5 HBase Vs. RDBMS Top Functional Differences

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Here're the differences between RDBMS and HBase. HBase in the Big data context has a lot of benefits over RDBMS. The listed differences below make it understandable why HBASE is popular in Hadoop (or Bigdata) platform.  5 HBase Vs. RDBMS Top Functional Differences Here're the differences unlock now. Random Accessing HBase handles a large amount of data that is store in a distributed manner in the column-oriented format while RDBMS is systematic storage of a database that cannot support a random manner for accessing the database. Database Rules RDBMS strictly follows Codd's 12 rules with fixed schemas and row-oriented manner of database and also follows ACID properties. HBase follows BASE properties and implements complex queries. Secondary indexes, complex inner and outer joins, count, sum, sort, group, and data of page and table can easily be accessible by RDBMS. Storage From small to medium storage application there is the use of RDBMS that provides the solution with MyS

AI Project 5 things You need to be Successful

Suppose you have got an opportunity to create a project on AI. Try implementing these five before the start. These five are Learning, Programming Language, Knowledge representation, Problem Solving, and Hardware.

Ensure These 5 Things Done, if you want to be your AI Project Successful

1. Learning Process.

What is learning? - adding knowledge to the storehouse, and improving its performance. The success of an AI program depends on two things- the extent of wisdom it has and how frequently it acquires it. Learning agents consist of four main components. They are the:
  • The Learning element - is part of the agent responsible for improving its performance. 
  • The Performance element- is the part that chooses the actions to take. 
  • Critics, that tell the learning element of how the agent is doing. 
  • The Problem generator - suggests actions that could lead to new information experiences.

2. Programming Language.

  • LISP and Prolog are the primary languages used in AI programming.
  • LISP (List Processing): LISP is an AI programming language developed by John McCarthy in 1950. LISP is a symbolic processing language that represents information in lists and manipulates lists to derive information.
  • PROLOG (Programming in Logic): Prolog, which is developed by Alain Colmeraver and P. Roussel at Marseilles University in France in the early 1970s. 
  • Prolog uses the syntax of predicate logic to perform symbolic, logical computations.

Artificial Intelligence Project Know these Five Before Start
Artificial Intelligence Project Know these Five Before Start

3. Knowledge Representation.

The quality of the result depends on how much knowledge the system acquires. You should represent the current knowledge efficiently. Hence, knowledge representation is a vital component of the system. The best-known representations schemes are:
  • Associative Networks or Semantic Networks
  • Frames
  • Conceptual Dependencies and
  • Scripts

4. Problem Solving.

The objective of this particular area of research is how to implement the procedures on AI systems to solve problems as humans do.

The inference process should also be equally fit to obtain satisfactory results. Inference-process, you can be divided into brute and heuristic search procedures.

5. Hardware.

Most of the AI programs, implemented on Von Neumann machines. However, for AI programming, dedicated workstations have emerged - classified into one of the following four categories:
  • SISD, Single Instruction Single Data Machines
  • SIMD, Single Instruction Multiple Data Machines
  • MISD, Multiple Instruction Single Data Machines
  • MIMD, Multiple Instruction Multiple Data Machines

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