In the existing time bank system, the issuance and settlement functions of time dollar are completely centralized on a central node. This central way not only suffers from many security problems including single point failure of central node and data tampering, but also has some problems such as lack of transparency in time dollar issuance and circulation, the dependance on centralized settlement agency in time dollar settlement process. In order to solve these problems, a public welfare time bank system based on blockchain was proposed. Firstly, the issuance function and settlement function of time dollar were separated from the central node. Then, the separated issuance function was gradually decentralized, and the separated settlement function was directly decentralized by the use of advantages of blockchain such as distributed decentration, collective maintenance and the feature of not easy to tamper, after that Public Welfare Time Blockchain (PWTB) was formed. Finally, PWTB used blockchain to decentralize the time bank system from a single node maintaining ledger to the collective maintaining distributed shared ledger, so the issuance and circulation of time dollar became open and transparent, and the settlement of time dollar did not rely on a central node. The security analysis shows that the PWTB can achieve safe information transmission and storage as well as safe data sharing.

Traditional data cleaning approaches usually implement cleaning rules specified by business requirements through hard-coding mechanism, which leads to well-known issues in terms of reusability, scalability and flexibility. In order to address these issues, a new Dynamic Rule-based Data Cleaning Method (DRDCM) was proposed, which supports the complex logic operation between various types of rules and three kinds of dirty data repair behavior. It integrates data detection, error correction and data transformation in one system and contributes several unique characteristics, including domain-independence, reusability and configurability. Besides, the formal concepts and terms regarding data detection and correction were defined, while necessary procedures and algorithms were also introduced. Specially, the supported multiple rule types and rule configurations in DRDCM were presented in detail. At last, the DRDCM approach was implemented. Experimental results show that the implemented system provides a high accuracy on the discarded behavior of dirty data repair with real-life data sets. Especially for the attribute required to comply with the statutory coding rules (such as ID card number), whose accuracy can reach 100%. Moreover, these results also indicate that this reference implementation of DRDCM can successfully support multiple data sources in cross-domain scenarios, and its performance does not sharply decrease with the increase of the number of rules. These results further validate that the proposed DRDCM is practical in real-world scenarios.

Automatic Number Plate Recognition (ANPR) data is easier to obtain than private Global Positioning System (GPS) data, and it contains more useful information, but the relatively mature GPS track data mining with vehicle group method did not apply to ANPR data, the existing accompanying vehicle group mining algorithm pays attention to the similarity of the trajectory and ignores the time factor when dealing with small amount of ANPR data. A clustering method based on trajectory feature to excavate the accompanying vehicle group was proposed. Aiming at the fact that the sampling points are fixed and the sampling time is uncertain in the ANPR data, whether two objects were accompanied was determined by the number of co-occurrence in the trajectory. The co-occurrence definition introduced the Hausdorff distance, taking into account the location, direction and time characteristics of the trajectory. The accompanying vehicle group with different but adjacent sampling points and similar trajectories was minned to improve the mining efficiency. The experimental results show that the proposed method is more effective than the existing method to excavate the vehicle group, and improves the efficiency by nearly two times when identifying the non-accompanying mode data.