Data Storage and Physical Design: Essential Concepts for Effective Information Management

In this ppt slide we will discuss about data storage & physical design and below are the summaries point:- Data storage involves archiving or recording data into electromagnetic devices for later access. Physical design refers to the layout and arrangement of data storage, such as tables in a database. Proper data storage is crucial for organizations, with a focus on secure access and extraction of information for decision-making.

Data storage and Physical Design
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Introduction
 Data storage refers to the archiving or recording of data into
electromagnetic devices where they can be accessed later by a
computer or a device.
 Physical design refers to the architecture layout or the physical
arrangement of how data are stored; such as the constraints and
the table. In database, entities are represented by tables
 Storage of data plays a vital role in every organization and the
society at large.
 There is a high demand for proper and appropriate storage of
these data where they can be accessed and stored in a secure
manner.

Objective
 The objective is not only to just give proper storage for the data, but
also the extraction of information to apprehend decision making by
the relevant people and groups in a particular organization
Data
 Information (processed data and presented inform of charts reports, tabulations, graphs)

Decisions

Physical design
 In a database, the physical database design interprets the LDM (logical
data model) into some sets of structured query languages (SQL).
 Physical Design is attained in several steps, which include expanding a
models of business into a fully attributed model (FAM) and later on
transforming the FAM into a model of physical design, (Laallam et al.,
2014).
 The data gathered during the logic model stage design is converted into
a description of the physical design during the physical design process.
 In physical design, the entities become tables, attributes become
column, unique identifiers become primary key and the relationships are
modeled as foreign keys
 Spaces are not allowed in between the name of the entities since they
translate into SQL calls when creating tables

Physical design structure
 Translating schemas into actual database structure involves creating of :-
 Tablespaces that need to be separated by differences
 Partitions so as to improve performance
 Indexes for ease in accessibility
 Constraints for ensuring the database is working within the intended
purpose
 Elucidate the physical structure
 Create the database/data storage environment
 Setting up appropriate and strong security mechanism
 Tuning techniques from indexing the entities to partitioning the storage
and data aggregation
 If applicable, the online analytical processes databases can also be
designed in the process

Physical design process
 The process of physical design can diagrammatically be
represented as shown below

Data storage and Database design
 A database is an organized structure that has been developed
to hold and store data, (Varadarajan et al., 2014). It consists of
a collection of several grouped data that have been stored and
can be accessed.
 For an effective data storage system, there must be an
effective information system that comprises of people,
hardware, software, databases and applications that transform
the data into meaningful information for easy interpretation.
 The information system should promote data integrity, prevent
anomalies and data redundancy that may arise thus promoting
the efficiency in the provision of data access

Data Storage Methods
 There are two main data storage devices in a computer system
 Hard disk
 Random Access Memory (RAM)
 Other methods of data storage include floppy disks, tape storage
and optical disks
 In a data warehouse, the data formats are continuously
integrated using an ETL process which comprises of the
extraction, transformation and the loading processes.
 In data mining, data warehouses are often designed and
optimized for analytical processing

Data accessibility
 The growing number of data on the internet makes the
accessibility of information difficult.
 Semantic support to the web can minimize the problem
as it assists the system to interpret the meaning of data
and helps the users to quickly get access to information
more conveniently.
 The standard data model for exchanging and sharing data
in semantic web is the RDF – Resource Description
Framework ,(Karan and Irizarry, 2015).
 Thus, it is essential to construct and implement a scalable
RDF storage system

Data storage tips
 The periodic back up of data accelerates duplication of data
which consume a lot of storage space.
 This can be minimized by use of inline reduplication
technology through :
 Improving the performance of compression redundant
data method of detection
 Advancing the performance of the recovery of data from
the linear delta chain
 Strengthening the security of the data in the respective
reduplicated systems
 Increasing the throughput of the reduplicated systems

Conclusion
 For one to implement an effective data storage and physical
design, one should consider:
 The situation and structure of the organization; the operational
components and how they function and interact.
 The problem and constraints that are existing and how they can
be resolved or minimized
 The scope and boundaries of the mold such as data structures,
size of data storage, type and the number of entities.

References
 Karan, E. P., & Irizarry, J. (2015). Extending BIM interoperability to
preconstruction operations using geospatial analyses and semantic web
services. Automation in Construction, 53, 1-12.
 Laallam, F. Z., Kherfi, M. L., & Benslimane, S. M. (2014). A survey on the
complementarity between database and ontologies: principles and research
areas. International Journal of Computer Applications in Technology, 49(2),
166-187.
 Varadarajan, R., Bharathan, V., Cary, A., Dave, J., & Bodagala, S. (2014,
March). DBDesigner: A customizable physical design tool for Vertica analytic
database. In Data Engineering (ICDE), 2014 IEEE 30th International
Conference on (pp. 1084-1095). IEEE.