Discover the Power of Vector Databases in AI: Transforming Data Handling & Insight Generation. Learn how vector embeddings revolutionize search, enhance recommendation systems, enable semantic search, and fuel AI innovation.
Cette bande dessinée didactique a été produite par le Service commun de la Documentation de l'Université de Guyane. Elle s'adresse à un public de doctorants et de chercheurs dans un objectif d'accompagnement à ces nouvelles pratiques scientifiques.
["slug" being an entity attribute]
Spring Data offers an existsBy query method, which we can define in the PostRepository, as follows:
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@Repository
public interface PostRepository
extends JpaRepository<Post, Long> {
boolean existsBySlug(String slug);
}
[another] option to emulate existence is using a CASE WHEN EXISTS native SQL query:
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@Repository
public interface PostRepository
extends JpaRepository<Post, Long> {
@Query(value = """
SELECT
CASE WHEN EXISTS (
SELECT 1
FROM post
WHERE slug = :slug
)
THEN 'true'
ELSE 'false'
END
""",
nativeQuery = true
)
boolean existsBySlugWithCase(@Param("slug") String slug);
}
@Repository
public interface PostRepository extends BaseJpaRepository<Post, Long> {
@Query("""
select p
from Post p
where date(p.createdOn) >= :sinceDate
"""
)
@QueryHints(
@QueryHint(name = AvailableHints.HINT_FETCH_SIZE, value = "25")
)
Stream<Post> streamByCreatedOnSince(@Param("sinceDate") LocalDate sinceDate);
}
The FETCH_SIZE JPA query hint is necessary for PostgreSQL and MySQL to instruct the JDBC Driver to prefetch at most 25 records. Otherwise, the PostgreSQL and MySQL JDBC Drivers would prefetch all the query results prior to traversing the underlying ResultSet.
Welcome to the Qualitative Data Sharing (QDS) Toolkit We believe in the benefits of data transparency. We created this toolkit to support qualitative data sharing in an ethical manner.
Abstract. In order to support web applications to understand the content of HTML pages an increasing number of websites have started to annotate structured data within their pages using markup formats such as Microdata, RDFa, Microformats. The annotations are used by Google, Yahoo!, Yandex, Bing and Facebook to enrich search results and to display entity descriptions within their applications. In this paper, we present a series of publicly accessible Microdata, RDFa, Microformats datasets that we have extracted from three large web corpora dating from 2010, 2012 and 2013.
More and more websites have started to embed structured data describing products, people, organizations, places, and events into their HTML pages using markup standards such as Microdata, JSON-LD, RDFa, and Microformats. The Web Data Commons project extracts this data from several billion web pages. So far the project provides 11 different data set releases extracted from the Common Crawls 2010 to 2022. The project provides the extracted data for download and publishes statistics about the deployment of the different formats.
Für viele Forscher und Statistiker gilt Jupyter Notebook als De-facto-Standard, wenn es um schnelles Prototyping und die explorative Datenanalyse geht. Außer auf Notebook wirft der Artikel aber auch einen Blick auf Jupyter Lab, das als nächste Generation von Notebooks in den Startlöchern steht.