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This study aims to develop an interface management risk interaction modeling and analysis methodology applicable to complex systems in high-speed rail construction projects, reveal the interaction mechanism of interface management risk and provide theoretical support for project managers to develop appropriate interface management risk response strategies.

This paper introduces the association rule mining technique to improve the complex network modeling method. Taking China as an example, based on the stakeholder perspective, the risk factors and significant accident types of interface management of high-speed rail construction projects are systematically identified, and a database is established. Then, the Apriori algorithm is used to mine and analyze the strong association rules among the factors in the database, construct the complex network, and analyze its topological characteristics to reveal the interaction mechanism of the interface management risk of high-speed rail construction projects.

The results show that the network is both scale-free and small-world, implying that construction accidents are not random events but rather the result of strong interactions between numerous interface management risks. Contractors, technical interfaces, mechanical equipment, and environmental factors are the primary direct causal factors of accidents, while owners and designers are essential indirect causal factors. The global importance of stakeholders such as owners, designers, and supervisors rises significantly after considering the indirect correlations between factors. This theoretically explains the need to consider the interactions between interface management risks.

The interaction mechanism between interface management risks is unclear, which is an essential factor influencing the decision of risk response measures. This study proposes a new methodology for analyzing interface management risk response strategies that incorporate quantitative analysis methods and considers the interaction of interface management risks.

The purpose of this paper is to develop and implement benchmarking methods on food safety and hygiene between suppliers of fruits and vegetables, regardless the food safety management systems they implement.

A tailor made questionnaire was prepared of 64 questions, divided into ten sectors regarding hygiene and food safety. The suppliers were selected from the inventory of a large chain of retail stores. The audits were performed by a food safety expert on the site of the company. Totally, 72 audits were conducted in several geographic regions of Greece. The data collected was statistically analyzed for benchmarking with technical, geographical or financial criteria.

The large size companies have a level from satisfactory to very satisfactory in the transport and need improvement on their packaging materials. Improvement in procedures of cleaning and disinfection is required by companies which are in the region of Attica and the Peloponnese.

The proposed methodology and analysis can be used to benchmark groups of food companies with common commercial profile in order to prioritize both the frequency of audits and the importance of interventions and preventive measures.

Customer lifetime value (CLV) scoring is highly effective when applied to marketing databases. Some researchers have extended the traditional association rule problem by associating a weight with each item in a transaction. However, studies of association rule mining have considered the relative benefits or significance of “items” rather than “transactions” belonging to different customers. Because not all customers are financially attractive to firms, it is crucial that their profitability be determined and that transactions be weighted according to CLV. This study aims to discover association rules from the CLV perspective.

This study extended the traditional association rule problem by allowing the association of CLV weight with a transaction to reflect the interest and intensity of customer values. Furthermore, the authors proposed a new algorithm, frequent itemsets of CLV weight (FICLV), to discover frequent itemsets from CLV-weighted transactions.

Experimental results from the survey data indicate that the proposed FICLV algorithm can discover valuable frequent itemsets. Moreover, the frequent itemsets identified using the FICLV algorithm outperform those discovered through conventional approaches for predicting customer purchasing itemsets in the coming period.

This study is the first to introduce the optimum approach for discovering frequent itemsets from transactions through considering CLV.

Mining sequential patterns in large databases has become an important data mining task with broad applications, such as business analysis, web mining, security, and bio‐sequences analysis. The purpose of this paper is to propose the notion of condensed frequent sequential pattern base (SP base) with guaranteed maximal error bound.

A subset of frequent sequential patterns is computed, and then used to approximate the supports of arbitrary frequent sequential patterns with guaranteed maximal error bound, because in many applications it is sufficient to generate only frequent sequential patterns with support frequency in close‐enough approximation instead of in full precision.

The concept of condensed frequent SP base is introduced, and an efficient algorithm for mining condensed SP bases is developed.

A condensed frequent SP base can significantly reduce the set of sequential patterns that need to be mined, stored, and analyzed, while providing guaranteed error bound for frequencies of sequential patterns not in the base.

A much smaller base of patterns can help users to comprehend the mining results. Computing a much smaller pattern base also leads to better efficiency.

The paper shows that by adopting a novel pruning technology, the algorithm out‐performs the previous work by one order of magnitude.

This study aims to overcome the problem of traditional association rules relying almost entirely on expert experience to set relevant interest indexes in mining. Second, this project can effectively solve the problem of four types of rules being present in the database at the same time. The traditional association algorithm can only mine one or two types of rules and cannot fully explore the database knowledge in the decision-making process for library recommendation.

The authors proposed a Markov logic network method to reconstruct association rule-mining tasks for library recommendation and compared the method proposed in this paper to traditional Apriori, FP-Growth, Inverse, Sporadic and UserBasedCF algorithms on two history library data sets and the Chess and Accident data sets.

The method used in this project had two major advantages. First, the authors were able to mine four types of rules in an integrated manner without having to set interest measures. In addition, because it represents the relevance of mining in the network, decision-makers can use network visualization tools to fully understand the results of mining in library recommendation and data sets from other fields.

The time cost of the project is still high for large data sets. The authors will solve this problem by mapping books, items, or attributes to higher granularity to reduce the computational complexity in the future.

The authors believed that knowledge of complex real-world problems can be well captured from a network perspective. This study can help researchers to avoid setting interest metrics and to comprehensively extract frequent, rare, positive, and negative rules in an integrated manner.

The paper aims to address the issue of Web service providers facing a major issue of estimating the potential effects of changing a Web service to other services, especially in large ecosystems of Web services which have become more common nowadays. Web service providers make constant changes to their Web services to meet the ever-changing business requirements.

The paper proposes an approach to predict change impact by mining a version history of a Web service ecosystem. The proposed approach extracts patterns of Web services that have been changed together from the version history by using association rule data mining techniques. The approach then uses this knowledge of co-changed patterns for predicting the impact of future changes based on the assumption that Web services which have been changed together frequently in the past will likely be changed together in future.

An empirical validation based on the Amazon’s ecosystem of 46 Web services indicates the effectiveness of the proposed approach. After an initial change, the proposed approach can correctly predict up to 25 per cent of further Web services to be changed with the precision of up to 82 per cent.

Traditional approaches to predict change impact in Web services tend to rely on having a dependency graph between Web services. However, in practice, building and maintaining inter-service dependencies that capture the precise semantics and behaviours of the Web services are challenging and costly. The proposed approach offers a novel alternative which only requires mining the existing version history of Web services.

The purpose of this paper is to provide a detailed discussion for different types of graph queries and a different mechanism for indexing and querying graph databases.

The paper reviews the existing approaches and techniques for indexing and querying graph databases. For each approach, the strengths and weaknesses are discussed with particular emphasis on the target application domain. Based on an analysis of the state‐of‐the‐art of research literature, the paper provides insights for future research directions and untouched challenging research aspects.

Several graph indexing and querying techniques have been proposed in the literature. However, there is still a clear room for improvement and further research issues in that domain.

The paper identifies the advantages and disadvantages of the different graph indexing mechanisms and their suitability for different practical applications. The paper provides some guidelines and recommendations which are useful for future research in the area of graph databases.

The paper has practical implications for social networks, protein networks, chemical compounds, multimedia database, and semantic web.

The paper contributes to the implementation of an efficient indexing and querying mechanism for graph databases in different application domains.

The novel Coronavirus (COVID-19) pandemic, which started in late December 2019, has spread to more than 200 countries. As no vaccine is yet available for this pandemic, government and health agencies are taking draconian steps to contain it. This pandemic is also trending on social media, particularly on Twitter. The purpose of this study is to explore and analyze the general public reactions to the COVID-19 outbreak on Twitter.

This study conducts a thematic analysis of COVID-19 tweets through VOSviewer to examine people’s reactions related to the COVID-19 outbreak in the world. Moreover, sequential pattern mining (SPM) techniques are used to find frequent words/patterns and their relationship in tweets.

Seven clusters (themes) were found through VOSviewer: Cluster 1 (green): public sentiments about COVID-19 in the USA. Cluster 2 (red): public sentiments about COVID-19 in Italy and Iran and a vaccine, Cluster 3 (purple): public sentiments about doomsday and science credibility. Cluster 4 (blue): public sentiments about COVID-19 in India. Cluster 5 (yellow): public sentiments about COVID-19’s emergence. Cluster 6 (light blue): public sentiments about COVID-19 in the Philippines. Cluster 7 (orange): Public sentiments about COVID-19 US Intelligence Report. The most frequent words/patterns discovered with SPM were “COVID-19,” “Coronavirus,” “Chinese virus” and the most frequent and high confidence sequential rules were related to “Coronavirus, testing, lockdown, China and Wuhan.”

The methodology can be used to analyze the opinions/thoughts of the general public on Twitter and to categorize them accordingly. Moreover, the categories (generated by VOSviewer) can be correlated with the results obtained with pattern mining techniques.

This study has a significant socio-economic impact as Twitter offers content posting and sharing to billions of users worldwide.

According to the authors’ best knowledge, this may be the first study to carry out a thematic analysis of COVID-19 tweets at a glance and mining the tweets with SPM to investigate how people reacted to the COVID-19 outbreak on Twitter.

In recent years, practitioners have identified a number of problems with traditional performance management (PM) systems, arguing that PM is broken and needs to be fixed. In this chapter, we review criticisms of traditional PM practices that have been mentioned by journalists and practitioners and we consider the solutions that they have presented for addressing these concerns. We then consider these problems and solutions within the context of extant scholarly research and identify (a) what organizations should do going forward to improve PM practices (i.e., focus on feedback processes, ensure accountability throughout the PM system, and align the PM system with organizational strategy) and (b) what scholars should focus research attention on (i.e., technology, strategic alignment, and peer-to-peer accountability) in order to reduce the science-practice gap in this domain.

The purpose of this study is to analyse the suitability of photo-sharing platforms, such as Flickr, to extract relevant knowledge on tourists’ spatial movement and point of interest (POI) visitation behaviour and compare the most prominent clustering approaches to identify POIs in various application scenarios.

The study, first, extracts photo metadata from Flickr, such as upload time, location and user. Then, photo uploads are assigned to latent POIs by density-based spatial clustering of applications with noise (DBSCAN) and k-means clustering algorithms. Finally, association rule analysis (FP-growth algorithm) and sequential pattern mining (generalised sequential pattern algorithm) are used to identify tourists’ behavioural patterns.

The approach has been demonstrated for the city of Munich, extracting 13,545 photos for the year 2015. POIs, identified by DBSCAN and k-means clustering, could be meaningfully assigned to well-known POIs. By doing so, both techniques show specific advantages for different usage scenarios. Association rule analysis revealed strong rules (support: 1.0-4.6 per cent; lift: 1.4-32.1 per cent), and sequential pattern mining identified relevant frequent visitation sequences (support: 0.6-1.7 per cent).

As a theoretic contribution, this study comparatively analyses the suitability of different clustering techniques to appropriately identify POIs based on photo upload data as an input to association rule analysis and sequential pattern mining as an alternative but also complementary techniques to analyse tourists’ spatial behaviour.

From a practical perspective, the study highlights that big data sources, such as Flickr, show the potential to effectively substitute traditional data sources for analysing tourists’ spatial behaviour and movement patterns within a destination. Especially, the approach offers the advantage of being fully automatic and executable in a real-time environment.

The study presents an approach to identify POIs by clustering photo uploads on social media platforms and to analyse tourists’ spatial behaviour by association rule analysis and sequential pattern mining. The study gains novel insights into the suitability of different clustering techniques to identify POIs in different application scenarios.

本论文旨在分析图片分享平台Flickr对截取游客空间动线信息和景点（POI）游览行为的适用性, 并且对比最知名的几种聚类分析手段, 以确定不同情况下的POI。

本论文首先从Flickr上摘录下图片大数据, 比如上传时间、地点、用户等。其次, 本论文使用DBSCAN和k-means聚类分析参数来将上传图片分配给POI隐性变量。最后, 本论文采用关联规则挖掘分析（FP-growth参数）和序列样式勘探分析（GSP参数）以确认游客行为模式。

本论文以慕尼黑城市为样本, 截取2015年13,545张图片。POIs由DBSCAN和k-means聚类分析将其分配到有名的POIs。由此, 本论文证明了两种技术对不同用法的各自优势。关联规则挖掘分析显示了显著联系（support：1%−4.6%；lift：1.4%−32.1%）, 序列样式勘探分析确立了相关频率游览次序（support：0.6%−1.7%。

本论文的理论贡献在于, 根据图片数据, 通过对比分析不同聚类分析技术对确立POIs, 并且证明关联规则挖掘分析和序列样式勘探分析各有千秋又互相补充的分析技术以确立游客空间行为。

本论文的现实意义在于, 强调了大数据的来源, 比如Flickr,证明了其对于有效代替传统数据的潜力, 以分析在游客在一个旅游目的地的空间行为和动线模式。特别是这种方法实现了实时自动可操作性等优势。

本论文展示了一种方法, 这种方法通过聚类分析社交媒体上的上传图片以确立POIs, 以及通过关联规则挖掘分析和序列样式勘探分析来分析游客空间行为。本论文对于不同聚类分析以确立不同适用情况下的POIs的确立提出了独到见解。