アスペクト ハッケン : ワールド ワイド ウェブ ニ オケル ブンミャク ジョウホウ ノ マイニング ニ カンスル ケンキュウ

京都大学0048新制・課程博士博士(情報学)甲第11722号情博第151号新制||情||34(附属図書館)23365UT51-2005-D471京都大学大学院情報学研究科社会情報学専攻(主査)教授 田中 克己, 教授 石田 亨, 教授 西田 豊明学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDA

Assessing a risk-avoidance navigation system based on localized torrential rain data

Localized torrential rainfall events and related traffic problems are increasing in Japan, suggesting the need for a navigation-alert system to help drivers avoid such risks. Based on ongoing developments of weather radar systems for early detection of localized torrential rain and a cross-data collaboration platform for traffic optimization, in this study we tested the application of a route-guidance system that can help drivers avoid heavy rainfall. Participants were given equivalent levels of pre-training un the early detection of rainfall and the relationship between rainfall and accidents, then allowed to test a driving simulator set up with four alert methods, three route options, and four levels of possible risk avoidance. Using this system, the heavy rain avoidance rate was 85.63%, suggesting that such a system would be socially acceptable and useful, though further research is needed to refine the specific approach

Report on a Hackathon for Car Navigation Using Traffic Risk Data

Car drivers select their routes based on the information obtained about accidents and traffic congestion along the route. In recent years, nowcasting and forecasting of various traffic risk events is being performed by using diverse sensor data. However, there is no clarity as yet on what and how to communicate to the driver in case there are traffic risks on the route. In this paper, we have developed an environment that enables non UI experts to quickly create car navigation prototypes by using traffic risk data. This paper includes our report on a hackathon that we held using this environment. The hackathon theme was "Develop a new car navigation system equipped with a mechanism that makes the driver aware of traffic risks and helps them determine the most appropriate driving routes." Twenty three researchers and professionals from the field of traffic engineering participated. Our results have brought certain common problems to the awareness of the experts. The information obtained from this report will be very beneficial for our community to determine the direction of collaboration

Mining Periodic-Frequent Patterns in Irregular Dense Temporal Databases Using Set Complements

Periodic-frequent patterns are a vital class of regularities in a temporal database. Most previous studies followed the approach of finding these patterns by storing the temporal occurrence information of a pattern in a list. While this approach facilitates the existing algorithms to be practicable on sparse databases, it also makes them impracticable (or computationally expensive) on dense databases due to increased list sizes. A renowned concept in set theory is that the larger the set, the smaller its complement will be. Based on this conceptual fact, this paper explores the complements, redefines the periodic-frequent pattern and proposes an efficient depth-first search algorithm that finds all periodic-frequent patterns by storing only non-occurrence information of a pattern in a database. Experimental results on several databases demonstrate that our algorithm is efficient

Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases

Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing approaches miss interesting patterns that exhibit partial periodic behavior in a database. With this motivation, this paper proposes a novel model for finding partial periodic patterns that may exist in temporal databases. An efficient pattern-growth algorithm, called Partial Periodic Pattern-growth (3P-growth), is also presented, which can effectively find all desired patterns within a database. Substantial experiments on both real-world and synthetic databases showed that our algorithm is not only efficient in terms of memory and runtime, but is also highly scalable. Finally, the effectiveness of our patterns is demonstrated using two case studies. In the first case study, our model was employed to identify the highly polluted areas in Japan. In the second case study, our model was employed to identify the road segments on which people regularly face traffic congestion

Vertical profile of tropospheric ozone derived from synergetic retrieval using three different wavelength ranges, UV, IR, and Microwave: sensitivity study for satellite observation

Rouzé Michel. Cinq millions d'années. In: Raison présente, n°19, Juillet – Août – Septembre 1971. Télévision, pouvoir et liberté. p. 113

Precisely and Persistently Identifying and Citing Arbitrary Subsets of Dynamic Data

International audiencePrecisely identifying arbitrary subsets of data so that these can be reproduced is a daunting challenge in data-driven science, the more so if the underlying data source is dynamically evolving. Yet an increasing number of settings exhibit exactly those characteristics. Larger amounts of data are being continuously ingested from a range of sources (be it sensor values, online questionnaires, documents, etc.), with error correction and quality improvement processes adding to the dynamics. Yet, for studies to be reproducible, for decision-making to be transparent, and for meta studies to be performed conveniently, having a precise identification mechanism to reference, retrieve, and work with such data is essential. The Research Data Alliance (RDA) Working Group on Dynamic Data Citation has published 14 recommendations that are centered around time-stamping and versioning evolving data sources and identifying subsets dynamically via persistent identifiers that are assigned to the queries selecting the respective subsets. These principles are generic and work for virtually any kind of data. In the past few years numerous repositories around the globe have implemented these recommendations and deployed solutions. We provide an overview of the recommendations, reference implementations, and pilot systems deployed and then analyze lessons learned from these implementations. This article provides a basis for institutions and data stewards considering adding this functionality to their data systems