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

Slides:

Advertisements

Similar presentations

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

Advertisements

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.

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

Computer Science and Engineering Inverted Linear Quadtree: Efﬁcient Top K Spatial Keyword Search Chengyuan Zhang 1,Ying Zhang 1,Wenjie Zhang 1, Xuemin.

Efficient Processing of Top- k Queries in Uncertain Databases Ke Yi, AT&T Labs Feifei Li, Boston University Divesh Srivastava, AT&T Labs George Kollios,

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.

Ariel Rosenfeld Network Traffic Engineering. Call Record Analysis. Sensor Data Analysis. Medical, Financial Monitoring. Etc,

Maintaining Variance over Data Stream Windows Brian Babcock, Mayur Datar, Rajeev Motwani, Liadan O ’ Callaghan, Stanford University ACM Symp. on Principles.

Engineering a Set Intersection Algorithm for Information Retrieval Alex Lopez-Ortiz UNB / InterNAP Joint work with Ian Munro and Erik Demaine.

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.

CircularTrip: An Effective Algorithm for Continuous kNN Queries Muhammad Aamir Cheema Database Research Group, The School of Computer Science and Engineering,

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.

Aggregate Query Processing in Cache- Aware Wireless Sensor Networks Khaled Ammar University of Alberta.

Ming Hua, Jian Pei Simon Fraser UniversityPresented By: Mahashweta Das Wenjie Zhang, Xuemin LinUniversity of Texas at Arlington The University of New South.

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

Small-world Overlay P2P Network

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.

Communication-Efficient Distributed Monitoring of Thresholded Counts Ram Keralapura, UC-Davis Graham Cormode, Bell Labs Jai Ramamirtham, Bell Labs.

Probabilistic Similarity Search for Uncertain Time Series Presented by CAO Chen 21 st Feb, 2011.

Continuous Data Stream Processing

Chain: Operator Scheduling for Memory Minimization in Data Stream Systems Authors: Brian Babcock, Shivnath Babu, Mayur Datar, and Rajeev Motwani (Dept.

1 SINA: Scalable Incremental Processing of Continuous Queries in Spatio-temporal Databases Mohamed F. Mokbel, Xiaopeng Xiong, Walid G. Aref Presented by.

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.

Probabilistic Skyline Operator over sliding Windows Wan Qian HKUST DB Group.

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

Energy-efficient Self-adapting Online Linear Forecasting for Wireless Sensor Network Applications Jai-Jin Lim and Kang G. Shin Real-Time Computing Laboratory,

Kyriakos Mouratidis, Spiridon Bakiras, Dimitris Papadias SIGMOD

1 Efficient Computation of Frequent and Top-k Elements in Data Streams.

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

Energy-Aware Scheduling with Quality of Surveillance Guarantee in Wireless Sensor Networks Jaehoon Jeong, Sarah Sharafkandi and David H.C. Du Dept. of.

Searching for Extremes Among Distributed Data Sources with Optimal Probing Zhenyu (Victor) Liu Computer Science Department, UCLA.

Towards Robust Indexing for Ranked Queries Dong Xin, Chen Chen, Jiawei Han Department of Computer Science University of Illinois at Urbana-Champaign VLDB.

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.

Jianmin Wang 1, Shaoxu Song 1, Xiaochen Zhu 1, Xuemin Lin 2 1 Tsinghua University, China 2 University of New South Wales, Australia 1/23 VLDB 2013.

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

1 Approximating Quantiles over Sliding Windows Srimathi Harinarayanan CMPS 565.

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,

Distributed Spatio-Temporal Similarity Search Demetrios Zeinalipour-Yazti University of Cyprus Song Lin

Efficient Processing of Top-k Spatial Preference Queries

Spatio-temporal Pattern Queries M. Hadjieleftheriou G. Kollios P. Bakalov V. J. Tsotras.

The university of Hong Kong Department of Computer Science Continuous Monitoring of Top-k Queries over Sliding Windows Authors: Kyriakos Mouratidis, Spiridon.

All right reserved by Xuehua Shen 1 Optimal Aggregation Algorithms for Middleware Ronald Fagin, Amnon Lotem, Moni Naor (PODS01)

Hutchison Telephone (Macau) Company Limited Online Marketing Proposal (January 2011)

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.

03/02/20061 Evaluating Top-k Queries Over Web-Accessible Databases Amelie Marian Nicolas Bruno Luis Gravano Presented By: Archana and Muhammed.

EASE: An Energy-Efficient In-Network Storage Scheme for Object Tracking in Sensor Networks Jianliang Xu Department of Computer Science Hong Kong Baptist.

Continuous Monitoring of Distributed Data Streams over a Time-based Sliding Window MADALGO – Center for Massive Data Algorithmics, a Center of the Danish.

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.

Online Web Marketing Services

A Unified Algorithm for Continuous Monitoring of Spatial Queries

A Uniﬁed Framework for Efﬁciently Processing Ranking Related Queries

Stochastic Skyline Operator

Fast Approximate Query Answering over Sensor Data with Deterministic Error Guarantees Chunbin Lin Joint with Etienne Boursier, Jacque Brito, Yannis Katsis,

TT-Join: Efficient Set Containment Join

Spatio-temporal Pattern Queries

On Efficient Graph Substructure Selection

Edge computing (1) Content Distribution Networks

Brian Babcock, Shivnath Babu, Mayur Datar, and Rajeev Motwani

Approximate Frequency Counts over Data Streams

Efficient Subgraph Similarity All-Matching

Presented by: Mahady Hasan Joint work with

Publishing in Top Venues

Resource Allocation for Distributed Streaming Applications

Efficient Processing of Top-k Spatial Preference Queries

Maintaining Stream Statistics over Sliding Windows

Presentation transcript:

Computer Science and Engineering Loyalty-based Selection: Retrieving Objects That Persistently Satisfy Criteria Presented By: Zhitao Shen Joint work with Muhammad Aamir Cheema, Xuemin Lin The University of New South Wales, Australia

2 Introduction Loyalty of an object The loyalty of an object shows how long the object satisfying the given criteria during the last T time units. Loyalty Queries Find the objects satisfying the given criteria for the majority of the most recent time (top loyal objects). Threshold Loyalty Queries Top-k Loyalty Queries Online processing. Applications location based services, wireless sensor network, stock market, traffic monitoring, internet applications, etc.

3 Motivation Monitoring Area Example: Car park advertising –Find the cars appearing in the monitoring area for the majority of the recent time.

4 Preliminaries Map objects to loyalty-time space. Example: 2 objects; top-1 loyalty query Time Loyalty Monitoring Area Sliding Window (T) Find the upper envelope Update Echo Update Top loyal objects

5 Contributions Propose a novel measure, loyalty of the object, for a variety of applications. First to study threshold and top-k loyalty queries over sliding windows. The worst cost for processing each update is log (N), which is optimal.

6 Our Solution Sweep line algorithm 1. Handle the updates Create potential events 2. Handle the valid events Create potential events Time Loyalty Top Border Bottom Event Queue Invalid U1U1 U2U2 U3U3 U4U4 U5U5 U6U6 Example: Top-2 Loyalty Query

7 What else in the paper We prove that the cost for processing each update is log (N) We show that the lower bound cost for each update in the worst case is log(N). (optimality) Pruning Rule Further ignore the unnecessary updates If the object is not possible to be a border object in the next D time, then the updates in the next D time can be ignored.

8 Experimental Settings Synthetic data. –a two state Markov chain Model Compare with classic Bently-Ottmann algorithm (BO) Varying k Varying window size (x1000)

9 Question and Answer Thank You! Any Questions?