Semi-Structured Database

A Semi-Structured Database is a structured database with semi-structured records.

• AKA: Semi-Structured Data Store, Semi-Structured Dataset, Hybrid Database.
• Context:
  • It can typically store Semi-Structured Data Records with flexible schema.
  • It can typically support Schema Evolution through semi-structured data models.
  • It can typically enable Hierarchical Data Storage through nested semi-structured records.
  • It can typically facilitate Document-Oriented Querying through semi-structured query languages.
  • It can typically provide Flexible Data Representation through semi-structured data formats.
  • ...
  • It can often contain Mixed Data Types within semi-structured record collections.
  • It can often support NoSQL Operations through semi-structured data interfaces.
  • It can often enable Metadata-Rich Storage through semi-structured annotations.
  • It can often facilitate Cross-Format Integration through semi-structured data adapters.
  • ...
  • It can range from being a Small Semi-Structured Database to being a Large-Scale Semi-Structured Database, depending on its semi-structured database size.
  • It can range from being a Simple Semi-Structured Database to being a Complex Semi-Structured Database, depending on its semi-structured schema complexity.
  • It can range from being a Homogeneous Semi-Structured Database to being a Heterogeneous Semi-Structured Database, depending on its semi-structured data variety.
  • It can range from being a Read-Heavy Semi-Structured Database to being a Write-Heavy Semi-Structured Database, depending on its semi-structured database access pattern.
  • It can range from being a Domain-Specific Semi-Structured Database to being a General-Purpose Semi-Structured Database, depending on its semi-structured database application scope.
  • ...
  • It can be accessed through Semi-Structured Query Languages for semi-structured data retrieval.
  • It can be managed by a Semi-Structured Database Management System using semi-structured indexing.
  • It can be integrated with Semi-Structured Data Processing Pipelines for semi-structured data transformation.
  • It can be optimized through Semi-Structured Database Tuning for semi-structured query performance.
  • ...
• Example(s):
  • Document-Oriented Semi-Structured Databases, such as:
    • JSON-Based Semi-Structured Databases, such as:
      • MongoDB Database for document-oriented semi-structured storage.
      • CouchDB Database for distributed semi-structured document storage.
      • Amazon DocumentDB for cloud-based semi-structured document management.
    • XML-Based Semi-Structured Databases, such as:
      • BaseX Database for XML semi-structured data processing.
      • MarkLogic Database for enterprise semi-structured content management.
      • eXist-db Database for native XML semi-structured storage.
  • Graph-Based Semi-Structured Databases, such as:
    • Property Graph Semi-Structured Databases, such as:
      • Neo4j Database for graph-based semi-structured relationship storage.
      • Amazon Neptune Database for cloud semi-structured graph management.
    • RDF Semi-Structured Databases, such as:
      • Apache Jena Database for semantic semi-structured data storage.
      • Virtuoso Database for linked semi-structured data management.
  • Key-Value Semi-Structured Databases, such as:
    • Redis Database for in-memory semi-structured data caching.
    • Amazon DynamoDB for serverless semi-structured data storage.
    • Apache Cassandra Database for distributed semi-structured column storage.
  • Scientific Semi-Structured Databases, such as:
    • Genomic Semi-Structured Databases, such as:
      • GenBank Database for genetic sequence semi-structured storage.
      • UniProt Database for protein semi-structured information management.
    • Chemical Semi-Structured Databases, such as:
      • PubChem Database for chemical compound semi-structured storage.
      • ChEMBL Database for bioactive molecule semi-structured data.
  • Reference Semi-Structured Databases, such as:
    • Lexical Semi-Structured Databases, such as:
      • WordNet Database for lexical semi-structured relationship storage.
      • FrameNet Database for semantic frame semi-structured annotation.
    • Citation Semi-Structured Databases, such as:
      • CrossRef Database for academic citation semi-structured linking.
      • PubMed Database for biomedical literature semi-structured indexing.
  • Web-Based Semi-Structured Databases, such as:
    • Web Crawl Semi-Structured Databases, such as:
      • Common Crawl Database for web page semi-structured archiving.
      • Internet Archive Database for historical web semi-structured preservation.
    • Social Media Semi-Structured Databases, such as:
      • Twitter Firehose Database for social media semi-structured streaming.
  • Time-Series Semi-Structured Databases, such as:
    • InfluxDB Database for time-stamped semi-structured metric storage.
    • Prometheus Database for monitoring semi-structured data collection.
  • ...
• Counter-Example(s):
  • Fully-Structured Database, which enforces rigid schema and lacks semi-structured flexibility.
  • Unstructured Database, which stores raw unstructured data without semi-structured organization.
  • Relational Database, which requires predefined schema and structured table formats.
  • Flat File Database, which lacks hierarchical semi-structured capability.
  • In-Memory Cache, which provides temporary storage without persistent semi-structured structure.
• See: Structured Database, NoSQL Database, Document Database, Semi-Structured Data Processing Task, Database Management System, Data Model.

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References

2013

• (Moniruzzaman & Hossain, 2013) ⇒ A. B. M. Moniruzzaman, and Syed Akhter Hossain. (2013). “Nosql Database: New Era of Databases for Big Data Analytics-classification, Characteristics and Comparison." arXiv preprint arXiv:1307.0191

2004

• (Kenji et al., 2004) ⇒ A. B. E. Kenji, Shinji Kawasoe, Hiroshi Sakamoto, Hiroki Arimura, and Setsuo Arikawa. (2004). “Efficient Substructure Discovery from Large Semi-structured Data." IEICE TRANSACTIONS on Information and Systems 87, no. 12

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