A scalable approach for content based image retrieval in cloud datacenter

The emergence of cloud datacenters enhances the capability of online data storage. Since massive data is stored in datacenters, it is necessary to effectively locate and access interest data in such a distributed system. However, traditional search techniques only allow users to search images over exact-match keywords through a centralized index. These techniques cannot satisfy the requirements of content based image retrieval (CBIR). In this paper, we propose a scalable image retrieval framework which can efficiently support content similarity search and semantic search in the distributed environment. Its key idea is to integrate image feature vectors into distributed hash tables (DHTs) by exploiting the property of locality sensitive hashing (LSH). Thus, images with similar content are most likely gathered into the same node without the knowledge of any global information. For searching semantically close images, the relevance feedback is adopted in our system to overcome the gap between low-level features and high-level features. We show that our approach yields high recall rate with good load balance and only requires a few number of hops

筑波大学計算科学研究センター 平成21年度 年次報告書

1.平成21年度 基本方針、重点施策・改善目標等　……　42.平成21年度 実績報告　……　71.素粒子宇宙研究部門　……　131.1.素粒子分野　……　131.2.宇宙分野　……　262.物質生命研究部門　……　482.1.物質工学理論グループ　……　482.2.生命物理グループ　……　552.3.計算物性グループ　……　662.4.原子核理論グループ　……　803.地球生物環境研究部門　……　883.1.地球環境学分野　……　883.2.生物分野　……　964.超高速計算システム研究部門　……　1025.計算情報学研究部門　……　1105.1.計算知能分野　……　1105.2.計算メディア分野　……　12

筑波大学計算科学研究センター 平成30年度 年次報告書

まえがき ...... 21 センター組織と構成員 ...... 42 平成30 年度の活動状況 ...... 83 各研究部門の報告 ...... 15I. 素粒子物理研究部門 ...... 15II. 宇宙物理研究部門 ....... 40III. 原子核物理研究部門 ...... 65IV. 量子物性研究部門 ...... 83V. 生命科学研究部門 ...... 110 V-1. 生命機能情報分野 ...... 110 V-2. 分子進化分野 ...... 125VI. 地球環境研究部門 ...... 140VII. 高性能計算システム研究部門 ...... 155VIII. 計算情報学研究部門 ...... 207 VIII-1. データ基盤分野 ...... 207 VIII-2. 計算メディア分野 ...... 22

QueueLinker: データストリームのための並列分散処理フレームワーク

早大学位記番号:新6373早稲田大

筑波大学計算科学研究センター 平成25年度 年次報告書

1 平成25 年度重点施策および改善目標の達成状況 ...... 22 自己評価と課題 ...... 83 各研究部門の報告 ...... 10I. 素粒子物理研究部門 ...... 10II. 宇宙・原子核物理研究部門 ...... 32II-1. 宇宙物理理論グループ ...... 32II-2. 原子核分野 ...... 56III. 量子物性研究部門 ...... 69IV. 生命科学研究部門 ...... 83IV-1. 生命機能情報分野 ...... 83IV-2. 分子進化分野 ...... 93V. 地球環境研究部門 ....... 104VI. 高性能計算システム研究部門 ...... 118VII. 計算情報学研究部門 ...... 148VII-1. データ基盤分野 ...... 148VII-2. 計算メディア分野 ...... 16

Business process model repositories : efficient process retrieval

As organizations increasingly work in process-oriented manner, the number of business process models that they develop and have to maintain increases. As a consequence, it has become common for organizations to have collections of hundreds or even thousands of business process models. When a collection contains such a large number of business process models, it is impossible to manage that collection manually. Therefore, Business Process (BP) Model Repositories are required that store large collections of process models and provide techniques for managing these collections automatically and efficiently. The goal of research described in this thesis is to improve on existing BP Model Repositories, by improving the management techniques that are supported by these repositories on an aspect that has received little attention so far. Looking ahead at the results of the research, the aspect that will be selected for improvement is the process retrieval aspect. The two main research activities that will be carried in the context of this research are the following. Firstly, a survey of Business Process Model Repositories is performed to identity an unsolved aspect to be enhanced. The functionality of existing BP Model Repositories is listed and summarized as a framework for BP Model Repositories. After comparing the functionality that is provided by existing BP Model Repositories, based on the framework, efficient process retrieval is selected as the aspect that will be improved. This aspect is selected, because, although existing BP Model Repositories provide techniques for process retrieval, none of them focus on the efficiency of process retrieval. Secondly, an indexing technique for process retrieval (both process similarity search and process querying) is proposed. The index is constructed using features of process models. Features are small and characteristic fragments of process models. As such, by matching features of a given query/search model and features of a process model in a collection, a small set of models in the collection that potentially match the query/search model can be retrieved efficiently through the index. Techniques are also proposed to further check whether a potential match is an actual match for the query/search model. All of the above techniques are implemented as a component of the AProMoRe (an Advanced Process Model Repository) process repository. To evaluate the proposed process retrieval techniques, experiments are run using both real-life and synthetic process model collections. Experimental results show that on average the process retrieval techniques proposed in this thesis performs at least one order of magnitude faster than existing techniques