["slug" being an entity attribute]
Spring Data offers an existsBy query method, which we can define in the PostRepository, as follows:
1
2
3
4
5
6
@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:
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
@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.
M. Atzmueller, F. Puppe, and H. Buscher. Proc. 19th International Joint Conference on Artificial Intelligence (IJCAI-05), page 647--652. Edinburgh, Scotland, (2005)
M. Atzmueller, J. Baumeister, and F. Puppe. Proc. 19th International Florida Artificial Intelligence Research Society Conference 2006 (FLAIRS-2006), page 402--407. AAAI Press, (2006)
M. Atzmueller, F. Puppe, and H. Buscher. Proc. 10th International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2005), page 46--51. Aberdeen, Scotland, (2005)
M. Atzmueller, J. Baumeister, and F. Puppe. Proc. 15th Intl. Conference on Applications of Declarative Programming and Knowledge Management (INAP 2004), page 203--213. Potsdam, Germany, (2004)
M. Atzmueller, J. Baumeister, and F. Puppe. Artificial Intelligence in Medicine. Special Issue on Intelligent Data Analysis in Medicine, 37 (1):
19--30(2006)
M. Atzmueller, J. Baumeister, and F. Puppe. Medical Data Analysis, Proc. 4th Intl. Symposium on Medical Data Analysis (ISMDA 2003), LNCS 2868, page 23-30. (2003)
J. Baumeister, M. Atzmueller, and F. Puppe. Advances in Case-Based Reasoning, volume 2416 of LNAI, page 28-42. (2002)Proc. 6th European Conference on Case-Based Reasoning (ECCBR 2002).