To prevent and curb viral outbreaks, such as COVID-19, it is important to increase vaccination coverage while resolving vaccine hesitancy and refusal. To understand why COVID-19 vaccination coverage had rapidly increased in Japan, we analyzed Twitter posts (tweets) to track the evolution of people's stance on vaccination and clarify the factors of why people decide to vaccinate. We collected all Japanese tweets related to vaccines over a five-month period and classified the vaccination stances of users who posted those tweets by using a deep neural network we designed. Examining diachronic changes in the users' stances on this large-scale vaccine dataset, we found that a certain number of neutral users changed to a pro-vaccine stance while very few changed to an anti-vaccine stance in Japan. Investigation of their information-sharing behaviors revealed what types of users and external sites were referred to when they changed their stances. These findings will help increase coverage of booster doses and future vaccinations.

Twitter is currently one of the most influential microblogging services on which users interact with messages. It is imperative to grasp the big picture of Twitter through analyzing its huge stream data. In this study, we develop a two-stage clustering method that automatically discovers coarse-grained topics from Twitter data. In the first stage, we use graph clustering to extract micro-clusters from the word co-occurrence graph. All the tweets in a micro-cluster share a fine-grained topic. We then obtain the time series of each micro-cluster by counting the number of tweets posted in a time window. In the second stage, we use time series clustering to identify the clusters corresponding to coarse-grained topics. We evaluate the computational efficacy of the proposed method and demonstrate its systematic improvement in scalability as the data volume increases. Next, we apply the proposed method to large-scale Twitter data (26 million tweets) about the COVID-19 Vaccination in Japan. The proposed method separately identifies the reactions to news and the reactions to tweets.

High utility itemset mining is an important model in data mining. It involves discovering all itemsets in a quantitative transactional database that satisfy a user-specified minimum utility (minUtil) constraint. MinUtil controls the minimum value that an itemset must maintain in a database. Since the model evaluates an itemset’s interestingness using only the minUtil constraint, it implicitly assumes that all items in the database have similar utility values. However, some items have high utility, while others may have relatively low utility in a database. If minUtil is set too high, the user will miss all itemsets containing low utility items. To find itemsets that involve both high and low utility items, minUtil has to be set very low. However, this may cause a combinatorial explosion as the items with high utility may combine with others in all possible ways. This dilemma is called the low utility item problem. This paper proposes a flexible model of relative high utility itemset to address this problem. We introduce a new null-invariant measure, called utility ratio, to evaluate the interestingness of an itemset in the database. We also present a fast single scan algorithm to find all desired itemsets in the database. Experimental results demonstrate that the proposed algorithm is efficient. Finally, a case study on Yahoo! JAPAN retail data shows that the proposed model is useful.

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, this model's prosperous industrial use has been hindered by the problem of combinatorial explosion of patterns, which is the generation of too many redundant patterns, most of which may be useless to the user. We propose a novel model of closed periodic-frequent patterns that may exist in a temporal database to address this problem. Closed periodic-frequent patterns represent a concise lossless subset that uniquely preserves the complete information of all periodic-frequent patterns in a database. An efficient depth-first search algorithm, called Closed Periodic-Frequent Pattern Miner (CPFP-Miner), has been introduced to find all the database's desired patterns. Experimental results demonstrate that CPFP-Miner is not only memory, runtime, and energy-efficient, but also highly scalable. The usefulness of our model has also been shown with a case study on traffic congestion analytics.

Large events with many attendees cause congestion in the traffic network around the venue. To avoid accidents or delays due to this kind of unexpected congestion, it is important to predict the level of congestion in advance of the event. This study aimed to forecast congestion triggered by large events. However, historical congestion information alone is insufficient to forecast congestion at large venues when non-recurrent events are held there. To address this problem, we utilize microblog posts that refer to future events as an indicator of event attendance. We propose a regression model that is trained with microblog posts and historical congestion information to accurately forecast congestion at large venues. Experiments on next 24-hour congestion forecasting using real-world traffic and Twitter data demonstrate that our model reduces the prediction errors over those of the baseline models (autoregressive and long short term memory) by 20% - 50%.

Periodic-frequent pattern mining (PFPM) is an important data mining model having many real-world applications. However, the successful industrial application of this model has been hindered by the problem of combinatorial explosion of patterns, that is the generation of too many redundant patterns, most of which may be useless to the user. To address this problem, this paper proposes a novel model of maximal periodic- frequent pattern that may exist in a temporal database. A new pattern-growth algorithm, called Maximum Periodic-Frequent Pattern-growth (maxPFP-growth), has also been introduced to efficiently find all desired patterns in the data. Experimental results demonstrate that maxPFP-growth is not only memory and runtime efficient, but also highly scalable as well. The usefulness of our model has also been demonstrated with a case study on traffic congestion analytics.

Frequent pattern mining is an important model in data mining. It involves finding all patterns in a transactional database that satisfy the user-specified minimum support (minSup) constraint. The minSup controls the minimum number of transactions that a pattern must cover in a transactional database. Since only minSup is used to evaluate a pattern's interestingness, the frequent pattern model implicitly assumes that spatial information of the items will not impact the interestingness of a pattern in the database. This assumption limits the applicability of the frequent pattern model in many real-world applications. It is because patterns whose items are close to each other are typically more attractive to the user than the patterns whose items are far from each other in a coordinate system. With this motivation, this paper proposes a novel model of frequent spatial pattern that may exist in a spatiotemporal database. An efficient pattern-growth algorithm, called Frequent Spatial Pattern-growth (FSP-growth), has also been presented to mine all desired patterns in a database. Experimental results demonstrate that our algorithm is efficient. The usefulness of the proposed patterns has also been shown with a real-world application.

Finding partial periodic patterns in very large databases is a challenging problem of great importance in many real-world applications. Most previous work focused on finding these patterns in temporal (or transactional) databases and did not recognize the spatial characteristics of items. In this paper, we propose a more flexible model of partial periodic spatial pattern that may be present in spatiotemporal database. Three constraints, maximum inter-arrival time(maxIAT), minimum period-support(minPS) and maximum distance(maxDist), have been employed to determine the interestingness of a pattern in a spatiotemporal database. The maxIAT controls the maximum duration in which a pattern must reappear to consider its occurrence as periodic within the data. The minPS controls the minimum number of periodic occurrences of a pattern within the data. The maxDist controls the maximum distance between the items in a pattern. All patterns satisfying these three constraints are returned. An efficient algorithm, called SpatioTemporal-Equivalence CLAss Transformation (ST-ECLAT), has also been described to discover all partial periodic spatial patterns in a spatiotemporal database. This algorithm employs a novel smart depth-first search technique to discover desired patterns effectively. Experimental results demonstrate that the proposed algorithm is efficient. We also present a case study in which we apply our model to find useful information in the air pollution database.

In real-world databases, high utility itemset (HUI) is an important class of regularities. Most previous studies have focused on mining HUIs in transactional databases and did not consider the spatiotemporal characteristics of items. In this study, a more flexible model of spatial HUIs (SHUIs) that exist in spatiotemporal databases is proposed. In a spatiotemporal database (STD), an itemset is said to be an SHUI if its utility is not less than a user-specified minimum utility and the distance between any two of its items is not more than a user-specified maximum distance. Identifying SHUIs is very challenging because the generated itemsets do not satisfy the anti-monotonic property. In this study, we present two novel pruning techniques for reducing computational costs. Moreover, a fast single scan algorithm is presented for effectively evaluating all SHUIs in a STD. Furthermore, two case studies are presented, in which the proposed model is used to identify useful information in traffic congestion data and air pollution data.

Weighted Frequent Itemset (WFI) mining is an important model in data mining. The popular adoption and successful industrial application of this model has been hindered by the following two obstacles: (i) finding WFIs is a computationally expensiveness process as these itemsets do not satisfy the downward closure property and (ii) lack of parallel algorithms to find WFIs in very large databases (e.g. astronomical data and twitter data). This paper makes an effort to address these two obstacles. Two pattern-growth algorithms, Sequential Weighted Frequent Pattern-growth and Parallel Weighted Frequent Pattern-growth, have been introduced to discover WFIs efficiently. Both algorithms employ three novel pruning techniques to reduce the computational cost effectively. The first pruning technique prunes some of the uninteresting items by employing a criterion known as cutoff weight. The second pruning technique, called conditional pattern base elimination, eliminates the construction of conditional pattern bases if a suffix item is an uninteresting item. The third pruning technique, called pattern-growth termination, defines a new terminating condition for the pattern-growth technique. Experimental results demonstrate that the proposed algorithms are memory and runtime efficient, and highly scalable as well.

Traffic accidents are still troubling our society. The number of drive recorders sold has increased, and therefore we can collect large-scale vehicle recorder data to be used to support traffic safety. We have developed a system for detecting potentially risky crossroads on the basis of vehicle recorder data, road shapes, and weather information. Visualization combining space and time in a single display called a “space time cube (STC)” helps us to understand and analyze spatio-temporal mobility data on caution crossroads. The STC enables us to simultaneously explore not only shapes and positions of vehicle trajectories but also their temporal distributions. However, it is difficult for users to manually find good viewpoints for understanding such characteristics of trajectories. In this paper, we propose an optimal viewpoint selection method for visualizing spatio-temporal characteristics of vehicle trajectories on a large set of crossroads using an STC. Major contributions of this paper are as follows: (1) We provide an algorithm based on viewpoint entropy weighted by angles of trajectories with a horizontal line as a measure of a viewpoint quality on a projected 2D image. (2) We demonstrate our solution can be adapted to crossroads with different trajectory shapes. We also extend the proposed method to find an optimal viewpoint for multiple crossroads. (3) We verify the proposed method through users' evaluations. (4) We construct an overviewing catalog of potentially risky crossroads detected from real vehicle recorder big data to discuss and analyze them with stakeholders.

We present a method for estimating road safety using large-scale and heterogeneous vehicle recorder data. Our assumption in this study is that occurrences of traffic accidents on crossings could be estimated by their road shape, driving behaviors over them, and photos taken by dash cams. Utilizing 3 years dataset from 1,500 drivers in a fleet delivery company, we try to detect risky crossings based on supervised machine learning. Results show that our method provides good performance, and can extract potentially risky crossings.

We introduce our developing system which can analyze drivers' driving behavior collected from vehicle recorder and other datasources such as weather reports and road maps. We show some performance issues while (pre-)processing large-scale data and discuss the requirements for the practical system.

A temporal database is a collection of transactions, ordered by their timestamps. Discovering partial periodic itemsets in temporal databases has numerous applications. However, to the best of our knowledge, no work has considered finding these itemsets in temporal databases, despite that this type of data is very common in real-life. Discovering partial periodic itemsets in temporal databases is challenging. It requires defining (i) an appropriate measure to assess the periodic interestingness of itemsets, and (ii) an algorithm to efficiently find all partial periodic itemsets. While a pattern-growth algorithm can be employed for the second sub-task, the first sub-task has not been addressed. Moreover, how these two tasks are combined has significant implications. In this paper, we address this challenge. We introduce a model to find partial periodic itemsets in temporal databases. A new measure, called periodic-frequency, has been proposed to determine the periodic interestingness of itemsets by taking into account their number of cyclic repetitions in the entire data. Moreover, the paper introduces a pattern-growth algorithm to discover all partial periodic itemsets. Experimental results demonstrate that our model is efficient.

It is vital for the transportation industry, which performs most of their work by automobiles, to reduce its number of traffic accidents. Many local governments in Japan have made potential risk maps of traffic accident spots. However, making such maps in wide areas and with the time information had been difficult because most of them are made based on an investigation. Utilizing long-term driving records can extract wide area spatio-temporal caution spots. This paper proposes a visual interaction method for exploring caution spots from large-scale vehicle recorder data. Our method provides (i) a flexible filtering interface for driving operations using various combinations of attribute values such as velocity and acceleration, and (ii) a 3D visual environment for spatio-temporal exploration of caution spots. We demonstrate the usefulness of our novel visual exploration environment using real data given by one of the biggest transportation companies in Japan. Exploration results show our environments can extract caution spots where some accidents have actually occurred or that are on very narrow roads with bad visibility.

We propose an analysis method of driving behaviors based on large-scale and long-term vehicle recorder data to support fleet driver management by classifying drivers by their skill, safety, physical/mental fatigue, aggressiveness, and so on. Previous studies rely on precise data with small number of drivers, which are difficult to extrapolate to general drivers. In this study, we examine ability of a dataset that is sparse but large-scale (over 100 fleet drivers) and long-term (10 months' worth). We focus on classifying drivers recently involved in accidents, and examine correlation with driving behaviors. We propose two models for the classification; entropy-like model and KL divergence model that aim to emphasize the behavioral difference from average drivers. From experiments, we will show some informative findings on behaviors that might cause accidents.

Partial periodic patterns are an important class of regularities that exist in a time series. A key property of these patterns is that they can start, stop, and restart anywhere within a series. We classify partial periodic patterns into two types: (i) regular patterns−patterns exhibiting periodic behavior throughout a series with some exceptions and (ii) recurring patterns−patterns exhibiting periodic behavior only for particular time intervals within a series. Past studies on partial periodic search have been primarily focused on finding regular patterns. One cannot ignore the knowledge pertaining to recurring patterns. This is because they provide useful information pertaining to seasonal or temporal associations between events. Finding recurring patterns is a non-trivial task because of two main reasons. (i) Each recurring pattern is associated with temporal information pertaining to its durations of periodic appearances in a series. Obtaining this information is challenging because the information can vary within and across patterns. (ii) Finding all recurring patterns is a computationally expensive process since they do not satisfy the anti-monotonic property. In this paper, we propose recurring pattern model by addressing the above issues. We also propose Recurring Pattern growth algorithm along with an efficient pruning technique to discover these patterns. Experimental results show that recurring patterns can be useful and that our algorithm is efficient.

Twitter is a "what's-happening-right-now" tool that enables interested parties to follow thoughts and commentary of individual users in nearly real-time. While it is a valuable source of information for real-time topic detection and tracking, Twitter data are not clean because of noisy messages and users, which significantly diminish the reliability of obtained results.

Transportation systems in mega-cities are often affected by various kinds of events such as natural disasters, accidents, and public gatherings. Highly dense and complicated networks in the transportation systems propagate confusion in the network because they offer various possible transfer routes to passengers. Visualization is one of the most important techniques for examining such cascades of unusual situations in the huge networks. This paper proposes visual integration of traffic analysis and social media analysis using two forms of big data: smart card data on the Tokyo Metro and social media data on Twitter. Our system provides multiple coordinated views to visually, intuitively, and simultaneously explore changes in passengers' behavior and abnormal situations extracted from smart card data and situational explanations from real voices of passengers such as complaints about services extracted from social media data. We demonstrate the possibilities and usefulness of our novel visualization environment using a series of real data case studies about various kinds of events.

Coverage pattern mining is an important model in data mining. It provides useful information pertaining to the sets of items that have coverage interesting to the users in a transactional database. The coverage patterns do not satisfy the anti-monotonic property. This increases the search space in the itemset lattice, which in turn increases the computational cost of mining these patterns. An Apriori-like algorithm known as CMine has been proposed in the literature to discover the patterns. It employs a pruning technique to reduce the search space. We have observed that there exists further scope for reducing the search space effectively. In this paper, we theoretically analyze different measures used in the pattern model, and introduce a novel pruning technique to reduce the search space. An Apriori-like algorithm, called CMine++, has also been proposed to discover the patterns. The performance study shows that mining coverage patterns with CMine++ is efficient.

Identifying and tracking new information on the Web is important in sociology, marketing, and survey research, since new trends might be apparent in the new information. Such changes can be observed by crawling the Web periodically. In practice, however, it is impossible to crawl the entire expanding Web repeatedly. This means that the novelty of a page remains unknown, even if that page did not exist in previous snapshots. In this paper, we propose a novelty measure for estimating the certainty that a newly crawled page appeared between the previous and current crawls. Using this novelty measure, new pages can be extracted from a series of unstable snapshots for further analysis and mining to identify new trends on the Web. We evaluated the precision, recall, and miss rate of the novelty measure using our Japanese web archive, and applied it to a Web archive search engine.

In recent years, several information retrieval methods using information about Web-links have been developed, such as HITS and trawling. In order to analyze Web-links dividing into links inside each Web site (local-links) and links between Web sites (global-links)for information retrieval, a proper model of the Web site is required. In existing research, a Web server is used as a model of the Web site. This idea works relatively well when a Web site corresponds to a server, as is the case for public Web sites, but works poorly when multiple Web sites correspond to a server, as is the case for private Web sites on rental Web servers. We propose a new model of the Web site, "directory-based site", to handle typical private sites, and a method to identify them using information about the URL and Web-links. We verify the method can approximately identify, at a rate of 66% of over 110,000 servers, whether each server has multiple directory-based sites or not, and extract over 500,000 directory-based sites and 4 million global-links by computational experiments using jp-domain URLs and Web-link data contains over 23 million URLs and 100 million Web-links, collected from July to August 2000, by Toyoda and Kitsuregawa. We also propose a new framework of Web-link based information retrieval that uses directory-based sites and global-links instead of Web pages and whole Web-links respectively, and examine the effectiveness of our framework by comparing a result of trawling on our framework to one on the existing framework.