Publishing in Top Venues

Slides:

Advertisements

Similar presentations

Lazy Updates: An Efficient Technique to Continuously Monitoring Reverse kNN Presented By: Ying Zhang Joint work with Muhammad Aamir Cheema, Xuemin Lin,

Advertisements

Multi-Guarded Safe Zone: An Effective Technique to Monitor Moving Circular Range Queries Presented By: Muhammad Aamir Cheema 1 Joint work with Ljiljana.

Computer Science and Engineering Diversified Spatial Keyword Search On Road Networks Chengyuan Zhang 1,Ying Zhang 2,1,Wenjie Zhang 1, Xuemin Lin 3,1, Muhammad.

Weiren Yu 1, Jiajin Le 2, Xuemin Lin 1, Wenjie Zhang 1 On the Efficiency of Estimating Penetrating Rank on Large Graphs 1 University of New South Wales.

1 Query Processing in Spatial Network Databases presented by Hao Hong Dimitris Papadias Jun Zhang Hong Kong University of Science and Technology Nikos.

Finding the Sites with Best Accessibilities to Amenities Qianlu Lin, Chuan Xiao, Muhammad Aamir Cheema and Wei Wang University of New South Wales, Australia.

13/04/20151 SPARK: Top- k Keyword Query in Relational Database Wei Wang University of New South Wales Australia.

Counting Distinct Objects over Sliding Windows Presented by: Muhammad Aamir Cheema Joint work with Wenjie Zhang, Ying Zhang and Xuemin Lin University of.

Probabilistic Skyline Operator over Sliding Windows Wenjie Zhang University of New South Wales & NICTA, Australia Joint work: Xuemin Lin, Ying Zhang, Wei.

School of Computer Science and Engineering Finding Top k Most Influential Spatial Facilities over Uncertain Objects Liming Zhan Ying Zhang Wenjie Zhang.

Test Review Critical Reading 8 Miss Kuffa

Click to edit Present’s Name SLICE: Reviving Regions-Based Pruning for Reverse k Nearest Neighbors Queries Shiyu Yang 1, Muhammad Aamir Cheema 2,1, Xuemin.

Planning a PhD Prof. Bob Givan. There are many paths and styles to a successful Phd There are many motives for getting a PhD My comments represent one.

SPARK: Top-k Keyword Query in Relational Databases Yi Luo, Xuemin Lin, Wei Wang, Xiaofang Zhou Univ. of New South Wales, Univ. of Queensland SIGMOD 2007.

Probabilistic Threshold Range Aggregate Query Processing over Uncertain Data Wenjie Zhang University of New South Wales & NICTA, Australia Joint work:

Quantile-Based KNN over Multi- Valued Objects Wenjie Zhang Xuemin Lin, Muhammad Aamir Cheema, Ying Zhang, Wei Wang The University of New South Wales, Australia.

Getting physics education research published …sharing experiences… Manjula Sharma.

A Unified Approach for Computing Top-k Pairs in Multidimensional Space Presented By: Muhammad Aamir Cheema 1 Joint work with Xuemin Lin 1, Haixun Wang.

Presented by: Duong, Huu Kinh Luan March 14 th, 2011.

Efficient Computation of the Skyline Cube Yidong Yuan School of Computer Science & Engineering The University of New South Wales & NICTA Sydney, Australia.

Module 2b: Modeling Information Objects and Relationships IMT530: Organization of Information Resources Winter, 2007 Michael Crandall.

Click to edit Present’s Name Trends in Location-based Services Muhammad Aamir Cheema.

Information Technology Trends in Location Based Services Muhammad Aamir Cheema Monash University, Australia Contact:

Introduction to the Concept of Demand side Groundwater Management Date: 30 July 2007 Time: to hrs. Venue: Aalankrita Resort, Hyderabad Topic:

Tips Prepared by Jackson Yeung (CSE) Research Methods.

Outline Who am I? What is research? My Research Higher studies opportunities in Australia Getting jobs in IT industry Presented by: Muhammad Aamir Cheema,

Computer Science and Engineering Loyalty-based Selection: Retrieving Objects That Persistently Satisfy Criteria Presented By: Zhitao Shen Joint work with.

Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach Wenjie Zhang, Xuemin Lin The University of New South Wales & NICTA Ming Hua,

Language Development: The Course Jan. 6, The Course Designed to give students a comprehensive understanding of language development, primarily in.

Top-k Similarity Join over Multi- valued Objects Wenjie Zhang Jing Xu, Xin Liang, Ying Zhang, Xuemin Lin The University of New South Wales, Australia.

Queensland University of Technology CRICOS No J PhD - How do I get one? PhD - How do I get one? Practical Analysis with Case Scenarios Dr. Tony Sahama.

THE WRITING PROCESS:PRELIMINARY QUESTIONS What am I going to write about? Have I got something to say of my own? What is it? ◦ Hypothesis ◦ Thesis statement.

Computer Science and Engineering Efficiently Monitoring Top-k Pairs over Sliding Windows Presented By: Zhitao Shen 1 Joint work with Muhammad Aamir Cheema.

Web Image Retrieval Re-Ranking with Relevance Model Wei-Hao Lin, Rong Jin, Alexander Hauptmann Language Technologies Institute School of Computer Science.

K-Hit Query: Top-k Query Processing with Probabilistic Utility Function SIGMOD2015 Peng Peng, Raymond C.-W. Wong CSE, HKUST 1.

Influence Zone: Efficiently Processing Reverse k Nearest Neighbors Queries Presented By: Muhammad Aamir Cheema Joint work with Xuemin Lin, Wenjie Zhang,

Student Confrences By Jack Trees.  I feel that I have successfully researched a very hard topic in my opinion. I felt glad that I did this well. I also.

Research of Database UNSW Some slides are taken from Wenjie Zhang.

1 CHBE 551 Lect 03 Planning A Research Career. 2 Outline Objective: Overview of how you build a research career What are key skills that need to be developed.

Computer Science and Engineering TreeSpan Efficiently Computing Similarity All-Matching Gaoping Zhu #, Xuemin Lin #, Ke Zhu #, Wenjie Zhang #, Jeffrey.

Information Technology Selecting Representative Objects Considering Coverage and Diversity Shenlu Wang 1, Muhammad Aamir Cheema 2, Ying Zhang 3, Xuemin.

Information Technology (Some) Research Trends in Location-based Services Muhammad Aamir Cheema Faculty of Information Technology Monash University, Australia.

MIDDLEWARE SYSTEMS RESEARCH GROUP MSRG.ORG Distributed Ranked Data Dissemination in Social Networks Joint work with: Mo Sadoghi Vinod Muthusamy Hans-Arno.

Research Methods Technical Writing Thesis Conference/Journal Papers

Computer Science and Engineering Jianye Yang 1, Ying Zhang 2, Wenjie Zhang 1, Xuemin Lin 1 Influence based Cost Optimization on User Preference 1 The University.

Click to edit Present’s Name AP-Tree: Efficiently Support Continuous Spatial-Keyword Queries Over Stream Xiang Wang 1*, Ying Zhang 2, Wenjie Zhang 1, Xuemin.

Course Information Andy Wang CIS 5930 Computer Systems Performance Analysis.

Cohesive Subgraph Computation over Large Graphs

A Unified Algorithm for Continuous Monitoring of Spatial Queries

CS240A: Databases and Knowledge Bases Introduction

A Uniﬁed Framework for Efﬁciently Processing Ranking Related Queries

Kevin C. Chang University of Illinois, Urbana-Champaign

Stochastic Skyline Operator

SOME GENERAL POINTS TO CONSIDER FOR AN EFFECTIVE SEMINAR

Arlington, Virginia, USA July 11, 2008

Beijing University of Technology, China, September 28, 2006

TT-Join: Efficient Set Containment Join

Comparative Essay.

Endocannabinoids Seminar: Fall 2015

CSC 682: Advanced Computer Security

Stochastic Order and Skyline

On Efficient Graph Substructure Selection

Some General Points To Consider For an Effective Seminar

Dr. Zhen Jiang Computer Science Department West Chester University

Orexin/Hypocretin Seminar: Spring 2015

Probabilistic n-of-N Skyline Computation over Uncertain Data Streams

in Intelligent Tutoring Systems with Fuzzy Logic Techniques

Presented by: Mahady Hasan Joint work with

Data Engineering Research Group

Wei Wang University of New South Wales, Australia

Presentation transcript:

Publishing in Top Venues Xuemin Lin School of Computer Science University of New South Wales Australia

Database Group@UNSW (2003-) 2/25/2019 Database Group@UNSW (2003-) 3 4 faculty members : Prof Xuemin Lin Dr. John Shepherd Dr. Wei Wang Dr. Raymond Wong 4 research fellows (research assistant Prof) Dr. Lijun Chang --- Graph Dr. Muhammad Aamir Cheema (ARC DECRA) --- Spatial Temporal Dr. Wenjie Zhang (ARC DECRA) --- Uncertain Dr. Ying Zhang (ARC APD) --- Stream 20+PhD students. Research Interests: core topics in DB, DM, IR, MM. DBG@UNSW Dr. Wei Wang @ CSE, UNSW

Enjoyable? Make an interesting story and sell it… Tough game but…let us love this game!

Outline Topic selection (故事的主题） Techniques Developing （故事的展开） Paper writing （故事的写作） Experiment （故事的现实性）

Topic Selection (new vs existing topics) New topics: New areas tough to promote but go for it Very sound applications (e.g. association rules, data cube, etc) New problem formalization tough to promote avoid delta variations Period-dependent (NP-completeness, probabilistic queries, etc) Semantics validation

Topic Selection (new vs existing topics) Existing problems Need a “big” story Complexity breakthroughs! Critical observations!

Developing Techniques Single ideas vs multiple ideas? My personal choice: single idea and framework Multiple ideas: completeness of your selection How many enough? Interesting enough? Nothing to do with quantity Interesting “insights”! (minimum: 3 interesting spots?) No space left for reviewers to imagine an immediate improvements.

Writing-up Easy for “busy” people to read. Make the early parts most interesting. Clearly structured. No holes! Make a good story in the introduction part 40% contribution to your success Your short bio/CV

Experiments Final chance to market your technical developments Avoid to have “fun” and mess-up things around! Results presented aim to verify your insights.