投稿時間:2022-04-15 09:44:16 RSSフィード2022-04-15 09:00 分まとめ(53件)

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IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 皮まで食べられる国産高級バナナ「蜜の月バナナ」　KOKUBOが通年販売	https://www.itmedia.co.jp/business/articles/2204/14/news179.html	itmedia	2022-04-15 08:39:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] ツイッターの「編集機能」に賛否　専門家「誹謗中傷の責任逃れ」を懸念	https://www.itmedia.co.jp/business/articles/2204/15/news067.html	itmedia	2022-04-15 08:24:00
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] ワークマンの靴が人気を集める理由	https://www.itmedia.co.jp/business/articles/2204/15/news066.html	itmedia	2022-04-15 08:11:00
TECH	Techable（テッカブル）	メルセデス・ベンツのコンセプトカー「Vision EQXX」がフル充電で1000キロ走行達成！	https://techable.jp/archives/177070	visioneqxx	2022-04-14 23:00:24
AWS	AWS Japan Blog	EKS on Bottlerocket で EFS を永続ストレージに使用する	https://aws.amazon.com/jp/blogs/news/persistent-storage-using-efs-for-eks-on-bottlerocket/	永続ストレージは、長時間実行されるステートフルアプリケーションが高可用性のために状態を永続化したり、共有データセットを中心にスケールアウトするために必要です。	2022-04-14 23:52:25
AWS	AWS Japan Blog	Bottlerocket が NVIDIA GPU をサポートしました	https://aws.amazon.com/jp/blogs/news/bottlerocket-support-for-nvidia-gpus/	年月、Bottlerocketがコンピューティングワークロードの高速化のためにNVIDIAGPUをサポートするようになったことをお知らせします。	2022-04-14 23:52:13
python	Pythonタグが付けられた新着投稿 - Qiita	Jupyterでイメージ表示	https://qiita.com/makaishi2/items/beda14ecb409b9efea63	importimagefilenameabcj	2022-04-15 08:48:37
python	Pythonタグが付けられた新着投稿 - Qiita	AtCoder Beginner Contest 247 参戦記	https://qiita.com/c-yan/items/0a266b2ae6b51f5ae2c3	tprintsabcbuniquenickname	2022-04-15 08:04:57
技術ブログ	Developers.IO	DynamoDB Transactionsで複数テーブルにまたがる書き込み処理をしてみる	https://dev.classmethod.jp/articles/dynamodb-transactions-multiple-tables/	amazon	2022-04-15 00:00:03
海外TECH	Ars Technica	Physicists devise precise laser-based method to measure a baseball’s drag	https://arstechnica.com/?p=1846726	measurements	2022-04-14 23:50:48
海外TECH	MakeUseOf	Amazon Rebrands IMDb TV to Freevee: What You Need to Know	https://www.makeuseof.com/imdb-tv-is-now-freevee-what-to-know/	extra	2022-04-14 23:36:19
海外TECH	MakeUseOf	Microsoft Want to Make the Default Windows 11 Wallpaper More Dynamic	https://www.makeuseof.com/windows-11-spotlight-default-wallpaper/	default	2022-04-14 23:21:54
海外TECH	DEV Community	Lifecycle in React Component	https://dev.to/yohanesss/lifecycle-in-react-component-28g7	Lifecycle in React ComponentEverything in React is made up of components or parts of components and every components follow a certain lifecycle almost like the lifecycle of any living thing on earth They are born grow and eventually die The phase when they are born is called mount When they are grow is called update The last phase of death is called unmount This whole process is called the Component Lifecycle For each of these phases React renders certain built in methods called lifecycle methods that control the behavior of components We can see on the diagram below all of React lifecycle methods associated with the mounting updating umounting of the component diagram credit dan abramov I will explain in following section about each methods that available for each lifecycle in more detail Mounting Lifecycle MethodsThe mounting phase is a phase where is the component is created and inserted into the DOM constructor constructor is the very first method called as the component is created This method is used for two purposes To initialize the local state of a componentTo bind an event handling method to an instanceHere is an example of constructor method in action constructor props super props this state fruit Apple this eatFruit this eatFruit bind this Note that constructor is the first method invoked before the component is mounted into the DOM We should not introduce any side effect in this method getDerivedFromProps getDerivedStateFromProps is a new React lifecycle method as of React and designed to replace componentWillReceiveProps The purpose of this function is to make sure that the state and props are in sync for when it is required getDerivedStateFromProps lifecycle runs after the constructor method and before the componentDidMount lifecycle run This function accepts two paramenters props and state We have to return an object to update state or null to indicate that nothing has changed To get better understanding of how getDerivedStateFromProps works let see the following code import React from react class FavFruit extends React Component constructor super props this state favFruit Banana render return lt h gt My Favorite Fruit is this state favFruit lt h gt When the component mount we will see My Favorite Fruit is Banana is showed up in the browser How can we change our state from Banana into Apple before render Actually we can do it via getDerivedStateFromProps import React from react class FavFruit extends React Component constructor super props this state favFruit Banana Not Recommended For explaining purpose only static getDerivedStateFromProps props state return favFruit Apple render return lt h gt My Favorite Fruit is this state favFruit lt h gt When the component mount we will see My Favorite Fruit is Apple is showed up in the browser rather than Banana How can this works By returning an object getDerivedStateFromProps can utilize its data and doing update for favFruit before render method is called to render the DOM Noted that getDerivedStateFromProps is takes argument the first argument is props and the second argument is state This short example is contrived and not really representative of the way how to use getDerivedStateFromProps But it s helpful for understanding the basics However just because we can update state via getDerivedStateFromProps it doesn t mean we should There are specific use cases on when we should use getDerivedStateFromProps If we use it in the wrong context we ll be solving a problem with the wrong tool When should we use the getDerivedStateFromProps We must know why this method is created in first place There are some cases where the component is needed update the internal state in response to a prop change Component state in this manner is referred to as derived state render render method is called after getDerivedStateFromProps is called import React from react class HelloWorld extends React Component render return lt h gt Hello World lt h gt What we return in render will be rendered into the DOM In the example above we are returning JSX But we can also return an array of JSX string number or if we don t want to render anything we could return a boolean or nullimport React from react class HelloWorld extends React Component render return lt div key gt Hello World lt div gt lt div key gt Hello World lt div gt Note that in example above we add key property in jsx It is used by React to identify and keep track for which items in the list are changed updated or deleted componentDidMount After render is called The component is mounted into the DOM componentDidMount will be invoked This method is the place when you should do a side effect thing Like make a subscription to an API data fetching or maybe make a change into the DOM import React from react class ChangeDocTitle extends React Component componentDidMount document title Hello World render return lt h gt This component will change the title of your current tab lt h gt Updating Lifecycle MethodsThe update phase is a phase where the component doing a re render updating the state that has been triggered because of state or prop change getDerivedFromProps This method is also invoked when the component doing an update Since I already give an explanation of getDerivedFromProps on mounting phase please refer on that Note that getDerivedFromProps is invoked when the component is mounted and when the component is doing re render shouldComponentUpdate After getDerivedProps is called shouldComponentUpdate is invoked This method accepts two arguments the first argument is the nextProps and the second argument is nextState The purpose of this function is to determine is wheter the component is will be re render by returning true or not by returing false import React from react class FavFood extends Component constructor props super props this state favMeal French Fries shouldComponentUpdate nextProps nextState let s assume that the currentProps in this example favDrink Cola if nextProps favDrink Cola The component is won t be updated if the currentProps favDrink is still same with the nextProps favDrink even when the nextState is different with currentState return false else return true render return lt div gt lt h gt My Fav Drink is this props favDrink lt h gt lt h gt My Fav Meal is this state favMeal lt h gt lt button onClick gt this setState favMeal Burger gt Change My Meal lt button gt lt div gt Notice that in the contrived example above we can click Change My Meal to change the state of favMeal however in shouldComponentUpdate we make a conditioning where if the nextProps of favDrink is still Cola still the same with currentProps then the component will be not be updated shouldComponentUpdate is a powerful method However as proverb says With Great Power Comes Great Responsibility we must treat this method with caution If we didn t careful with our conditioning and accidentally returning false the component is not updated and this can be a problem and it will be hard to debug it render render method is called immediately depending on the returned value from shouldComponentUpdate which defaults to true getSnapshotBeforeUpdate Once render is called getSnapshotBeforeUpdate is invoked just before the DOM is being rendered It is used to store the previous values of the state after the DOM is updated Any value returned by getSnapshotBeforeUpdate will be used as a parameter for componentDidUpdate which will be explained after this getSnapshotBeforeUpdate accepts two arguments which is prevProps and prevState import React from react class FavSuperHero extends React Component constructor props super props this state mySuperHero Thor ️ componentDidMount setTimeout gt this setState mySuperHero IronMan getSnapshotBeforeUpdate prevProps prevState document getElementById prevHero innerHTML Previous Super Hero prevState mySuperHero componentDidUpdate document getElementById newHero innerHTML New Super Hero prevState mySuperHero render return lt div gt lt h id prevHero gt lt h gt lt h id newHero gt lt h gt lt div gt From the code above we can get the prevState and showing Previous Super Hero Thor ️ which is the old state that have been updated into New Super Hero IronMan which is the current state Note that it is highly recommended to never directly to set state in getSnapshotBeforeUpdate otherwise it will trigger render method componentDidUpdate componentDidUpdate is invoked as soon as the render method called update happens The common use case for the componentDidUpdate method is to updating the DOM in response to prop or state changes This method accept three arguments the first is prevProps second is prevState and the third argument is the value that has returned from getSnapshotBeforeUpdate method We can also call setState within this method However please be careful incorrect usage of setState within this componentDidUpdate can cause an infinite loop Note that you will need to wrap setState in a condition to check for state or prop changes from previous one componentDidUpdate prevProps if this props accessToken null amp amp prevProps accessToken null this getUser accessToken then user gt if user this setState user catch e gt console log Error fetching user data In the example above we do a condition where if the accessToken is not null we can fetch the user data and then updating our user state If we are does not have access token componentDidUpdate will not call getUser method hence preventing to set the user state Unmounting lifecycle methodThe unmounting phase is a phase where the component will be unmounted destroyed from the DOM componentWillUnmount This method will be called when the component is unmounted destroyed from DOM This is the place where you perform for any cleanup method cancel the network request or purge the unwanted subscriptions that was created in the componentDidMount method import React from react class Building extends React Component componentWillUnmount console log The building is destroyed render return lt h gt My Building lt h gt class DestroyBuilding extends React Component constructor props super props state showBuilding true render let building if this state showBuilding building lt Building gt return lt div gt building lt button onClick gt this setState showBuilding false gt Detonate Building lt button gt lt div gt When you click Detonate Building button The building is destroyed text will be logged into our console	2022-04-14 23:37:10
海外TECH	DEV Community	Interesting libraries, fonts and more	https://dev.to/matiascarpintini/interesting-libraries-fonts-and-more-32hf	Interesting libraries fonts and moreShare yours below Web Tools libraries and moreTypescale Great tool for all devs that are obsessed with getting the typography rightUI Colors Create Tailwind CSS color familyDaisyUI The most popular free and open source Tailwind CSS component libraryConfetti Performant confetti animation in the browserMantime React UI LibraryNocodb Open Source Airtable Alternative turns any MySQL Postgres database into a collaborative spreadsheet with REST APIs Meilisearch Powerful fast and an easy to use search engineZinc Zinc Search engine A lightweight alternative to elasticsearch that requires minimal resources written in Go Partytown Relocate resource intensive third party scripts off of the main thread and into a web worker Asciinema Record and share your terminal sessions the simple way Not paid Client did not pay Add opacity to the body tag and decrease it every day until their site completely fades away FontsManrope Google Fonts Space GroteskRecoletaNewakeSonderModernistArchiaTechniqueCamptonTrackPlus Jakarta Sans open source Jobs searchRelocate me Niche job boardfor techies looking to relocateWorkby io Hiring remote devs around the globe self promotion	2022-04-14 23:11:52
海外TECH	DEV Community	Model evaluation - MAP@K	https://dev.to/mage_ai/model-evaluation-mapk-13i6	Model evaluation MAP K TLDRIn this Mage Academy lesson we ll be going over how to evaluate ranking models built for recommendation amp personalization using mean average precision at K OutlineIntroductionRanking recommendationsCoding the algorithmMAP K evaluationMagical solution IntroductionWhen building a model for ranking items it s common to use them for recommending Ranking is flexible in terms of a number or a category and can be used for content recommendation or product recommendation For an e commerce shop you need to select what will be the best for each user browsing it at a given time as spending habits and buying habits shift fast E commerce product recommendation based on views Source GrowForce In this lesson we ll be using collaborative filtering a machine learning method to group users together and make suggestions based on what the other similar users have clicked This algorithm is best used to tailor results to a specific individual by matching users with similar audiences This allows for an output that is crafted especially for the group or cohort Collaborative filtering Source BC We ll be looking at an example similar to how video streaming services such as Netflix Disney or Crunchyroll can implement it using machine learning Additionally we ll also cover how to measure its performance which can be applied to other kinds of advanced models such as those used in computer vision or image recognition Ranking RecommendationsFor this lesson we ll be using the movie ratings dataset to curate a list of movies for each user based on movies they ve watched in the past and their ratings for each Our dataset will contain a list of movie titles from the American Film Industry AFI based on the top American movies of all time This movie ratings dataset will contain the user id movies watched and their rating Training the ModelFrom our dataset we ll be trying to select columns to determine the goals of our ranking model Using the ratings dataset we ll want to increase the likelihood of a user purchasing more products In other words we ll be ranking products for a user to increase purchases aka rating Now that we have an idea of what kind of model we can evaluate let s dive into the calculations for “mean average precision at k mAP K We ll break it down into K P K AP K and mAP K While this may be called mean average precision at K don t let its name fool you It s not calculated by simply taking the average of all precision values What exactly is K K represents the number of values you d like the top results to be For instance a K of will only return the very best suggestion while a K of will return the top ten suggestions This is very useful if you have many products when you recommend them you don t want to send all of them back just the ones that are more likely to be clicked When training a ranking model this is the number of objects we want to rank in this would be the limit of products we can show on the recommendation Precision at K amp Average Precision at KSo if “K is the number of objects to rank precision is the individual value of each object to rank So for precision at K we calculate the precision of only the top scoring values up to K To recap from our precision lesson it s calculated by getting the predicted value of the total positives In other words the matches that were positive Then to get the average precision you may be thinking that we would take the sum of all individual values and divide by k But that s not it instead we need to consider that it s at k as well This is to account for edge cases where you may want the top k but you only have a list of movies to begin with Mean Average Precision at KIf your model only has one customer in it then you re done here Otherwise you ll want to check the mean average precision to get an idea of just how diverse each user is This is the pinnacle and the calculation will need to represent the entire model not just a specific individual As a result we ll be taking all the AP K values for each unique chosen group aka user to movie Then we ll average that by taking the sum and dividing by the total number of users Coding the algorithmFor this lesson we re going to focus on the evaluation portion We ve already pre cleaned this dataset and trained the model using linear regression As a result it came up with a list of outputs as predictions so now let s take a dive into the performance PythonStarting with Python we re going to code the functions from scratch using the values determined from the linear regression model First we re going to write a function to calculate the Average Precision at K It will take in three values the value from the test set and value from the model prediction and finally the value for K This code can be found in the Github for the ml metrics Python Library def apk actual predicted k if len predicted gt k predicted predicted k score num hits for i p in enumerate predicted if p in actual and p not in predicted i num hits score num hits i if not actual return return score min len actual k This first function only calculates the Average Precision at K To break this down we take a subset of the predictions only up to k Note that this subset is only applied to the model s predictions not the actual truth set Next we iterate over the values to calculate the total precision and divide by the lesser value between the test set and k By using this apk function we can then calculate the mean using the same library np mean apk a p k for a p in zip actual predicted Similarly this will unpack the values and take the mean using the sum of the return divided by the number of times apk was called Map K evaluationNow that you know how to calculate the values let s quickly discuss what would be good or bad values Generally a machine learning model is good if it s better than the baseline values When you increase K if your model s MAP K value is rising that s a good sign that the recommendation works On the other hand if your model s values are less than the baseline something is wrong with the model and you may need more data Some additional features you should consider for retraining your recommendation model could be “views “watchtime or “genre Choosing MAP KWhen using MAP K for a recommendation model I consider MAP K the one metric MAP K is one metric to rule them all Source LotR While MAP K may be an advanced metric for the specialization of recommendations it trumps other model evaluation metrics and can be used as the one metric that matters Precision over recallWhen recommending products precision focusing on the positives i e they ll click is better than its counterpart recall looking at the negatives i e they won t click Especially when ranking on a positive or increasing output Likewise because recall doesn t matter as much F Score is no longer the “best metric K more than a scoreSimilarly “K is a variable length that can be split apart When performing an evaluation and making predictions it s good to take into account trends in the present and the future This is why it can be better than the ROC AUC score which creates a broadened generalization ignoring short bursts or trendy data Magical solutionMAP K Is an advanced metric that has yet to be explored more within machine learning Mage is specialized in creating ranking models that support collaborative filtering in order to make recommendation models accessible for small businesses Mage is simple and breaks down the ranking model flow into basic questions Who is it for What do you want to rank What should increase as a result Then you may follow our suggestions to think like a data scientist and train a model PerformanceOnce that s complete and you have a model you re greeted by the overview amp statistics screen For recommendation models Mage is smart enough to use Mean average precision and provides it at and Here we see that the model did beat the baseline and its overall performance is excellent because it beat the baseline We also notice that for better performance it s best to have higher K s Now a value of Average performance means that a user is likely to click on any of the recommended as it expands to choices the likelihood rises which shows the model is working as intended RecommendationsUnder the samples tag you can see what factors mattered For instance in the case of user because they loved The Godfather Part II and disliked Double Indemnity we use this information to suggest other movies similar users liked Because you reviewed the movies like this we suggest that you try watchingIsn t that great Now you can use AI to do things that enterprises require a large team of dedicated professionals for We hope you enjoyed this lesson on MAP K and recommendation models For more knowledge on machine learning visit Mage Academy If you re interested in this low code AI tool please sign up and try Mage	2022-04-14 23:05:50
海外TECH	Engadget	HBO Max exec admits to the app’s early flaws	https://www.engadget.com/hbo-max-exec-owns-up-to-the-apps-early-flaws-233115087.html?src=rss	HBO Max exec admits to the app s early flawsViewers have long complained about the early HBO Max app s tendency to crash and its lack of discoverability features There have been a number of overhauls and fixes since then Now we know why Turns out that HBO Max launched its apps before they were ready in order to keep up with its competitors The app was “never intended to go global or to suit the needs of a direct to consumer market according to an interview that Sarah Lyons HBO Max s head of product experience gave Protocol The network wanted to build an audience first and then fix the app s flaws as the service scaled up While Lyons admits that the early days of HBO Max were rocky she thinks the company made the right decision We ve been changing out the engine of the plane while we re flying the plane she said “I do think it was the right decision to try to balance both said Lyons HBO Max first released its app in May to join an already saturated streaming ecosystem that included Netflix Hulu Disney Apple TV and others At the time both HBO Go the network s on demand app for cable subscribers and HBO Now the standalone app for cord cutters were still available a fact that confused many subscribers The network has since retired both apps Viewers have flocked to Reddit since the app s initial launch with complaints that spanned platforms and devices “We ve been trying to watch the Harry Potter movies and literally every min or so we get an ERROR message and have to force close the app Another time it froze completely It s absolute garbage I don t have this problem with any other app or streaming service wrote one user in a thread on the r HBOMax subreddit from January entitled “Why does this app suck so hard For many viewers long accustomed to advanced recommendation algorithms on Netflix and other streaming platforms it was hard to get used to HBO Max s lack of discoverability features Lyons admitted that HBO Max wasn t built with discovery in mind and the app tried to address this by putting every new show on the app s home page “You didn t have to go find anything because whatever show you were looking for was going to be at the top of the home page said Lyons While Engadget s early review of HBO Max detailed its flaws we pointed out that it was still a “smart bet for the company Since then the service has made many improvements including a new Apple TV app and updates to its apps for Roku Playstation Android TV and others But following a recent billion merger with Discovery the biggest change is yet to come The plan is to merge both Discovery Plus and HBO Max into one unified platform HBO Max ended with million subscribers when combined with the network s cable subscribers of HBO who also have access to the streaming service It ll soon absorb at least another million subscribers from Discovery Plus While there could be more bumps down the road viewers can at least be assured that HBO Max has more experience under its belt now	2022-04-14 23:32:58
海外科学	NYT > Science	Gina McCarthy, Top Climate Adviser, Is Said to Be Planning Departure	https://www.nytimes.com/2022/04/14/climate/gina-mccarthy-climate-adviser.html	Gina McCarthy Top Climate Adviser Is Said to Be Planning DepartureMs McCarthy was tapped by President Biden to lead an ambitious domestic climate agenda Associates say she is frustrated by the slow pace of progress	2022-04-14 23:31:42
金融	JPX マーケットニュース	[OSE]国債先物における受渡適格銘柄及び交換比率一覧表の更新	https://www.jpx.co.jp/derivatives/products/jgb/jgb-futures/02.html	銘柄	2022-04-15 09:00:00
金融	金融総合：経済レポート一覧	65歳以上の給与所得者の増加と在職老齢年金の見直し	http://www3.keizaireport.com/report.php/RID/492444/?rss	給与所得	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	３億人の‘新市民’市場と保険サービス（中国）：基礎研レター	http://www3.keizaireport.com/report.php/RID/492445/?rss	研究所	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	シンガポール通貨庁、半年で３度目の引き締め決定、引き締めペースも強化～シンガポールは財政、金融政策の両面で引き締めが進むなど、ポスト・コロナを目指す動きが前進：Asia Trends	http://www3.keizaireport.com/report.php/RID/492446/?rss	asiatrends	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	受注好調でも物足りなさを覚える局面：Market Flash	http://www3.keizaireport.com/report.php/RID/492447/?rss	marketflash	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	日本銀行が中銀デジタル通貨（CBDC）第1段階実証実験の報告書を公表：木内登英のGlobal Economy ＆ Policy Insight	http://www3.keizaireport.com/report.php/RID/492450/?rss	lobaleconomypolicyinsight	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	FX Daily（4月13日）～ドル円、2002年5月以来の高値圏	http://www3.keizaireport.com/report.php/RID/492451/?rss	fxdaily	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	ドル円は約20年ぶりの円安水準に～今後の相場展開を考える：市川レポート	http://www3.keizaireport.com/report.php/RID/492487/?rss	三井住友	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	カナダ金融政策（2022年4月）～利上げペース加速とQT開始を決定：マーケットレター	http://www3.keizaireport.com/report.php/RID/492488/?rss	投資信託	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	インド準備銀行11会合連続政策金利据え置き～インフレ対策に重点を置く姿勢を示す：新興国レポート	http://www3.keizaireport.com/report.php/RID/492489/?rss	据え置き	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	投資のヒント「排出権市場が機能マヒ、今後の展開は？～ESGニュース　気になるトピック（4月）」	http://www3.keizaireport.com/report.php/RID/492490/?rss	三井住友トラスト	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	【ACI】ヘルスケア・マンスリー・レポート（2022年3月）～ヘルスケア株は絶対値で大幅に上昇...	http://www3.keizaireport.com/report.php/RID/492491/?rss	野村アセットマネジメント	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	暗号資産への投資に対する警告文書の発出（欧州）～欧州金融監督当局から公表された文書の紹介：基礎研レター	http://www3.keizaireport.com/report.php/RID/492492/?rss	Detail Nothing	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	不動産はインフレヘッジになるか：不動産マーケットリサーチレポート	http://www3.keizaireport.com/report.php/RID/492504/?rss	三菱ufj信託銀行	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	国民健康保険における予防・健康づくりに関する調査分析事業 報告書	http://www3.keizaireport.com/report.php/RID/492526/?rss	事業報告書	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	韓国中銀、次期総裁就任前も、物価対応を理由に追加利上げを決定～政策委員の「タカ派」傾斜が確認されるなか、次期体制下でも追加利上げに動く可能性は高い：Asia Trends	http://www3.keizaireport.com/report.php/RID/492529/?rss	asiatrends	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	足元の債券利回りの上昇が確定給付企業年金に与える影響について～退職給付会計と非継続基準の観点から	http://www3.keizaireport.com/report.php/RID/492556/?rss	確定給付企業年金	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	マテリアリティの開示が株主資本コストに及ぼす影響について	http://www3.keizaireport.com/report.php/RID/492564/?rss	日本政策投資銀行	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	ながさき暮らしのデータBOX：キャッシュレス決済、今後は？～6割近くが、「キャッシュレス決済が現金より多い」	http://www3.keizaireport.com/report.php/RID/492573/?rss	長崎	2022-04-15 00:00:00
金融	金融総合：経済レポート一覧	最近のウクライナ情勢とESG投資について～ロシアのウクライナ侵攻後の論説のサーベイと長期投資の観点からの考察	http://www3.keizaireport.com/report.php/RID/492584/?rss	長期	2022-04-15 00:00:00
ニュース	BBC News - Home	El Shafee Elsheikh: Guilty verdict for Islamic State jihadist	https://www.bbc.co.uk/news/world-us-canada-61112787?at_medium=RSS&at_campaign=KARANGA	elsheikh	2022-04-14 23:24:03
ニュース	BBC News - Home	Child sex abuse victims want Online Safety Bill strengthened	https://www.bbc.co.uk/news/technology-61082449?at_medium=RSS&at_campaign=KARANGA	predators	2022-04-14 23:01:44
ニュース	BBC News - Home	Crypto boss says fraudsters have 'special place in hell'	https://www.bbc.co.uk/news/business-61011151?at_medium=RSS&at_campaign=KARANGA	cryptocurrency	2022-04-14 23:12:31
ニュース	BBC News - Home	Ukraine: Returning home to a country still at war	https://www.bbc.co.uk/news/world-europe-61105013?at_medium=RSS&at_campaign=KARANGA	ukrainians	2022-04-14 23:00:55
ニュース	BBC News - Home	The Papers: Migrant plan 'inhumane' and Sussexes visit Queen	https://www.bbc.co.uk/news/blogs-the-papers-61114859?at_medium=RSS&at_campaign=KARANGA	asylum	2022-04-14 23:35:44
ニュース	BBC News - Home	Shanghai lockdown: How Covid tests China's 'Great Firewall'	https://www.bbc.co.uk/news/world-asia-china-61102809?at_medium=RSS&at_campaign=KARANGA	lockdown	2022-04-14 23:01:20
ニュース	BBC News - Home	Sienna Miller: Anatomy of a Scandal actress on the 'ignorance' of privilege	https://www.bbc.co.uk/news/entertainment-arts-61107909?at_medium=RSS&at_campaign=KARANGA	ordinary	2022-04-14 23:12:42
ニュース	BBC News - Home	Brazil: Wild animals take to the streets in Rio de Janeiro	https://www.bbc.co.uk/news/world-latin-america-61112864?at_medium=RSS&at_campaign=KARANGA	animals	2022-04-14 23:01:21
ニュース	BBC News - Home	Africa's week in pictures: 8-14 April 2022	https://www.bbc.co.uk/news/world-africa-61092881?at_medium=RSS&at_campaign=KARANGA	africa	2022-04-14 23:19:42
ニュース	BBC News - Home	Jerusalem on edge as festivals fall amid tensions	https://www.bbc.co.uk/news/world-middle-east-61095047?at_medium=RSS&at_campaign=KARANGA	israeli	2022-04-14 23:05:31
ニュース	BBC News - Home	Durban floods: Is it a consequence of climate change?	https://www.bbc.co.uk/news/61107685?at_medium=RSS&at_campaign=KARANGA	clear	2022-04-14 23:11:18
ニュース	BBC News - Home	The book that sank on the Titanic and burned in the Blitz	https://www.bbc.co.uk/news/uk-england-london-57683638?at_medium=RSS&at_campaign=KARANGA	april	2022-04-14 23:39:47
ビジネス	ダイヤモンド・オンライン - 新着記事	ロシア巡洋艦が沈没、ウクライナは「ミサイル攻撃」主張 - WSJ発	https://diamond.jp/articles/-/301705	沈没	2022-04-15 08:03:00
北海道	北海道新聞	Ｂジェイズ加藤がメジャー再昇格　今季２度目、負傷者と交代	https://www.hokkaido-np.co.jp/article/669751/	大リーグ	2022-04-15 08:16:00
海外TECH	reddit	Admiral Igor Osipov, commander of the Black Sea Fleet, has been arrested by people in civilian clothes. Witnesses report that the detention was rather harsh - the admiral's adjutant was beaten very badly.	https://www.reddit.com/r/UkrainianConflict/comments/u3tud0/admiral_igor_osipov_commander_of_the_black_sea/	Admiral Igor Osipov commander of the Black Sea Fleet has been arrested by people in civilian clothes Witnesses report that the detention was rather harsh the admiral x s adjutant was beaten very badly submitted by u tomispev to r UkrainianConflict link comments	2022-04-14 23:02:36
GCP	Cloud Blog	The definitive guide to databases on Google Cloud: Part 1 - Data modeling basics	https://cloud.google.com/blog/topics/developers-practitioners/definitive-guide-databases-google-cloud-part-1-data-modeling-basics/	The definitive guide to databases on Google Cloud Part Data modeling basicsThe next best thing since sliced bread As I wake up to the smell of tea and freshly ground cardamom every morning all I could think of is how nice it would be with some soft buttery sliced bread Hmm if only it wasn t for the gluten allergy Anyway the one thing that is half as old as sliced bread but twice as good as that is Database The set of comprehensive application programs that can be leveraged to store access manage and update data whilst assuring structure recovery security concurrency and more Yup exactly years ago E F Codd the father of Database Management Systems DBMS propounded and formalized these as commandments that are in fact in number starting from I know right that make up a DBMS We have evolved since the s when we used one database to store and secure information to these modern times where we use one database per stage in the data lifecycle in fact one database per data stage type and structure in most cases In this blog we are going to discuss the business attributes technical aspects design questions considerations to keep in mind while “Designing the Database Model and if you hang on till the end a simple contest to ensure you have been entertained thoroughly And if you ask…Is there a quick and dirty choice The short answer is NO However your selection of database can be derived based on the answers to a multitude of database business requirement questions including the top mandatory ones below What is the stage in the data lifecycle Starting from Ingestion  landing data from different sources in one place  Storage  staging in a persistent location for use later  Processing  transformational stages for analysis to Visualization  derive and present insights from the analysis we need to be aware of the stage in the lifecycle of data for which we are designing discussing storage requirements What is the type of data you are bringing in There are broadly types of data we would be dealing with that would highly influence the choice of database and storage a  Application that covers transactional and event based datab  Live Streaming Real time that covers data from real time sourcesc   Batch that covers bulk scheduled interval and event triggered dataReal time data is immediate and constantly up to date the integration of this type of data needs to be carried out at the time of the event Whereas Batch data process is scheduled at specific times and amounts Is your data Structured Unstructured or Hybrid a  Structured data is modeled with rows columns and are mostly transactional and analytical in natureb  Unstructured could be anything like images audio files etc The amount of unstructured data is much larger than that of structured data Unstructured data makes up a whopping   or more of all enterprise data and the percentage keeps growing Per the Forbes article on the IDC Report Data Age sponsored by Seagate Technology Unstructured data has been predicted to increase by zettabytes by so the methods by which we store such data is more important than ever This means that companies not taking unstructured data into account are missing out on a lot of crucial business intelligence c   The semi structured hybrid data are the ones with attributes defined but could vary for each record The major differentiating factor for each kind of semi structured data is in the way they are retrieved accessedEngineering or architecting ask the right data questions There is always this question around the responsibilities concerning data If you design data architecture manage business and technology requirements around the architecture involve in design of data extraction transformation loading and provide direction to the team for methods of organizing formats and presentation of data then you are an Architect As an Engineer you create applications and develop solutions to enable data for distribution processing analysis and participate in one or more of those activities directly But in either case you are an expert you need to ask the right questions and need to set the right expectation as you approach the technical aspects of data It is not always possible to get the one solution with these questions below but will definitely help get started and eliminate the mismatches easily right off the start Volume and Scalability What is the size of data you are going to be dealing with at the time of design and at each stage in the lifecycle of the data How much do you expect it to scale with time Velocity What is the rate schedule at which the data needs to be sent and processed Veracity What is the variation expected to be seen in the data incoming Security How much access restriction does your data need Row level object level fine grained levels of access control encryption privacy and compliance And other most common areas of design consideration are Availability Resilience Reliability and Portability Choosing the right databaseHaving assessed all these questions and considerations the logical next step is to choose from eliminate from the database types out there We have the good old Relational Database for the Online Transaction Processing OLTP that typically follow normalization rules and Online Analytical Processing OLAP that are typically used for Data Mart and Data Warehouse applications This type requires Structured Query Language to define manipulate query and manage And then we have the NOSQL database types for the semi structured i e less structured than Relational database There is no formal model or normalization requirement for this type Key Value pair DB Document DB Wide Column DB Graph DB are some types of NOSQL databases  More on each of these technologies to be covered in upcoming episodes code labs and PoCs of the blog series  Before I goPhew you would think that s it for now Not quite Let me leave you on a fun note All this while we have been talking about the types requirements design aspects database choices and what not Here is a simple exercise to flex your understanding so far How would you model a NoSQL solution for an application that needs to query the lineage between individual entities that are represented in pairs E g If A B B C C E A E D F are the row of records that are in pairs your application should represent A B C E belonging to one lineage and D F belonging to another  Tips for Modeling a NoSQL database What are the design questions that come to your mind Does NOSQL have a schema Sometimes it s misleading when we hear that NoSQL options are schema less They do not have a schema in the same strict way as the relational databases However they have an underlying structure that is used to store the data Each of the four main types of NoSQL databases is based on a specific way of storing data This provides the logic for a data model in each case Document databases store data in document data type which is similar to a JSON document Each document stores pairs of fields and values with a wide variety of data types and data structures being used as valuesKey Value database items consist of a key and a value making this the simplest type of database The data model consists of two parts a string with some relationship to the data and the data Data is retrieved using the direct request method provide the key and get the data rather than through the use of a query languageWide column databases use a table form but in a flexible and scalable way Each row consists of a key and one or more related columns which are called column families Each row s key column family can have different numbers of columns and the columns can have different kinds of data Data is retrieved using a query language This column structure enables fast aggregation queriesGraph Databases consist of nodes connected by edges Data items are stored in the nodes and the edges store information about how the nodes are related Node and relationship information is typically retrieved using specialized query languages sometimes SQL as wellNext Steps…Take a few minutes to think through the exercise mentioned in the above section Also you can go through more details of this reference architecture and database options here   Once you have had the time to do these I would love to hear ideas and feedback from you on LinkedIn	2022-04-14 23:30:00