日本データベース学会の皆様 筑波大学の天笠です．度々失礼します． 先ほどお送りした参加募集ですが，一部誤って昨年の内容が入って おりました．お詫びして訂正させていただきます．今年は青山学院 大学での開催ですので，どうぞお間違えのないようお気をつけくだ さいますようお願い致します． ====================================================================== iDB2010ワークショップ講演会 http://db-event.jpn.org/idb2010/index.php?route=invited-talks.html 主催: 日本データベース学会，情報処理学会データベースシステム研究会 電子情報通信学会データ工学研究会 ====================================================================== 日時: 2010年8月3日 13:00〜17:00 （iDBワークショップ2010内イベント） 会場: 青山学院大学　青山キャンパス 総研ビル(14号館) 12F 大会議室 http://www.aoyama.ac.jp/other/map/aoyama.html 住所: 〒150-8366 東京都渋谷区渋谷4-4-25 参加費: 無料 参加登録: 不要 8月2日から4日にかけて青山学院大学青山キャンパスで開かれますiDBワーク ショップ2010にて，データベースおよびデータ工学分野で世界的に活躍されて いる著名海外研究者による講演会を開催致します．参加登録，参加費は無料で す．ぜひ奮ってご参加いただけますようよろしくお願い致します． なお，8月4日には情報処理学会データベースシステム研究会・情報基礎とアク セス技術研究会および電子情報通信学会データ工学研究会が開催されます．各 研究会への参加を予定されている皆様、ぜひこちらの講演にも参加をご検討く ださい． ■講演会プログラム (13:00〜17:00) 1. TransDec: A Data-Driven Framework for Decision-Making in Transportation System Prof. Cyrus Shahabi (University of Southern Colifornia) 2. A Unified Graph Model for Sentence-based Opinion Retrieval Prof. Kam-Fai Wong (The Chinese University of Hong Kong) 3. Architecture-Driven Modelling Methodologies with the following content Prof. Bernhard Thalheim 4. Networking the Asian WordNet on WordNet Management System (WNMS) Dr. Virach Sornlertlamvanich (NECTEC, Thailand) 5. Good Papers and Good Presentations Dr. Tetsuya Sakai (Microsoft Research Asia, China) ■講演概要 1. TransDec: A Data-Driven Framework for Decision-Making in Transportation System Prof. Cyrus Shahabi (University of Southern Colifornia) The vast amounts of transportation datasets (traffic flow, incidents, etc.) collected by various federal and state agencies are extremely valuable in 1) real-time decision-making, planning, and management of the transportation systems, and 2) conducting research to develop new policies to enhance the efficacy of the transportation systems. In this talk, I will present our data-driven framework, dubbed TransDec (short for Transportation Decision-Making), which enables real-time integration, visualization, querying, and analysis of dynamic and archived transportation data. I will show that considering the large size of the transportation data, variety of the data (different modalities and resolutions), and frequent changes of the data, implementation of such a scalable system that allows for effective querying and analysis of both archived and real-time data is an intrinsically challenging data management task. Subsequently, I will focus on a route-planning problem where the weights on the road-network edges vary as a function of time due to the variability of traffic congestion. I will show that naive approaches to address this problem are either inaccurate or slow, motivating the need for new solutions. Consequently, I will discuss our initial approach to this problem and demonstrate its implementation within the TransDec framework. 2. A Unified Graph Model for Sentence-based Opinion Retrieval Prof. Kam-Fai Wong (The Chinese University of Hong Kong) There is a growing research interest in opinion retrieval for on-line users’opinions are becoming more and more popular in business, social network, etc. Practically speaking, the goal of opinion retrieval is to retrieve documents, which entail opinions or comments, relevant to a target specified by the user's query. A fundamental challenge in opinion retrieval is information representation. Existing approaches are document-based and documents are represented by bag-of-word. However, this representation cannot maintain the association between topic relevance and opinion relevance due to loss of contextual information. For this reason, existing systems fail to capture the pairing information between an opinion and its corresponding target, and the relationship among opinions on an identical topic is often overlooked. This in turn seriously affects opinion retrieval performance. In this paper, we propose a sentence-based opinion retrieval method. We define word pairs to capture intra-sentence contextual information. Additionally, we consider inter-sentence information to capture the relationships among the opinions on the same topic. Finally, two types of information are combined in a novel unified graph-based model, which can effectively rank the documents. Compared with existing approaches, experimental results on the COAE08 and COAE09 datasets show that our model has achieved significant improvement. 3. Architecture-Driven Modelling Methodologies with the following content Prof. Bernhard Thalheim Classical software development methodologies take architectural issues as granted or pre-determined. They thus neglect the impact decisions for architecture have within the development process. This omission is toleratable as long as we are considering monolithic systems. It cannot however been kept whenever we move to distributed systems. Web information systems pay far more attention to users support and thus require sophisticated layout and playout systems. These systems go beyond what has been known for presentation systems. We thus discover that architecture plays a major role during systems analysis, design and development. We thus target on building a framework that is based on early architectural decisions or on integration of new solutions into existing architectures. We aim at development of novel approaches to web information systems development that allow a co-evolution of architectures and software systems. 4. Networking the Asian WordNet on WordNet Management System (WNMS) Dr. Virach Sornlertlamvanich (NECTEC, Thailand) WordNet has been recognized as an important language resource of lexical semantic. Each sense of word is assigned a set of synonyms called synset which plays an important role in representing the meaning of the word. Moreover, many other lexical semantic relations namely antonym, hypernym, hyponym, holonym, and meronym are provided to construct a large-scaled network of lexical semantic. The formalism of semantic representation in WordNet has a great advantage in terms of building a computation lexical database. Up to the present day, many approaches in information retrieval, query expansion, machine translation, word sense disambiguation, text classification and so on have shown the promising results in using WordNet to increase the performance. As a result, several efforts have been put to create WordNet for other languages. Asian WordNet (AWN) is one of the approaches to build the WordNet for Asian languages by translating and networking the synsets through the defined synset ID of Princeton WordNet. To prepare an initial WordNet for a certain language, we assign the synset to a list of words from the existing bi-lingual dictionaries based on an assignment algorithm. The degree of confidence in the synset assignment has been proposed by computing the distance between a word to a member of a synset. Word synonyms are also used to serve in finding a candidate of synset. As a result, the list of candidate synsets is proposed to a word entry together with a degree of confidence score. In our approach, we show the efficiency in nominating the synset candidate by using the most common lexical information. The algorithm is evaluated against the implementation of Thai-English, Indonesian-English, and Mongolian-English bi-lingual dictionaries. The experiment also shows the effectiveness of using the same type of dictionary from different sources. The results are then reviewed collaboratively online via http://www.asianwordnet.org/. To exhibit a cross language access to the WordNet, we use the synset in the Princeton WordNet (PWN) as a key to retrieve a set of words in the target language. Moreover, the environment for developing the WordNet for Asian languages is designed in a distributed manner on the WordNet Management System (WNMS). Each language may take care of the environment and share its own resulted WordNet through a common API of a web service protocol. Currently, Asian WordNet (AWN) can serve some languages depending on the progress of the percentage of translation namely, Bengali (0.90%), Hindi (7.44%), Indonesian (27.58%), Japanese (30.35%), Korean (35.93%), Lao (33.05%), Mongolian (1.38%), Burmese (16.95%), Napali (0.03%), Sinhala (0.23%), Sundanese (0.06%), Thai (55.20%), and Vietnamese (10.43%). On the WNMS, not only to browse the WordNet of each language, the implementation in cross language WordNet and multilingual dictionary can be seen by configuration on the provided web API. 5. Good Papers and Good Presentations Dr. Tetsuya Sakai (Microsoft Research Asia, China) What makes a good research paper? What if your paper gets rejected? What makes a good presentation at a conference? I will share with you my experiences as an author, a Senior Program Committee member and a Best Paper Committee member of ACM SIGIR, so that you might want to answer these questions for yourself.