Cities have been our most elaborate creation since the beginning of human society, therefore urban heritages would always be the carrier of culture and collective memory of a place. Nowadays, however, there are already globally developed system for the conservation of tangible urban heritages, in most cases, monuments, with the loss of historic meaning, which means, the public, including local residents and tourists, can hardly perceive the historical authenticity of the city in the modern society. As a result, they can barely understand the sense of place and historical layering process of the city. From the perspective of Historic Urban Landscape, this paper sets out the experience and approaches of public perceptions of historical authenticity in China through literature review, comprehensive survey and network data analysis, and take The Five Avenues District in Tianjin as a case study in order to propose strategies for enhancing the public's perceived authenticity of the historic area, in other words, make up for the gap between the expression of urban heritages and the acceptance of the public. By doing this, the public can have a better understanding of the meaning of urban heritage conservation, which would contribute to its distinctive identity, sense of place, as well as the management of change and sustainable use of urban heritages.

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Recently, high utility pattern mining (HUPM) is one of the most important research issues in data mining. Because it can consider the non-binary frequency values of items in a transaction and the different profit values of each item. It has been widely used. First of all, this paper briefly describes the related concepts, formulas and examples of application for HUPM. Secondly, the key technologies for HUMP are introduced in detail, and they are divided into main methods including Apriori-based, tree-based, projection-based, list-based, data format-based, and index-based and so on. The paper further compares data sets, uses, advantages and disadvantages of algorithms, laid the foundation for the next research direction. Then, this article outlines the high utility derivative patterns, including high average utility pattern, high utility sequential pattern, and high utility compact pattern and so on. Because static data is difficult to meet the actual needs, this paper summarizes the efficient use of HUPMs' methods over data streams, mainly based on incremental methods, based on the sliding window model methods, based on the time decay model methods and based on the landmark model methods and so on. INDEX TERMS Survey, pattern mining, high utility pattern, data streams, incremental databases.

High utility itemset mining(HUIM) with negative utility is an emerging data mining task. However, the setting of the minimum utility threshold is always a challenge when mining high utility itemsets(HUIs) with negative items. Although the top-k HUIM method is very common, this method can only mine itemsets with positive items, and the problem of missing itemsets occurs when mining itemsets with negative items. To solve this problem, we first propose an effective algorithm called THN (Top-k High Utility Itemset Mining with Negative Utility). It proposes a strategy for automatically increasing the minimum utility threshold. In order to solve the problem of multiple scans of the database, it uses transaction merging and dataset projection technology. It uses a redefined sub-tree utility value and a redefined local utility value to prune the search space. Experimental results on real datasets show that THN is efficient in terms of runtime and memory usage, and has excellent scalability. Moreover, experiments show that THN performs particularly well on dense datasets.

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