dimensional modeling techniques Focuses on tough modeling problems, including creating and maintaining keys and modeling calendars, hierarchies, transactions, and data qualitydoi:10.1007/3-540-48298-9_1Claudia
Relational models consist of a set of relations, each of which is represented as a normalized two-dimensional data table. Some basic terms are introduced in Table 2. Table 2. Basic terms of relational data structures TermExplanation Relation A relation usually corresponds to a table in relational...
《Translating Embeddings for Modeling Multi-relational Data》阅读笔记 Abstract We propose TransE, embedding entities and relationships of multi-relational data in low-dimensional vector space. It significantly outperforms state-of-the-art methods in link prediction on t......
论文阅读笔记: Modeling Relational Data with Graph Convolutional Networks,程序员大本营,技术文章内容聚合第一站。
Biomedical big data, as a whole, covers numerous features, while each dataset specifically delineates part of them. “Full feature spectrum” knowledge discovery across heterogeneous data sources remains a major challenge. We developed a method called bo
“Model + Solver” paradigm that was and is prevalent in AI [6], natural language processing [7], machine learning [8], and data mining [9]: instead of outlining how a solution should be computed, we specify what the problem is in terms some high-level modeling language and solve it ...
PCA error detection has limited applicability to certain data types and cannot effectively handle high-dimensional data. In contrast, as an unsupervised ensemble learning approach for error detection, Feature bagging [67] combines the results of multiple error detection algorithms, whose every error dete...
Knowledge graphs enable a wide variety of applications, including question answering and information retrieval. Despite the great effort invested in their creation and maintenance, even the largest (e.g., Yago, DBPedia or Wikidata) remain incomplete. We
Cloud databases have also gained prominence, offering scalability and accessibility by hosting data on remote servers. Additionally, vector databases have emerged to cater to the specific needs of machine learning applications, efficiently storing and querying high-dimensional vectors. Popular Databases Manag...
Data integration applications are essentially a cross between the transactional and BI sides and are best implemented using a hybrid data model. Such hybrids incorporate elements from the 3NF format to support data updates, then layer on advanced dimensional modeling techniques, such as the use of...