Lecture 0: Course Intro

Transcription

1 Databases (3): NoSQL & Deductive Databases Department of Applied Informatics Faculty of Mathematics, Physics and Informatics Comenius University in Bratislava 25 Sep 2018

2 Part I: NoSQL Databases

3 NoSQL Databases Use different model from the traditional relational database model: Key-value stores Document stores Native XML databases Object databases Graph databases

4 Key-value stores Systems storing associative arrays in from of key-value pairs. Data (the values) are treated as opaque structures and stored differently from RDBMs More efficient storage and retrieval of values Implementations: BerkeleyDB, Aerospike, Apache Cassandra, Amazon DynamoDB, Oracle NoSQL DB,...

5 Document stores More complex than key-value stores: values are no longer opaque, but semistructured documents in some format. Documents are processed for optimization. JSON, XML, and other document types Processing of metadata Key management Retrieval and update of whole documents and subparts Implementations: MongoDB, CouchDB, MUMPS DB, MS DocumentDB,...

6 Native XML databases Document databases with XML document format. Optimized for XML storage and retrieval XML Query languages: XPath, XQuery Implementations: BaseX, exist

7 Object databases Optimized for storing objects from object-oriented programming platforms. Combine DB and OOP capabilities Integrated with programming platforms Implementations: db4o, GemStone, JODB, ObjectDatabase++,...

8 Graph databases Data represented in graph model as nodes and edges. Nodes represent values Edges represent semantic relations between values Links between values (relations) directly stored by DB unlike in RDBMs (where they are stored as data) Effective retrieval of chunks of interlinked values Query languages: Gremlin, Cypher Implementations: Neo4j, OrientDB

9 Part II: Semantic & Deductive Databases

10 RDF Triple Stores Based on RDF data model, emphasizing triples of the form: subject property object Ontological organization: objects, classes, properties Ontology-mediated data access Query language: SPARQL Implementations: Virtuoso, Stardog Linked Open Data: Large datasets freely available on the web

11 Deductive Databases Store knowledge facts and rules and enable to make deductions from the stored knowledge Inference Production systems procedural semantics Logical systems model-based semantics Deduction of implicit knowledge Extensions with uncertainty, etc. Implementations: Dedicated deductive DBs, e.g. DLV Logic programming platforms interfacing RDBMs: SWI-Prolog, DLV DB,...

12 Other Column-based DBs Spatial databases

13 About the Course

14 About the Course Stuff to do: Lectures Labs (obligatory) Projects (obligatory) Project reviews

15 Evaluation Evaluation: 100% from projects (50% each). One project from each part of syllabus Each project: Implement an application on top of selected DB. Submission should contain: Specification Data model Source code Short experience report: (a) What did you learn on this project? (b) In what sense was work with the chosen DB different from traditional relational DBs?

16 Evaluation (2) Submission and review: Grading: Submission via git repo (GitLab) Each students reviews 2 projects You can fix any problems before final submission and grading of your project Each project graded A Fx If you fail to review: one grade lower Final grade: average of two grades