NoSQL Database Study Material PDF

NoSQL DATABASE Relational Database (Traditional Database) store data in structured tables with predefined schemas, where relationships between different types of data are established using keys and constraints. Ex MySQL, Oracle Database, Microsoft SQL Server. NoSQL databases store data in a fundam...

NoSQL DATABASE Relational Database (Traditional Database) store data in structured tables with predefined schemas, where relationships between different types of data are established using keys and constraints. Ex MySQL, Oracle Database, Microsoft SQL Server. NoSQL databases store data in a fundamentally different way compared to traditional relational databases that use structured tables. Instead of the rigid, predefined schemas and table structures found in relational systems, NoSQL databases offer a variety of flexible data models that cater to specific types of unstructured, semi-structured, or structured data. These databases are designed to handle massive volumes of data and efficiently manage high user loads, making them well-suited for \` applications that require scalability and agility. NoSQL databases are categorized into several types based on their underlying data model. The main types include **document stores, key-value stores, wide-column stores, and graph databases**. Each of these types has unique characteristics and advantages, making them ideal for different kinds of use cases: ###### 1. Document Databases Document databases store data as documents, typically in formats like **JSON**, **BSON**, or **XML**. These documents are collections of key-value pairs that can contain complex, nested structures, making them highly flexible. Unlike relational databases, document databases do not require a fixed schema, allowing for different types of data to be stored in the same collection. They are particularly useful for managing content such as user profiles, product catalogs, and real-time analytics, where data formats may vary and evolve over time. - **Example:** **MongoDB** is a popular document database that uses a JSON-like data format, making it a go-to choice for applications where data structure flexibility is a priority. ###### 2. Key-Value Databases In key-value stores, data is organized as simple key-value pairs, where each key is unique, and it is used to retrieve the corresponding value. The simplicity of this model allows for extremely fast read and write operations. Key-value databases are commonly used for caching, session management, and applications that require quick lookups of data without complex querying.  - **Example:** **Redis** and **Amazon DynamoDB** are well-known key-value databases, often used in scenarios where low-latency data access is crucial. ###### 3. Wide-Column Databases Wide-column databases, also known as **column-family stores**, organize data in columns rather than rows. Unlike traditional relational databases that use fixed schemas for tables, wide-column stores allow dynamic schema changes and are optimized for querying large datasets across distributed systems. This makes them suitable for handling huge amounts of data that require high performance in distributed environments, such as time-series data, logs, and large-scale analytics. - **Example:** **Apache Cassandra** and **HBase** are leading wide-column stores that are often used in applications with high write-throughput and large datasets, such as IoT, telecommunications, and recommendation engines. ###### 4. Graph Databases Graph databases focus on storing and managing relationships between entities. Data is represented as nodes (entities) and edges (relationships), making it easy to model complex, interconnected data. This is particularly useful for scenarios where relationships are just as important as the data itself, such as social networks, fraud detection, and recommendation engines. Queries in graph databases focus on traversing relationships, which is more efficient in this model than in traditional relational databases. - **Example:** **GraphDB** and **Neo4j**  are prominent graph databases, widely used in applications that require deep exploration of data relationships, such as social media platforms, supply chain management, and network security.  GraphDB GraphDB is a graph database that is designed specifically for managing, querying, and storing large amounts of interconnected data. It is used for storing and querying RDF (Resource Description Framework) data, which is a standard format used by the Semantic Web to represent information. It is developed by Ontotext, and it is designed to support semantic technologies such as SPARQL (a query language for RDF) and OWL (Web Ontology Language) for reasoning and inferencing over data. ###### RDF (Resource Descriptive Framework) RDF is a way to represent information about things and their relationships in a structured format. You can think of it to describe data using simple statements that humans and computers can understand. The World Wide Web Consortium (W3C) sets the rules and standards for how RDF works, including its key ideas and different formats. An RDF statement consists of three components, referred to as a *triple*: 1. **Subject** is a resource being described by the triple. 2. **Predicate** describes the relationship between the subject and the object. 3. **Object** is a resource that is related to the subject. For example: - \"The book \'Harry Potter\' **has author** J.K. Rowling\" is an RDF triple: - **Subject**: \"Harry Potter\" - **Predicate**: \"has author\" - **Object**: \"J.K. Rowling\"  The subject and object are nodes (resources) that represent things. The RDF standard provides three different types of nodes: 1. **IRI Nodes**: IRI nodes are nodes that represent resources identified by Internationalized Resource Identifiers. For example ,https://w3id.org/bot\#Building could serve as a URI node representing a specific class called \"Building.\" **URI** - [https://w3id.org/bot\#](https://w3id.org/bot) \[ Following the URI leads to a location that defines various resources, including \"Building.\] **IRI -** \[ Following the IRI takes you to a location that defines what a \"Building\" is.\] 2. **Blank Nodes**: Also known as anonymous nodes, blank nodes are used to represent resources that do not have a specific URI. They are useful for representing data that is relevant but does not need a unique identifier. For example, if you have information about an author that doesn't need a full URI, you might use a blank node. 3. **Literal Nodes**: These nodes represent values or data that are not resources, such as strings, numbers, dates, etc. For example, \"The Great Gatsby\" and 42 would be literal nodes, as they provide actual data rather than pointing to a resource. Serialization Although the XML syntax was the only syntax option specified at first, standards for encoding RDF statements now include the following three syntaxes: - **Turtle** is the most popular text syntax for RDF statements. The W3C describes it as a \"compact and natural text form\" that includes abbreviations for commonly used patterns. - **JSON-LD** uses the JSON syntax for RDF statements. - **N-Triples** is a subset of the Turtle syntax, designed to be a simpler text-based format for RDF statements for improved ease of use by humans writing statements. The simpler format also makes it easier for programs to create and parse RDF statements. ###### RDF Schema RDF Schema (RDFS) is a semantic extension of the Resource Description Framework (RDF) that provides a framework for defining the structure and semantics of RDF data. It allows for the creation of vocabularies to describe resources and their relationships, enhancing the ability to infer knowledge and reason about data. [RDF]{.smallcaps} :Alice a :Person. :Alice :hasName \"Alice\". :Alice :hasAge \"30\"\^\^xsd:integer. Statements define about Alice. [RDFS]{.smallcaps} :Person a rdfs:Class. :hasName a rdf:Property; rdfs:domain :Person; rdfs:range xsd:string. :hasAge a rdf:Property; rdfs:domain :Person; rdfs:range xsd:integer. Statements define the structure (classes and properties) of the data. In this way, RDFS and RDF work together: RDFS provides the framework and definitions, while RDF provides the actual data and instances based on that framework. :Person a rdfs:Class. :hasName a rdf:Property; rdfs:domain :Person; rdfs:range xsd:string. :hasAge a rdf:Property; rdfs:domain :Person; rdfs:range xsd:integer. Alice a :Person. :Alice :hasName \"Alice\". :Alice :hasAge \"30\"\^\^xsd:integer. ###### ###### Ontology Ontology is set of RDF statements which generally represents a set of concepts within a domain and the relationship between those concepts. It provides a structured framework to describe knowledge about a specific area. ###### Knowledge Graph A knowledge graph a set of RDF statement representing of a network of real-world entities and their interrelations. **Ontology + Data = Knowledge Graph**  ###### SPARQL It is a powerful query language specifically designed for querying and manipulating data stored in RDF format. SPARQL allows users to retrieve and manipulate data from various RDF datasets, including those found in knowledge graphs and databases. Queries are constructed using triple patterns, which consist of a subject, predicate, and object. SPARQL allows users to perform a variety of operations on RDF data, such as: 1. **Querying**: Retrieve specific data based on patterns, filtering, and constraints. 2. **Inserting**: Add new triples to an RDF dataset. 3. **Updating**: Modify existing triples. 4. **Deleting**: Remove triples from an RDF dataset. ***Query*** PREFIX ex: \ SELECT ?title WHERE { ?book ex:hasauthor ?author. ?author ex:name \"F. Scott Fitzgerald\". ?book ex:title ?title. ?book ex:publishedInYear ?year. } *Components of the Query:* 1. *PREFIX*: This defines a namespace for the terms used in the query. Here, ex: is a shorthand for http://example.org/. 2. *SELECT ?title*: This part specifies that you want to retrieve the variable ?title. However, note that in the WHERE clause, there is no mention of ?title, which suggests a missing pattern if you intend to retrieve the book title. 3. *WHERE Clause:* This is where the conditions for the query are defined: - ?book ex:hasauthor ?author.: This matches any ?book that has an ex:hasauthor predicate pointing to an ?author. - ?author ex:name \"F. Scott Fitzgerald\".: This condition specifies that the ?author must have the name \"F. Scott Fitzgerald\". - ?book ex:publishedInYear ?year.: This condition matches the ?book with a publishedInYear, but note that the ?year is not used in the output. ###### **Result** +-----------------------------------------------------------------------+ | ----------- | | **title** | | ----------- | +=======================================================================+ | ---------------------- | | \"The Great Gatsby\" | | ---------------------- | +-----------------------------------------------------------------------+ | ------------------------- | | \"Tender Is the Night\" | | ------------------------- | +-----------------------------------------------------------------------+ Let's delve into constructing a family tree using RDF, RDFS, and ontology concepts, illustrating the entire chain from the basics of RDF to the application of an ontology. **Step 1: Defining the Family Tree** Imagine we want to represent a simple family tree with the following relationships: - Alice is the mother of Bob. - Bob is the father of Charlie. - Charlie is the son of Bob. **Step 2: Representing Relationships with RDF** Using RDF, we represent these relationships as triples. Each triple consists of a subject, predicate, and object. **RDF Triples** 1. **Alice has a child (Bob)**: - Subject: ex:Alice - Predicate: ex:hasChild - Object: ex:Bob 2. **Bob has a child (Charlie)**: - Subject: ex:Bob - Predicate: ex:hasChild - Object: ex:Charlie 3. **Alice is the mother of Bob**: - Subject: ex:Alice - Predicate: ex:isMotherOf - Object: ex:Bob 4. **Bob is the father of Charlie**: - Subject: ex:Bob - Predicate: ex:isFatherOf - Object: ex:Charlie 5. **Bob is the son of Alice**: - Subject: ex:Bob - Predicate: ex:isSonOf - Object: ex:Alice **Step 3: Defining Classes and Properties with RDFS** Next, we can use RDFS to define classes and properties that describe the relationships in our family tree. This helps organize the data and provides a structure for understanding relationships. **Class Definitions** - **Classes**: - ex:Person: A general class for all individuals. - ex:Parent: A subclass of ex:Person representing individuals who are parents. - ex:Child: A subclass of ex:Person representing individuals who are children. **Property Definitions** - **Properties**: - ex:hasChild: Indicates a parent-child relationship. - ex:isMotherOf: Indicates a specific mother-child relationship. - ex:isFatherOf: Indicates a specific father-child relationship. - ex:isSonOf: Indicates a child-parent relationship. **Step 4: Building an Ontology** An ontology formalizes the concepts and relationships, providing more detailed semantics. It describes how the classes and properties relate to one another and can include rules or constraints. **Example Ontology Elements** - **Class Hierarchy**: - ex:Person - ex:Parent (inherits from ex:Person) - ex:Child (inherits from ex:Person) - **Properties**: - ex:hasChild (domain: ex:Parent, range: ex:Child) - ex:isMotherOf (domain: ex:Parent, range: ex:Child) - ex:isFatherOf (domain: ex:Parent, range: ex:Child) - ex:isSonOf (domain: ex:Child, range: ex:Parent) **Step 5: Putting It All Together** Now we have a structured way to represent the family tree using RDF, RDFS, and ontology: **Complete RDF Representation** - **Triples**: 1. ex:Alice ex:hasChild ex:Bob 2. ex:Bob ex:hasChild ex:Charlie 3. ex:Alice ex:isMotherOf ex:Bob 4. ex:Bob ex:isFatherOf ex:Charlie 5. ex:Bob ex:isSonOf ex:Alice **Example Queries and Results** 1. **Query for all children of Alice**: - **Query**: Find all resources where ex:hasChild has ex:Alice. - **Result**: ex:Bob 2. **Query for the father of Charlie**: - **Query**: Find all resources where ex:isFatherOf has ex:Charlie. - **Result**: ex:Bob 3. **Query for relationships involving Bob**: - **Query**: Retrieve all properties related to ex:Bob. - **Result**: - ex:hasChild ex:Charlie - ex:isMotherOf ex:Alice - ex:isFatherOf ex:Charlie **Conclusion** This construction of a family tree using RDF, RDFS, and an ontology illustrates how to represent relationships and hierarchies clearly and semantically. - **RDF** provides the foundational structure for representing relationships. - **RDFS** organizes these relationships and defines the types of entities involved. - **Ontology** enriches this representation by formalizing the relationships and hierarchies, allowing for more complex queries and reasoning about the data. Together, they create a rich knowledge graph that can be easily queried and understood. ###### Using GraphDB ###### Installation - Download the GraphDB Desktop installer file. - Double-click it and get a virtual disk on your desktop. Copy the program from the virtual disk to your hard disk **Applications** folder, and you're set. - Start GraphDB Desktop by clicking the application icon. The GraphDB Workbench opens at . ###### Creating Repository - A repository in GraphDB is a named store where you can save and manage RDF graphs. Each repository can contain its own distinct set of RDF data and configurations. - Under Setup -\> Repositories -\> New repository -\> GraphDB Repository -\> Enter Repository Name in Repository ID field -\> Click on Create below. ###### Import Knowledge Graph - Choose the Repository and Import the Knowledge Graph. Once a knowledge graph is imported into GraphDB, the RDF statements can be visualized as graphs and queried using SPARQL. ###### Further Reading RDF - RDFS -- https://www.w3.org/TR/rdf-schema/ SPARQL - GraphDB - https://graphdb.ontotext.com/documentation/10.6/