㅮ₇
C1.

Yutaka Kabutoya, Tomoharu Iwata, Hiroyuki Toda, and Hiroyuki Kitagawa, "A
Probabilistic

Model

for

Diversifying

Recommendation

Lists", Proc.

15th

Asia-Pacific Web Conference (APWeb 2013), Sydney, Australia, pp. 348-359, April
4-6, 2013.
C2.

Salman Shaikh and Hiroyuki Kitagawa, "Fast Top-k Distance-based Outlier
Detection on Uncertain Data", Proc. 14th International Conference on Web-Age
Information Management (WAIM 2013), Beidaihe, China, LNCS7923, pp. 301-313,
June 14-16, 2013.

C3.

Hiroyuki

Inoue,

Toshiyuki

Amagasa,andHiroyuki

Kitagawa, "AnETL

Framework for Online Analytical Processing of Linked Open Data", Proc. 14th
International Conference on Web-Age Information Management (WAIM 2013),
Beidaihe, China, LNCS7923, pp. 111-117, June 14-16, 2013.

-160-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩

C4.

Tadashi Murakami, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "DBPowder: A
Flexible

Object-Relational

Mapping

Framework

based

on

a

Conceptual

Model", Proc. 37th IEEE International Computer Software and Applications
Conference (COMPSAC 2013), Kyoto, Japan, pp. 589-598, July 22-26, 2013.
C5.

Yusuke Kozawa, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "Parallel andDistributed

MiningofProbabilistic

Frequent

Itemsets

Using

MultipleGPUs", Proc. 24th International Conference on Database and Expert Systems
Applications (DEXA 2013), Prague, Czech Republic, pp. 145-152, August 26-30,
2013.
C6.

Kenji

Gonnokami,

AtsuyukiMorishima,

ShigeoSugimoto,

Hiroyuki

Kitagawa, "Condition-Task-Store: A Declarative Abstraction for Microtask-based
Complex Crowdsourcing", Proc. First VLDB Workshop on Databases andCrowdsourcing (DBCrowd 2013), pp. 20-25, August 26-30, 2013.
C7.

Kousuke Nakabasami, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "Querying
MongoDB with LINQ in a Server-side JavaScript Environment", Proc. 2nd
International Workshop on Advances in Data Engineering and Mobile Computing(DEMoC 2013), Gwangju, Korea, pp. 344-349, September 4-6, 2013.

C8.

Yuto Yamaguchi, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "Landmark-Based
User Location Inference in Social Media", Proc. 1st ACM Conference on Online
Social Networks (COSN 2013), Boston, USA, pp. 223-234, October 7-8, 2013.

C9.

Atsuyuki Morishima, Erika Yumiya, Masami Takahashi, Shigeo Sugimoto,
Hiroyuki Kitagawa, "Efficient Filtering and Ranking Schemes for FindinInclusion Dependencies on the Web", Proc. 22nd International Conference on
Information and Knowledge Management (CIKM), pp. 763-768, October 29, 2013.

C10. Yuta Sakakura, Yuto Yamaguchi, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "A
Local Method for ObjectRank Estimation", Proc. 15th International Conference on
Information Integration and Web-based Applications & Services (iiWAS 2013),
Vienna, Austria, pp. 92-101, December 2-4, 2013.
C11. Eri Kataoka, Toshiyuki Amagasa, and Hiroyuki Kitagawa, "A System for Social
Reading based on EPUB3", Proc. 15th International Conference on Information
Integration and Web-based Applications & Services (iiWAS 2013), Vienna, Austria,
pp. 72-76, December 2-4, 2013.
(3) ᅜෆᏛ఍࣭◊✲఍Ⓨ⾲A) ᣍᚅㅮ₇
ヱᙜ࡞ࡋ

-161-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩B) ࡑࡢ௚ࡢⓎ⾲P1.

⚟ゅ㥴, ᳃ᔱཌ⾜, ရᕝᚨ⚽, ໭ᕝ༤அ, "DB ᢳ㇟໬࡜ࢤ࣮࣒⌮ㄽ࡟ᇶ࡙ࡃ࣐࢖ࢡࣟ
ࣈࣟࢢ࠿ࡽࡢᵓ㐀ࢹ࣮ࢱᢳฟ GWAP ࡢ㛤Ⓨ", ᪥ᮏࢹ࣮ࢱ࣮࣋ࢫᏛ఍, 2013 ᖺ 6 ᭶ 22

᪥P2.

Salman Ahmed Shaikh, Hiroyuki Kitagawa, "Differential Outlier Detection on
Uncertain Streams of the Gaussian Distribution", The 5th International
Workshop with Mentors on Databases, Web and Information Management for
Young Researchers (iDB2013), Sapporo, Japan, July 21 - 23, 2013.

P3.

Yuto Yamaguchi, Toshiyuki Amagasa, Hiroyuki Kitagawa, "Landmark-Based User
Location Inference on Social Media", The 5th International Workshop with
Mentors on Databases, Web and Information Management for Young Researchers(iDB2013), Sapporo, Japan, July 21 - 23, 2013.

P4.

Yuta Sakakura, Yuto Yamaguchi, Toshiyuki Amagasa, Hiroyuki Kitagawa,"A Local Method for ObjectRank Estimation", The 5th International Workshop
with Mentors on Databases, Web and Information Management for Young
Researchers (iDB2013), Sapporo, Japan, July 21 - 23, 2013.

P5.

Hiroyuki Inoue, Toshiyuki Amagasa, Hiroyuki Kitagawa, "An ETL Framework forOnline Analytical Processing of Linked Open Data", The 5th International
Workshop with Mentors on Databases, Web and Information Management for
Young Researchers (iDB2013), Sapporo, Japan, July 21 - 23, 2013.

P6.

୰ ᮧ 㧗 ኈ , ᪩ ℩ ᗣ ⿱ , ໭ ᕝ ༤ அ , " ࢯ ࣇ ࢺ ࢘ ࢙ ࢔ ࣉ ࣟ ࢲ ࢡ ࢺ 㛫 ࡛ ࡢ Logical
Coupling ᳨ฟ࡟ྥࡅࡓணഛⓗ࡞ㄪᰝ", ➨ 182 ᅇࢯࣇࢺ࢙࢘࢔ᕤᏛ◊✲Ⓨ⾲఍, 2013
ᖺ 10 ᭶ 24㹼10 ᭶ 25 ᪥.

P7.

ᑠ⃝ భ௓, ኳ➟ ಇஅ, ໭ᕝ ༤அ, "ࢹ࣮ࢱศ๭࡜༠ㄪⓗ࣐࣮ࢪ࡟ᇶ࡙ࡃ GPU ୖࡢຠ⋡ⓗࢯ࣮ࢺ࢔ࣝࢦࣜࢬ࣒", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ
(DEIM 2014), A2-1, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.

P8.

Savong Bou, Toshiyuki Amagasa, Hiroyuki Kitagawa, "Path-based Keyword
Search over XML Streams", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014), A1-5, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.

P9.

すᮧ ┤Ꮥ, ᕝᓥ ⱥஅ, "ࣜࣥࢡ㞟⣙࡜ࣃࢱࣥ࢟ࣕࢵࢩࣗࢆ⏝࠸ࡓ」ྜ࢖࣋ࣥࢺฎ⌮ࡢ㧗ᛶ⬟໬", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014),
D3-3, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.

P10. ஭ୖ ᐶஅ, ኳ➟ ಇஅ, ໭ᕝ ༤அ, "LINQ ࢆ⏝࠸ࡓ Linked Open Data ࡟ᑐࡍࡿၥྜ
ࡏ", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014), D7-2,
2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
P11. ᆏ಴ ᝆኴ, ᒣཱྀ ♸ே, ኳ➟ ಇஅ, ໭ᕝ ༤அ, "㒊ศࢢࣛࣇ࡟ᇶ࡙ࡃຠ⋡ⓗ࡞-162-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩

PageRank ᥎ᐃ", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM2014), D6-4, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
P12. ᯘ ྐᑛ, ᑠ⃝ భ௓, ኳ➟ ಇஅ, ໭ᕝ ༤அ, "GPU ࢆ⏝࠸ࡓ Canopy ࢡࣛࢫࢱࣜࣥࢢ
ࡢ㧗㏿໬", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014),
D5-4, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
P13. ⋤ ᒾ, ໭ᕝ ༤அ, "An Efficient Execution Scheme for Designated Event-based
Stream Processing", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ
(DEIM 2014), D3-2, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
P14. 㯤 ᓧ, ᑠ⃝ భ௓, ኳ➟ ಇஅ, ໭ᕝ ༤அ, "GPGPU ࢆ⏝࠸ࡓ୙☜ᐇ᫬⣔ิࢹ࣮ࢱ㢮ఝ᳨⣴ࡢ㧗㏿໬", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM2014), D5-1, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
P15. ୰ᤃ ᫭௓, ໭ᕝ ༤அ, ኳ➟ ಇஅ, "ࢫࢺ࣮࣒ࣜฎ⌮࢚ࣥࢪࣥࢆ⏝࠸ࡓࢫࢺ࣮࣒ࣜࢹ
࣮ࢱࡢ OLAP ฎ⌮", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ
(DEIM 2014), D3-1, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
P16. ᒸ⏣ Ⳁዉ, Ώ㎶ ▱ᜨ⨾, ໭ᕝ ༤அ, "ࢯ࣮ࢩࣕࣝࢿࢵࢺ࣮࣡ࢡࢹ࣮ࢱࡢ㊥㞳㛵ಀࡢኚ໬ࢆᢚไࡍࡿ k ༏ྡ໬࢔ࣝࢦࣜࢬ࣒", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014), E5-4, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
P17. ᑠᰗ ᾴ௓, ኳ➟ ಇஅ, ໭ᕝ ༤அ, "ࢸ࢟ࢫࢺ࠾ࡼࡧᵓ㐀ࡢ㢮ఝᗘ࡟ᇶ࡙࠸ࡓ XML
ࢹ࣮ࢱ࡟ᑐࡍࡿຠ⋡ⓗ࡞㢮ఝ᳨⣴", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014), D7-5, 2014 ᖺ 3 ᭶ 3 ᪥㹼3 ᭶ 5 ᪥.
P18. ᶒᏲ೺Ⴙ, ᳃ᔱཌ⾜, ໭ᕝ༤அ, "࣐࢖ࢡࣟࢱࢫࢡᆺࢡࣛ࢘ࢻࢯ࣮ࢩࣥࢢฎ⌮ࡢኚ᥮", ➨ 6 ᅇࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ (DEIM 2014), 2014 ᖺ 3
᭶ 3 ᪥.
P19. ⚟⏣ ᏹᶞ, ᪩℩ ᗣ⿱, ໭ᕝ ༤அ, "ࢡࣛࢫྡࡢᩥἲᵓ㐀࡜࿘㎶ࡢ㆑ูᏊࢆ⏝࠸ࡓࢡࣛࢫࡢ࿨ྡᨭ᥼", ᝟ሗฎ⌮Ꮫ఍➨ 76 ᅇ඲ᅜ኱఍ (IPSJ ඲ᅜ኱఍ 2014), 2M-5, 2014
ᖺ 3 ᭶ 11 ᪥㹼3 ᭶ 13 ᪥.
P20. ኱す ㄔ, ໭ᕝ ༤அ, "ࢽ࣮ࣗࢫグ஦ࡢຠ⋡ⓗ࡞ࣜ࢔ࣝࢱ࢖࣒ࢯ࣮ࢩࣕࣝ࢔ࣀࢸ࣮ࢩ
ࣙࣥᡭἲ", ᝟ሗฎ⌮Ꮫ఍➨ 76 ᅇ඲ᅜ኱఍ (IPSJ ඲ᅜ኱఍ 2014), 3M-8, 2014 ᖺ 3 ᭶
11 ᪥㹼3 ᭶ 13 ᪥.
P21. ᑠᰗᾴ௓㸪ኳ➟ಇஅ㸪໭ᕝ༤அ, "኱つᶍ XML ࢹ࣮ࢱ࡟࠾ࡅࡿຠ⋡ⓗ࡞㔜」ࢹ࣮ࢱ
᳨ฟ", ᝟ሗฎ⌮Ꮫ఍➨ 76 ᅇ඲ᅜ኱఍ (IPSJ ඲ᅜ኱఍ 2014), 3N-8, 2014 ᖺ 3 ᭶ 11
᪥㹼3 ᭶ 13 ᪥.
P22. ᒸ⏣Ⳁዉ㸪Ώ㎶▱ᜨ⨾㸪໭ᕝ༤அ, "ࣀ࣮ࢻ㛫ࡢ㊥㞳㛵ಀࢆ⪃៖ࡋࡓࢯ࣮ࢩࣕࣝࢿࢵࢺ
࣮࣡ࢡ࡟࠾ࡅࡿ k ༏ྡ໬", ᝟ሗฎ⌮Ꮫ఍➨ 76 ᅇ඲ᅜ኱఍ (IPSJ ඲ᅜ኱఍ 2014),
5N-5, 2014 ᖺ 3 ᭶ 11 ᪥㹼3 ᭶ 13 ᪥.
P23. ⇃ᮏ࿴ṇ㸪ኳ➟ಇஅ㸪୸ᶫᘯ἞㸪໭ᕝ༤அ, "ࢸࣥࢯࣝศゎࢆ⏝࠸ࡓࣞࣅ࣮ࣗࢹ࣮ࢱࡢ
-163-

⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩ศᯒ", ᝟ሗฎ⌮Ꮫ఍➨ 76 ᅇ඲ᅜ኱఍ (IPSJ ඲ᅜ኱఍ 2014), 6N-7, 2014 ᖺ 3 ᭶ 11
᪥㹼3 ᭶ 13 ᪥.
(4) ⴭ᭩ࠊゎㄝグ஦➼஭ୖඞ㑻㸪ᴋᮏ┿஧㸪ᚋ⸨ཌᏹ㸪㬼ᯘᑦ㟹㸪໭ᕝ༤அ, "ᐇ㊶ⓗ᝟ሗᩍ⫱༠ാࢿࢵࢺ࣮࣡
ࢡ enPiT", ᝟ሗฎ⌮, Vol.55, No.2, pp.194-197, February 2014.
 ␗ศ㔝㛫㐃ᦠ࣭ᅜ㝿㐃ᦠ࣭ᅜ㝿άື➼z ᆅ⌫⎔ቃ◊✲㒊㛛࡜ࡢ㐃ᦠ㸸Ẽ㇟ᗇẼ㇟ணሗࢹ࣮ࢱ࣮࣋ࢫࠕGPV/JMA ࢔࣮࢝࢖ࣈࠖ
(http://gpvjma.ccs.hpcc.jp)ࡢ㛤Ⓨ㸪⟶⌮㸪㐠⏝㸬z ⣲⢏Ꮚ≀⌮◊✲㒊㛛࡜ࡢ㐃ᦠ㸸Japan Lattice Data Grid (JLDG), International
Lattice Data Grid (ILDG)ࡢ㐠Ⴀ㸬z ⏘ᴗᢏ⾡⥲ྜ◊✲ᡤ࡜ࡢ㐃ᦠ㸸኱つᶍ࣭␗✀ࡢ᫬✵㛫ࢹ࣮ࢱ⤫ྜ࡛⏕ࡌࡿ▩┪ࢆチᐜࡍࡿࢧ࢖࢚ࣥࢫࢡࣛ࢘ࢻᇶ┙࡟㛵ࡍࡿ◊✲㸬z Carnegie Mellon University ࡜ࡢᅜ㝿ඹྠ◊✲࡟㛵ࡍࡿ‽ഛ㸬㸦2014 ᖺᗘࡼࡾඹྠ◊✲ᐇ᪋୰㸬㸧 ⟶⌮࣭㐠Ⴀ໭ᕝ༤அᩍᤵz Ꮫእ¾ ᩥ㒊⛉Ꮫ┬࣭᝟ሗᢏ⾡ேᮦ⫱ᡂࡢࡓࡵࡢᐇ㊶ᩍ⫱ࢿࢵࢺ࣮࣡ࢡᙧᡂ஦ᴗࠕศ㔝࣭ᆅᇦࢆ㉺࠼ࡓᐇ㊶ⓗ᝟ሗᩍ⫱༠ാ NWࠖ ࣅࢪࢿࢫ࢔ࣉࣜࢣ࣮ࢩࣙࣥศ㔝௦⾲㸬z Ꮫෆ¾ ࢩࢫࢸ࣒᝟ሗᕤᏛ◊✲⛉ࢥࣥࣆ࣮ࣗࢱࢧ࢖࢚ࣥࢫᑓᨷ㸸㧗ᗘ IT࣭ᐇ㊶ NW ⤫ᣓ㸬¾ ィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮㸸ィ⟬᝟ሗᏛ◊✲㒊㛛㛗㸪ィ⟬⛉Ꮫ᣺⯆ᐊ㛗㸬ኳ➟ಇஅ෸ᩍᤵz Ꮫእ¾ ࡘࡃࡤࣛ࢖ࣇࢧ࢖࢚ࣥࢫ᥎㐍༠㆟఍ ᝟ሗࢩࢫࢸ࣒ᵓ⠏ WG ጤဨ㛗z Ꮫෆ¾ CS ᑓᨷ࣭᝟ሗ㸦⛉㸧Ꮫ㢮Ꮫ⏕ጤဨ఍ ጤဨ㛗 ♫఍㈉⊩࣭ᅜ㝿㈉⊩໭ᕝ༤அᩍᤵz ᅜ㝿ጤဨ➼¾ ᅜ㝿ࢪ࣮ࣕࢼࣝ⦅㞟ጤဨ㸸IEEE Transactions on Knowledge and Data
Engineering, World Wide Web Journal-164-⟃Ἴ኱Ꮫィ⟬⛉Ꮫ◊✲ࢭࣥࢱ࣮ᖹᡂ  ᖺᗘᖺḟሗ࿌᭩¾ ᅜ㝿఍㆟㐠Ⴀጤဨ㸸WAIM Steering Committee Member, DASFAA Steering
Committee Member Emeritus¾ ᅜ㝿఍㆟ඹྠጤဨ㛗㸸WAIM2013¾ ᅜ㝿఍㆟ඹྠ᭱ඃ⚽ㄽᩥጤဨ㛗㸸DASFAA2013¾ ᅜ㝿఍㆟ඹྠࣃࢿࣝጤဨ㛗㸸ASONAM2014¾ ᅜ㝿఍㆟ࣉࣟࢢ࣒ࣛጤဨ఍ጤဨ㸸DASFAA2013, MDM2013, PAKDD2013,
DEXA2013, IDEAS2013, CoopIS2013, DASFAA2014, MDM2014, PAKDD2014,
IDEAS2014, DEXA2014¾ ᅜ㝿఍㆟࢔ࢻࣂ࢖ࢨࣜጤဨ㸸SRDS2014z ᅜෆጤဨ➼¾ ᪥ᮏᏛ⾡఍㆟㐃ᦠ఍ဨ¾ ᪥ᮏࢹ࣮ࢱ࣮࣋ࢫᏛ఍๪఍㛗¾ 㸦⊂㸧⛉Ꮫᢏ⾡᣺⯆ᶵᵓ࣭ᡓ␎ⓗ๰㐀◊✲᥎㐍஦ᴗࠕࣅࢵࢢࢹ࣮ࢱ⤫ྜ฼ά⏝ࡢࡓࡵࡢḟୡ௦ᇶ┙ᢏ⾡ࡢ๰ฟ࣭య⣔໬ࠖ㡿ᇦ࢔ࢻࣂ࢖ࢨ࣮
¾ 㸦⊂㸧᝟ሗ㏻ಙ◊✲ᶵᵓ࣭㧗ᗘ㏻ಙ࣭ᨺ㏦◊✲㛤Ⓨጤク◊✲ホ౯ጤဨ఍ጤဨ
¾ බ┈㈈ᅋἲேᅜ㝿⛉Ꮫᢏ⾡㈈ᅋ࣭ᅜ㝿⛉Ꮫᢏ⾡㈈ᅋ 2014 ᖺ◊✲ຓᡂ㑅⪃ጤဨ
¾ FIT2014 ➨ 13 ᅇ᝟ሗ⛉Ꮫᢏ⾡ࣇ࢛࣮࣒ࣛ⌧ᆅᐇ⾜๪ጤဨ㛗ኳ➟ಇஅ෸ᩍᤵz ᅜ㝿ጤဨ➼¾ ᅜ㝿Ꮫ఍ࣉࣟࢢ࣒ࣛጤဨ㸸BSI2013, AICCSA 2013, WAIM2013, DEMoC2013,
SITIS2013, FutureTech 2013, iiWAS2013z ᅜෆጤဨ➼¾ ࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ࡟㛵ࡍࡿࣇ࢛࣮࣒ࣛ㸦DEIM ࣇ࢛࣮࣒ࣛ㸧2014 ࣉࣟࢢ࣒ࣛጤဨ㛗¾ 㟁Ꮚ᝟ሗ㏻ಙᏛ఍ㄽᩥㄅࠕࢹ࣮ࢱᕤᏛ࡜᝟ሗ࣐ࢿࢪ࣓ࣥࢺ≉㞟ྕࠖ⦅㞟ᖿ஦¾ ᪥ᮏࢹ࣮ࢱ࣮࣋ࢫᏛ఍ㄽᩥㄅ⦅㞟ጤဨ¾ ᝟ሗ⛉Ꮫᢏ⾡ࣇ࢛࣮࣒ࣛ㸦FIT㸧2013 ࣉࣟࢢ࣒ࣛጤဨ

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