Digital Forensics
Data Acquisation, Data imaging, Data Extraction,Data cloning.
Data Acquisition System
Data Acquisition System is an information System that collects, stories and distributes information.
It is used in industrial and commercial electronics and environmental and scientific equipment to capture electrical signals or environmental conditions on a computer device.
Include different tools and technologies that are designed to accumulate data.
(DAS or DAQ)
Data Acquisition System consists of :-
Sensor
Signal conditioning
Data conversion
Data processing
Multiplexing
Data Handling
Associated Transmission, Storage and Display devices.
Block Diagram of DAS.
Transducer
It is used to convert the physical quantity coming from the field into electrical signals.
It is used to measure directly the electrical quantities like Voltage, current, frequency, resistance.
Signal Conditioning Unit.
Output signals of transducers are very weak signals which cannot be used for further processing.
To make the signals strong, various signal conditioners are used. And this conditioners can be like to Amplifiers, filters, modifiers.
Multiplexer.
Accept multiple analog inputs. And provide a single output according to the requirements.
A/D Converter.
Convert analog data into digital data.
Easy Processing.
Easy transmission.
Digital display and storage.
Recorders and Display devices.
Data is displayed in suitable form in order to monitor the input signals.
Ex:- Oscilloscopes, Numerical Displays, Panel meters.
Data can be either permanently or temporary stored or recorded.
Ex:- optical recorders, Ultaviolet recorders, Stylus and ink recorders.
Objective of Data Acquisition System.
Must acquire the necessary data, at correct speed.
Use of all data efficieutly to inform the operation to operator about the state of the input.
Must monitor the complete plant operation to maintain on-line optimum and safe operations.
Must be able to summarize and store data for diagnosis of operation and record purpose.
Must be Flexible and capable of being expanded for future requirements.
Must be reliable and not have a down time greater than 0.1%.
Must provide an effective human communication system.
Applications Uses of DAS.
Analog DAS is used when wide frequency width is required or when lower accuracies can be tolerated.
Digital DAS is used when physical quantity being monitored has narrow band width and also when high accuracy and low per channel cost is required.
Digital are more complex than analog both in terms of instrumentation involved and the volume and complexity of data they can handle.
Data Extraction
As the first step of ETL process in which data from various source system is extracted.
Successful extraition converts data into a single format for standardized processing.
Types of Extraction
1. Logical Extraction
2. Physical Extraction
3. Live extraction
Logical Extraction
It further have two methods.
1. Full Extraction
In this method data is completely from the source system There is no need to keep track of changes.
2. Incremental Extraction
In this method, Data extracted after a well defined point/event in times.
The changes in source data need to be tracked since last Successful extraction only the changes in data will be extracted and then loaded.
Physical Data Extraction
It has two methods.
1. Online Extraction
In this method, Extraction process directly connect to the source system and extract the source data.
Data Extracted directly from the source system.
May access source tables through an intermediate System.
2. Offline Extraction
In this method, Data is not extracted directly from source system, instead stayed explicitly outside the original source System.
Data is either already structured or was created by extraction routine.
Live Extraction
In the context of data and technology, “live extraction” can refer to the process of extracting data in real-time from a source. This could include data extraction from databases, websites, sensors, or other data streams as they are generated. Live data extraction is often used for real-time analytics, monitoring, or reporting.
Data Imaging
What is a Data Images
A data image represents how data is organized and represented at different levels within an organization, including at the physical level — how the data might be physically stored — and at the logical level — how the information is presented to applications or computer programs.
Why is A Data Images Important
Enterprise data often is stored in multiple silos, separated by disparate storage practices in the different lines of business, which increases the complexity of combining data from multiple sources. One way to reduce that complexity is to map the separate physical data images into a standardized logical data image that presents a more consistent view for applications. This abstraction of the data image using a model-driven architecture is a powerful way to simplify application development and increase the speed to develop and deploy new capabilities within the enterprise
How C3 Ai Enables Organizations to Use A Data Images
The C3 AI Platform helps integrate, federate, and unify all disparate enterprise and external data into a logical data image. Developers and data engineers can use metadata transformation expressions to combine data entities, leverage AI-based reconciliation capabilities, maintain data lineage, and enforce data governance. The C3 AI Platform provides flexibility in making fine-tuned decisions to ingest and persist data within the platform’s data stores for faster
performance or to use a virtual data image to maintain value in existing data lake investments.
Data Cloning
Data cloning, in the context of computer systems and data management, refers to the process of creating an exact or near-exact duplicate or copy of data, typically for various purposes such as backup, data migration, testing, and more. This process ensures that the cloned data is identical to the original, and any changes made to the original data are also reflected in the cloned data.
What are the benefits of Data Cloning
There are several benefits of database virtualization, also known as data cloning, including:
1. Improved agility: Database virtualization enables organizations to quickly and easily provision data to different teams and departments, accelerating application development and reducing time to market.
2. Reduced costs: By cloning data instead of creating multiple physical copies, database virtualization reduces the need for additional hardware and storage, resulting in cost savings for organizations.
3. Increased productivity: Database virtualization eliminates the need for manual data copying and synchronization, freeing up resources to focus on more critical tasks.
4. Enhanced security: Database virtualization solutions can include features such as data masking and encryption to ensure sensitive data remains secure.
5. Better collaboration: Database virtualization enables teams to work on the same data sets, ensuring consistency and accuracy across the organization.
Overall, database virtualization provides organizations with a flexible, scalable, and cost-effective way to manage data, which is essential in today’s data-driven business environment.
