2018年11月10日に開催された「FOSS4G 2018 Tokyo コアデイ」での発表資料です。
FOSS4G 2018 Tokyo コアデイ
https://www.osgeo.jp/events/foss4g-2018/foss4g-2018-tokyo/foss4g-2018-tokyo-coreday
This document discusses and compares Neptune and JanusGraph graph databases. It provides an overview of Neptune's features like multi-AZ deployment and storage in S3. It also describes how to access Neptune using Gremlin and SPARQL query languages. The document then introduces JanusGraph and notes some key differences when using Gremlin APIs with Neptune versus JanusGraph. It shares the results of a performance test loading Amazon product graph data into both systems. Finally, it discusses options for loading and querying data between Neptune, Athena, Kinesis and other AWS services.
The document discusses graph databases and their properties. Graph databases are structured to store graph-based data by using nodes and edges to represent entities and their relationships. They are well-suited for applications with complex relationships between entities that can be modeled as graphs, such as social networks. Key graph database technologies mentioned include Neo4j, OrientDB, and TinkerPop which provides graph traversal capabilities.
The document demonstrates various geospatial queries in MongoDB including indexing locations, saving a document with a location, and queries to find documents within a distance, box, or circle of a point. It shows how to find the nearest document to a point, limit the maximum distance, and use spherical geometry for more accurate distance calculations over large areas.
Linked Open Data勉強会2020 後編:SPARQLの簡単な使い方、SPARQLを使った簡単なアプリ開発KnowledgeGraph
Linked Open Data勉強会2020
後編:SPARQLの簡単な使い方、SPARQLを使った簡単なアプリ開発
前編:https://www.slideshare.net/KnowledgeGraph/linked-open-data2020-lod
The document demonstrates various geospatial queries in MongoDB including indexing locations, saving a document with a location, and queries to find documents within a distance, box, or circle of a point. It shows how to find the nearest document to a point, limit the maximum distance, and use spherical geometry for more accurate distance calculations over large areas.
Linked Open Data勉強会2020 後編:SPARQLの簡単な使い方、SPARQLを使った簡単なアプリ開発KnowledgeGraph
Linked Open Data勉強会2020
後編:SPARQLの簡単な使い方、SPARQLを使った簡単なアプリ開発
前編:https://www.slideshare.net/KnowledgeGraph/linked-open-data2020-lod
MySQL 5.7は、地図情報を使ったアプリケーションやJSONを扱うアプリケーションとの親和性が向上しています。本セッションでは、MySQL 5.7で刷新されたGIS(地理情報システム)機能や、MySQL 5.7で実装されたJSONデータ型やJSON関数等について、ご紹介いたします。地図情報を使ったアプリケーションや、JSONを扱うアプリケーションに関わられている方は、是非ご参加下さい!!
MySQL 5.7は、地図情報を使ったアプリケーションやJSONを扱うアプリケーションとの親和性が向上しています。本セッションでは、MySQL 5.7で刷新されたGIS(地理情報システム)機能や、MySQL 5.7で実装されたJSONデータ型やJSON関数等について、ご紹介いたします。地図情報を使ったアプリケーションや、JSONを扱うアプリケーションに関わられている方は、是非ご参加下さい!!
The document discusses MySQL JSON UDFs (User Defined Functions), which add functions for handling JSON to MySQL. It provides setup instructions, demonstrates various JSON functions like json_valid(), json_search(), json_extract(), and json_replace() on sample JSON data. It notes that the JSON UDFs are currently experimental and available on labs.mysql.com. It encourages testing and providing feedback on bugs.