約 3,922,550 件
https://w.atwiki.jp/mw3_wiki/pages/8.html
動画(youtube) @wikiのwikiモードでは #video(動画のURL) と入力することで、動画を貼り付けることが出来ます。 詳しくはこちらをご覧ください。 ＝＞http //atwiki.jp/guide/17_209_ja.html また動画のURLはYoutubeのURLをご利用ください。 ＝＞http //www.youtube.com/ たとえば、#video(http //youtube.com/watch?v=kTV1CcS53JQ)と入力すると以下のように表示されます。
https://w.atwiki.jp/byakumu2/pages/3110.html
製作者：alw プレット 性別：不明 年齢：不明 身長：１３０cm 体重：６８kg 一人称：なし 二人称：なし 所属：能力科学研究所 森原和哉に作られたモンスター。フードをかぶった顔も見えずしゃべらないため謎が多い存在。元々は空間を捻じ曲げて人間に悪戯をするのが大好きなモンスターだったが、改造の結果実戦でもその能力を使えるようになっている。また、他のモンスターと混合することで、怪光線や隕石落としなど多数の武装を所持している。なお、身長は低めだが、全身マントに包んだ怪しい存在としてドリーム＝ツヴァイと間違えられることもあるらしい。 プレット プレット, プレット, -, モンスター, AAAA, 180 特殊能力なし 128, 145, 146, 148, 165, 155, 強気 ＳＰなし ori_tko_050.bmp, -.mid プレット プレット, ぷれっと, (モンスター), 1, 2 陸, 2, S, 4800, 150 特殊能力 テレポートLv2=空間移動 10 反射=空間湾曲 !突接武音精 100 10 緊急テレポートLv3=緊急空間移動 1 10 攻撃属性=夢 夢=解説 夢干渉 現実世界と意識世界の狭間に存在するモノをとらえる攻撃。 3000, 150, 700, 80 BABA, ori_tko_160_d.bmp 空間湾曲 , 500, 1, 1, +20, -, 5, -, AAAA, +99, 固転L3 怪光線 , 1100, 1, 4, +10, -, 10, -, AAAA, +10, Ｂ 隕石落とし, 1400, 1, 3, +5, -, 20, 110, AAAA, +10, 魔火 プレット 回避, …………？ ダメージ小, ………… ダメージ中, …………！ ダメージ大, …………！！ 破壊, …………！！！ 射程外, ………… 攻撃, ………… プレット 空間湾曲(準備), - 空間湾曲(攻撃), BeamSaber.wav 空間湾曲(命中), 投げ飛ばし 怪光線, 怪光線 隕石落とし(準備), Houring(2).wav 隕石落とし(攻撃), 隕石 Missile.wav 隕石落とし(命中), 地面衝撃 赤 Explode.wav; 2; 炎 指定アイコン パイロットアイコンおよびユニットアイコンはtko様のori_tko_050.bmpおよびori_tko_160_d.bmpを指定させていただきます。 自由記入欄 ここから先は、何かを追加したいときに追記する欄です。 何か追加したい設定がありましたら、作者以外の方もご自由にお書きください。
https://w.atwiki.jp/fpsclanicanfly/
クランICF 現在 クラン一時凍結中 そのため、 ※隊員は募集しておりません。 ※クラン戦/交流戦の募集はしておりません。 また、クラン凍結解除の目途も立っておりません。 ご迷惑をお掛けしますが よろしくお願い致します。 クラン概要 2010年4月にPS3版「Call of Duty Modern Warfare2」の CLANを結成 （現在は「Call of Duty Modern Warfare3」をメインに活動中） 現在所属隊員は12名。S Dを中心に活動 クランタグは【ICF】、活動時間は毎週金曜日・土曜日の夜(23時～翌日2時) また、両日ともIN出来ない場合は「BBSのクラン活動日欠席届スレ」に書き込みをしてください 活動中はTCでの会話がメイン(VCはほとんど使用しません) 不定期＆諸事情により活動日/時間に出来ない隊員 saito55 jackson505 so-yama 以上の隊員以外のクラン活動無断欠席は認められません クランNEWS 12/15 副隊長解任 12/18 やまだん 副隊長就任 12/23 交流戦実施 【RetR様】 12/25 ble-ch07 再入隊 1/20 sisya Lions 脱退 1/21 交流戦実施 【ALIT様】 1/23 IneMurl-MisakA 入隊 3/1 IneMurl-MisakA 脱退 3/1 Megumode-jpn 脱退 3/4 クランICF 一時凍結
https://w.atwiki.jp/maguroxz/
クランBHLのHPです。 明るく！（Brightly） 楽しく！（Happily） 賑やかに！（Lively） をモットーにしたホモクランです。入隊条件は以下の通り。 ①マナーを守れる方。 ②ネタをネタと分かってくれる方。 VCやスカイプなどはなくても構いません。クランの掛け持ち、脱退の際は言ってもらえれば基本承知します。 ホモは大歓迎です。 ※このクランはホモが多いです。ノンケの方は気を付けましょう。
https://w.atwiki.jp/bullet-bb/pages/55.html
[部分編集] バレットコンボパーツ集(BBCP) 確立されたコンボレシピについては、コンボレシピのページをご覧ください。 [部分編集] 略記 技名 コマンド 略称語 ワッドカット･エンゲージ ドライブ攻撃中D(ヒートアップ時限定) ワッド フリントシューター 236+A(タメ可) フリント、溜めフリント カッティングシア 623+B シア エクスプロード･エンゲージ カッティングシア中22+D(ヒートアップ時限定) エクス ミュクレットキャプチャー 41236+C キャプ ピアッシング･エンゲージ ミュクレットキャプチャー中236+D(ヒートアップ時限定) ピア スナップハンスフィスト 623+C(空中可) ハンス フレシェット･エンゲージ スナップハンスフィスト中623+D(ヒートアップ時限定) フレ アフターバーナー 214+D(タメ可) バーナー レイジアグレッサー 2363214+C レイジ サーペンタインアサルト レバー2回転+A アサルト フランジブル･エンゲージ サーペンタインアサルト中レバー2回転+D フラン ブラックアウト フランジブル･エンゲージ中レバー3回転+D アウト 632146+D AH クラッシュトリガー A+B CT [部分編集] コンボ注意点 2Cは特に表記がない限り2段目まで当てます。 ミュクレットキャプチャーはしゃがみには当たりません。 A ：ノーマル、ヒートアップ問わず繋がる。 N ：ノーマル限定で繋がる。/ノーマル時推奨 H ：ヒートアップ時レベル関係なく繋がる。 H1：ヒートアップレベル1限定で繋がる。/H1時推奨 H2：ヒートアップレベル2限定で繋がる。 位置限定なしA通常 しゃがみ 空中食らい 〆パーツ FC 位置限定なしH通常 位置限定なしH2 中央A 中央H 中央H2～ピア 端A通常 ～キャプ ～ハンス壁ビタ 端H通常 端H2通常 コメント欄 [部分編集] 位置限定なしA 通常 ～2C D H+1 迷ったらこれ。 ～D/JD RC {B,C,6B,D等}～ -50%、H+1 ヒート二段階あげるもよし、追加出して火力優先もよし。 6投げ ステB～ 4投げ 溜めフリント～ 4投げ 6B～ ～C cOD B～ バースト消費 しゃがみ ～C 3C キャプ～ しゃがみ食らい時にキャプに繋ぎたい時のためのパーツ。 C 3Cはしゃがみ限定。 空中食らい ～6A 溜めフリント 6A～ ～6A 溜めフリント 6C jc JC～ ～6A 溜めフリント JB JC 6B～ ～C (3C )キャプ～ ～6A 2B～ ～6A CT～ 25%消費 ～6B JC 6B～ ～JC JA 6B~ ～JC A 6B～ ～JD RC 6B jc JB jc JB JC ハンス フレ レイジ 殺しきり ～6C jc OD{ JB JC} ハンス～ 補正により{}内が入る 〆パーツ ～6B jc{ JB jc JB} JC JD H+1 エリアルはだいたいこれ。 {}内はNだとJDが当たらない可能性が、 また時間制限により途中で受身される可能性がある。 ～B JB dlJC 6B hjc{ JB jc JB} JC JD 5B少し低めで拾った時などに。 {}内はNだとJDが当たらない可能性が、 また時間制限により途中で受身される可能性がある。 JC JA jc JB JC JD ～6B レイジ 50%消費 6Bが当たればどの高さでも入ったはず FC ～C 6C ～C 溜めフリ 屈限 [部分編集] [部分編集] 位置限定なしH 通常 ～ピア ステB～ H→N アサルト フラン 6A～ -50% 補正がキツいのでとっととH回収して締める。 アサルト フラン OD キャプ～ -50%、バースト消費 [部分編集] [部分編集] 位置限定なしH2 ～フレ ステ6A H→N [部分編集] 中央A [部分編集] [部分編集] 中央H [部分編集] [部分編集] 中央H2 ～ピア ～ピア ステ6A H→N ～ピア 低ダJB JC～ H→N 着地j JB JCとかで拾える [部分編集] 端A 通常 6投げ B 6B ハンス 4投げ 溜めフリント 6B/6C ハンス 端維持。後に6Cを使う予定なら6Bで。 ～6C 裏周り2B～ ～キャプ ～キャプ B C 2C D ～キャプ B 6B hjc JC JD ～キャプ 6B jc JC B～ キャラ限 ～ハンス壁ビタ ～ハンス壁ビタ ステ6C jc 降りJC～ ～ハンス壁ビタ ステ6C 裏周り着地B～ ～ハンス壁ビタ ステキャプ～ ～ハンス壁ビタ 低ダJC 6B～ ～ハンス壁ビタ ステCT～ 25％消費 [部分編集] [部分編集] 端H 通常 ～ワッド B～ H→N ～ピア 裏周り6B ハンス H→N 端維持 [部分編集] [部分編集] 端H2 通常 ～ワッド ステ6A 溜めフリント～ H→N コメント欄 すべてのコメントを見る（未作成） 名前
https://w.atwiki.jp/tock_t9710/pages/265.html
{{Infobox Musician | Name = AsaMinG☆ | Img = | Img_capt = | Img_size = | Landscape = | Background = singer | Birth_name = 坂本麻実 | Alias = あさみん、さかもっちゃん | Blood = O型 | School_background = | Born = Template 生年月日と年齢 | Died = | Origin = Template JPN岐阜県高山市 | Instrument = 歌 | Genre = J-POP | Occupation = 歌手、作詞家 | Years_active = 2007年 - | Label = | Production = [B-feeling] | Associated_acts = | Influences = | URL = | Notable_instruments = }} AsaMinG☆（アサミン、1983年12月23日 - ）は坂本麻実のアーティスト名。Gを読んだり☆マークを読んだりはしない。岐阜県出身。所属事務所はB-feeling。 名前 アーティスト活動を行うときは AsaMinG☆ 表記になっており、それ以外では 坂本麻実 となっている。 坂本麻実で検索すると、一部の検索サイトではもしかして 阪本麻美と表示される。 プロフィール 趣味/特技 3日坊主、パソコンを使うこと 好き クッピーラムネ、キキララ、緑色,素直な人、ガリガリ君、コーヒー 苦手 プレッシャー、ゲーム、上っ面だけの人、爬虫類、虫、初対面 ディスコグラフィー SuperCospresson vol.2に参加 (2008年3月12日 KDSC-3002) 収録曲は「愛してるって言わせたい!!」と「サンセットビーチ」の2曲 備考 クッピーラムネが好きでライブで配っていたことがある。 2007年10月からアイドルイベントのからくりかまきりぃにてMCを行っている。 外部リンク AsaMinG☆のぽっけ公式ブログ Template DEFAULTSORT あさみん
https://w.atwiki.jp/cbah/pages/13.html
人気商品一覧 @wikiのwikiモードでは #price_list(カテゴリ名) と入力することで、あるカテゴリの売れ筋商品のリストを表示することができます。 カテゴリには以下のキーワードがご利用できます。 キーワード 表示される内容 ps3 PlayStation3 ps2 PlayStation3 psp PSP wii Wii xbox XBOX nds Nintendo DS desctop-pc デスクトップパソコン note-pc ノートパソコン mp3player デジタルオーディオプレイヤー kaden 家電 aircon エアコン camera カメラ game-toy ゲーム・おもちゃ全般 all 指定無し 空白の場合はランダムな商品が表示されます。 ※このプラグインは価格比較サイト@PRICEのデータを利用しています。 たとえば、 #price_list(game-toy) と入力すると以下のように表示されます。 ゲーム・おもちゃ全般の売れ筋商品 #price_list ノートパソコンの売れ筋商品 #price_list 人気商品リスト #price_list
https://w.atwiki.jp/sunetyama/pages/4.html
ニュース @wikiのwikiモードでは #news(興味のある単語) と入力することで、あるキーワードに関連するニュース一覧を表示することができます 詳しくはこちらをご覧ください。 ＝＞http //atwiki.jp/guide/17_174_ja.html たとえば、#news(wiki)と入力すると以下のように表示されます。 【グランサガ】リセマラ当たりランキング - グランサガ攻略wiki - Gamerch(ゲーマチ) Among Us攻略Wiki【アマングアス・アモングアス】 - Gamerch(ゲーマチ) 【ひなこい】最強ひな写ランキング - ひなこい攻略Wiki - Gamerch(ゲーマチ) マニュアル作成に便利な「画像編集」機能を提供開始！ - ナレッジ共有・社内wikiツール「NotePM」 - ヨコハマ経済新聞 【Apex Legends】ヴァルキリーの能力と評価【エーペックス】 - Gamerch(ゲーマチ) モンハンライズ攻略Wiki｜MHRise - AppMedia(アップメディア) 【ウインドボーイズ】リセマラ当たりランキング（最新版） - ウインドボーイズ攻略Wiki - Gamerch(ゲーマチ) ポケモンBDSP(ダイパリメイク)攻略wiki - AppMedia(アップメディア) 【テイルズオブルミナリア】リセマラ当たりランキング - TOルミナリア攻略Wiki - Gamerch(ゲーマチ) SlackからWikiへ！シームレスな文章作成・共有が可能な「GROWIBot」リリース - アットプレス（プレスリリース） 【ウマ娘】チャンピオンズミーティングの攻略まとめ - Gamerch(ゲーマチ) 【ウマ娘】ナリタブライアンの育成論｜URAシナリオ - Gamerch(ゲーマチ) 【ウマ娘】ヒシアケボノの育成論｜URAシナリオ - Gamerch(ゲーマチ) 【ウマ娘】フジキセキの育成論｜URAシナリオ - Gamerch(ゲーマチ) ドラゴンクエストけしケシ攻略Wiki - Gamerch(ゲーマチ) サモンズボード攻略wiki - GameWith 【スタオケ】カード一覧【金色のコルダスターライトオーケストラ】 - Gamerch(ゲーマチ) 【ブレフロレゾナ】リセマラ当たりランキング【ブレイブフロンティアレゾナ】 - ブレフロR攻略Wiki - Gamerch(ゲーマチ) 【ポケモンユナイト】サーナイトの評価と性能詳細【UNITE】 - Gamerch(ゲーマチ) 【ガーディアンテイルズ】ギルドレイド戦（秘密の研究所）の攻略とおすすめキャラ【ガデテル】 - Gamerch(ゲーマチ) 仲村トオル、共演者は事前に“Wiki調べ” - 沖縄タイムス 【ENDER LILIES】攻略チャートと全体マップ【エンダーリリィズ】 - Gamerch(ゲーマチ) 【ウマ娘】あんしん笹針師の選択肢はどれを選ぶべき？ - Gamerch(ゲーマチ) 【ポケモンユナイト】アップデート情報・キャラ調整まとめ - ポケモンユナイト攻略Wiki - Gamerch(ゲーマチ) 【Apex】シーズン11の新要素と最新情報まとめ【エーペックス】 - Gamerch(ゲーマチ) ロストジャッジメント攻略Wiki - Gamerch(ゲーマチ) 【Among us】新マップThe Airship（エアシップ）の解説【アモングアス】 - Gamerch(ゲーマチ) ハーネスについて小児科医の立場から考える（坂本昌彦） - 個人 - Yahoo!ニュース - Yahoo!ニュース ゼルダ無双攻略Wiki｜厄災の黙示録 - AppMedia(アップメディア) ウマ娘攻略Wiki - AppMedia(アップメディア) ゲトメア（ゲートオブナイトメア）攻略Wiki - Gamerch(ゲーマチ) 【白夜極光】リセマラ当たりランキング - 白夜 極光 wiki - Gamerch(ゲーマチ) お蔵入りとなった幻の『スーパーマリオ』 オランダの博物館でプレイ可能？（リアルサウンド） - Yahoo!ニュース - Yahoo!ニュース ナレッジ共有・社内wikiツール「NotePM」が「ITreview Best Software in Japan 2021」のTOP50に選出 - PR TIMES 真女神転生5攻略Wiki｜メガテン5 - AppMedia(アップメディア) 【B4B】近接ビルドデッキにおすすめのカード【back4blood】 - Gamerch(ゲーマチ) ポケモンスナップ攻略wiki - AppMedia(アップメディア) 富野由悠季「ブレンパワード」作り直したい！ファンを前に意欲（シネマトゥデイ） - Yahoo!ニュース - Yahoo!ニュース 【ウマ娘】査定効率から見た取るべきスキルとおすすめキャラ【プリティーダービー】 - Gamerch(ゲーマチ) 【スマブラSP】カズヤの評価とコンボ【スマブラスペシャル】 - Gamerch(ゲーマチ) ナレッジ共有・社内wiki「NotePM」が「ITreview Grid Award 2021 Fall」で、チームコラボレーションとマニュアル作成部門において「Leader」を5期連続でW受賞！ - PR TIMES メモ・ドキュメント・wiki・プロジェクト管理などオールインワンのワークスペース「Notion」が日本語ベータ版提供開始 - TechCrunch Japan 【ギアジェネ】リセマラ当たりランキング【コードギアス】 - ギアジェネ攻略Wiki - Gamerch(ゲーマチ) モンスターファーム2(MF2)攻略wiki｜アプリ・Switch移植版 - AppMedia(アップメディア) 【ブラサジ】最強キャラTierランキング【ブラックサージナイト】 - Gamerch(ゲーマチ) 【パワプロ】鬼滅の刃コラボ情報まとめ - Gamerch(ゲーマチ) 【SPAJAM2021】第3回予選大会は「クイズ!WIKIにゃんず!」を開発したチーム「かよちゃんず」が最優秀賞！ | gamebiz - SocialGameInfo 検索結果における「ナレッジパネル」の役割とは・・・ウィキメディア財団とDuckDuckGoの共同調査 - Media Innovation ナレッジ共有・社内wikiツール「NotePM」が「BOXIL SaaS AWARD 2021 Autumn」にて「コラボレーション部門」を受賞！ - PR TIMES 【ポケモンユナイト】カメックスの評価と立ち回り【UNITE】 - Gamerch(ゲーマチ) Wikipediaが「中国人編集者の身の安全を守るため」に一部の編集者アカウントをBANに - GIGAZINE 【ドッカンバトル】3.5億ダウンロードキャンペーン最新情報 - ドッカンバトル攻略Wiki - Gamerch(ゲーマチ) BTS（防弾少年団）のV、8月のWikipedia閲覧数が韓国アーティストで1位！グループでは4ヶ月連続トップ - Kstyle 【イース6オンライン】リセマラ当たりランキング｜召喚ガチャの開放条件は？ - Gamerch(ゲーマチ) BacklogからNotePMへwiki情報を自動API連携する「Backlog to NotePM」をSaaStainerに掲載開始 - PR TIMES ライザのアトリエ2攻略Wiki - AppMedia(アップメディア) 真女神転生3リマスター攻略Wiki｜メガテン3 - AppMedia(アップメディア) タスクも文書もWikiもデータベースもまとめて管理できる「Notion」とは？ - ASCII.jp ナレッジ共有・社内wikiツール「NotePM」が、見るだけ専用ユーザー『無料』の新プランを発表！ - PR TIMES 【かのぱず】リセマラ当たりランキング【彼女お借りします】 - Gamerch(ゲーマチ) 【乃木フラ】リセマラの必要はある？【乃木坂的フラクタル】 - Gamerch(ゲーマチ) メトロイド ドレッド攻略Wiki - Gamerch(ゲーマチ) 【パワプロ】生放送まとめ｜パワフェス2021 - パワプロ攻略Wiki - Gamerch(ゲーマチ) ルーンファクトリー5攻略wiki｜ルンファク5 - AppMedia(アップメディア) シャーマンキングふんばりクロニクル攻略Wiki - Gamerch(ゲーマチ) 簡単操作で自分専用Wikiを構築できるMarkdownエディタ「Obsidian」のモバイル版を使ってみた - GIGAZINE ディーサイドトロイメライ攻略Wiki - Gamerch(ゲーマチ) 情報マネジメントツール「Huddler」がwiki機能を刷新 - PR TIMES シェアエコ配送アプリ「DIAq(ダイヤク)」のアンカーアプリで、高層ビル・商業施設の入館方法などお役立ち情報をまとめた「DIAqwiki」を公開 - アットプレス（プレスリリース） 異常熱波のカナダで49.6度、いま北米で起きていること（森さやか） - 個人 - Yahoo!ニュース - Yahoo!ニュース 【ツイステ】マスターシェフの攻略～辛味のふるさと～【料理イベント】 - Gamerch(ゲーマチ) 【ラグナロクオリジン】リセマラは不要？おすすめ職業は？【ラグオリ】 - Gamerch(ゲーマチ) 白夜極光攻略wiki - AppMedia(アップメディア) 【バイオミュータント】2.02アプデ｜アップデート1.4情報 - バイオミュータント攻略Wiki - Gamerch(ゲーマチ) ニーアレプリカントリメイク攻略wiki｜ver.1.22 - AppMedia(アップメディア) 【ウマ娘】ゴルシウィークはいつから？キャンペーン情報まとめ - Gamerch(ゲーマチ) シーズン66 - 【超速GP】ミニ四駆 超速グランプリ攻略まとめwiki - 電撃オンライン 乃木坂的フラクタル攻略Wiki - Gamerch(ゲーマチ) 「こんなことになるとは…」13年前のエイプリルフールについた“嘘”がネットで… ある男の告白（BuzzFeed Japan） - Yahoo!ニュース - Yahoo!ニュース 【ウマ娘】DMM版のデータ連携のやり方とメリット【プリティーダービー】 - Gamerch(ゲーマチ) 整理不要の情報共有ツール(社内Wiki)「Nerve」シードラウンドで総額約3500万円の資金調達を実施 - PR TIMES Nerve - 整理不要の情報共有ツール(社内Wiki) ローンチカスタマー募集開始のお知らせ - PR TIMES パニシンググレイレイヴン(パニグレ)攻略wiki - Gamerch(ゲーマチ) 稲作アクションRPG『天穂のサクナヒメ』における「農林水産省攻略wiki説」は本当なのか？ - AUTOMATON スタスマ攻略Wiki【スタースマッシュ】 - Gamerch(ゲーマチ) 無料とは思えない多機能っぷりなWikiインフラ「Wiki.js」レビュー、自前でホスト＆外部サービスと連携可能 - GIGAZINE Microsoft Teamsの基本と活用(24) TeamsのWikiを使う - マイナビニュース 『ゲーミングお嬢様』での提起が話題に “企業系wiki”に横たわる問題点とは - リアルサウンド 「エイリアンのたまご」，自動周回機能と公式wikiが登場 - 4Gamer.net 【リゼロス】Re ゼロから始める異世界生活 Lost in Memories攻略まとめwiki - 電撃オンライン 【世界初！】モノの背景を全方位で執筆できるVintage Wiki「VOV」を正式リリース - PR TIMES 足もとのベストアンサーを“編集”！ Wikiペディキュア！ - ビューティ特集 | SPUR - SPUR.JP パワプロ2021／2020攻略Wiki - Gamerch(ゲーマチ) ヌーラボ、「Backlog」の絵文字入力の補完機能やWiki編集の自動マージ機能を改善 - CodeZine（コードジン） ヌーラボ、プロジェクト管理ツール「Backlog」の絵文字入力の補完機能・Wiki編集の自動マージ機能を修正改善 - PR TIMES アサルトリリィ Last Bullet 攻略Wiki【ラスバレ】 - Gamerch(ゲーマチ) SHOW BY ROCK!! Fes A Live攻略wiki - Gamerch(ゲーマチ) Backlog、Wikiにファイル添付が容易にできる機能をリリース -- グローバルバーの視認性改善なども実施 - PR TIMES GK川島、パンチング失点でWiki書き換え炎上 「セネガル代表」「プロボクサー」... - J-CASTニュース Wikipediaで「ヒト」を象徴する画像が、タイの男女に決まるまで 5年の激論を経て選ばれた「1枚」の物語 - WIRED.jp
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IBM Research Laboratory, San Jose, California Future users of large data banks must be protected from having to know how the data is organized in the machine (the internal representation). A prompting service which supplies such information is not a satisfactory solution. Activities of users at terminals and most application programs should remain unaffected when the internal representation of data is changed and even when some aspects of the external representation are changed. Changes in data representation will often be needed as a result of changes in query, update, and report traffic and natural growth in the types of stored information. Existing noninferential, formatted data systems provide users with tree-structured files or slightly more general network models of the data. In Section 1, inadequacies of these models are discussed. A model based on n-ary relations, a normal form for data base relations, and the concept of a universal data sublanguage are introduced. In Section 2, certain operations on relations (other than logical inference) are discussed and applied to the problems of redundancy and consistency in the user’s model. KEY WORDS AND PHRASES data bank, data base, data structure, data organization, hierarchies of data, networks of data, relations, derivability, redundancy, consistency, composition, join, retrieval language, predicate calculus, security, data integrity CR CATEGORIES 3.70, 3.73, 3.75, 4.20, 4.22, 4.29 1. Relational Model and Normal Form 1 .I. INTR~xJ~TI~N This paper is concerned with the application of elementary relation theory to systems which provide shared access to large banks of formatted data. Except for a paper by Childs [l], the principal application of relations to data systems has been to deductive question-answering systems. Levein and Maron [2] provide numerous references to work in this area. In contrast, the problems treated here are those of data independence-the independence of application programs and terminal activities from growth in data types and changes in data representation-and certain kinds of data inconsistency which are expected to become troublesome even in nondeductive systems. Volume 13 / Number 6 / June, 1970 The relational view (or model) of data described in Section 1 appears to be superior in several respects to the graph or network model [3,4] presently in vogue for noninferential systems. It provides a means of describing data with its natural structure only-that is, without superimposing any additional structure for machine representation purposes. Accordingly, it provides a basis for a high level data language which will yield maximal independence between programs on the one hand and machine representation and organization of data on the other. A further advantage of the relational view is that it forms a sound basis for treating derivability, redundancy, and consistency of relations-these are discussed in Section 2. The network model, on the other hand, has spawned a number of confusions, not the least of which is mistaking the derivation of connections for the derivation of relations (see remarks in Section 2 on the “connection trap”). Finally, the relational view permits a clearer evaluation of the scope and logical limitations of present formatted data systems, and also the relative merits (from a logical standpoint) of competing representations of data within a single system. Examples of this clearer perspective are cited in various parts of this paper. Implementations of systems to support the relational model are not discussed. 1.2. DATA DEPENDENCIESIN PRESENTS YSTEMS The provision of data description tables in recently developed information systems represents a major advance toward the goal of data independence [5,6,7]. Such tables facilitate changing certain characteristics of the data representation stored in a data bank. However, the variety of data representation characteristics which can be changed without logically impairing some application programs is still quite limited. Further, the model of data with which users interact is still cluttered with representational properties, particularly in regard to the representation of collections of data (as opposed to individual items). Three of the principal kinds of data dependencies which still need to be removed are ordering dependence, indexing dependence, and access path dependence. In some systems these dependencies are not clearly separable from one another. 1.2.1. Ordering Dependence. Elements of data in a data bank may be stored in a variety of ways, some involving no concern for ordering, some permitting each element to participate in one ordering only, others permitting each element to participate in several orderings. Let us consider those existing systems which either require or permit data elements to be stored in at least one total ordering which is closely associated with the hardware-determined ordering of addresses. For example, the records of a file concerning parts might be stored in ascending order by part serial number. Such systems normally permit application programs to assume that the order of presentation of records from such a file is identical to (or is a subordering of) the Communications of the ACM 377 stored ordering. Those application programs which take advantage of the stored ordering of a file are likely to fail to operate correctly if for some reason it becomes necessary to replace that ordering by a different one. Similar remarks hold for a stored ordering implemented by means of pointers. It is unnecessary to single out any system as an example, because all the well-known information systems that are marketed today fail to make a clear distinction between order of presentation on the one hand and stored ordering on the other. Significant implementation problems must be solved to provide this kind of independence. 1.2.2. Indexing Dependence. In the context of formatted data, an index is usually thought of as a purely performance-oriented component of the data representation. It tends to improve response to queries and updates and, at the same time, slow down response to insertions and deletions. From an informational standpoint, an index is a redundant component of the data representation. If a system uses indices at all and if it is to perform well in an environment with changing patterns of activity on the data bank, an ability to create and destroy indices from time to time will probably be necessary. The question then arises Can application programs and terminal activities remain invariant as indices come and go? Present formatted data systems take widely different approaches to indexing. TDMS [7] unconditionally provides indexing on all attributes. The presently released version of IMS [5] provides the user with a choice for each file a choice between no indexing at all (the hierarchic sequential organization) or indexing on the primary key only (the hierarchic indexed sequent,ial organization). In neither case is the user’s application logic dependent on the existence of the unconditionally provided indices. IDS [8], however, permits the fle designers to select attributes to be indexed and to incorporate indices into the file structure by means of additional chains. Application programs taking advantage of the performance benefit of these indexing chains must refer to those chains by name. Such programs do not operate correctly if these chains are later removed. 1.2.3. Access Path Dependence. Many of the existing formatted data systems provide users with tree-structured files or slightly more general network models of the data. Application programs developed to work with these systems tend to be logically impaired if the trees or networks are changed in structure. A simple example follows. Suppose the data bank contains information about parts and projects. For each part, the part number, part name, part description, quantity-on-hand, and quantity-on-order are recorded. For each project, the project number, project name, project description are recorded. Whenever a project makes use of a certain part, the quantity of that part committed to the given project is also recorded. Suppose that the system requires the user or file designer to declare or define the data in terms of tree structures. Then, any one of the hierarchical structures may be adopted for the information mentioned above (see Structures l-5). 378 Communications of the ACM Structure 1. Projects Subordinate to Parts File Segment Fields F PART part # part name part description quantity-on-hand quantity-on-order PROJECT project # project name project description quantity committed Structure 2. Parts Subordinate to Projects File Sqmeut Fields F PROJECT project # project name project description PART part # part name part description quantity-on-hand quantity-on-order quantity committed Structure 3. Parts and Projects as Peers Commitment Relationship Subordinate to Projects File Segment Fields F PART part # part name part description quantity-on-hand quantity-on-order G PROJECT project # project name project description PART part # quantity committed Structure 4. Parts and Projects as Peers Commitment Relationship Subordinate to Parts File Segnren1 Fields F PART part # part description quantity-on-hand quantity-on-order PROJECT project # quantity committed G PROJECT project # project name project description Structure 5. Parts, Projects, and Commitment Relationship as Peers FCZC .% -,,ZC,,t Ficlds F PART part # part name part description quantity-on-hand quantity-on-order G PROJECT project # project name project description H COMMIT part # project # quantity committed Volume 13 / Number 6 / June, 1970 Now, consider the problem of printing out the part number, part name, and quantity committed for every part used in the project whose project name is “alpha.” The following observations may be made regardless of which available tree-oriented information system is selected to tackle this problem. If a program P is developed for this problem assuming one of the five structures above-that is, P makes no test to determine which structure is in effect- then P will fail on at least three of the remaining structures. More specifically, if P succeeds with structure 5, it will fail with all the others; if P succeeds with structure 3 or 4, it will fail with at least 1,2, and 5; if P succeeds with 1 or 2, it will fail with at least 3, 4, and 5. The reason is simple in each case. In the absence of a test to determine which structure is in effect, P fails because an attempt is made to exceute a reference to a nonexistent file (available systems treat this as an error) or no attempt is made to execute a reference to a file containing needed information. The reader who is not convinced should develop sample programs for this simple problem. Since, in general, it is not practical to develop application programs which test for all tree structurings permitted by the system, these programs fail when a change in ructure becomes necessary. Systems which provide users with a network model of the data run into similar difficulties. In both the tree and network cases, the user (or his program) is required to exploit a collection of user access paths to the data. It does not matter whether these paths are in close correspondence with pointer-defined paths in the stored representation-in IDS the correspondence is extremely simple, in TDMS it is just the opposite. The consequence, regardless of the stored representation, is that terminal activities and programs become dependent on the continued existence of the user access paths. One solution to this is to adopt the policy that once a user access path is defined it will not be made obsolete until all application programs using that path have become obsolete. Such a policy is not practical, because the number of access paths in the total model for the community of users of a data bank would eventually become excessively large. 1.3. A RELATIONAL VIEW OF DATA The term relation is used here in its accepted mathematical sense. Given sets X1 , S, , . . . , S, (not necessarily distinct), R is a relation on these n sets if it is a set of ntuples each of which has its first element from S1, its second element from Sz , and so on.’ We shall refer to Si as the jth domain of R. As defined above, R is said to have degree n. Relations of degree 1 are often called unary, degree 2 binary, degree 3 ternary, and degree n n-ary. For expository reasons, we shall frequently make use of an array representation of relations, but it must be remembered that this particular representation is not an essential part of the relational view being expounded. An ar- 1 More concisely, R is a subset of the Cartesian product 81 X sz x *.* x 87%. ray which represents an n-ary relation R has the following properties (1) Each row represents an n-tuple of R. (2) The ordering of rows is immaterial. (3) All rows are distinct. (4) The ordering of columns is significant-it corresponds to the ordering S1, Sz , . . . , S, of the domains on which R is defined (see, however, remarks below on domain-ordered and domain-unordered relations ) . (5) The significance of each column is partially conveyed by labeling it with the name of the corresponding domain. The example in Figure 1 illustrates a relation of degree 4, called supply, which reflects the shipments-in-progress of parts from specified suppliers to specified projects in specified quantities. supply (supplier part project quantity) 1 2 5 17 1 3 5 23 2 3 7 9 2 7 5 4 4 1 1 12 FIG. 1. A relation of degree 4 One might ask If the columns are labeled by the name of corresponding domains, why should the ordering of columns matter? As the example in Figure 2 shows, two columns may have identical headings (indicating identical domains) but possessd istinct meanings with respect to the relation. The relation depicted is called component. It is a ternary relation, whose first two domains are called part and third domain is called quantity. The meaning of component (2, y, z) is that part x is an immediate component (or subassembly) of part y, and z units of part 5 are needed to assemble one unit of part y. It is a relation which plays a critical role in the parts explosion problem. component (part part quantity) 1 5 9 2 5 7 3 5 2 2 6 12 3 6 3 4 7 1 6 7 1 FIG. 2. A relation with-two identical domains It is a remarkable fact that several existing information systems (chiefly those based on tree-structured files) fail to provide data representations for relations which have two or more identical domains. The present version of IMS/360 [5] is an example of such a system. The totality of data in a data bank may be viewed as a collection of time-varying relations. These relations are of assorted degrees. As time progresses, each n-ary relation may be subject to insertion of additional n-tuples, deletion of existing ones, and alteration of components of any of its existing n-tuples. Volume 13 / Number 6 / June, 1970 Communications of the ACM 379 In many commercial, governmental, and scientific data banks, however, some of the relations are of quite high degree (a degree of 30 is not at all uncommon). Users should not normally be burdened with remembering the domain ordering of any relation (for example, the ordering supplier, then part, then project, then quantity in the relation supply). Accordingly, we propose that users deal, not with relations which are domain-ordered, but with relationships which are their domain-unordered counterparts.2 To accomplish this, domains must be uniquely identifiable at least within any given relation, without using position. Thus, where there are two or more identical domains, we require in each case that the domain name be qualified by a distinctive role name, which serves to identify the role played by that domain in the given relation. For example, in the relation component of Figure 2, the first domain part might be qualified by the role name sub, and the second by super, so that users could deal with the relationship component and its domains-sub.part super.part, quantity-without regard to any ordering between these domains. To sum up, it is proposed that most users should interact with a relational model of the data consisting of a collection of time-varying relationships (rather than relations). Each user need not know more about any relationship than its name together with the names of its domains (role qualified whenever necessary) Even this information might be offered in menu style by the system (subject to security and privacy constraints) upon request by the user. There are usually many alternative ways in which a relational model may be established for a data bank. In order to discuss a preferred way (or normal form), we must first introduce a few additional concepts (active domain, primary key, foreign key, nonsimple domain) and establish some links with terminology currently in use in information systems programming. In the remainder of this paper, we shall not bother to distinguish between relations and relationships except where it appears advantageous to be explicit. Consider an example of a data bank which includes relations concerning parts, projects, and suppliers. One relation called part is defined on the following domains (1) part number (2) part name (3) part color (4) part weight (5) quantity on hand (6) quantity on order and possibly other domains as well. Each of these domains is, in effect, a pool of values, some or all of which may be represented in the data bank at any instant. While it is conceivable that, at some instant, all part colors are present, it is unlikely that all possible part weights, part 2 In mathematical terms, a relationship is an equivalence class of those relations that are equivalent under permutation of domains (see Section 2.1.1). * Naturally, as with any data put into and retrieved from a computer system, the user will normally make far more effective use of the data if he is aware of its meaning. names, and part numbers are. We shall call the set of values represented at some instant the active domain at that instant. Normally, one domain (or combination of domains) of a given relation has values which uniquely identify each element (n-tuple) of that relation. Such a domain (or combination) is called a primary key. In the example above, part number would be a primary key, while part color would not be. A primary key is nonredundant if it is either a simple domain (not a combination) or a combination such that none of the participating simple domains is superfluous in uniquely identifying each element. A relation may possess more than one nonredundant primary key. This would be the case in the example if different parts were always given distinct names. Whenever a relation has two or more nonredundant primary keys, one of them is arbitrarily selected and called the primary key of that relation. A common requirement is for elements of a relation to cross-reference other elements of the same relation or elements of a different relation. Keys provide a user-oriented means (but not the only means) of expressing such crossreferences. We shall call a domain (or domain combmation) of relation R a foreign key if it is not the primary key of R but its elements are values of the primary key of some relation S (the possibility that S and R are identical is not excluded). In the relation supply of Figure 1, the combination of supplier, part, project is the primary key, while each of these three domains taken separately is a foreign key. In previous work there has been a strong tendency to treat the data in a data bank as consisting of two parts, one part consisting of entity descriptions (for example, descriptions of suppliers) and the other part consisting of relations between the various entities or types of entities (for example, the supply relation). This distinction is difficult to maintain when one may have foreign keys in any relation whatsoever. In the user’s relational model there appears to be no advantage to making such a distinction (there may be some advantage, however, when one applies relational concepts to machine representations of the user’s set of relationships). So far, we have discussed examples of relations which are defined on simple domains-domains whose elements are atomic (nondecomposable) values. Nonatomic values can be discussed within the relational framework. Thus, some domains may have relations as elements. These relations may, in turn, be defined on nonsimple domains, and so on. For example, one of the domains on which the relation employee is defined might be salary history. An element of the salary history domain is a binary relation defined on the domain date and the domain salary. The salary history domain is the set of all such binary relations. At any instant of time there are as many instances of the salary history relation in the data bank as there are employees. In contrast, there is only one instance of the employee relation. The terms attribute and repeating group in present data base terminology are roughly analogous to simple domain 380 Communications of the ACM Volume 13 / Number 6 / June, 1970 and nonsimple domain, respectively. Much of the confusion in present terminology is due to failure to distinguish between type and instance (as in “record”) and between components of a user model of the data on the one hand and their machine representation counterparts on the other hand (again, we cite “record” as an example). 1.4. NORMAL FORM A relation whose domains are all simple can be represented in storage by a two-dimensional column-homogeneous array of the kind discussed above. Some more complicated data structure is necessary for a relation with one or more nonsimple domains. For this reason (and others to be cited below) the possibility of eliminating nonsimple domains appears worth investigating! There is, in fact, a very simple elimination procedure, which we shall call normalization. Consider, for example, the collection of relations exhibited in Figure 3 (a). Job history and children are nonsimple domains of the relation employee. Salary history is a nonsimple domain of the relation job history. The tree in Figure 3 (a) shows just these interrelationships of the nonsimple domains. employee I jobhistory I salaryhistory children employee (man#, name, birthdate, jobhistory, children) jobhistory (jobdate, title, salaryhistory) salaryhistory (salarydate, salary) children (childname, birthyear) FIG. 3(a). Unnormalized set employee’ (man#, name, birthdate) jobhistory’ (man#, jobdate, title) salaryhistory’ (man#, jobdate, salarydate, salary) children’ (man#, childname, birthyear) FIG. 3(b). Normalized set Normalization proceeds as follows. Starting with the relation at the top of the tree, take its primary key and expand each of the immediately subordinate relations by inserting this primary key domain or domain combination. The primary key of each expanded relation consists of the primary key before expansion augmented by the primary key copied down from the parent relation. Now, strike out from the parent relation all nonsimple domains, remove the top node of the tree, and repeat the same sequence of operations on each remaining subtree. The result of normalizing the collection of relations in Figure 3 (a) is the collection in Figure 3 (b). The primary key of each relation is italicized to show how such keys are expanded by the normalization. 4 M. E. Sanko of IBM, San Jose, independently recognized the desirability of eliminating nonsimple domains. If normalization as described above is to be applicable, the unnormalized collection of relations must satisfy the following conditions (1) The graph of interrelationships of the nonsimple domains is a collection of trees. (2) No primary key has a component domain which is nonsimple. The writer knows of no application which would require any relaxation of these conditions. Further operations of a normalizing kind are possible. These are not discussed in this paper. The simplicity of the array representation which becomes feasible when all relations are cast in normal form is not only an advantage for storage purposes but also for communication of bulk data between systems which use widely different representations of the data. The communication form would be a suitably compressed version of the array representation and would have the following advantages (1) It would be devoid of pointers (address-valued or displacement-valued ) . (2) It would avoid all dependence on hash addressing schemes. (3) It would contain no indices or ordering lists. If the user’s relational model is set up in normal form, names of items of data in the data bank can take a simpler form than would otherwise be the case. A general name would take a form such as R (g).r.d where R is a relational name; g is a generation identifier (optional); r is a role name (optional); d is a domain name. Since g is needed only when several generations of a given relation exist, or are anticipated to exist, and r is needed only when the relation R has two or more domains named d, the simple form R.d will often be adequate. 1.5. SOME LINGUISTIC ASPECTS The adoption of a relational model of data, as described above, permits the development of a universal data sublanguage based on an applied predicate calculus. A firstorder predicate calculus s ices if the collection of relations is in normal form. Such a language would provide a yardstick of linguistic power for all other proposed data Ianguages, and would itself be a strong candidate for embedding (with appropriate syntactic modification) in a variety of host Ianguages (programming, command- or problemoriented). While it is not the purpose of this paper to describe such a language in detail, its salient features would be as follows. Let us denote the data sublanguage by R and the host language by H. R permits the declaration of relations and their domains. Each declaration of a relation identifies the primary key for that relation. Declared relations are added to the system catalog for use by any members of the user community who have appropriate authorization. H permits supporting declarations which indicate, perhaps less permanently, how these relations are represented in stor- Volume 13 / Number 6 / June, 1970 Communications of the ACM 381 age. R permits the specification for retrieval of any subset of data from the data bank. Action on such a retrieval request is subject to security constraints. The universality of the data sublanguage lies in its descriptive ability (not its computing ability). In a large data bank each subset of the data has a very large number of possible (and sensible) descriptions, even when we assume (as we do) that there is only a finite set of function subroutines to which the system has access for use in qualifying data for retrieval. Thus, the class of qualification expressions which can be used in a set specification must have the descriptive power of the class of well-formed formulas of an applied predicate calculus. It is well known that to preserve this descriptive power it is unnecessary to express (in whatever syntax is chosen) every formula of the selected predicate calculus. For example, just those in prenex normal form are adequate [9]. Arithmetic functions may be needed in the qualification or other parts of retrieval statements. Such functions can be defined in H and invoked in R. A set so specified may be fetched for query purposes only, or it may be held for possible changes. Insertions t,ake the form of adding new elements to declared relations without regard to any ordering that may be present in their machine representation. Deletions which are effective for the community (as opposed to the individual user or subcommunities) take the form of removing elements from declared relations. Some deletions and updates may be triggered by others, if deletion and update dependencies between specified relations are declared in R. One important effect that the view adopted toward data has on the language used to retrieve it is in the naming of data elements and sets. Some aspects of this have been discussed in the previous section. With the usual network view, users will often be burdened with coining and using more relat,ion names than are absolutely necessary, since names are associated with paths (or path types) rather than with relations. Once a user is aware that a certain relation is stored, he will expect to be able to exploit5 it using any combination of its arguments as “knowns” and the remaining arguments as “unknowns,” because the information (like Everest) is there. This is a system feature (missing from many current informat.ion systems) which we shall call (logically) symmetric expZoitation of relations. Naturally, symmetry in performance is not to be expected. To support symmetric exploitation of a single binary relation, two directed paths are needed. For a relation of degree n, the number of paths to be named and controlled is n factorial. Again, if a relational view is adopted in which every nary relation (n 2) has to be expressed by the user as a nested expression involving only binary relations (see Feldman’s LEAP System [lo], for example) then 2n - 1 names have to be coined instead of only n + 1 with direct n-ary notation as described in Section 1.2. For example, the 6 Exploiting a relation includes query, update, and delete. 4-ary relation supply of Figure 1, which entails 5 names in n-ary notation, would be represented in the form P (supplier, (part, R (project, quantity))) in nested binary notation and, thus, employ 7 names. -4 further disadvantage of this kind of expression is its asymmetry. Although this asymmetry does not prohibit symmetric exploitation, it certainly makes some bases of interrogation very awkward for the user to express (consider, for example, a query for those parts and quantities related to certain given projects via and R). 1.6. EXPRESSIBLE, NAMED, AND STORED RELATIONS Associated with a data bank are two collections of relations the named set and the expressible set. The named set is the collection of all those relations that the community of users can identify by means of a simple name (or identifier). A relation R acquires membership in the named set when a suitably authorized user declares R; it loses membership when a suitably authorized user cancels the declaration of R. The expressible set is the total collection of relations that can be designated by expressions in the data language. Such expressions are constructed from simple names of relations in the named set; names of generations, roles and domains; logical connectives; the quantifiers of the predicate calcu- 1~s;~ and certain constant relation symbols such as = , . The named set is a subset of the expressible set-usually a very small subset. Since some relations in the named set may be time-independent combinations of others in that set, it is useful to consider associating with the named set a collection of statements that define these time-independent constraints. We shall postpone further discussion of this until we have introduced several operations on relations (see Section 2). One of the major problems confronting the designer of a data system which is to support a relational model for its users is that of determining the class of stored representations to be supported. Ideally, the variety of permitted data representations should be just adequate to cover the spectrum of performance requirements of the total collection of installations. Too great a variety leads to unnecessary overhead in storage and continual reinterpretation of descriptions for the structures currently in effect. For any selected class of stored representations the data system must provide a means of translating user requests expressed in the data language of the relational model into corresponding-and elhcient-actions on the current stored representation. For a high level data language this presents a challenging design problem. Nevertheless, it is a problem which must be solved-as more users obtain concurrent access to a large data bank, responsibility for providing efficient response and throughput shifts from the individual user to the data system. 6 Because each relation in a practical data bank is a finite set at every instant of time, the existential and universal quantifiers can be expressed in terms of a function that counts the number of elements in any finite set. 382 Communications of the ACM Volume 13 / Number 6 / June, 1970 2. Redundancy and Consistency 2.1. OPERATIONS ON RELATIONS Since relations are sets, all of the usual set operations are applicable to them. Nevertheless, the result may not be a relation; for example, the union of a binary relation and a ternary relation is not a relation. The operations discussed below are specifically for relations. These operations are introduced because of their key role in deriving relations from other relations. Their principal application is in noninferential information systems- systems which do not provide logical inference services-although their applicability is not necessarily destroyed when such services are added. Most users would not be directly concerned with these operations. Information systems designers and people concerned with data bank control should, however, be thoroughly familiar with them. 2.1.1. Permutation. A binary relation has an array representation with two columns. Interchanging these columns yields the converse relation. More generally, if a permutation is applied to the columns of an n-ary relation, the resulting relation is said to be a permutation of the given relation. There are, for example, 4! = 24 permutations of the relation supply in Figure 1, if we include the identity permutation which leaves the ordering of columns unchanged. Since the user’s relational model consists of a collection of relationships (domain-unordered relations), permutation is not relevant to such a model considered in isolation. It is, however, relevant to the consideration of stored representations of the model. In a system which provides symmetric exploitation of relations, the set of queries answerable by a stored relation is identical to the set answerable by any permutation of that relation. Although it is logically unnecessary to store both a relation and some permutation of it, performance considerations could make it advisable. 2.1.2. Projection. Suppose now we select certain columns of a relation (striking out the others) and then remove from the resulting array any duplication in the rows. The final array represents a relation which is said to be a projection of the given relation. A selection operator ?r is used to obtain any desired permutation, projection, or combination of the two operations. Thus, if L is a list of lc indices7 L = i1, ii, - . - , ik and R is an n-ary relation (n 2 k ), then rrL (R ) is the k-ary relation whose jth column is column ii of R (j = 1,2, * * . ,k) except that duplication in resulting rows is removed. Consider the relation supply of Figure 1. A permuted projection of this relation is exhibited in Figure 4. Note that, in this particular case, the projection has fewer n-tuples than the relation from which it is derived. 2.1.3. Join. Suppose we are given two binary relations, which have some domain in common. Under what circumstances can we combine these relations to form a 7 When dealing with relationships, we use domain names (rolequalified whenever necessary) instead of domain positions. ternary relation which preserves all of the information in the given relations? The example in Figure 5 shows two relations R, S, which are joinable without loss of information, while Figure 6 shows a join of R with S. A binary relation R is joinable with a binary relation S if there exists a ternary relation U such that 7r1(2U ) = R and ‘1~23 (U) = S. Any such ternary relation is called a join of R with S. If R, S are binary relations such that ~2 (R) = ~1 (S), then R is joinable with S. One join that always exists in such a case is the natural join of R with S defined by R*S = {(a, b, c) R(a, b) A S(b, c)) where R (a, b) has the value true if (a, b) is a member of R and similarly for S(b, c). It is immediate that and TB(R*S) = R T33(R*S) = S. Note that the join shown in Figure 6 is the natural join of R with S from Figure 5. Another join is shown in Figure 7. II31 hPPb/) (project supplier) 5 1 5 2 1 4 7 2 FIG. 4. A permuted projection of the relation in Figure 1 R (supplier Part) S (part project) 1 1 1 1 2 1 1 2 2 2 2 1 FIG. 5. Two joinable relations R*S (supplier part project) 1 1 1 1 1 2 2 1 1 2 1 2 2 2 1 FIG. 6. The natural join of R with S (from Figure 5) U (supplier part project) 1 1 2 2 1 1 2 2 1 FIG. 7. Another join of R with S (from Figure 5) Inspection of these relations reveals an element (element 1) of the domain part (the domain on which the join is to be made) with the property that it possessesm ore than one relative under R and also under S. It is this ele- Volume 13 / Number 6 / June, 1970 Communications of the ACM 383 ment which gives rise to the plurality of joins. Such an element in the joining domain is called a point of ambiguity with respect to the joining of R with S. If either ~1 (R) or S is a function no point of ambiguity can occur in joining R with S. In such a case, the natural join of R with S is the only join of R with S. Note that the reiterated qualification “of R with S” is necessary, because S might be joinable with R (as well as R with S), and this join would be an entirely separate consideration. In Figure 5, none of the relations R, 7r21(R), S, ?rzl(S) is a function. Ambiguity in the joining of R with S can sometimes be resolved by means of other relations. Suppose we are given, or can derive from sources independent of R and S, a relation T on the domains project and supplier with the following properties (1) m(T) = m(S), (2) m(T) = al(R), (3) T(i s +~P(R( P A S(P,~))~ (4) R(s, P -+ 3j(Soj\,.i) * T(.A s)), (5) S@,.i) + 3s(T(.?, s A R(s, P , then we may form a three-way join of R, S, T; that is, a ternary relation such that mz(U) = R, 7r23(U) = s, ml(U) = T. Such a join will be called a cyclic 3-join to distinguish it from a linear S-join which would be a quaternary relation V such that m(V) = R, lr23W) = s, lr34(V) = T. While it is possible for more than one cyclic 3-join to exist (see Figures 8,9, for an example), the circumstances under which this can occur entail much more severe constraints R (s P) s (P 23 T 0’ s) 1 a a d d 1 2 a 2 b b Ii d 2 e 2 b e e 2 FIG. 8. Binary relations with a plurality of cyclic 3-joins u b P 8 TJ’ (s P i) 1 a d 1 a d 2 a e 2 a d 2 b d 2 a e 2 b e 2 b d 2 b e FIG. 9. Two cyclic 3-joins of the relations in Figure 8 than those for a plurality of 2-joins. To be specific, the relations R, S, T must possess points of ambiguity with respect to joining R with S (say point x), S with T (say 8 A function is a binary relation, which is one-one or many-one, but not one-many. y), and T with R (say a), and, furthermore, y must be a relative of x under S, z a relative of y under T, and x a relative of z under R. Note that in Figure 8 the points 2 = a; y = d; x = 2 have this property. The natural linear 3-join of three binary relations R, S, T is given by R*S*T = { (a, b, c, d) R (a, b) A S (b, c) A T (c, d)} where parentheses are not needed on the left-hand side because the natural 2-join (*) is associative. To obtain the cyclic counterpart, we introduce the operator y which produces a relation of degree n - 1 from a relation of degree n by tying its ends together. Thus, if R is an n-ary relation (n 2 2), the tie of R is defined by the equation r(R) = {(a~, a2, .-. , a,-l) R(ul, az, ... , a,-~, a,) A al = a,). We may now represent the natural cyclic S-join of R, S, T by the expression y (R*S*T). Extension of the notions of linear and cyclic S-join and their natural counterparts to the joining of n binary relations (where n 2 3) is obvious. A few words may be appropriate, however, regarding the joining of relations which are not necessarily binary. Consider the case of two relations R (degree r ), S (degree s) which are to be joined on p of their domains (p T, p s). For simplicity, suppose these p domains are the last p of the r domains of R, and the first p of the s domains of S. If this were not so, we could always apply appropriate permutations to make it so. Now, take the Cartesian product of the first r-p domains of R, and call this new domain A. Take the Cartesian product of the last p domains of R, and call this B. Take the Cartesian product of the last s-p domains of S and call this C. We can treat R as if it were a binary relation on the domains A, B. Similarly, we can treat S as if it were a binary relation on the domains B, C. The notions of linear and cyclic S-join are now directly applicable. A similar approach can be taken with the linear and cyclic n-joins of n relations of assorted degrees. 2.1.4. Composition. The reader is probably familiar with the notion of composition applied to functions. We shall discuss a generalization of that concept and apply it first to binary relations. Our definitions of composition and composability are based very directly on the definitions of join and joinability given above. Suppose we are given two relations R, S. T is a camposition of R with S if there exists a. join U of R with S such that T = aI3 (U) . Thus, two relations are composable if and only if they are joinable. However, the existence of more than one join of R with S does not imply the existence of more than one composition of R with S. Corresponding to the natural join of R with S is the 384 Communications of the ACM Volume 13 / Number 6 / June, 1970 ndural composition9 of R with S defined by R.S = TH(R*S). Taking the relations R, S from Figure 5, their natural composition is exhibited in Figure 10 and another composition is exhibited in Figure 11 (derived from the join exhibited in Figure 7). R. S (project supplier) 1 1 1 2 2 1 FIG. 10. The natural composition of R with S (from Figure 5) T (project supplier) 1 2 2 1 FIQ. 11. Another composition of R with S (from Figure 5) When two or more joins exist, the number of distinct compositions may be as few as one or as many as the number of distinct joins. Figure 12 shows an example of two relations which have several joins but only one composition. Note that the ambiguity of point c is lost in composing R with S, because of unambiguous associations made via the points a, b, d, e. R (supplier part) S (part project) 1 1 z ; ; 1 c c f 2 2 8 is 2 e e f FICA 12. Many joins, only one composition Extension of composition to pairs of relations which are not necessarily binary (and which may be of different degrees) follows the same pattern as extension of pairwise joining to such relations. A lack of understanding of relational composition has led several systems designers into what may be called the connection trap. This trap may be described in terms of the following example. Suppose each supplier description is linked by pointers to the descriptions of each part supplied by that supplier, and each part description is similarly linked to the descriptions of each project which uses that part. A conclusion is now drawn which is, in general, erroneous namely that, if all possible paths are followed from a given supplier via the parts he supplies to the projects using those parts, one will obtain a valid set of all projects supplied by that supplier. Such a conclusion is correct only in the very special case that the target relation between projects and suppliers is, in fact, the natural composition of the other two relations-and we must normally add the phrase “for all time,” because this is usually implied in claims concerning path-following techniques. 0 Other writers tend to ignore compositions other than the natural one, and accordingly refer to this particular composition as the composition-see, for example, Kelley’s “General Topology.” 2.1.5. Restriction. A subset of a relation is a relation. One way in which a relation S may act on a relation R to generate a subset of R is through the operation restriction of R by S. This operation is a generalization of the restriction of a function to a subset of its domain, and is defined as follows. Let L, M be equal-length lists of indices such that L = iI, ., *** ,ik,M = jI,j2, e-s , jk where k 5 degree of R and k 6 degree of S. Then the L, M restriction of R by S denoted RLIMS is the maximal subset R’ of R such that ?rL(R’) = TM(S). The operation is defined only if equality is applicable between elements of ?T+(,R ) on the one hand and rjh (S) on the other for all h = 1, 2, . . . , k. The three relations R, S, R’ of Figure 13 satisfy the equation R = Rw~wsS. R (8 P ~3 s (P 8 R (8 P 3 1aA a A 1aA 2 a A c B 2 a A 2 a B b B 2 b B 2bA 2 b B FIG. 13. Example of restriction We are now in a position to consider various applications of these operations on relations. 2.2. REDUNDANCY Redundancy in the named set of relations must be distinguished from redundancy in the stored set of representations. We are primarily concerned here with the former. To begin with, we need a precise notion of derivability for relations. Suppose 0 is a collection of operations on relations and each operation has the property that from its operands it yields a unique relation (thus natural join is eligible, but join is not ). A relation R is O-derivable from a set S of relations if there exists a sequence of operations from the collection 0 which, for all time, yields R from members of S. The phrase “for all time” is present, because we are dealing with time-varying relations, and our interest is in derivability which holds over a significant period of time. For the named set of relationships in noninferential systems, it appears that an adequate collection contains the following operations projection, natural join, tie, and restriction. Permutation is irrelevant and natural composition need not be included, because it is obtainable by taking a natural join and then a projection. For the stored set of representations, an adequate collection e2 of operations would include permutation and additional operations concerned with subsetting and merging relations, and ordering and connecting their elements. 2.2.1. Strong Redundancy. A set of relations is strongly redundant if it contains at least one relation that possesses a projection which is derivable from other projections of relations in the set. The following two examples are intended to explain why strong redundancy is defined this way, and to demonstrate its practical use. In the first ex- Volume 13 / Number 6 / June, 1970 Communications of the ACM 385 ample the collection of relations consists of just the following relation employee (serial #, name, manager#, managername) with serial# as the primary key and manager# as a foreign key. Let us denote the active domain by A,, and suppose that A, (munuger#) c A, (serial#) and At (managername) C At (name) for all time t. In this case the redundancy is obvious the domain managername is unnecessary. To see that it is a strong redundancy as defined above, we observe that m4 (employee) = ~12 (empZoyee) r3 (employee). In the second example the collection of relations includes a relation S describing suppliers with primary key s#, a relation D describing departments with primary key d#, a relation J describing projects with primary key j#, and the following relations lJ (s#, d#, - * * t Q(s#,j#, .-- , R(d#,j#, **- , where in each case - - - denotes domains other than s#, d#, j#. Let us suppose the following condition C is known to hold independent of time supplier s supplies department d (relation P ) if and only if supplier s supplies some project j (relation Q) to which d is assigned (relation R). Then, we can write the equation m(P) = m(Q)-w(R) and thereby exhibit a strong redundancy. An important reason for the existence of strong redundancies in the named set of relationships is user convenience. A particular case of this is the retention of semiobsolete relationships in the named set so that old programs that refer to them by name can continue to run correctly. Knowledge of the existence of strong redundancies in the named set enables a system or data base administrator greater freedom in the selection of stored representations to cope more efficiently with current traffic. If the strong redundancies in the named set are directly reflected in strong redundancies in the stored set (or if other strong redundancies are introduced into the stored set), then, generally speaking, extra storage space and update time are consumed with a potential drop in query time for some queries and in load on the central processing units. 2.2.2. Weak Redundancy. A second type of redundancy may exist. In contrast to strong redundancy it is not characterized by an equation. A colIection of relations is weakly redundant if it contains a relation that has a projection which is not derivable from other members but is at all times a projection of some join of other projections of relations in the collection. We can exhibit a weak redundancy by taking the second example (cited above) for a strong redundancy, and assuming now that condition C does not hold at all times. The relations al2 (P), 7r1(2Q ), 7r1(2R ) are complexlo relations with the possibility of points of ambiguity occurring from time to time in the potential joining of any two. Under these circumstances, none of them is derivable from the other two. However, constraints do exist between them, since each is a projection of some cyclic join of the three of them. One of the weak redundancies can be characterized by the statement for all time, 1r1(2P ) is somec omposition of ~12(Q ) with ‘~~1(R ). The composition in question might be the natural one at some instant and a nonnatural one at another instant. Generally speaking, weak redundancies are inherent in the logical needs of the community of users. They are not removable by the system or data base administrator. If they appear at all, they appear in both the named set and the stored set of representations. 2.3. CONSISTENCY Whenever the named set of relations is redundant in either sense, we shall associate with that set a collection of statements which define all of the redundancies which hold independent of time between the member relations. If the information system lacks-and it most probably will-detailed semantic information about each named relation, it cannot deduce the redundancies applicable to the named set. It might, over a period of time, make attempts to induce the redundancies, but such attempts would be fallible. Given a collection C of time-varying relations, an associated set Z of constraint statements and an instantaneous value V for C, we shall call the state (C, 2, V) consistent or inconsistent according as V does or does not satisfy 2. For example, given stored relations R, S, T together with the constraint statement “nlz(T) is a composition of 5~1(R2 ) with ~12(X )“, we may check from time to time that the values stored for R, S, T satisfy this constraint. An algorithm for making this check would examine the first two columns of each of R, S, T (in whatever way they are represented in the system) and determine whether (1) ?rl(T) = rl(R), (2) ?rz(T) = 7r2@), (3) for every element pair (a, c) in the relation al2 (T) there is an element b such that (a, b) is in a12(R) and (b, c) is in 7r12(S). There are practica1 problems (which we shall not discuss here) in taking an instantaneous snapshot of a collection of relations, some of which may be very large and highly variable. It is important to note that consistency as defined above is a property of the instantaneous state of a data bank, and is independent of how that state came about. Thus, in particular, there is no distinction made on the basis of whether a user generated an inconsistency due to an act of omission or an act of commission. Examination of a simple IO A binary relation is complex if neither it nor its converse is a function. 386 Communications of the AMC Volume 13 / Number 6 / June, 1970 example will show the reasonableness of this (possibly unconventional) approach to consistency. Suppose the named set C includes the relations S, J, D, P, Q, R of the example in Section 2.2 and that P, Q, R possess either the strong or weak redundancies described therein (in the particular case now under consideration, it does not matter which kind of redundancy occurs). Further, suppose that at some time t the data bank state is consistent and contains no project j such that supplier 2 supplies project j and j is assigned to department 5. Accordingly, there is no element (2,5) in ~12 (P). Now, a user introduces the element (2, 5) into 7~1(2P ) by inserting some appropriate element into P. The data bank state is now inconsistent. The inconsistency could have arisen from an act of omission, if the input (2, 5) is correct, and there does exist a project j such that supplier 2 supplies j and j is assigned to department 5. In this case, it is very likely that the user intends in the near future to insert elements into Q and R which will have the effect of introducing (2, j) into al2 (Q) and (5, j) in W(R). On the other hand, the input (2, 5) might have been faulty. It could be the case that the user intended to insert some other element into P-an element whose insertion would transform a consistent state into a consistent state. The point is that the system will normally have no way of resolving this question without interrogating its environment (perhaps the user who created the inconsistency ). There are, of course, several possible ways in which a system can detect inconsistencies and respond to them. In one approach the system checks for possible inconsistency whenever an insertion, deletion, or key update occurs. Naturally, such checking will slow these operations down. If an inconsistency has been generated, details are logged internally, and if it is not remedied within some reasonable time interval, either the user or someone responsible for the security and integrity of the data is notified. Another approach is to conduct consistency checking as a batch operation once a day or less frequently. Inputs causing the inconsistencies which remain in the data bank state at checking time can be tracked down if the system maintains a journal of all state-changing transactions. This latter approach would certainly be superior if few nontransitory inconsistencies occurred. 2.4. SUMMARY In Section 1 a relational model of data is proposed as a basis for protecting users of formatted data systems from the potentially disruptive changes in data representation caused by growth in the data bank and changes in traffic. A normal form for the time-varying collection of relationships is introduced. In Section 2 operations on relations and two types of redundancy are defined and applied to the problem of maintaining the data in a consistent state. This is bound to become a serious practical problem as more and more different types of data are integrated together into common data banks. Many questions are raised and left unanswered. For example, only a few of the more important properties of the data sublanguage in Section 1.4 are mentioned. Neither the purely linguistic details of such a language nor the implementation problems are discussed. Nevertheless, the material presented should be adequate for experienced systems programmers to visualize several approaches. It is also hoped that this paper can contribute to greater precision in work on formatted data systems. Acknowledgment. It was C. T. Davies of IBM Poughkeepsie who convinced the author of the need for data independence in future information systems. The author wishes to thank him and also F. P. Palermo, C. P. Wang, E. B. Altman, and M. E. Senko of the IBM San Jose Research Laboratory for helpful discussions. RECEIVED SEPTEMBER, 1969; REVISED FEBRUARY, 1970 REFERENCES 1. CHILDS, D. L. Feasibility of a set-theoretical data structure -a general structure based on a reconstituted definition of relation. Proc. IFIP Cong., 1968, North Holland Pub. Co., Amsterdam, p. 162-172. 2. LEVEIN, R. E., AND MARON, M. E. A computer system for inference execution and data retrieval. Comm. ACM 10, 11 (Nov. 1967), 715-721. 3. BACHMAN, C. W. Software for random access processing. Datumation (Apr. 1965), 3641. 4. MCGEE, W. C. Generalized file processing. In Annual Review in Automatic Programming 6, 13, Pergamon Press, New York, 1969, pp. 77-149. 5. Information Management System/360, Application Description Manual H20-0524-1. IBM Corp., White Plains, N. Y., July 1968. 6. GIS (Generalized Information System), Application Description Manual H20-0574. IBM Corp., White Plains, N. Y., 1965. 7. BLEIER, R. E. Treating hierarchical data structures in the SDC time-shared data management system (TDMS). Proc. ACM 22nd Nat. Conf., 1967, MD1 Publications, Wayne, Pa., pp. 41-49. 8. IDS Reference Manual GE 625/635, GE Inform. Sys. Div., Pheonix, Ariz., CPB 1093B, Feb. 1968. 9. CHURCH, A. An Introduction to Mathematical Logic I. Princeton U. Press, Princeton, N.J., 1956. 10. FELDMAN, J. A., AND ROVNER, P. D. An Algol-based associative language. Stanford Artificial Intelligence Rep. AI-66, Aug. 1, 1968. Volume 13 / Number 6 / June, 1970 Communications of the ACM 38’7
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Ohm imageプラグインエラー ご指定のファイルが見つかりません。ファイル名を確認して、再度指定してください。 (Ohm.png) ゲーム内説明 可変チョークで射程を切替可能な指向性エネルギー兵器 ゲーム内説明(英語) 性能 分類 ヘビーウェポン 解除条件 DLC「Ascendance」 ダメージ(最大-最小) 35-35-33(HS倍率×1.1) 射撃方式 フルオート 装弾数(拡張マガジン装備時) 40/120(60/120) 射程 3発 ～1400(～約35.6m) 最大威力距離3発 ～8000(～約203m) 4発 8000～(約203m～) レート（ラピッドファイア装備時） 666（833） アタッチメント アタッチメント 解除条件 レッドドットサイト 20kill ハイブリッドサイト 40kill オートフォーカスサイト 60kill ターゲットエンハンサー 80kill サーマル 100kill ACOGスコープ 120kill レーザーサイト 腰撃ち10kill ストック 腰撃ち20kill 拡張マガジン 腰撃ち40kill トラッカー 腰撃ち60kill クイックドロー ADS10kill パラボラマイク ADS20kill アドバンスドライフル ADS40kill ラピッドファイア ADS60kill サプライドロップ 名前(Name) 分類(Tier) ダメージ(Damage) 精度(Accuracy) レート(Fire Rate) 射程(Range) 操縦性(Handling) 機動性(Mobility) その他 Unending Elite -1 -1 -1 装弾数増加(60発) Restless Elite -1 +3 装弾数減少 Heart Seeker Professional +2 -1 -1 レーザーサイト標準装備 Watcher Professional +2 -2 Bloodletter Professional +2 -1 予備弾数減少 Lycanthrope Enlisted +1 -1 Lunacy Enlisted +1 -1 Blood Moon Enlisted -1 +1 Enlisted Revamp Marksman +2 装弾数減少(30発) Werewolf -1 +1 特徴、考察、補足 当然だがLMGモードとSGモードでは特性が大きくことなる。 LMGモード時は高めの威力設定によりかなりの距離まで3発キルできる。また、エネルギー兵器だが壁抜きが可能。 ヘビーウェポン特有の動作の遅さはあるが、クイックドローの解除は非常に簡単なので苦労するのは最初のみだろう。 また、上・左右方向共に反動が大きく、グリップも装着できないため遠距離以遠の戦闘は難しい。 SGモード時では連射速度を犠牲に最低威力が上昇したS-12といった性能。 装弾数が40発とSGとしては非常に多いため、S-12以上に無茶な運用が可能。 ただし連射速度では劣るので無茶は禁物。また、少しでも離れるとSGモードよりLMGモードを使ったほうが早い。 共通する欠点として、エネルギー兵器故にサプレッサーが装備不可能。 また、弾薬がLMGとSGモードで共通な点にも注意。 コメント 要望した者です、ありがとうございました。 - 2015-05-05 10 00 38 S12の上位互換だな。 S12いらなくね？ - 2015-05-05 10 03 18 連射速度が段違いなんだよなぁ… - 2015-05-05 12 58 23 機動力が段違いなんだよなぁ…。 - 2015-05-20 20 22 47 全距離3発の物、レート750の物があるらしい… - 2015-05-05 10 09 13 とりあえず、わかる範囲でヴァリアント編集しました。 - 2015-05-05 10 16 54 編集のしかたがよくわからないのでここに知ってる範囲内のバリアント書きます。参考までにどうぞ。エリート「Unending」 装弾数アップ×３ 精度、レート、射程ダウン×１ 「Restless」精度アップ×３ ダメージダウン×１、装弾数ダウン×２ PRO 「Heart Seeker」精度アップ×２ レート、射程ダウン×１ レーザーサイト付き 「Watcher」精度アップ×２ レートダウン×２ エンリ 「Lycanthrope」ダメージアップ×１ レートダウン×１ 「Lunacy」レートアップ×１ 機動性ダウン×１ 以上になります。どれも持っていませんので、装弾数の増減についてはどのくらいなのか分かりません。編集できる方は申し訳ありませんが編集をお願いします。 - 2015-05-05 15 16 55 ↑追記 PRO 「Bloodletter」操縦性アップ×２ 機動性ダウン×１、予備弾ダウン×１ エンリ 「Blood Moon」操縦性アップ×１ 射程ダウン×１ 以上です。海外のサイトなどからの情報ですので間違っている箇所かあるかもしれませんが参考にどうぞ。 - 2015-05-05 17 46 58 装弾数にモノをいわせた中距離からSG乱射で敵陣に突っ込むのすげぇ楽しい(生き残れるとは言ってない - 2015-05-06 14 56 39 Ohmのエリート「Unending」出たので使ってみました。 - 2015-05-08 23 38 12 使ってみた感想としては、1マガの弾数が大幅に増えたのはいいですけど、命中率下がってるのでブレが余計にひどくてLMGでは運用しないほうがいいと思いました。 - 2015-05-08 23 43 05 どのくらい弾数増えてましたか？ - 2015-05-09 00 03 58 確か、元々40発ぐらいでしたっけ？60発に増えてましたね。大幅に増量したのは言い過ぎでしたすいませんorz - 2015-05-09 09 51 13 あと連射速度、射程もさがってるので、アドライ、ラピファつけたほうがいいです。といっても、マイナス部分が相殺か少し伸びる程度なのでその辺は微妙です。 - 2015-05-09 10 03 43 上の方々の情報で編集しました。他に正確な情報が入りましたら御協力の程、宜しくお願い致します。 - 2015-05-13 01 50 55 正直、使ってみたいバリアントがHeart SeekerとLunacyくらいしかないんだよな～ なんか全部微妙。 - 2015-05-13 16 40 58 Lycanthorpe当たったので、報告します。威力+1、レート-1で確定。使ってみた感想は威力+1により3発キルの距離がかなり伸びたと感じました。以前全距離3発のバリアントがあるらしいとおっしゃっていた方がいましたが、おそらくこれがそれに相当するものと思われます。SGモード時の火力も上がっているように感じました。ただ、レートが下がってしまっているので、よりLMGとSGの使い分けが重要になると感じました。長文失礼しました。 - 2015-05-14 20 59 04 ブレの制御も一層重要になってきそうですね。フォアグリップ付けれないので - 2015-05-15 10 07 15 Lunacy当たったので、報告します。レート+1、機動性-1で確定。感想はレート上昇による恩恵が大きいですね。特にLMGモードでですが。レートが上がったことでLMGモードでの近距離戦能力がある程度上がっていると感じました。中距離以降もレートでごり押しできないこともないですが、無理は禁物です。SGモードはちょこっとだけレートがあがったかな？という感じです。正直、ノーマルと大差ないです。レートはLMGモードでの影響が大きいですね。あと、機動性が下がっているので人にもよると思いますが、クイドロつけたほうがいいかもです。以上、長文失礼しました。 - 2015-05-20 00 13 51 追記。レートが上がったことで弾の消費が激しくなっているので、その点も注意です。 - 2015-05-20 00 18 40 SGModeは性能固定かも？ - 2015-05-21 00 16 38 確かにSGモードはそうかもしれませんね。SGモードの時だけ体感に違いを感じませんでしたし。あと、Ohmはアドラ、レーザー、ラピファでSG凸するとけっこう強いですね。何だろう、このオマケが本体感…。 - 2015-05-21 01 59 42 KF5の初弾5発,XMGのロックダウンのスペックは固定でアタッチメント,バリアントの影響を受けないから多分Ohmも固定だと思う。 - 2015-05-29 03 14 45 Restlessは装弾数３０発だと思われる、拾い物のため不明 - 2015-05-24 14 12 27 海外のサイトで見てきましたが、そこには30発とあったので、その可能性は高いですね。あと、SGモードの性能は全バリアント一緒みたいですね。 - 2015-05-24 17 45 42 SGモードは、ダメージ20-15-10 、レンジ225-600、レート500で固定だと思います。ソースhttps //docs.google.com/spreadsheets/d/1z3-syTEbrn5J2FKt_2yAEuHjsQ3DXtzbW_tTdCvAyo4/edit#gid=383776988 - 2015-05-29 15 15 34 SGモードは、ダメージ20-15-10 、レンジ225-600、レート500で固定だと思います。ソースhttps //docs.google.com/spreadsheets/d/1z3-syTEbrn5J2FKt_2yAEuHjsQ3DXtzbW_tTdCvAyo4/edit#gid=383776988 - 2015-05-29 15 15 35 Ohmの射程って、結局アメリペイテイクと同じ1400で決まりなの？ - 2015-05-31 00 00 56 Heart Seeker出たので報告します。やはり精度+2の恩恵はでかいですね。欠点である、ブレが大分小さくなってます。ただ、射程とレートが下がっているので、まずアドラは必須ですね。ラピファはお好みでという感じです。 - 2015-06-04 00 12 05 Lycanthropeがパッと見強いかと思ってたけど、中間威力が8000まであるから全マップ全距離3発ぽい - 2015-06-09 09 58 23 結論：ノ基本Ohm - 2015-06-09 10 05 33 誤爆 結論：基本ノーマルが一番強い。ただしLunacyだけはノーマルよりも強い - 2015-06-09 10 07 03 Lunacy Unending BloodMoon Lunacyはデメリットが皆無、ステータスでレートが上がってる為精度に影響が出ない。 Unendingはレートが下がってるとは言え、20発増加はデカい、射程は下がっても影響なし。BloodMoonはUnendingと同じで射程は意味がない、ただ腰だめ1上がったお得なバリアント。 - 2015-07-25 22 59 45 Restlessに拡マガ・クイドロ・ラピファで使ったら結構強かった。ラピつけても中・遠距離狙えるしSGモードもレート上がってゴリ押せた。 - 2015-09-21 21 22 19