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python Pythonタグが付けられた新着投稿 - Qiita 坂道グループの歌詞を解析して、頻出名詞を抜き出してみた。 https://qiita.com/y-nara/items/519db29be23565f4d246 今後について思ったこと秋元康先生は「川の流れのように」のような名曲も作詞されています。 2021-05-14 01:20:34
Program [全てのタグ]の新着質問一覧|teratail(テラテイル) ssl.confでドキュメントルートを設定すると証明書が外れる。 https://teratail.com/questions/338193?rss=all sslconfでドキュメントルートを設定すると証明書が外れる。 2021-05-14 01:46:00
Program [全てのタグ]の新着質問一覧|teratail(テラテイル) 【JavaScript】アキネーターのような「正解絞り込みクイズ」を設計したい! https://teratail.com/questions/338192?rss=all 2021-05-14 01:45:45
Program [全てのタグ]の新着質問一覧|teratail(テラテイル) C言語コンパイルエラー。型について https://teratail.com/questions/338191?rss=all C言語コンパイルエラー。 2021-05-14 01:42:40
Program [全てのタグ]の新着質問一覧|teratail(テラテイル) Android studioのエラー https://teratail.com/questions/338190?rss=all AndroidstudioのエラーYouTubeを見ながらAndroidstudioをインストールしました。 2021-05-14 01:38:36
Program [全てのタグ]の新着質問一覧|teratail(テラテイル) 【python】read_sqlのエラー https://teratail.com/questions/338189?rss=all 【python】readsqlのエラーpythonのpyodbcモジュールでDBに接続しようと試みた結果、以下のようなエラー文。 2021-05-14 01:12:17
Ruby Rubyタグが付けられた新着投稿 - Qiita 草野球の出欠確認Webアプリを作ろう! part.6 https://qiita.com/tomodachi_uec/items/b1b72b30713e97443230 【jQuery】HTMLのtableの行にリンクを設定するRailsでjQueryの導入方法yarnaddjqueryconfigwebpackerenvironmentjsconstenvironmentrequirerailswebpackerconstwebpackrequirewebpackenvironmentpluginsprependProvidenewwebpackProvidePluginjquerysrcjqueryjQueryjquerysrcjquerymoduleexportsenvironmentappjavascriptpacksapplicationjs略importjquery略※jqueryを導入した際にrailsサーバーを起動中だったら、一度サーバーを停止して再度起動しなおすほうがよい過去バージョンでの経験則jqueryを使えるようにした後は、そのjqueryを使用して一覧画面のテーブルの行をクリックしたときに、編集画面に遷移するように準備する。 2021-05-14 01:54:38
Docker dockerタグが付けられた新着投稿 - Qiita Jetson Xavier NX上でOpenGLを呼び出せるDocker環境を構築する https://qiita.com/B-SKY-Lab/items/46b1e7d457d9684c60e0 2021-05-14 01:27:23
Ruby Railsタグが付けられた新着投稿 - Qiita 草野球の出欠確認Webアプリを作ろう! part.6 https://qiita.com/tomodachi_uec/items/b1b72b30713e97443230 【jQuery】HTMLのtableの行にリンクを設定するRailsでjQueryの導入方法yarnaddjqueryconfigwebpackerenvironmentjsconstenvironmentrequirerailswebpackerconstwebpackrequirewebpackenvironmentpluginsprependProvidenewwebpackProvidePluginjquerysrcjqueryjQueryjquerysrcjquerymoduleexportsenvironmentappjavascriptpacksapplicationjs略importjquery略※jqueryを導入した際にrailsサーバーを起動中だったら、一度サーバーを停止して再度起動しなおすほうがよい過去バージョンでの経験則jqueryを使えるようにした後は、そのjqueryを使用して一覧画面のテーブルの行をクリックしたときに、編集画面に遷移するように準備する。 2021-05-14 01:54:38
Ruby Railsタグが付けられた新着投稿 - Qiita 【rails】テキストエディターの実装(summernote) https://qiita.com/tani__san929/items/e8657e0283c30cf967f5 公式Github参考記事①完成形・見た事があるテキストエディターが表示され、投稿詳細にも反映されています。 2021-05-14 01:54:01
海外TECH Ars Technica Microsoft puts Windows 10X variant on the back burner https://arstechnica.com/?p=1764827 microsoft 2021-05-13 16:20:23
海外TECH Ars Technica Musk: Bitcoin is bad for climate (and you can’t buy Teslas with it anymore) https://arstechnica.com/?p=1764840 flops 2021-05-13 16:00:56
海外TECH DEV Community Why C++ is most preferred for competitive programming? https://dev.to/rishabh062/why-c-is-most-preferred-for-competitive-programming-8nk Why C is most preferred for competitive programming Competitive coding is like a mind sport where you run your brain and apply logic for solving questions But I guess you always struck with a questions that why maximum programmers preferred C for doing so Here are the reasons It offers a library called STL Standard Template Library STL is a collection of C template classes that provides common data structures and functions C is a Object Oriented Programming language as well as Procedural Oriented Programming language Which make it so easy to use on given conditions C is little faster as compare to others languages like Python or Java Writing functions in C is more easy and efficient as compare to java while writing functions in Python is also efficient but major factor to analyse is speed Implementing Data structures is quite easy C is totally a beginner friendly language which seems to be very addictive once you fall in love with it That s a short introduction about it hope you find it useful Thank youRishabh Dwivedi star codechef 2021-05-13 16:27:16
海外TECH DEV Community DEBS Grand Challenge: Real-Time Stream Processing with YoMo https://dev.to/fanweixiao/debs-grand-challenge-real-time-stream-processing-with-yomo-p8i DEBS Grand Challenge Real Time Stream Processing with YoMo IntroductionDEBS or the ACM International Conference on Distributed Event Based Systems is a top conference that aims to provide a forum dedicated to the dissemination of original research the discussion of practical insights and the reporting of experiences relevant to event based computing that were previously scattered across several scientific and professional communities reference The DEBS Grand Challenge Smart Grid is the th ACM International Conference on Distributed Event Based Systems focused on two problems which are relevant to the industry real time load prediction and anomaly detection The data for the challenge was collected from a number of smart home installations deployed in Germany In traditional processing we often store data to database at first After we have gathered enough data we will process it in order to get useful information The traditional architecture However with stream processing we are able to run real time analytics on data streams quickly and easily By YoMo an open source stream processing serverless framework we can solve this challenge in a serverless and real time way The organization of this article is as follows First we will take a quick look at what kind of data we are dealing with Then we will introduce two queries which were originally proposed by ACM DEBS Finally we will install YoMo which is an open source framework for real time stream processing and implement both queries as described DataFrom Jerzak and Ziekow For the DEBS Grand Challenge we assume a hierarchical structure with a house identified by a unique house id being the topmost entity Every house contains one or more households identified by a unique household id within a house Every household contains one or more smart plugs each identified by a unique plug id within a household Every smart plug contains exactly two sensors load sensor measuring current load with Watt as unit work sensor measuring total accumulated work since the start or reset of the sensor with kWh as unit The input stream is defined as follows id a unique identifier of the measurement bit unsigned int timestamp timestamp of measurement bit unsigned int value the measurement bit float property type of the measurement for work or for load boolean plug id a unique identifier within a household of the smart plug bit unsigned int household id a unique identifier of a household within a house where the plug is located bit unsigned int house id a unique identifier of a house where the household with the plug is located bit unsigned int The complete data file is available under this link For demonstration purposes we will generate mock data using this file In real life we should be dealing with direct sensor data QueriesLoad PredictionLet s divide the whole period from t start to t end covered by the dataset into N equal slices of s seconds and call them s s s etc The average load for slice s i is given byL s i avgLoad s i median avgLoad s j s j is a nonempty set defined by s i n k where k is the number of slices within a hour period and n is a natural number between and floor i k OutliersFor this query we will compute the percentage of plugs which have a median load during the last hour greater than the median load of all plugs YoMo what is it and why do we use it YoMo is an open source streaming serverless framework for building low latency edge computing applications Built atop QUIC transport protocol and functional reactive programming interface it makes real time data processing reliable secure and easy Getting StartedFollow the instructions here to install YoMo Assuming that GOPATH has been set on your device you should be able to see a directory with the name GOPATH src github com yomorun yomo Run the following commands to create a new project Don t forget to replace YOUR GITHUB USERNAME with your actual GitHub username mkdir p GOPATH src github com YOUR GITHUB USERNAME amp amp cd yomo init debs flowNow cd into GOPATH src github com YOUR GITHUB USERNAME It should have the following structure └ーdebs flow ├ーapp go ├ーgo mod ├ーgo sum └ーsl soWe will be focusing on the Handler function in app go which defines how we want the input stream to be processed Git clone the yomo source example repository For clarity we will call it debs source git clone git github com yomorun yomo source example gitCreate a file named workflow yaml with the following content Place it under debs source name servicehost localhostport flows name debs host localhost port sinks name mock db host localhost port Make sure the GOPATH src github com YOUR GITHUB USERNAME directory contains the following files ├ーdebs flow│├ーapp go│├ーgo mod│├ーgo sum│└ーsl so└ーdebs source ├ーgo mod ├ーgo sum ├ーmain go └ーworkflow yaml Main DishBy default the Handler function in debs flow app go should look as follows func Handler rxstream rx RxStream rx RxStream stream rxstream Subscribe x OnObserve decoder Map printer Encode x return stream Subscribe x Subscribe to the input stream x is the key It is defined in debs source main go OnObserve decoder Decode byte to interface Empty interfaces are often used by code that handles values of unknown type Map printer Print out the data For query we need to define two functions in addition to what we discussed above We will call them average and predict func Handler rxstream rx RxStream rx RxStream stream rxstream Subscribe x OnObserve decoder Map printer BufferWithTime ss e ss stands for slice size Map average Map predict Encode x return stream compute the average load for each plug and save the values to db which is a global variable in this examplefunc average context Context i interface interface error convert interface to interface lst ok i interface if ok err fmt Sprintf expected type interface got v instead reflect TypeOf i fmt Printf average v n err return nil fmt Errorf err plug gt value total make map string float count make map string float for elem range lst convert interface to measurement x ok elem Measurement if ok err fmt Sprintf expected type measurement got v instead reflect TypeOf elem fmt Printf average v n err return nil fmt Errorf err if x Property load plug x toString total plug x Value count plug save to db fmt Println average for plug v range total avg v count plug fmt Printf s v v v n idx plug avg ok db plug if ok db plug make map uint float db plug idx avg fmt Println return i nil make predictions based on what we have in dbfunc predict context Context i interface interface error k t ss fmt Println predict l idx k if l fmt Println not enough data else possible values for j lst make uint l for m range lst n uint m j idx n k lst m j for plug range db average load for s j data make float l for m j range lst data m db plug j pred db plug idx median data fmt Printf s v v v n idx plug pred fmt Println idx slice return nil For query we will define a function called outliers func Handler rxstream rx RxStream rx RxStream stream rxstream Subscribe x OnObserve decoder Map printer BufferWithTime ss e Map outliers Encode x return stream which plugs have a median load greater than the median load of all plugs func outliers context Context i interface interface error convert interface to interface lst ok i interface if ok err fmt Sprintf expected type interface got v instead reflect TypeOf i fmt Printf outliers v n err return nil fmt Errorf err all make float len lst indiv make map string float plug gt values for elem range lst convert interface to measurement x ok elem Measurement if ok err fmt Sprintf expected type measurement got v instead reflect TypeOf elem fmt Printf outliers v n err return nil fmt Errorf err if x Property load all append all x Value plug x toString indiv plug append indiv plug x Value v median all fmt Printf all plugs v n v fmt Println outliers for plug vs range indiv m median vs if m gt v fmt Printf w v v v n idx plug m fmt Println idx return nil Now to run the code we need tocd into debs flow and type yomo run app gocd into debs source and type yomo wf run workflow yaml PORT go run main goYou might want to try a different set of hyperparameters ResultsFor query you should see something similar to the following load work load work load work load work average s s predict s s For query something like this load work load work load work load work load work load work load work load work load work load workall plugs outliers s About AuthorIvy Guo currently studying Computer Science at the University of Washington Thanks for reading to the end If you have any questions please feel free to email me at zhifeig cs washington edu Further ReadingRohit Gupta Rinku Shah and Apurva Mhetre In Memory High Speed Stream Processing In Proceedings of the th ACM International Conference on Distributed Event Based Systems DEBS Association for Computing Machinery New York NY USA DOI Abhinav Sunderrajan Heiko Aydt and Alois Knoll Real Time Load Prediction and Outliers Detection using STORM DEBS Proceedings of the th ACM International Conference on Distributed Event Based Systems ResearchGateACM DEBS Grand Challenge implementation using Apache Flink GithubDEBS Proceedings of the th ACM International Conference on Distributed Event Based Systems all research papers ReferencesZbigniew Jerzak and Holger Ziekow The DEBS grand challenge In Proceedings of the th ACM International Conference on Distributed Event Based Systems DEBS Association for Computing Machinery New York NY USA DOI More about YoMo 2021-05-13 16:10:18
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