A Storage Optimization Scheme for Blockchain Transaction Databases

As the typical peer-to-peer distributed networks, blockchain systemsrequire each node to copy a complete transaction database, so as to ensure newtransactions can by verified independently. In a blockchain system (e.g., bitcoinsystem), the node does not rely on any central organization, and every node keepsan entire copy of the transaction database. However, this feature determines thatthe size of blockchain transaction database is growing rapidly. Therefore, with thecontinuous system operations, the node memory also needs to be expanded tosupport the system running. Especially in the big data era, the increasing networktraffic will lead to faster transaction growth rate. This paper analyzes blockchaintransaction databases and proposes a storage optimization scheme. The proposedscheme divides blockchain transaction database into cold zone and hot zone usingexpiration recognition method based on Least Recently Used (LRU) algorithm. Itcan achieve storage optimization by moving unspent transaction outputs outsidethe in-memory transaction databases. We present the theoretical analysis on theoptimization method to validate the effectiveness. Extensive experiments showour proposed method outperforms the current mechanism for the blockchaintransaction databases.

Concurrency Control of Single-Level Transaction in WHYMX Object-Oriented Database System

The data model of WHYMX complicates transaction management. Traditional locking method is not powerful enough to solve the new problem of concurrency control of WHYMX's transaction. This paper presents a number of concurrency control algorithms based on the extended locking method.

Resources Snapshot Model for Concurrent Transactions in Multi-Core Processors

Transaction parallelism in database systems is an attractive way of improving transaction performance. There exists two levels of transaction parallelism, inter-transaction level and intra-transaction level. With the advent of multi- core processors, new hopes of improving transaction parallelism appear on the scene. The greatest execution efficiency of concurrent transactions comes from the lowest dependencies of them. However, the dependencies of concurrent transactions stand in the way of exploiting parallelism. In this paper, we present Resource Snapshot Model （RSM） for resource modeling in both levels. We propose a non-restarting scheduling algorithm in the inter-transaction level and a processor assignment algorithm in the intra-transaction level in terms of multi-core processors. Through these algorithms, execution performance of transaction streams will be improved in a parallel system with multiple heterogeneous processors that have different number of cores.

Generalized Multidimensional Association Rules

The problem of association rule mining has gained considerableprominence in the data mining community for its use as an important tool of knowledge discovery from large-scale databases. And there has been a spurt of researchactivities around this problem. Traditional association rule mining is limited tointratransaction. Only recently the concept of N-dimensional inter-transaction association rule (NDITAR) was proposed by H.J. Lu. This paper modifies and extendsLu's definition of NDITAR based on the analysis of its limitations, and the generalized multidimensional association rule (GMDAR) is subsequently introduced, whichis more general, flexible and reasonable than NDITAR.