Database Research at Bilkent University ?zgür
Ulusoy
Bilkent University
Computer Engineering Department
Bilkent, Ankara, TURKEY
oulusoy@https://www.doczj.com/doc/c579471.html,.tr
1.Introduction
This report provides a brief description of the research activities of the Database Research Group of Bilkent University. The current research of the group is mainly focused on the topics of Multimedia Databases (in particular video database management and content-based retrieval of document images), Web Databases (in particular the modeling and querying of Web resources and focused crawling), and Mobile Computing (in particular moving object processing and mobile data management).
An overview of each research topic investigated is provided together with the list of researchers, any external funding information, and a selection of associated publications. Interested readers should contact ?zgür Ulusoy at oulusoy@https://www.doczj.com/doc/c579471.html,.tr for further information. Most of the publications listed in this report are available at:
https://www.doczj.com/doc/c579471.html,.tr/~oulusoy/~pubs.html.
2.Multimedia Databases
Researchers: ?. Ulusoy, U. Güdükbay, E. ?etin, E. ?aykol, C. Alper, I. S. Alt?ng?vde, T. Sevilmi?.
Past Researchers: M. E. D?nderler, U. Arslan, G. ünel, A. K. Sinop.
Funding Sources: Scientific and Technical Research Council of Turkey (TüB?TAK) under grant number EEEAG-199E025, Turkish State Planning Organization (DPT) under grant number 2004K120720, and European Commission 6th Framework Program, MUSCLE NoE Project, under grant number FP6-507752.
2.1BilVideo: A Video Database Management System
We have developed a prototype video database management system, called BilVideo [1, 2, 3]. The architecture of BilVideo is original in that it provides full support for spatio-temporal queries that contain any combination of directional, topological, 3D-relation, object-appearance, trajectory-projection, and similarity-based object-trajectory conditions by a rule-based system built on a knowledge-base, while utilizing an object-relational database to respond to semantic (keyword, event/activity, and category-based), color, shape, and texture queries. The knowledge-base of BilVideo consists of a fact-base and a comprehensive set of rules implemented in Prolog. The rules in the knowledge-base significantly reduce the number of facts that need to be stored for spatio-temporal querying of video data [4]. To respond to user queries containing both spatio-temporal and semantic conditions, the query processor interacts with both the knowledge-base and a feature database, where the system stores fact-based and semantic metadata, respectively. Intermediate query results returned from these two system components are integrated seamlessly by the query processor, and final results are sent to Web clients. Raw video data and its features are stored in a separate database. The feature database contains video semantic properties to support keyword, event/activity, and category-based queries. The video-annotator tool, which we developed as a Java application, generates and maintains the features. The fact-base, which is a part of the knowledge-base, is populated by the fact-extractor tool, which is also a Java application [3].
BilVideo provides support for retrieving any segment of a video clip, where the given query conditions are satisfied, regardless of how video data is semantically partitioned. Object trajectories, object-appearance relations, and spatio-temporal relations between video objects are represented as Prolog facts in a knowledge-base, and they are not explicitly related to the semantic units of videos. Thus, precise answers can be returned for user queries, when requested, in terms of frame intervals. BilVideo has a simple, yet very powerful SQL-like query language, which currently supports a broad range of spatio-temporal queries on video data [5]. We are currently working on integrating support for semantic and low-level (color, shape, and texture) video queries as well. We completed our work on semantic video modeling, which was reported in [6]. As for the low-level queries, our Fact-Extractor tool also extracts color, shape, and texture histograms of the salient objects in video keyframes. We have also developed a Web-based visual query interface for specifying video queries visually over the Internet. Furthermore, we have completed our work on the optimization of spatio-temporal video queries [7].
The BilVideo query language is designed to be used for any application that needs video query processing facilities. Hence, the language provides query support through external predicates for application-dependent data.
The Web-based Query Interface of BilVideo and its user manual are available at https://www.doczj.com/doc/c579471.html,.tr/. A demo of the Web-based Query Interface can be seen at:
https://www.doczj.com/doc/c579471.html,.tr/~bilmdg/bilvideo/webclient.avi.
2.2Ottoman Archive Content-Based Retrieval System
The Ottoman Archive Content-Based Retrieval system is a Web-based program that provides electronic access to digitally stored
Ottoman document images. The Ottoman script is a connected script based on the Arabic alphabet. A typical word consists of compounded letters as in handwritten text. We have developed a framework for content-based retrieval of historical documents in the Ottoman Empire archives [8]. The documents are stored as textual images, which are compressed by constructing a library of symbols that occur in a document. The symbols in the original image are then replaced by the pointers into the codebook library. For symbol extraction, we use the features in wavelet and spatial domain based on angular and distance span shapes. Symbols are extracted from document images by a scale-invariant process. User can specify a query as a rectangular region in an input image, and content-based retrieval is achieved by applying the same symbol extraction process to the query region. The query is processed on the codebook of documents, and the resulting documents are ranked in the decreasing order of their similarity to the query image, which is determined by the total number of symbols matched with the query region. The resulting documents are presented by identifying the matched region of each document in a rectangle. The querying process does not require decompression of images.
The techniques we use in our work are not specific to documents with Ottoman script. They can easily be tailored to other domains of archives containing printed or handwritten documents.
The web site of the Ottoman Archive Content-Based Retrieval system is:
https://www.doczj.com/doc/c579471.html,.tr/bilmdg/ottoman/webclient.html.
A step-by-step user manual describing how to access the Web query interface and specify queries is available at:
https://www.doczj.com/doc/c579471.html,.tr/~bilmdg/ottoman/manual/manual.ht m.
References
[1]M.E. D?nderler, E. Saykol, ?. Ulusoy, U. Güdükbay.
BilVideo: A Video Database Management System. IEEE Multimedia, 10, 5 (January/March 2003), 66-70.
[2]?. Ulusoy, U. Güdükbay, M.E. D?nderler, E. Saykol, C.
Alper. BilVideo Video Database Management System (demo paper). In Proceedings of the International Conference on Very Large Databases (VLDB'04). (Toronto, Canada, August-September 2004), 1373-1376.
[3]M.E. D?nderler, E. Saykol, U. Arslan, ?. Ulusoy, U.
Güdükbay. BilVideo: Design and Implementation of a Video Database Management System. To appear in Multimedia Tools and Applications, 2005.
[4]M.E. D?nderler, ?. Ulusoy, U. Güdükbay. A Rule-based
Video Database System Architecture. Information Sciences, 143, 1-4 (June 2002), 13-45.
[5]M.E. D?nderler, ?. Ulusoy, U. Güdükbay. Rule-based
Spatio-temporal Query Processing for Video Databases.
VLDB Journal, 13, 1 (January 2004), 86-103.
[6]U. Arslan, M.E. D?nderler, E. ?aykol, ? Ulusoy, U.
Güdükbay. A Semi-Automatic Semantic Annotation Tool for Video Databases. In Proceedings of the Workshop on
Multimedia Semantics (SOFSEM'02). (Milovy, Czech Republic, November 2002), 1-10.
[7]G, ünel, M.E. D?nderler, ?. Ulusoy, U. Güdükbay. An
Efficient Query Optimization Strategy for Spatio-Temporal Queries in Video Databases. Journal of Systems and Software, 73, 1 (September 2004), 113-131.
[8] E. ?aykol, A. K. Sinop, U. Güdükbay, ?. Ulusoy, E. ?etin.
Content-Based Retrieval of Historical Ottoman Documents Stored as Textual Images. IEEE Transactions on Image Processing, 13, 3 (March 2004), 314-325.
3.Web Databases
Researchers: ?. Ulusoy, ?. S. Alt?ng?vde, ?. N. Subakan.
Past Researchers: S. A. ?zel, M. Kutlutürk.
Collaborators: G. ?zsoyo?lu, Z. M. ?zsoyo?lu, A. Al-Hamdani (Case Western Reserve University).
Funding Sources: A joint grant of Scientific and Technical Research Council of Turkey (TüB?TAK) under grant number 100U024 and National Science Foundation of the USA under grant number INT-9912229.
3.1Metadata-Based Modeling of the Web
A recent approach to increase the quality of Web search is associating metadata to the resources on the Web. To this end, there are various standardization efforts and initiatives, such as the Dublin Core Framework, RDF, Semantic Web and Topic Maps. In [9, 10], we address the problem of modeling Web information resources using expert knowledge and personalized user information for improved Web searching capabilities. We propose a “Web information space” model, which is composed of Web-based information resources (HTML/XML documents on the Web), expert advice repositories (domain-expert-specified metadata for information resources), and personalized information about users (captured as user profiles that indicate users’ preferences about experts as well as users’ knowledge about topics).
Expert advice, the heart of the Web information space model, is specified using topics and relationships among topics (called metalinks), along the lines of the recently proposed topic maps. Topics and metalinks constitute metadata that describe the contents of the underlying HTML/XML Web resources. The metadata specification process is semi-automated. It exploits XML DTDs to allow domain-expert guided mapping of DTD elements to topics and metalinks. In particular, the domain expert specifies a mapping between the entities of our metadata model (topics and metalinks) and the XML DTDs in the corresponding domain(s). An agent then traverses the Web, extracts topics and metalinks for those XML files conformant with the input DTD, and stores them into a local object-relational database management system, which will then serve as an expert advice (metadata) repository for these visited Web resources.
To demonstrate the practicality and usability of the proposed Web information space model, we have created a prototype expert
advice repository of more than one million topics and metalinks for DBLP (Database and Logic Programming) Bibliography data set.
3.2Querying Web Metadata: Native Score Management and Text Support in Databases
In this work, we discuss the issues involved in adding a native score management system to object-relational databases, to be used in querying web metadata (that describes the semantic content of web resources) [11, 12]. As described in the preceding section, the web metadata model is based on topics (representing entities), relationships among topics (metalinks), and importance scores (sideway values) of topics and metalinks. We extend database relations with scoring functions and importance scores. We add to SQL score-management clauses with well-defined semantics, and propose the sideway-value algebra (SVA), to evaluate the extended SQL queries.
SQL extensions include clauses for propagating input tuple importance scores to output tuples during query processing, clauses that specify query stopping conditions, threshold predicates—a type of approximate similarity predicates for text comparisons, and user-defined-function-based predicates. The propagated importance scores are then used to rank and return a small number of output tuples. The query stopping conditions are propagated to SVA operators during query processing. We show that our SQL extensions are well-defined, meaning that, given a database and a query Q, the output tuples of Q and their importance scores stay the same under any query processing scheme.
3.3Focused Web Crawling
A preliminary step for extracting and querying metadata for Web resources is gathering all and only the relevant Web resources for
a particular application domain or topic of interest. To this end, in
[13] we propose a rule-based focused crawling strategy to crawl the Web and construct a repository of relevant Web pages.A focused crawler is an agent that concentrates on a particular target topic, and tries to visit and gather only relevant pages from the Web. In the literature, one of the approaches for focused crawling is using a canonical topic taxonomy and a text classifier to train the crawler so that those URLs that most probably point to on-topic pages will be identified and prioritized. Our research explores using simple rules derived from the linkage statistics among the topics of a taxonomy while deciding on the crawler’s next move, i.e., to select the URL to be visited next. The rule based approach improves the harvest rate and coverage of the taxonomy-based focused crawler and also enhances it to support tunneling. More specifically, the rule based crawler can follow a path of off-topic pages that may at last lead to high quality on-topic pages. More information on our projects in Web Databases can be found through https://www.doczj.com/doc/c579471.html,.tr/~bilweb. References
[9]I. S. Altingovde, S. A. ?zel, ?. Ulusoy, G. ?zsoyo?lu, Z. M.
?zsoyo?lu. Topic-Centric Querying of Web Information Resources. In Proceedings of the Database and Expert
Systems Applications (DEXA'01), Lecture Notes in Computer
Science (Springer Verlag), vol.2113, (Munich, Germany, September 2001) 699-711.
[10]S. A. ?zel, I. S. Altingovde, ?. Ulusoy, G. ?zsoyo?lu, Z. M.
?soyoglu. Metadata-Based Modeling of Information Resources on the Web. Journal of the American Society for
Information Science and Technology (JASIST), 55, 2 (January 2004), 97-110.
[11]G. ?zsoyo?lu, Abdullah Al-Hamdani, I. S. Altingovde, S. A.
?zel, ?. Ulusoy, Z. M. ?zsoyo?lu. Sideway Value Algebra
for Object-Relational Databases. In Proceedings of the International Conference on Very Large Databases (VLDB'02). (Hong Kong, August 2002), 59-70.
[12]G. ?zsoyo?lu, I. S. Altingovde, A. Al-Hamdani, S. A. ?zel,
?. Ulusoy, Z. M. ?zsoyo?lu. Querying Web Metadata: Native Score Management and Text Support in Databases.
ACM Transactions on Database Systems, 29, 4 (December 2004), 581-634.
[13]I. S. Altingovde, ?. Ulusoy. Exploiting Interclass Rules for
Focused Crawling. IEEE Intelligent Systems, 19, 6 (November-December 2004), 66-73.
4.Mobile Computing
Researchers: ?. Ulusoy, M. Karakaya, ?. K?rpeo?lu, S. ??rac?. Past Researchers: , Y. Sayg?n, E. Kayan, J. Tayeb, G. G?k, I. Yoncac?, G. Yava?.
Collaborators: O. Wolfson (University of Illinois at Chicago), K. Y. Lam (City University of Hong Kong), D. Katsaros, Y. Manolopoulos (Aristotle University),A. K. Elmagarmid (Purdue University).
Funding Sources: Scientific and Technical Research Council of Turkey (TüB?TAK) under grant number EEEAG-246, NATO Collaborative Research Program under grant number CRG 960648, and the bilateral program of scientific cooperation between Turkey and Greece (TüB?TAK grant number 102E021 and Γ.Γ.E.T.).
4.1Moving Object Processing
Our earlier work on moving object processing includes indexing locations of moving objects. In [14], we propose an indexing technique for moving objects based on a variant of the quadtree data structure in which the indexing directory is in primary memory and the indexed data resides in secondary storage. The method is useful in any application that involves dynamic attributes whose values vary continuously according to a given function of time. Our approach is based on the key idea of using a linear function of time for each dynamic attribute that allows us to predict its value in the future. We contribute an algorithm for regenerating the quadtree-based index periodically to minimize CPU and disk access cost.
Another important issue in moving object database management is to provide support for processing location-dependent queries, where the answer to a query depends on the current location of
the user who issued the query. A location-dependent query can become more difficult to process when it is submitted as a continuous query for which the answer changes as the user moves. In [15], we present an efficient method to monitor the locations of moving objects so that timely and accurate results can be returned to location dependent continuous queries, while minimizing the location update cost. Location-dependent queries from mobile clients may also be associated with timing constraints on their response times. In [16], we propose a method to monitor the locations of moving users/objects based on the real-time criticality of location dependent continuous queries, so that higher levels of accuracy can be achieved for the results returned to the queries categorized with higher criticality. Another related issue in processing location dependent continuous queries is the transmission of query results to the users. In [17], various methods that can be used to determine the transmission time of query results are investigated with the goal of minimizing data transmission costs.
In [18], we present a data mining algorithm for the prediction of user movements in a mobile computing system. The algorithm proposed is based on mining the mobility patterns of users, forming mobility rules from these patterns, and finally predicting
a mobile user’s future movements by using these rules.
4.2Mobile Data Management
Dissemination of data by broadcasting may induce high access latency in case the number of broadcast data items is large. In [19], we propose two methods to reduce client access latency for broadcast data. Our methods are based on analyzing the broadcast history (i.e., the chronological sequence of items that have been requested by clients) using data mining techniques. The proposed methods are implemented on a Web log, and it is shown that the proposed rule-based methods are effective in improving the system performance in terms of average latency as well as cache hit ratio of mobile clients. Our other work on broadcast scheduling considers a pull-based data delivery environment [20]. We propose a variant of the Longest Wait First heuristic in scheduling data broadcast, which provides a practical implementation of the heuristic by avoiding the decision overhead.
One of the features that a mobile computer should provide is disconnected operation, which is performed by hoarding. The process of hoarding can be described as loading the data items needed in the future to the client cache prior to disconnection. Automated hoarding is the process of predicting the hoard set without any user intervention. In [21], we describe a generic, application-independent technique for determining what should be hoarded prior to disconnection. Our method utilizes association rules that are extracted by data mining techniques for determining the set of items that should be hoarded to a mobile computer prior to disconnection.
While there has been much research interest in mobile computing issues, an issue that has not received much attention is the management of the database of a mobile system under timing constraints. In [22], we present a mobile database system model that takes into account the timing requirements of applications supported by mobile computing systems. We provide a transaction execution model with alternative execution strategies for mobile transactions and evaluate the performance of the system under various mobile system characteristics, such as the number of mobile hosts in the system, the handoff process, disconnection, coordinator site relocation, and wireless link failure.
References
[14]J. Tayeb, ?. Ulusoy, O. Wolfson, A Quadtree Based
Dynamic Attribute Indexing Method. The Computer Journal,
41, 3 (1998) 185-200.
[15]K. Y. Lam, ?. Ulusoy, et al., An Efficient Method for
Generating Location Updates for Processing of Location-
Dependent Continuous Queries. In Proceedings of the
International Conference on Database Systems for Advanced
Applications (DASFAA'01). (Hong Kong, April 2001) 218-
225.
[16]?. Ulusoy, I. Yoncac?, K. Y. Lam. Evaluation of a
Criticality-Based Method for Generating Location Updates.
In Proceedings of Workshop on Database Mechanisms for
Mobile Applications, Lecture Notes in Informatics, vol.43,
(Karlsruhe, Germany, April 2003) 94-105.
[17]G. G?k, ?. Ulusoy. Transmission of Continuous Query
Results in Mobile Computing Systems. Information Sciences, 125, 1-4 (2000) 37-63.
[18]G. Yava?, D. Katsaros, ?. Ulusoy, Y. Manolopoulos. A Data
Mining Approach for Location Prediction in Mobile Environment. Data and Knowledge Engineering, 54, 2 (2005) 121-146.
[19]Y. Sayg?n, ?. Ulusoy. Exploiting Data Mining Techniques
for Broadcasting Data in Mobile Computing Environments.
IEEE Transactions on Knowledge and Data Engineering, 14,
6 (November/December 2002) 1387-1399.
[20]M. Karakaya, ?. Ulusoy. Evaluation of a Broadcast
Scheduling Algorithm. In Proceedings of Advances in Databases and Information Systems (ADBIS'01), Lecture Notes in Computer Science (Springer Verlag), vol.2151,
(Vilnius, Lithuania, September 2001) 182-195.
[21]Y. Sayg?n, ?. Ulusoy, A. K. Elmagarmid. Association Rules
for Supporting Hoarding in Mobile Computing Environments. In Proceedings of IEEE International Workshop on Research Issues on Data Engineering (RIDE'00), (San Diego, CA, USA, February 2000) 71-78. [22]E. Kayan, ?. Ulusoy. An Evaluation of Real-Time
Transaction Management Issues in Mobile Database Systems.The Computer Journal, 42, 6 (November 1999)
501-510.
On choice-o?ering imperatives Maria Aloni? 1Introduction The law of propositional logic that states the deducibility of either A or B from A is not valid for imperatives(Ross’s paradox,cf.[9]).The command (or request,advice,etc.)in(1a)does not imply(1a)(unless it is taken in its alternative-presenting sense),otherwise when told the former,I would be justi?ed in burning the letter rather then posting it. (1) a.Post this letter!? b.Post this letter or burn it! Intuitively the most natural interpretation of the second imperative is as one presenting a choice between two actions.Following[2](and[6])I call these choice-o?ering imperatives.Another example of a choice-o?ering imperative is (2)with an occurence of Free Choice‘any’which,interestingly,is licensed in this context. (2)Take any card! Like(1a),this imperative should be interpreted as carrying with it a permission that explicates the fact that a choice is being o?ered. Possibility statements behave similarly(see[8]).Sentence(3b)has a read-ing under which it cannot be deduced from(3a),and‘any’is licensed in(4). (3) a.You may post this letter.? b.You may post this letter or burn it. (4)You may take any card. In[1]I presented an analysis of modal expressions which explains the phe-nomena in(3)and(4).That analysis maintains a standard treatment of‘or’as logical disjunction(contra[11])and a Kadmon&Landman style analysis of‘any’as existential quanti?er(contra[3]and[4])assuming,however,an in-dependently motivated‘Hamblin analysis’for∨and?as introducing sets of alternative propositions.Modal expressions are treated as operators over sets of propositional alternatives.In this way,since their interpretation can depend on the alternatives introduced by‘or’(∨)or‘any’(?)in their scope,we can account for the free choice e?ect which arises in sentences like(3b)or(4).In this article I would like to extend this analysis to imperatives.The resulting theory will allow a uni?ed account of the phenomena in(1)-(4).We will start by presenting our‘alternative’analysis for inde?nites and disjunction. ?ILLC-Department of Philosophy,University of Amsterdam,NL,e-mail:M.D.Aloni@uva.nl
Introdution
Mike Jian
INTRODUCTION ?Section A: ?Comprises 8 two mark and 4 one mark multiple choice questions. ?Section B: ?Four 10 mark questions. ?Two 20 mark questions.
INTRODUCTION The examination is a three hour paper with 15 minutes reading and planning time. All questions are compulsory. Some questions will adopt a scenario/case study approach. All those questions will require some form of written response although questions on planning or review may require the calculation and interpretation of some basic ratios.
1.Which TWO of the following should be included in an audit engagement letter? ①Objective and scope of the audit ②Results of previous audits ③Management’s responsibilities ④Need to maintain professional scepticism A.① and ② B.① and ③ C.② and ④ D.③ and ④ (2 marks)
毕业设计说明书 英文文献及中文翻译 学 专 指导教师： 2014 年 6 月
Introduction to Linux 1.1 History 1.1.1 UNIX In order to understand the popularity of Linux, we need to travel back in time, ab out 30 years ago... Imagine computers as big as houses, even stadiums. While the sizes of those com puters posed substantial problems, there was one thing that made this even worse: eve ry computer had a different operating system. Software was always customized to ser ve a specific purpose, and software for one given system didn't run on another system. Being able to work with one system didn't automatically mean that you could work w ith another. It was difficult, both for the users and the system administrators. Computers were extremely expensive then, and sacrifices had to be made even after th e original purchase just to get the users to understand how they worked. The total cost of IT was enormous. Technologically the world was not quite that advanced, so they had to live with t he size for another decade. In 1969, a team of developers in the Bell Labs laboratories started working on a solution for the software problem, to address these compatibility issues. They developed a new operating system, which was simple and elegant written in the C programming language instead of in assembly code able to recycle code. The Bell Labs developers named their project "UNIX." The code recycling features were very important. Until then, all commercially av ailable computer systems were written in a code specifically developed for one system . UNIX on the other hand needed only a small piece of that special code, which is now commonly named the kernel. This kernel is the only piece of code that needs to be ad apted for every specific system and forms the base of the UNIX system. The operating system and all other functions were built around this kernel and written in a higher pr ogramming language, C. This language was especially developed for creating the UNI
自我介绍(self-introduction) ??? 1. Good morning. I am glad to be here for this interview. First let me introduce myself. My name is ***, 24. I come from ******,the capital of *******Province. I graduated from the ******* department of *****University in July ,2001.In the past two years I have been preparing for the postgraduate examination while I have been teaching *****in NO.****middle School and I was a head-teacher of a class in junior grade two. Now all my hard work has got a result since I have a chance to be interview by you . I am open-minded ,quick in thought and very fond of history.In my spare time,I have broad interests like many other youngsters.I like reading books, especially those about *******.Frequently I exchange with other people by making comments in the forum on line.
Canada Introduction Canada has a population just less than 30 million people in a country twice the area of the United States. The heritage of Canada was French and English; however, significant immigration from Asia and Europe's non-French and English countries has broadened Canada's cultural richness. This cultural diversity is considered a national asset, and the Constitution Act prohibits discrimination against individual citizens on the basis of race, color, religion, or sex. The great majority of Canadians are Christian. Although the predominant language in Canada is English, there are at least three varieties of French that are recognized: Quebecois in Quebec, Franco-Manitoban throughout Manitoba and particularly in the St. Boniface area of Winnipeg, and Acadian. The Italian language is a strong third due to a great influx of Italian immigrants following W.W.II. Canada's three major cities are distinctively, even fiercely different from one another even though each is a commercially thriving metropolitan center. Montreal, established in the 17th century and the largest French city outside France, has a strong influence of French architecture and culture. It is a financial and manufacturing center
英语口语集锦－介绍(introduction) making introductions 给人作介绍 1. jane, tom. tom, jane. 2. jane, this is tom, tom, this is jane. 3. jane, i’d like you to meet my friend tom. 4. jane, have you met tom? 5. jane, do you know tom? 6. look, tom’s he re. tome, come and meet jane. 7. jane, this is tom. he’s a friend from college. 8. jane, tom is the guy i was telling you about. 9. do you know each other? 10. have you two met ? 11. have you two been introduced? 12. allow me to introduce professor linda ferguson of harvard university. 13. let me introduce our guest of honor, mr.david morris. 14. if you want to be introduced to the author, i think i can arrange it.
making a self-introduction 作自我介绍 1. may i introduce myself 2. hello, i’m hanson smith. 3. excuse me, i don’t think we’ve met. my name’s hanson smith. 4. how do you do? i’m hanson smith. 5. i’m david anderson. i don’t believe i’ve had the pleasure. 6. first let me introduce myself. i’m peter white, production manager. 7. my name is david. i work in the marketing department. after being introduced. 被介绍与对方认识后. 1. i’m glad to meet you. 很高兴认识你. 2. nice meeting you. 很高兴认识你. (平时用得最多的是nice to meet you ) 3. how nice to meet you. 认识你真高兴. 4. i’ve heard so much about you. 我知道很多关于你的事儿. 5. helen has told me all about you. 海伦对我将了好多你的事儿. 6. i’ve been wanting to meet you for some time.
英语自我介绍例文模板： Sample1 My name is ________. I am graduate from ________ senior high school and major in ________. There are ________ people in my family. My father works in a computer company. And my mother is a housewife. I am the youngest one in my family. In my spare time, I like to read novels. I think reading could enlarge my knowledge. As for novels, I could imagine whatever I like such as a well-known scientist or a kung-fu master. In addition to reading, I also like to play PC games. A lot of grownups think playing PC games hinders the students from learning. But I think PC games could motivate me to learn something such as English or Japanese. My favorite course is English because I think it is interesting to say one thing via different sounds. I wish my English could be improved in the next four years and be able to speak fluent English in the future. Sample2: I am . I was born in . I graduate from senior high school and major in English. I started learning English since I was 12 years old. My parents ha ve a lot of American friends. That’s why I have no problem communicating with Americans or others by speaking English. In my spare time, I like to do anything relating to English such as listening to English songs, watching English movies or TV programs, or even attending the activities held by some English clubs or institutes. I used to go abroad for a short- term English study. During that time, I learned a lot of daily life English and saw a lot of different things. I think language is very interesting. I could express one substance by using different sounds. So I wish I could study and read more English literatures and enlarge my knowledge. Sample3: My name is . There are 4 people in my family. My father is a Chemistry teacher. He teaches chemistry in senior high school. My mother is an English teacher. She teaches English in the university. I have a younger brother, he is a junior high school student and is preparing for the entrance exam. I like to read English story books in my free time. Sometimes I surf the Internet and download the E- books to read. Reading E- books is fun. In addition, it also enlarges my vocabulary words because of the advanced technology and the vivid animations. I hope to study both English and computer technology because I am interested in both of the subjects. Maybe one day I could combine both of them and apply to my research in the future. Sample4: My name is . I am from . There are people in my family. My father works in a computer company. He is a computer engineer. My mother works in a international trade company. She is also a busy woman. I have a older sister and a younger brother. My sister is a junior in National Taiwan University. She majors in
Introduction 介绍 Making introductions 给人作介绍 1. Jane, Tom. Tom, Jane. 2. Jane, this is Tom, Tom, this is Jane. 3. Jane, I'd like you to meet my friend Tom. 4. Jane, have you met Tom? 5. Jane, do you know Tom? 6. Look, Tom's here. Tome, come and meet Jane. 7. Jane, this is Tom. He's a friend from college. 8. Jane, Tom is the guy I was telling you about. 9. Do you know each other? 10. Have you two met ? 11. Have you two been introduced? 12. Allow me to introduce Professor Linda Ferguson of Harvard University. 13. Let me introduce our guest of honor, Mr.David Morris. 14. If you want to be introduced to the author, I think I can arrange it. Making a self-introduction 作自我介绍 1. May I introduce myself 2. Hello, I'm Hanson Smith. 3. Excuse me, I don't think we've met. My name's Hanson Smith. 4. How do you do? I'm Hanson Smith. 5. I'm David Anderson. I don't believe I've had the pleasure. 6. First let me introduce myself. I'm Peter White, production manager. 7. My name is David. I work in the marketing department. After being introduced. 被介绍与对方认识后 1. I'm glad to meet you. 很高兴认识你。 2. Nice meeting you. 很高兴认识你。 3. How nice to meet you. 认识你真高兴。 4. I've heard so much about you. 我知道很多关于你的事儿。 5. Helen has told me all about you.
1 Introduction Greco-Roman mythology is the cultural reception of myths from the ancient Greeks and Romans. Along with philosophy and political thought, mythology represents one of the major survivals of classical antiquity throughout later Western culture. Greek mythology is the body of myths and legends belonging to the ancient Greeks, concerning their gods and heroes, the nature of the world, and the origins and significance of their own cult and ritual practices. They were a part of religion in ancient Greece and are part of religion in modern Greece and around the world as Hellenismos. Modern scholars refer to, and study the myths in an attempt to throw light on the religious and political institutions of Ancient Greece, its civilization, and to gain understanding of the nature of myth-making itself. Roman mythology is the combination of the beliefs, the rituals, and the observances of supernatural occurrences by the ancient Romans from early periods until Christianity finally completely replaced the native religions of the Roman Empire. The religion of the early Romans was so changed by the addition of numerous and conflicting beliefs in later times, and by the assimilation of a vast amount of Greek mythology, that it cannot be ever reconstructed precisely. This was because of the extensive changes in the religion before the literary tradition began. Most of the Greek deities were adopted by the Romans, although in many cases there was a change of name. Much of what became Roman mythology was borrowed from Greek mythology at a later date, as Greek gods were associated with their Roman counterparts. Greek mythology is embodied, explicitly, in a large collection of narratives, and implicitly in Greek representational arts, such as vase-paintings and votive gifts. Greek myth attempts to explain the origins of the world, and details the lives and adventures of a wide variety of gods, goddesses, heroes, heroines, and mythological creatures. These accounts initially were disseminated in an oral-poetic tradition; today the Greek myths are known primarily from Greek literature. The oldest known Greek literary sources, the epic poems Iliad and Odyssey, focus on events surrounding the Trojan War. Two poems by Homer's near contemporary Hesiod, the Theogony and the Works and Days, contain accounts of the genesis of the world, the
Introduction 介绍 一、Introducing Each other 介绍相识 高频语句 自我介绍 1.May I introduce myself to you? 我可以作自我介绍吗？ 2.Did you meet before? 我们见过面吗？ 3.Allow me to introduce myself. 请允许我作个自我介绍。 4.Hello, my name is Bill. 你好，我叫比尔。 5.Can you just introduce yourself to the other guests? 您向其他客人自我介绍一下， 好吗？ 6.Are you Mr. Smith? 你是史密斯先生吗？ 7.Do you mind if I join you? 我加入你们当中来，介意吗？ 8.Here is my card. 这是我的命。 9.It’s really an honor for me to meet you. 真的很荣幸认识你。 10.This is the first time we have met. 这是我们第一次见面。 介绍同事 1.I’d like you to meet Mary, my colleague. 我介绍你们认识玛丽，我的同事。 2.Will you introduce me to that lady? 把我介绍给那位女士认识一下，好吗？ 3.I don’t think you have known each other. 我想你们俩还互不认识吧。 4.Just go in and meet everyone. 进去和大家认识一下。 5.May I introduce Mr. Chen？让介绍一下陈先生好吗？ 相互寒暄 1.We have been looking forward to meeting you. 我们一直盼望着见到您。 2.I’m delighted to know you. 很高兴认识你。 3.Is this your first visit to Shanghai? 这是您第一次来上海？ 4.I can show you around while you’re here. 您在此逗留期间我可以带着您到处 走走。 5.Mr. Li has told me all about you. 李先生对我讲了好多你的事儿。 二、Products Introduction 产品介绍