What are the different data modeling concepts?

Data model is a conceptual representation of data structures. The data structures consist of data objects, relationship between data objects and rules that govern these operations. Often, analysis modeling begins with data modeling.
The inputs of data model comes from the planning and analysis stage. There are two outputs of the data model. First is an entity relationship diagram and second is a data document. The goal of the data model is to make sure that the all data objects required by the database are completely and accurately represented.

The different concepts of data modeling are:

- Data Objects are a representation of any composite information that is processed by software. A data object can be an external entity, thing, occurrence, event, role, an organizational unit, place or a structure. The description of the data object includes data object and its attributes. A data object contains only data.

- Data Attributes name a data object, describe its characteristics and sometimes make reference to another object. One or more attributes must be identified as a key which acts as an identifier.

- Relationships indicate the manner in which data objects are connected to one another.

- Cardinality of a relationship is the actual number of related occurences for each of the two entities. It defines the maximum number of objects participating in relationship. It does not indicate whether a data object should participate in relationship or not.

- Modality of a relationship can be 0 or 1. It is 1 if an occurrence of relationship is must. It is 0 if an occurrence of relationship is optional.

What is Design Engineering? What are different design concepts?

Design engineering focuses on the creation of a representation or model that are concentrated on architecture of the software, data structures, interfaces and components that are necessary to implement the software.

The design process is an iterative process of refinement, i.e.from a higher level of abstraction to lower levels of abstraction. The design should implement both explicit and implicit requirements. It should be readable and understandable. It provides an over-all illustration of the software from the perspective of the data, function and behavior.

Design concepts provides the software engineer a foundation from which design methods can be applied. These concepts include:
- Abstractions: Software engineers different levels of abstractions as we design the blueprint of the software. Two types of abstraction called data and procedural abstractions are defined.
- Modularity: It leads to information hiding and encourages functional independence.
- Coupling: It is the degree of interconnectedness between design objects. Two types of coupling are used: Interaction and Inheritance Coupling.
- Cohesion: It is the measure to which an element contributes to a single purpose.
- Refinement: Abstraction complements refinement as they enable a software engineer to specify the behavior and data of a class or module yet suppressing low levels of detail.
- Refactoring: It simplifies the design of the component without changing its function and behavior.

The work product of the design engineering phase is the design model which consists of the architectural design, data design, interface design and component-level design and deployment level design.

How is data designed at architectural and component level?

Data Design at Architectural Level

Data design translates data objects defined during analysis model into data structures at the software component level and, when necessary,a database architecture at the application level.
There are small and large businesses that contains lot of data. There are dozens of databases that serve many applications comprising of lots of data. The aim is to extract useful information from data environment especially when the information desired is cross functional.
Techniques like data mining is used to extract useful information from raw data. However, data mining becomes difficult because f some factors:
- Existence of multiple databases.
- Different structures.
- Degree of detail contained with databases.Alternative solution is concept of data warehousing which adds an additional layer to data architecture. Data warehouse encompasses all data used by a business. A data warehouse is a large, independent database that serve the set of applications required by a business. Data warehouse is a separate data environment.

Data Design at Component Level

It focuses on representation of data structures that are directly accessed by one or more software components. Set of principles applicable to data design are:
- Systematic analysis principles applied to function and behavior should also be applied to data.
- All data structures and operations to be performed on each should be identified.
- The content of each data object should be defined through a mechanism that should be established.
- Low level data design decisions should be deferred until late in design process.
- A library of data structures and operations that are applied to them should be developed.
- The representation of data structure should only be known to those modules that can directly use the data contained within the structure.
- Software design and programming language should support the specification and realization of abstract data types.

Component Level Design - Designing Class based Components - Coupling

The complexity of the system increases as the amount of collaboration and communication increases. As the complexity increases, difficulty in implementing, testing, and maintaining software also increases.

Coupling is a qualitative measure of the degree to which classes are connected to each other. Coupling increases as the classes become more dependent on each other. The main objective of component level design is to minimize coupling as much as possible.

As the design for each software component is elaborated, the focus shifts to the design of specific data structures and procedural designs to manipulate the data structures. There are different categories of coupling :

- Content Coupling
Content coupling is when one module modifies or relies on the internal workings of another module. This violates the basic design concept i.e. information hiding.
- Common Coupling
In common coupling, two modules share the same global data. It can lead to uncontrolled error propagation and side effects.
- Control Coupling
In Control coupling, one module controls the flow of other module. The problem is that an unrelated change in second module can result in the change of the meaning of control flag.
- External Coupling
Two modules share an external data format. A component communicates or collaborates with infrastructure components. It is necessary but should be limited to small number of components or classes.
- Data Coupling
Data coupling is when modules share data through. The bandwidth of communication grows and the complexity also increases.
- Stamp Coupling
In this type of coupling, modules share a composite data structure. It occurs when class B is declared as an argument of an operation of class A and therefore the system becomes more complex.
- Routine Call Coupling
It occurs when one operation invokes another. This type of coupling is quite necessary but it does increase the interconnectedness of a system.
- Type Use Coupling
It occurs when component A uses a data type in component B. If the type definition changes, every component that uses the definition also changes.
- Inclusion Coupling
In this type of coupling, component A includes or imports a package or content of component B.

Introduction to Hashing

Hashing is a method to store data in an array so that storing, searching, inserting and deleting data is fast. For this every record needs an unique key.The basic idea is not to search for the correct position of a record with comparisons but to compute the position within the array. The function that returns the position is called the 'hash function' and the array is called a 'hash table'.

Main idea: Use an array of size m and the key k as address of the array.
A hash function h is used to map keys to [0::m-1].
Key technical issues :
- What is a good h? A good function avoids (but does not eliminate)collisions, and is quick to compute.
- How do we resolving collisions? Retrieval time is a function of collisions.
- What if we run out of space in the table?
- Can we rearranging keys upon an insertion?

A hash function is any well-defined procedure or mathematical function that converts a large, possibly variable-sized amount of data into a small datum, usually a single integer that may serve as an index to an array. The values returned by a hash function are called hash values, hash codes, hash sums, or simply hashes.A hash function may map two or more keys to the same hash value.Hash functions are related to (and often confused with) check sums,check digits, fingerprints, randomization functions, error correcting codes, and cryptographic hash functions. Although these concepts overlap to some extent, each has its own uses and requirements and is designed and optimized differently.

A hash table or hash map is a data structure that uses a hash function to efficiently map certain identifiers or keys (e.g., person names) to associated values (e.g., their telephone numbers).n general, a hashing function may map several different keys to the same index. Therefore, each slot of a hash table is associated with (implicitly or explicitly) a set of records, rather than a single record. For this reason, each slot of a hash table is often called a bucket, and hash values are also called bucket indices.