This diagram categorizes a wide range of operating systems (2024) by their functionality and development&support lineage:

1) Unix-based: Includes BSD variants (FreeBSD, OpenBSD, NetBSD, DragonFly BSD, TrueOS, MidnightBSD, GhostBSD, Darwin) and Linux distributions (Debian, Red Hat, Arch, Gentoo, SUSE, Slackware, Alpine, Void, Oracle Linux, Clear Linux), as well as Solaris (SunOS, OpenSolaris, illumos, SmartOS), HP-UX, and AIX.

2) Windows: Encompasses various versions including Windows NT (Windows 10, 11, Server, 8, 7), Windows 9x (95, 98, ME), and MS-DOS (FreeDOS).

3) Mobile: Lists mobile operating systems such as Android (LineageOS, GrapheneOS, OxygenOS, One UI), iOS, KaiOS, HarmonyOS, Sailfish OS, Tizen, Firefox OS, Ubuntu Touch, and Plasma Mobile.

4) Embedded: Features real-time operating systems (RTOS) like FreeRTOS, VxWorks, QNX, ThreadX, Contiki, RIOT, eCos, and proprietary embedded systems like Embedded Linux, Windows CE, Zephyr, Nucleus RTOS, Integrity, LynxOS, and Green Hills INTEGRITY.

5) Supercomputers: Operating systems used in high-performance computing, such as Cray OS, IBM AIX, SUSE Linux Enterprise Server, Red Hat Enterprise Linux, CentOS, Unicos, HPE Cray OS, and custom Linux distributions for TOP500 supercomputers.

6) Mainframes: Lists operating systems used in mainframes such as IBM z/OS, z/VM, z/VSE, MCP (Burroughs), GCOS (Honeywell), Fujitsu BS2000, Unisys ClearPath OS 2200, VM/CMS, and Multics.

7) Gadgets: Operating systems for wearable and small devices like Wear OS, watchOS, Fitbit OS, Tizen, Garmin OS, Pebble OS, AsteroidOS, Amazfit OS, MycroftOS, and Oculus OS.

8) Legacy: Encompasses older operating systems like DOS (MS-DOS, PC-DOS), OS/2 (eComStation, ArcaOS), BeOS (Haiku), AmigaOS (AROS, MorphOS), RISC OS, CP/M (MP/M), Atari TOS (MiNT), Classic Mac OS (System 1, System 7), and GEOS (NewDeal Office).

9) IoT Devices: Operating systems specifically designed for Internet of Things devices, such as Embedded Linux, Zephyr, RIOT, Contiki, FreeRTOS, Mbed OS, TinyOS, Brillo/Android Things, LiteOS, Huawei LiteOS, and AliOS Things.

10) Smart Network Devices: Operating systems used in networking devices such as Cisco IOS, Junos OS, FortiOS, Arista EOS, VyOS, MikroTik RouterOS, pfSense, OPNsense, and OpenWRT.

11) Others: Includes unique or niche operating systems like Haiku, ReactOS, Plan 9, AROS, Inferno, MINIX, TempleOS, MorphOS, Syllable, SkyOS, KolibriOS, HelenOS, Genode OS, 9front, SerenityOS, Redox OS, and Fuchsia.

This mindmap provides an overview of the diverse operating system landscape, highlighting their development and specific use cases across different platforms and devices.

This mindmap diagram provides a categorization of various programming languages (2024) split by their primary functionality and usage:

1) Functional Programming: Emphasizes functions and immutable data. Languages include Haskell, Lisp, Clojure, Erlang, F#, and OCaml.

2) Object-Oriented Programming: Focuses on objects and classes to structure code. Languages include Java, C++, Python, Ruby, Smalltalk, Swift, and C#.

3) Procedural Programming: Based on procedure calls. Languages include C, Pascal, Fortran, COBOL, Ada, and BASIC.

4) Scripting Languages: Designed for automating tasks. Languages include JavaScript, Perl, PHP, Python, Ruby, and Shell.

5) Logic Programming: Uses formal logic to express computation. Languages include Prolog, Datalog, and Mercury.

6) Concurrent Programming: Handles multiple computations simultaneously. Languages include Go, Rust, Erlang, Scala, Java, and Clojure.

7) Systems Programming: Focuses on system software development. Languages include Rust, C, C++, Go, Assembly, and Zig.

8) Web Development: Languages and technologies for web applications. Includes JavaScript, TypeScript, Ruby, PHP, Python, and HTML/CSS.

9) Mobile Development: Languages for mobile app development. Includes Swift, Kotlin, Java, Dart, and Objective-C.

10) Game Development: Specialized languages for game design. Includes C#, C++, Lua, Java, Python, and UnrealScript.

11) Scientific Computing: Languages for scientific research and numerical analysis. Includes MATLAB, R, Julia, Python, and Fortran.

12) Data Analysis: Languages and tools for analyzing data. Includes R, Python, Julia, SAS, MATLAB, and SPSS.

13) Machine Learning: Languages and frameworks for ML applications. Includes Python, R, Julia, JavaScript, and Scala.

14) Assembly Languages: Low-level languages for direct hardware manipulation. Includes x86 Assembly, ARM Assembly, MIPS Assembly, and more.

This mindmap diagram serves as a comprehensive guide to understanding the diverse landscape of programming languages available today, helping in selecting the right language based on specific requirements and use cases at common.

This mindmap diagram provides a categorization of various popular databases (2024) split by their primary functionality:

1) Relational Databases: Traditional databases that store data in rows and columns, ensuring data integrity and supporting complex queries through SQL. Main RDBMS’s include MySQL, PostgreSQL, Oracle Database, and Microsoft SQL Server.

2) NoSQL Databases: Designed for specific data models and flexible schemas, they are ideal for large-scale data storage and real-time web applications.

3) Document Stores: Store data as documents, typically in JSON or BSON format. Examples: MongoDB, CouchDB, Firestore.

4) Key-Value Stores: Store data as key-value pairs, offering high performance for simple queries. Main of them are Redis, DynamoDB, Memcached.

5) Column-Family Stores: Organize data into columns and rows, optimized for read and write performance. Main of them are Cassandra, HBase, Bigtable.

6) Graph Databases: Designed to handle data with complex relationships, representing it as nodes, edges, and properties. Main of them are Neo4j, Amazon Neptune, OrientDB.

7) Time Series Databases: Optimized for handling time series data, such as logs and sensor data, providing efficient querying and storage. Main of them are InfluxDB, TimescaleDB, Prometheus.

8) NewSQL Databases: Combine the scalability of NoSQL with the ACID guarantees of traditional relational databases. Main of them are Google Spanner, CockroachDB, VoltDB.

9) Search Engines: Specialized databases optimized for indexing and searching large datasets, often used for full-text search. Main of them are Elasticsearch, Solr, Algolia.

10) Multi-Model Databases: Support multiple data models (e.g., document, graph, key-value) within a single database engine. Main of them are ArangoDB, Cosmos DB, MarkLogic.

11) Vector Databases: Optimized for handling vector data, often used in AI and machine learning applications. Main of them are Pinecone, Weaviate, Milvus.

12) Legacy Databases: Older database systems that have been widely used historically and may still be in use today for certain applications. Main of them are IBM IMS, dBASE, Microsoft Access.

This mindmap serves as a comprehensive guide to understanding the diverse landscape of database technologies available today, helping in selecting the right database based on specific requirements and use cases.

Here are roughly summarized the key differences between RESTful and RESTless web services (it does not have to be strictly valid):
————————————————————-
1. Protocol
RESTful services use REST architectural style,
RESTless services use SOAP protocol.
————————————————————-
2. Business logic / Functionality
RESTful services use URL to expose business logic,
RESTless services use the service interface to expose business logic.
————————————————————-
3. Security
RESTful inherits security from the underlying transport protocols,
RESTless defines its own security layer, thus it is considered as more secure.
————————————————————-
4. Data format
RESTful supports various data formats such as HTML, JSON, text, etc,
RESTless supports XML format.
————————————————————-
5. Flexibility
RESTful is easier and flexible,
RESTless is not as easy and flexible.
————————————————————-
6. Bandwidth
RESTful services consume less bandwidth and resource,
RESTless services consume more bandwidth and resources.

Here are roughly summarized the key differences between RESTful and RESTless web services (it does not have to be strictly valid):
————————————————————-
1. Protocol
RESTful services use REST architectural style,
RESTless services use SOAP protocol.
————————————————————-
2. Business logic / Functionality
RESTful services use URL to expose business logic,
RESTless services use the service interface to expose business logic.
————————————————————-
3. Security
RESTful inherits security from the underlying transport protocols,
RESTless defines its own security layer, thus it is considered as more secure.
————————————————————-
4. Data format
RESTful supports various data formats such as HTML, JSON, text, etc,
RESTless supports XML format.
————————————————————-
5. Flexibility
RESTful is easier and flexible,
RESTless is not as easy and flexible.
————————————————————-
6. Bandwidth
RESTful services consume less bandwidth and resource,
RESTless services consume more bandwidth and resources.