投稿時間:2023-06-29 08:41:17 RSSフィード2023-06-29 08:00 分まとめ(46件)

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IT	気になる、記になる…	Beatsの新型ワイヤレスヘッドホン｢Beats Studio Pro｣のより詳細な仕様が明らかに	https://taisy0.com/2023/06/29/173464.html	beatsstudiopr	2023-06-28 22:55:06
IT	気になる、記になる…	Amazon、｢Kindle本8点まとめて買うと さらに15％ポイント還元｣キャンペーンを開催中	https://taisy0.com/2023/06/29/173457.html	amazon	2023-06-28 22:27:29
IT	気になる、記になる…	Apple、｢iOS 16.6｣と｢iPadOS 16.6｣のパブリックベータ4を提供開始	https://taisy0.com/2023/06/29/173462.html	apple	2023-06-28 22:27:11
IT	気になる、記になる…	Apple、｢macOS Ventura 13.5｣のパブリックベータ4を提供開始	https://taisy0.com/2023/06/29/173460.html	apple	2023-06-28 22:26:20
IT	ITmedia 総合記事一覧	[ITmedia ビジネスオンライン] 目標達成できず部下が落ち込んでいる　効果的なアドバイスは？	https://www.itmedia.co.jp/business/articles/2306/29/news008.html	itmedia	2023-06-29 07:30:00
IT	ITmedia 総合記事一覧	[ITmedia エグゼクティブ] 2万人以上のビジネスリーダーの睡眠を改善して分かってきた中高年のリーダーの睡眠が業績を左右する	https://mag.executive.itmedia.co.jp/executive/articles/2306/29/news044.html	itmedia	2023-06-29 07:01:00
TECH	Techable（テッカブル）	SEOのプロ集団が開発！知識ゼロでも少ないリソースで集客アップを目指せるSEOツール	https://techable.jp/archives/212937	非常	2023-06-28 22:30:32
TECH	Techable（テッカブル）	自律型ロボットの移動に必要な周辺情報を取得する「自己位置推定システム 」にAIカメラ採用	https://techable.jp/archives/212909	visualslam	2023-06-28 22:00:20
AWS	AWS Partner Network (APN) Blog	Ganit Transforms Fast Fashion Apparel Retail with Intelligent Demand Forecasting on AWS	https://aws.amazon.com/blogs/apn/ganit-transforms-fast-fashion-apparel-retail-with-intelligent-demand-forecasting-on-aws/	Ganit Transforms Fast Fashion Apparel Retail with Intelligent Demand Forecasting on AWSGauging market demand for the apparel retail industry is challenging The success of SKUs sold depends on customer preference fitting feel regional acceptance and latest trends which can change frequently Learn how Ganit has successfully deployed inventory management systems using intelligent demand forecasting at the core of its solutions This system has helped many clients optimize their inventory leading to efficient working capital deployment and improvement in topline and bottom line numbers	2023-06-28 22:09:51
AWS	AWS Database Blog	Migrate on-premises SQL Server workloads to Amazon RDS Custom for SQL Server using distributed availability groups	https://aws.amazon.com/blogs/database/migrate-on-premises-sql-server-workloads-to-amazon-rds-custom-for-sql-server-using-distributed-availability-groups/	Migrate on premises SQL Server workloads to Amazon RDS Custom for SQL Server using distributed availability groupsIn this post we provide a SQL Server Always On cluster database migration pattern solution to Amazon Relational Database Service Amazon RDS Custom for SQL Server using distributed availability groups This solution helps reduce the migration downtime through continuous data synchronization combined with a failover process This post is not a high availability and disaster …	2023-06-28 22:17:50
js	JavaScriptタグが付けられた新着投稿 - Qiita	[Chrome拡張]　ポップアップメニューからWebページを操作する方法	https://qiita.com/doran/items/c34be1c03353a02cd04f	chrome	2023-06-29 07:21:57
Ruby	Railsタグが付けられた新着投稿 - Qiita	Rails7+Tailwind css+esbuildでAction Textを使用する方法	https://qiita.com/MIDO-ruby7/items/fd2c31ba00d8420cf630	actiontext	2023-06-29 07:22:14
技術ブログ	Developers.IO	【セキュアアカウント切り戻し手順】AWS Security Hub無効化手順のご案内	https://dev.classmethod.jp/articles/secure-account-setting-switchback-securityhub/	awssecurityhub	2023-06-28 22:16:52
海外TECH	DEV Community	Why Google Zanzibar Shines at Building Authorization	https://dev.to/warrant/why-google-zanzibar-shines-at-building-authorization-47do	Why Google Zanzibar Shines at Building AuthorizationOver the last couple years authorization AKA “authz has become a hot topic of debate Proponents of various authz frameworks libraries and philosophies have voiced their opinions on how it should be implemented jockeying for position to become the de facto way to implement authz Among the contestants in this debate Google s Zanzibar has recently emerged as a popular way of not only modeling and enforcing authorization for modern fine grained use cases but also of scaling to meet the requirements of today s large scale cloud native applications When we started Warrant in we set out to build developer friendly authorization infrastructure that all engineering teams could use We knew that Warrant would be a core piece of infrastructure for our customers so our authz service had to be generic enough to model all of their use cases and scalable enough to support access checks across their authz models globally and with low latency After reading the seminal Zanzibar paper we decided to build Warrant s core authorization engine based on many of the concepts described in the paper e g tuples namespaces zookies etc more on these concepts later We believed that Zanzibar had zeroed in on a set of fundamental concepts and patterns that would help us build a generic solution to the authorization challenges of any application We launched Warrant to much discussion and debate and have tackled a wide variety of authorization challenges since then helping many companies build production ready authz In this post I ll talk about why we believe Zanzibar is a great foundation for implementing authorization discuss some areas where it falls short and share how we ve addressed those shortcomings with enhancements of our own A Flexible Uniform Data Model for AuthorizationZanzibar provides an intuitive and more importantly uniform data model for representing authorization Its authorization paradigm known as relationship based access control ReBAC is based on the principle that all resources in an application are related to each other via directed relationships e g user is owner of report abc and the application s authz rules i e the abilities granted to users of the application flow from these relationships either explicitly or implicitly Representing authorization in this way feels intuitive because it s similar to how most of us already design data models e g relational database schemas for our own applications making it easy to understand and reason about authz models in Zanzibar ReBAC is also extremely flexible capable of representing any authz model you can throw at it including other authz paradigms like role based access control RBAC and attribute based access control ABAC “Zanzibar provides a uniform data model and configuration language for expressing a wide range of access control policies from hundreds of client services at Google from Zanzibar Google s Consistent Global Authorization SystemIn practice each relationship between two resources is represented as a “tuple composed of three parts The object resource on which the relationship is being specified The relationship being specified The subject a resource or group of resources that will possess the specified relationship on the object Together the set of all tuples makes up a big graph of relationships in which the objects and subjects are the nodes and the relationships between them are the edges This graph is powerful because it can be traversed in various ways to determine the capabilities of users in an application For example a path between a user and a resource might mean that the user has write privileges on the resource In another scenario it might mean that the user is not allowed to perform writes on a different resource To dictate how the graph can be traversed and to assign semantic meaning for authz to the relationships it represents Zanzibar provides us with namespaces Authorization Logic Decoupled from Application LogicNamespaces allow us to assign meaning to the relationships represented by our graph for the purpose of authorization Each namespace defines the available relationships e g admin writer reader on a type of resource e g report and optionally a set of logical rules that specify how each relationship can be inferred from others e g an editor of a report is also a viewer of that report They are similar to database schemas in that they allow us to define the structure of an authorization model but unlike database schemas namespaces also allow us to express logic on top of that structure For example the namespace for a report object type might define three relationships admin editor and viewer In addition to defining these relationships the namespace can also specify A subject can only have the admin relationship on a report explicitly A subject can have the editor relationship on a report explicitly OR implicitly if it has the admin relationship on that report A subject can have the viewer relationship on a report explicitly OR implicitly if it has the editor relationship on that report The ability for namespaces to specify logical rules or policies like these between relationships makes it possible to separate authorization logic from application logic This makes application code much simpler The application only needs to confirm that a user has a particular capability e g editor before executing a section of code e g persisting a proposed edit on a document Stateful Centralized Query able PermissionsWhile modern policy driven authz solutions like Open Policy Agent OPA offer some of the features and benefits described so far one thing remains unique to Zanzibar It s a stateful centralized service meaning that all tuples the relationship graph and namespaces are pieces of data that are stored and updated centrally A major benefit of this design is the ability to query the data not only to check if a particular subject has access to a specific resource but also to get the list of resources a particular subject has access to This is extremely useful in practice for example to audit a user s privileges for regulatory compliance or to understand the impact of a change to the authorization model before applying it In our opinion having the ability to query permissions like this should be a requirement in any authorizaton system and can only be done in a stateful system with a global view of all authz data However as with any system design decision this approach comes with its trade offs Global Scale Low LatencySince Zanzibar stores all authorization data centrally client applications must make requests to it to check permissions making it a potential performance bottleneck for those applications To minimize the end to end response times of authz queries Zanzibar is distributed globally as close to client applications as possible and utilizes aggressive caching responding to queries from cache in single milliseconds whenever possible Because access patterns and freshness requirements for authorization data vary from application to application Zanzibar has the concept of a “zookie a global incrementing version number for each change made to the authorization data Zookies make caching feasible while still allowing client applications to dictate when they favor correctness over speed Our Take on ZanzibarWhile the concepts and features of Zanzibar are great Google never built a publicly available implementation because they built Zanzibar to solve their own authorization needs for services like Drive Docs YouTube and more Fortunately Warrant implements all of the concepts we ve discussed so far many of them with slight variations intended to either improve developer experience or add functionality that we feel Zanzibar lacks In Warrant tuples are known as warrants A warrant includes the same three major components object relationship and subject as a tuple but can also include an optional component that we call a policy The policy component is a user defined boolean expression that is evaluated at query time to determine whether the warrant is available to the query or not If a warrant matches a query and its policy evaluates to true that warrant is considered during the query Otherwise the warrant is ignored Policies can reference dynamic contextual data that is passed in by the query e g user is an approver of transaction abc if transaction amount lt Having the ability to do this makes Warrant capable of modeling attribute based access control ABAC scenarios in which external data e g the transaction amount is required to make an authorization decision something Zanzibar s purely ReBAC approach struggles with You can learn more about warrants in our documentation namespaces tuples object types warrantsNamespaces as described in Zanzibar are known as object types in Warrant Object types are represented as JSON and conform to a JSON schema specification Unlike Zanzibar s namespaces object types support the ability to restrict the types of objects that can possess each relationship making it easier for developers to reason about the scope of each object type s relationships Warrant also provides various pre built object types to standardize and simplify the implementation of common authorization use cases like RBAC feature entitlements and multi tenancy within Zanzibar s ReBAC paradigm You can learn more about object types and the various built in models Warrant offers in our documentation Looking Forward amp Open SourceMore than two years after choosing to build Warrant atop Zanzibar s core principles we re extremely happy with our decision Doing so gave us a solid technical foundation on which to tackle the various complex authorization challenges companies face today As we continue to encounter new scenarios and use cases we ll keep iterating on Warrant to ensure it s the most capable authorization service To share what we learn and what we build with the developer community we recently open sourced the core authorization engine that powers our fully managed authorization platform Warrant Cloud If you re interested in authorization or Zanzibar check it out and give it a star	2023-06-28 22:45:00
海外TECH	DEV Community	Introducing The Spatial Cypher Cheat Sheet	https://dev.to/lyonwj/introducing-the-spatial-cypher-cheat-sheet-3bph	Introducing The Spatial Cypher Cheat Sheet A Resource For Working With Geospatial Data In NeojIn this post we explore some techniques for working with geospatial data in Neoj We will cover some basic spatial Cypher functions spatial search routing algorithms and different methods of importing geospatial data into Neoj I recently went through the examples in the Spatial Cypher Cheat Sheet in an episode of the Neoj Livestream you can watch the recording below The first page of the Spatial Cypher Cheat Sheet introduces Cypher and the property graph data model the spatial types available in the Neoj database as well as some of the spatial functions available in Cypher We also touch on importing geospatial data into Neoj from CSV and GeoJSON as well as some of the path finding algorithms breadth first search Dijkstra s Algorithm and A Page of the Spatial Cypher Cheat Sheet covers using Neoj with Python First using the Neoj Python driver to query data from Neoj to build a GeoDataFrame We then explore using the OSMNx Python package for fetching data from OpenStreetMap to load a road network into Neoj You can download the PDF version of the Spatial Cypher Cheat Sheet or read on for the same content in the rest of this blog post Introduction To Geospatial Cypher Functions With NeojIntro To Cypher amp The Property Graph Data ModelSpatial Point TypeSpatial Cypher FunctionsData ImportRouting With Path Finding AlgorithmsUsing Neoj With PythonThe Neoj Python DriverCreating A GeoDataFrame From Data Stored In NeojWorking With OpenStreetMap DataLoading A Road Network With OSMNxAnalyzing and Visualizing Road Networks With Neoj Bloom And Graph Data Science Introduction To Geospatial Cypher Functions With Neoj Intro To Cypher And The Property Graph Data ModelNeoj is a database management system DBMS that uses the property graph data model which is composed of nodes relationships and properties to model store and query data as a graph Nodes can have one or more labels relationships have a single type and direction Key value pair properties can be stored on nodes and relationships The Cypher query language is used to query data and interact with Neoj Cypher is a declarative query language that uses ASCII art like syntax to define graph patterns that form the basis of most query operations Nodes are defined with parenthesis relationships with square brackets and can be combined to create complex graph patterns Common Cypher commands are MATCH find where the graph pattern exists CREATE add data to the database using the specified graph pattern and RETURN return a subset of the data as a result of a traversal through the graph MATCH p sfo Airport iata SFO FLIGHT TO gt rsw Airport iata RSW RETURN p Spatial Point TypeNeoj supports D or D geographic WGS or cartesian coordinate reference system CRS points Here we create a point by specifying latitude longitude WGS is assumed when using latitude longitude RETURN point latitude longitude Point data can be stored as properties on nodes or relationships Here we create an Airport node and set its location as a point CREATE a Airport SET a iata SFO a location point latitude longitude RETURN aDatabase indexes are used to speed up search performance Here we create a database index on the location property for Airport nodes This will help us find airports faster when searching for airports by location radius distance or bounding box spatial search CREATE POINT INDEX airportIndex FOR a Airport ON a location Spatial Cypher FunctionsRadius Distance SearchTo find nodes close to a point or other nodes in the graph we can use the point distance function to perform a radius distance searchMATCH a Airport WHERE point distance a location point latitude longitude lt RETURN aWithin Bounding BoxTo search for nodes within a bounding box we can use the point withinBBox function MATCH a Airport WHERE point withinBBox a location point longitude latitude point longitude latitude RETURN aSee also my other blog post that goes into a bit more detail on spatial search functionality with Neoj including point in polygon Spatial Search Functionality With NeojGeocodingTo geocode a location description into latitude longitude location we can use the apoc spatial geocode procedure By default this procedure uses the Nominatim geocoding API but can be configured to use other geocoding services such as Google Cloud CALL apoc spatial geocode SFO Airport YIELD location description San Francisco International Airport South Airport Boulevard South San Francisco San Mateo County CAL Fire Northern Region California United States longitude latitude Data ImportWe can use Cypher to import data into Neoj from formats such as CSV and JSON including GeoJSON CSVUsing the LOAD CSV Cypher command to create an airport routing graph Create a constraint on the field that identies uniquesness in this case Airport IATA code This ensures we won t create duplicate airports but also creates a database index to improve performance of our data import steps below CREATE CONSTRAINT FOR a Airport REQUIRE a iata IS UNIQUE Create Airport nodes storing their location name IATA code etc as node properties LOAD CSV WITH HEADERSFROM AS rowMERGE a Airport iata row IATA CODE ON CREATE SET a city row CITY a name row AIRPORT a state row STATE a country row country a location point latitude toFloat row LATITUDE longitude toFloat row LONGITUDE Create FLIGHT TO relationships connecting airports with a connecting flight Increment the num flights counter variable to keep track of the number of flights between airports per year autoLOAD CSV WITH HEADERSFROM AS rowCALL WITH row MATCH origin Airport iata row ORIGIN AIRPORT MATCH dest Airport iata row DESTINATION AIRPORT MERGE origin f FLIGHT TO gt dest ON CREATE SET f num flights f distance toInteger row DISTANCE ON MATCH SET f num flights f num flights IN TRANSACTIONS OF ROWS GeoJSONWe can also store arrays of Points to represent complex geometries like lines and polygons for example to represent land parcels CALL apoc load json YIELD valueUNWIND value features AS featureCREATE p Parcel SET p coordinates coord IN feature geometry coordinates point latitude coord longitude coord p feature properties Routing With Path Finding AlgorithmsShortest PathThe shortestPath function performs a binary breadth first search to find the shortest path between nodes in the graph MATCH p shortestPath Airport iata SFO FLIGHT TO gt Airport iata RSW RETURN pShortest Weighted PathOften we want to consider the shortest weighted path taking into account distance time or some other cost stored as relationship properties Dijkstra and A are two algorithms that take relationship or edge weights into account when calculating the shortest path Dijkstra s AlgorithmDijkstra s algorithm is similar to a breadth first search but takes into account relationship properties distance and prioritizes exploring low cost routes first using a priority queue MATCH origin Airport iata SFO MATCH dest Airport iata RSW CALL apoc algo dijkstra origin dest FLIGHT TO distance YIELD path weightUNWIND nodes path AS nRETURN airport n iata lat n location latitude lng n location longitude AS routeA AlgorithmThe A algorithm adds a heuristic function to choose which paths to explore In our case the heuristic is the distance to the final destination MATCH origin Airport iata SFO MATCH dest Airport iata RSW CALL apoc algo aStarConfig origin dest FLIGHT TO pointPropName location weight distance YIELD weight pathRETURN weight pathThere are additional path finding algorithms available in Neoj s Graph Data Science Library Using Neoj With Python For Geospatial Data The Neoj Python DriverIn this section we ll use the Neoj Python Driver to create a GeoDataFrame of our flight data We ll also compute weighted degree centrality so we can plot airport size relative to their “importance in the US airline network The Neoj Python Driver can be installed with pip install neojCreating A GeoDataFrame From Data Stored In NeojFirst we import the neoj Python package define our connection credentials for our Neoj instance here we are using a local Neoj instance and create a driver instance import neojNEOJ URI neoj localhost NEOJ USER neoj NEOJ PASSWORD letmeinnow NEOJ DATABASE neoj driver neoj GraphDatabase driver NEOJ URI auth NEOJ USER NEOJ PASSWORD Next we define a Cypher query to fetch data from Neoj In addition to fetching each flight between airports we compute weighted degree centrality a measure of each node s importance in the network the sum of all relationship weights connected to a given node in this case the number of flights per year for each airport We also return the geometry of our origin and destination airports and the flight route as Well Known Text WKT POINT for the airports and LINESTRING for the flight route We ll parse this WKT when defining the geometry in our GeoDataFrame AIRPORT QUERY MATCH origin Airport f FLIGHT TO gt dest Airport CALL WITH origin MATCH origin f FLIGHT TO RETURN sum f num flights AS origin centrality CALL WITH dest MATCH dest f FLIGHT TO RETURN sum f num flights AS dest centrality RETURN origin wkt POINT origin location longitude origin location latitude origin iata origin iata origin city origin city origin centrality origin centrality dest centrality dest centrality dest wkt POINT dest location longitude dest location latitude dest iata dest iata dest city dest city distance f distance num flights f num flights geometry LINESTRING origin location longitude origin location latitude dest location longitude dest location latitude Next we define a Python function to execute our Cypher query and process the results into a GeoPandas GeoDataFrame The Neoj Python driver has a to df method which will convert a Neoj result set to a Pandas DataFrame Note that we parse the WKT columns into GeoSeries and convert the pandas DataFrame into a GeoPandas GeoDataFrame def get airport tx results tx run AIRPORT QUERY df results to df expand True df columns origin city origin wkt dest city dest wkt origin centrality distance origin iata geometry num flights dest centrality dest iata df geometry geopandas GeoSeries from wkt df geometry df origin wkt geopandas GeoSeries from wkt df origin wkt df dest wkt geopandas GeoSeries from wkt df dest wkt gdf geopandas GeoDataFrame df geometry geometry return gdfwith driver session database NEOJ DATABASE as session airport df session execute read get airport We now have a GeoDataFrame where each row is a flight route between two airports We can plot the airport and routes using the centrality metric to size airport nodes more important airports should be larger world geopandas read file geopandas datasets get path naturalearth lowres ax world world continent North America plot color white edgecolor black flights gdf flights gdf set geometry origin wkt flights gdf plot ax ax markersize origin centrality flights gdf flights gdf set geometry geometry flights gdf plot ax ax markersize linewidth Working With OpenStreetMap DataIn this section we will import data from OpenStreetMap into Neoj using the OSMNx Python package Below is the property graph data model we will use to model the road network of Boston pip install osmnx Loading A Road Network With OSMNximport osmnx as oxG ox graph from place Boston MA USA network type drive fig ax ox plot graph G gdf nodes gdf relationships ox graph to gdfs G gdf nodes reset index inplace True gdf relationships reset index inplace True Here is our nodes GeoDataFrame Each row represents an intersection in the Boston road network Here is our relationships GeoDataFrame Each row represents a road segment connecting two intersections We ll define a Cypher query to add intersection nodes from the nodes GeoDataFrame and add road segments from the relationships GeoDataFrame connecting intersection nodes First let s create a constraint to ensure we don t have duplicate Intersection nodes this will also create a node index to improve lookups during import CREATE CONSTRAINT FOR i Intersection REQUIRE i osmid IS UNIQUEWe can also create an index on the osmid property of the ROAD SEGMENT relationship to improve import performance CREATE INDEX FOR r ROAD SEGMENT ON r osmidBecause our GeoDataFrames can be very large we break them up into batches to avoid sending too much data to the database at once node query UNWIND rows AS row WITH row WHERE row osmid IS NOT NULL MERGE i Intersection osmid row osmid SET i location point latitude row y longitude row x i ref row ref i highway row highway i street count toInteger row street count RETURN COUNT as total rels query UNWIND rows AS road MATCH u Intersection osmid road u MATCH v Intersection osmid road v MERGE u r ROAD SEGMENT osmid road osmid gt v SET r oneway road oneway r lanes road lanes r ref road ref r name road name r highway road highway r max speed road maxspeed r length toFloat road length RETURN COUNT AS total def insert data tx query rows batch size total batch while batch batch size lt len rows results tx run query parameters rows rows batch batch size batch batch size to dict records data print results total results total batch with driver session as session session execute write insert data node query gdf nodes drop columns geometry session execute write insert data rels query gdf relationships drop columns geometry In this post we introduced some of the spatial functionality natively supported by Neoj including the point type and related Cypher functions and demonstrated how to accomplish various spatial search operations as well as a brief look at routing with graph algorithms ResourcesCypher manual for spatial Cypher functionsSpatial search map example codeDaylight Earth TableImporting Daylight Earth Table points of interest into Neoj Python code Spatial search Leaflet js Neoj demo code	2023-06-28 22:18:00
Apple	AppleInsider - Frontpage News	Beats Studio Pro to feature improved battery life, Spatial Audio, and much more	https://appleinsider.com/articles/23/06/28/beats-studio-pro-to-feature-improved-battery-life-spatial-audio-and-much-more?utm_medium=rss	Beats Studio Pro to feature improved battery life Spatial Audio and much moreMore details have emerged about the rumored Beats Studio Pro which could offer longer battery life than Apple s AirPods Max and other Beats by Dre headphones Beats StudioEarly rumors regarding the new headphones first surfaced in May then showed up again in an FCC filing in June as well An earlier leak on Wednesday seems to reference the same documents discussed below which suggested a July release Read more	2023-06-28 23:00:05
Apple	AppleInsider - Frontpage News	Reddit client Apollo is shutting down on July 1st -- please decline your refund	https://appleinsider.com/articles/23/06/28/reddit-client-apollo-is-shutting-down-on-july-1st----please-decline-your-refund?utm_medium=rss	Reddit client Apollo is shutting down on July st please decline your refundReddit has forced third party clients like Apollo to shut down due to exorbitant fees and please refuse the subscription refund to help keep your favorite developers afloat Decline your Apollo refundLike with what happened to third party Twitter clients Reddit clients are being forced to shut down with almost zero notice However that leaves developers like Christian Selig holding the bill Read more	2023-06-28 22:14:29
海外科学	NYT > Science	Auto-Industry Group Assails Biden’s Plan to Electrify America’s Cars	https://www.nytimes.com/2023/06/28/climate/epa-electric-cars-auto-industry-criticism.html	Auto Industry Group Assails Biden s Plan to Electrify America s CarsThe E P A s plan is “neither reasonable nor achievable the lobbying group said using strong language in the face of the urgency to cut planet warming emissions from vehicles	2023-06-28 22:30:45
海外科学	BBC News - Science & Environment	Human remains thought to be found in Titan sub debris	https://www.bbc.co.uk/news/world-us-canada-66049789?at_medium=RSS&at_campaign=KARANGA	coast	2023-06-28 22:39:14
金融	金融総合：経済レポート一覧	FX Daily（6月27日）～米指標強く、144円台に上昇	http://www3.keizaireport.com/report.php/RID/543031/?rss	fxdaily	2023-06-29 00:00:00
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金融	金融総合：経済レポート一覧	欧州保険会社が2022年のSFCR（ソルベンシー財務状況報告書）を公表（４）～SFCRからの具体的内容の抜粋報告（その３）：保険・年金フォーカス	http://www3.keizaireport.com/report.php/RID/543040/?rss	保険会社	2023-06-29 00:00:00
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金融	金融総合：経済レポート一覧	グローバルREITの投資再考～世界経済の拡大とともに上昇してきたグローバルREIT	http://www3.keizaireport.com/report.php/RID/543081/?rss	世界経済	2023-06-29 00:00:00
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金融	金融総合：経済レポート一覧	Quarterly Market Outlook～高金利通貨に資金集まるも「逆回転」に警戒の時期へ	http://www3.keizaireport.com/report.php/RID/543083/?rss	quarterlymarketoutlook	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	2023年後半の日本株見通し：市川レポート	http://www3.keizaireport.com/report.php/RID/543084/?rss	三井住友	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	2023年7月の注目イベント～FOMCで利上げ実施か、日銀の長短金利操作の修正はあるか	http://www3.keizaireport.com/report.php/RID/543085/?rss	三井住友	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	米銀破綻後の米国商業用不動産ローン：経済の動き	http://www3.keizaireport.com/report.php/RID/543089/?rss	三井住友信託銀行	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	点描：円安が再び進行～対ユーロでの円安が目立つ	http://www3.keizaireport.com/report.php/RID/543094/?rss	総合研究所	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	為替150円接近で高まる物価見通し～再びの為替介入に現実味：Economic Trends	http://www3.keizaireport.com/report.php/RID/543101/?rss	economictrends	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	【第110回】令和の“住まい”と住宅ローン事情～20代・30代の住宅ローン、5人に1人はペアローンを利用	http://www3.keizaireport.com/report.php/RID/543109/?rss	三井住友トラスト	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	盗難通帳、インターネット・バンキング、盗難・偽造キャッシュカードによる預金等の不正払戻し件数・金額等に関するアンケート結果および口座不正利用に関するアンケート結果	http://www3.keizaireport.com/report.php/RID/543110/?rss	全国銀行協会	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	投資におけるESG及びSDGsの考慮に係る俯瞰研究に関する報告書	http://www3.keizaireport.com/report.php/RID/543135/?rss	年金積立金管理運用独立行政法人	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	金融仲介機能の発揮に向けたプログレスレポート	http://www3.keizaireport.com/report.php/RID/543136/?rss	金融庁	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	金融活動作業部会（FATF）による「暗号資産：FATF基準の実施状況についての報告書」	http://www3.keizaireport.com/report.php/RID/543137/?rss	金融活動作業部会	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	【注目検索キーワード】ワーケーション	http://search.keizaireport.com/search.php/-/keyword=ワーケーション/?rss	検索キーワード	2023-06-29 00:00:00
金融	金融総合：経済レポート一覧	【お薦め書籍】1300万件のクチコミでわかった超優良企業	https://www.amazon.co.jp/exec/obidos/ASIN/4492534628/keizaireport-22/	転職	2023-06-29 00:00:00
ニュース	BBC News - Home	Human remains thought to be found in Titan sub debris	https://www.bbc.co.uk/news/world-us-canada-66049789?at_medium=RSS&at_campaign=KARANGA	coast	2023-06-28 22:39:14
ニュース	BBC News - Home	Declan Rice: Arsenal reach agreement to sign West Ham midfielder in £105m deal	https://www.bbc.co.uk/sport/football/66048751?at_medium=RSS&at_campaign=KARANGA	declan	2023-06-28 22:29:38
ニュース	BBC News - Home	Wimbledon 2023: The ball boys from Barnardo's	https://www.bbc.co.uk/sport/tennis/66009652?at_medium=RSS&at_campaign=KARANGA	Wimbledon The ball boys from Barnardo x sWhen the list of the teenagers selected to be Wimbledon ball boys went up in their Barnardo s children s home it opened up a fortnight of unexpected opportunities	2023-06-28 22:47:48
マーケティング	MarkeZine	GMOメイクショップとリチカ、ECサイト向け動画の制作サービスを開始	http://markezine.jp/article/detail/42644	向け	2023-06-29 07:15:00
IT	週刊アスキー	＼本日肉の日／ロッテリア、バーミヤンなど特別メニュー	https://weekly.ascii.jp/elem/000/004/143/4143070/	肉の日	2023-06-29 07:05:00