# An Efficient Blockchain Transaction Retrieval System
## 1 Introduction
The current study mainly includes research on efficiency optimization of existing blockchain systems, query semantic retrieval function, and blockchain retrieval method research. Research on efficiency optimization of the existing blockchain system and query semantic retrieval function includes the following: [PERSON] [17] proposes the etherQL system, a query layer designed on the outer layer of the blockchain. The main idea of the system is to copy the blockchain data to the external database MongoDB and create the query layer with the help of the functional interface provided by the external database to realize the blockchain data query. VQL [18] stores blockchain data based on the external database and makes the intermediate results verifiable. In response to the shortcomings of hyperledger fabric in processing temporal data, the processing efficiency is improved by adding copies and inserting redundant data [19]. By combining the Merkle tree and B+ tree characteristics, the blockchain is upgraded from the traditional hash-based query to the key-based query [20]. However, this type of research mainly considers the introduction of external databases or inserting redundant data to improve the retrieval efficiency of the blockchain. While increasing the extra cost of the system, it does not explicitly enhance the ability of blockchain retrieval. It only carries out optimization at the application layer and is not suitable for the needs of this scenario. Blockchain retrieval method research includes Refs. [21; 22]. [PERSON] [21] proposes a DCOMB (dual combination bloom filter) method that combines the data stream of the Internet of Things with the timestamp of the blockchain to improve the versatility. [PERSON] [22] proposes a set of solutions including extracting the account address, establishing an inverted index between the account address and the block hash, and using the B+ tree to manage it. The program achieves good results. However, with the rapid expansion of the number of account addresses, retrieval efficiency is affected to a certain extent. In this work, we conducted a study on blockchain transaction retrieval based on past studies. We focus on analyzing the characteristics of block data, extracting universal retrieval features, proposing an improved retrieval data structure, and effectively managing retrieval features to boost retrieval efficiency. The contributions include the following:  
* For the blockchain transaction retrieval problem, based on the blockchain data semi-structured data features, this paper extracts the universal blockchain transaction characteristics, including account address and time, and establishes an efficient blockchain transaction retrieval system. * In response to the lack of an efficient search structure, this paper proposes a scalable and flexible secondary search structure BB+ tree based on bloom filters and B+ trees, which can effectively manage account addresses. The I2B+ tree is introduced to establish time retrieval structure, which can efficiently manage the time characteristics and increase the attributes of blockchain retrieval. * The experiment results prove that our system is superior to the existing methods in single feature retrieval, concurrent retrieval and multi-feature hybrid retrieval. The retrieval time under single feature retrieval is reduced by 40.54%, and the retrieval time is reduced by 43.16% under the multi-feature hybrid retrieval. It has better stability in the face of different block sizes and concurrent retrieval scales. The content of this article is arranged as follows: Section 2 introduces the development of retrieval data structure, the bloom filter and I2B+ tree. Section 3 introduces the overall architecture of the blockchain transaction retrieval system, the establishment and update of the retrieval structure by the new data structure BB+ tree and I2B+ tree, the single-feature retrieval process, and the multi-feature hybrid retrieval process. Section 4 shows the performance evaluation results and analysis.