Domains like social media analysis, e-commerce, and healthcare data management require querying through large chunks of structured and unstructured databases. In this modern world, there has been an ever-increasing requirement for the same in many other domains. However, current systems have been proven inefficient due to their inability to tackle the diverse obstacles presented when querying through databases comprising both structured and unstructured data.
Intending to integrate these two data types seamlessly within a unified framework, researchers from Fudan University and Transwarp have developed CHASE, which is a relational database framework designed to support hybrid queries natively.
Currently, there are relational database management systems for structured data and specialised unstructured data solutions. Both specialise in their specific data types and cannot handle hybrid queries. Structured data is highly rigid and needs a predefined set of rules for organisation, while unstructured data consists of texts, images, videos, etc, requiring a flexible system for their storage. When both data types come together, there is an immense increase in the computational load, and catering to their specific needs is challenging. Therefore, there is a need for a new method that can bridge the gap between these two data structure types, introducing latency in query processing and addressing scalability issues.
The proposed method, CHASE, introduces a sophisticated architecture to handle hybrid queries. The key functionalities include the following:
- Advanced Indexing for Unstructured Data: For efficient retrieval, an indexing system is introduced for all the different unstructured data types, such as images, audio and videos. This allows for effectively tackling complex queries, which was an issue due to the flexible nature of these databases.
- Dynamic Query Optimization: First, CHASE analyses the data types present in the query, and based on that, it optimises its approach in real time. With this tailored approach, the process becomes more efficient by reducing the processing time of the queries.
- Integration with Natural Language Processing (NLP): NLP enables CHASE to understand the natural language query, which allows it to gain contextual understanding rather than keyword matching. This provides the user with a better experience and also allows non-technical personnel to query the databases effectively.
CHASE was benchmarked on real-world datasets, with 23 scenarios for testing various functionalities. The execution time was, on average, 30% faster for CHASE than conventional systems. The benchmarks indicated reduced resource consumption while maintaining high performance levels, which is a testament to the efficiency of CHASE in handling hybrid datasets. CHASE showed linear scalability with the increased dataset size, proving its efficacy for enterprise-grade applications.
The paper has dealt with the critical need for a cohesive system in order to manage hybrid data queries by proposing the CHASE methodology, which is practical and scalable due to its immense performance and efficiency upgrade over traditional methods. Its novel architecture, complete query language, and strong benchmarking results position CHASE as a leading solution for the management of hybrid data. However, this research has some weaknesses, such as limited testing on real-world datasets with complex data relationships; therefore, it needs further validation to guarantee its long-term reliability and broad applicability in general and various domains. Overall, this research contributes meaningfully to the field because it proposes an intrinsic relational database designed for hybrid queries, which fills the critical gap in the management of data and establishes CHASE as a valuable tool for modern applications with the requirement to integrate structured and unstructured data seamlessly.
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