What are the data collection methods?
Data collection refers to the automatic gathering of non-electrical or power signals from analog and digital devices under test, such as Sensors and other instruments, and transmitting them to a host computer for analysis and processing. A data acquisition system is a flexible, user-defined measurement system that integrates software and hardware products based on a computer or specialized testing platform. This system allows users to customize their data collection processes according to specific needs.
Data acquisition, also known as DAQ, involves collecting external data and inputting it into an internal interface. This technology is widely used in various fields, including cameras, microphones, and other devices that capture real-world information. The collected data often represents physical quantities converted into electrical signals, such as temperature, pressure, wind speed, and water level. These can be either analog or digital. Data acquisition typically uses sampling methods, where data is collected repeatedly at regular intervals (sampling period). The results are usually instantaneous values or average characteristics over a certain time frame.
Accurate data measurement is fundamental to effective data collection. Measurement methods can be contact-based or non-contact-based, with various detection components used depending on the application. Regardless of the method, it is crucial to ensure that the object under test and its environment remain unaffected to maintain data accuracy. Data collection has a broad scope, including the continuous measurement of surface physical quantities. In computer-aided design, mapping, and drafting, digitizing images or graphics is also considered data acquisition, where geometric or physical properties like grayscale levels are captured.
With the rapid development of the internet industry, data collection has seen significant advancements, especially in distributed systems. Intelligent data acquisition systems have made great progress, both domestically and internationally. The number of bus-compatible data acquisition modules has increased, and more personal computer-compatible systems are now available. Various data acquisition devices have been introduced globally, ushering in a new era for data collection.
In today's big data landscape, the term "big data" is frequently used, but it hasn't lived up to expectations and has even been criticized as a "pseudo-concept." However, data collection remains the foundation of the big data industry. Many companies focus on big data applications and value mining, yet lack sufficient data to work with. It's like wanting gasoline without oil. The information infrastructure across industries, including government departments, is often fragmented, with massive data locked within different software systems. Data sources are diverse, large in volume, and change rapidly.
In the age of big data, data is the most valuable asset. But how do we effectively mine this data? What tools do we use? And how can we do it at the lowest cost?
**Data Acquisition Methods**
Today, we will explore three common software-based data collection methods, focusing on their implementation processes, advantages, and disadvantages.
**First, the Software Interface Method**
Many software vendors provide data interfaces to enable data collection and aggregation. The process includes:
- Coordinating with engineers from multiple software vendors to understand business processes and database structures.
- Designing and determining feasibility plans.
- Coding.
- Testing and debugging.
- Final delivery.
The advantage of this method is high data reliability and real-time transmission through the interface. However, it requires significant coordination between vendors, which can be complex and costly. Scalability is limited, and any changes in business requirements may necessitate major modifications to existing interfaces, making the process time-consuming and expensive.
**Second, the Open Database Method**
This approach directly accesses the target database to collect data. If two databases are on the same server, they can be accessed directly by specifying the database name and schema owner. For databases on different servers, a linked server or open query can be used, though this requires additional configuration.
This method offers high data accuracy and real-time performance. However, coordinating access to multiple vendor databases is challenging and can strain system performance. Additionally, many software vendors are reluctant to open their databases due to security concerns.
**Third, the Direct Data Collection Based on Underlying Data Exchange**
This method captures data at the network level by analyzing traffic between the software client and the database. It collects all data generated by the target software using I/O requests and network analysis, then converts and restructures the data for use in new databases.
Key features include:
- No need for cooperation with the original software vendor.
- Real-time data collection with sub-second response times.
- Strong compatibility with Windows-based systems.
- High-quality structured data for analytics.
- Automatic data association and quick implementation.
- Support for historical data import and AI-driven data writing.
- Simple configuration and short deployment time.
This method eliminates dependency on software vendors, reducing risks like code loss or team disintegration. It enables efficient, accurate, and real-time data extraction from various systems, supporting decision-making and operational efficiency while generating economic value.
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