- freely available
Information 2019, 10(10), 296; https://doi.org/10.3390/info10100296
2. Platform Architecture and Orientation
3. Key Technologies
3.1. SOOC Hybrid SQL/NoSQL Service
3.1.1. SOOC Hybrid SQL/NoSQL Service Architecture
- Cache Database primarily stores the cached data. During the processing of SOOC data, different data need to be obtained from various databases for testing, while intermediate data and results that need to be shared or stored in databases are generated. During experiments on algorithms, researchers mainly improve and iterate the internal processing of the algorithms, and the input data of the algorithms usually remain unchanged. Retrieving raw data from databases each time will lead to lower efficiency and exert pressure on the databases and servers; moreover, such data need to be organized and integrated to meet the requirements each time. By storing frequently accessed data in Cache Database, the testing data can be obtained in real time. By storing intermediate data in Cache Database, data sharing can be realized between multiple modules. In addition, the processing results can not only be stored in Cache Database temporarily but also be transferred to different persistent databases.
- SQL Database provides multiple patterns of query strategies to satisfy the storage and retrieval requirements of structured data. When the data requested do not exist in Cache Database, they can be obtained from SQL Database. The metadata of unstructured data in SQL Database can be used as an intermediate bridge to retrieve unstructured data. SQL Database can also store the processing results.
- Unstructured Database provides high-performance queries for unstructured data, which can be not only retrieved through the metadata in SQL databases but also queried directly from NoSQL databases. For processing results, such as images and text, Unstructured Database can effectively meet the storage requirements for the subsequent processing of these data.
3.1.2. SOOC Hybrid SQL/NoSQL Data Access Flow
- Part 1
- Retrieve cached data. When the module Data Retrieval and Integration receives the data query request (1: Request), it first queries data from the cache database (2: Request cached data), returns the result (3: Return), and judges whether cached data exists (4: Cached data exists?). If the cached data exists, the result will be returned to the user (5: Return cached data) without requesting data from MySQL or MongoDB; otherwise, Part 2 will be executed.
- Part 2
- Retrieve structured data and metadata of structured data. When the user requests structured data, these data can be obtained from MySQL (6: Query structured data and metadata) and returned to the module Data Retrieval and Integration (7: Return). The frequently accessed raw data can be stored in the cache database (8: Store result in cache database) to ensure data access efficiency. Owing to different user requirements, it is necessary to judge whether to retrieve unstructured data in Data Retrieval and Integration. When it is not necessary to obtain unstructured data, the result is directly returned to the users (12: Return); otherwise, Part 3 will be executed on the basis of the user requirements or the metadata of the unstructured data.
- Part 3
- Retrieve unstructured data. When unstructured data are requested, the query parameters can be obtained according to the user requirements or the metadata of the unstructured data acquired from Part 2. The unstructured data obtained from MongoDB (9: Query unstructured data) will be processed in Data Retrieval and Integration (10: Return) for ease of use and returned to the users (12: Return). The frequently accessed raw data can be stored in the cache database (11: Store result in cache database) to ensure data access efficiency.
3.1.3. Comparison of Different SOOC Data Services
3.2. SOOC Heterogeneous Function Integration Service
3.2.1. SOOC Algorithmic and Functional Module Integration
3.2.2. SOOC Online Service Integration
4. Platform Application Modes and Case Study
4.1. Data Level
4.1.1. Data Input and Retrieval Efficiency
4.1.2. SOOC Data Visualization
4.2. Algorithm Level
4.3. Development Level
5. Conclusions and Future Work
Conflicts of Interest
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|Item||Service Based on RDBMS and Hadoop||Hybrid SQL/NoSQL Service|
|Deployment||Not easy to deploy and not user-friendly||Easy to deploy and user-friendly|
|Maintenance||Difficult and the updates of Oracle and Hadoop are complex||Easy to maintain and update|
|Data Support||Support data of different structures, but unable to support variable data schemas||Support data of different structures and schemas, and also support new formats in existing documents|
|Access Interface||Do not provide a direct or unified access interface for users unfamiliar with SQL or NoSQL databases||Support to access all data through a unified interface or data of different structures separately|
|Cost||May need more money and time||Based on opensource software with less money and time|
|Purpose||Mainly for enterprise business applications and medium or large companies||Mainly for startup or smaller scientific research teams and companies|
|MySQL||Hybrid SQL/NoSQL Service|
|Data input of numerical data||Storing all the numerical data||MySQL stores the historical orbit ephemeris data and MongoDB stores the magnitude data|
|Data input of numerical and image data||Storing all the numerical data and image files||MySQL stores the historical orbit ephemeris data and MongoDB stores the magnitude data and image files|
|Health status check URL||http://address/health.json|
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