Answering Cross-Source Keyword Queries Over Biological Data Sources

Slides:

Advertisements

Similar presentations

…to Ontology Repositories Mathieu dAquin Knowledge Media Institute, The Open University From…

Advertisements

Oyster, Edinburgh, May 2006 AIFB OYSTER - Sharing and Re-using Ontologies in a Peer-to-Peer Community Raul Palma 2, Peter Haase 1 1) Institute AIFB, University.

Lukas Blunschi Claudio Jossen Donald Kossmann Magdalini Mori Kurt Stockinger.

1 gStore: Answering SPARQL Queries Via Subgraph Matching Presented by Guan Wang Kent State University October 24, 2011.

Shuai Ma, Yang Cao, Wenfei Fan, Jinpeng Huai, Tianyu Wo Capturing Topology in Graph Pattern Matching University of Edinburgh.

Search Engines. 2 What Are They?  Four Components  A database of references to webpages  An indexing robot that crawls the WWW  An interface  Enables.

Circumventing Data Quality Problems Using Multiple Join Paths Yannis Kotidis, Athens University of Economics and Business Amélie Marian, Rutgers University.

Toward Making Online Biological Data Machine Understandable Cui Tao.

Learning to Advertise. Introduction Advertising on the Internet = $$$ –Especially search advertising and web page advertising Problem: –Selecting ads.

Metadata Server system software laboratory. Overview metadata service in Grid environment Grid environment Metadata server User query data search information.

Disambiguation Algorithm for People Search on the Web Dmitri V. Kalashnikov, Sharad Mehrotra, Zhaoqi Chen, Rabia Nuray-Turan, Naveen Ashish For questions.

Web Projections Learning from Contextual Subgraphs of the Web Jure Leskovec, CMU Susan Dumais, MSR Eric Horvitz, MSR.

1 Structures and Strategies for State Space Search 3 3.0Introduction 3.1Graph Theory 3.2Strategies for State Space Search 3.3Using the State Space to Represent.

1 MARG-DARSHAK: A Scrapbook on Web Search engines allow the users to enter keywords relating to a topic and retrieve information about internet sites (URLs)

Sangam: A Transformation Modeling Framework Kajal T. Claypool (U Mass Lowell) and Elke A. Rundensteiner (WPI)

SEO for Web Designers By Alfredo Palconit, Jr.. I. What is SEO? A process of improving a site’s traffic and rank from organic search engine results. Notes:

Query Planning for Searching Inter- Dependent Deep-Web Databases Fan Wang 1, Gagan Agrawal 1, Ruoming Jin 2 1 Department of Computer.

Keyword Search in Relational Databases Jaehui Park Intelligent Database Systems Lab. Seoul National University

Information Visualization using graphs algorithms Symeonidis Alkiviadis

Social scope: Enabling Information Discovery On Social Content Sites

Grant Number: IIS Institution of PI: Arizona State University PIs: Zoé Lacroix Title: Collaborative Research: Semantic Map of Biological Data.

DBXplorer: A System for Keyword- Based Search over Relational Databases Sanjay Agrawal Surajit Chaudhuri Gautam Das Presented by Bhushan Pachpande.

DBease: Making Databases User-Friendly and Easily Accessible Guoliang Li, Ju Fan, Hao Wu, Jiannan Wang, Jianhua Feng Database Group, Department of Computer.

Graph and Topological Structure Mining on Scientific Articles Fan Wang, Ruoming Jin, Gagan Agrawal and Helen Piontkivska The Ohio State University The.

Automated Creation of a Forms- based Database Query Interface Magesh Jayapandian H.V. Jagadish Univ. of Michigan VLDB

Failure Recovery of Composite Semantic Web Services using Subgraph Replacement Hadi Saboohi Amineh Amini Hassan Abolhassani Karaj Islamic Azad University,

Supporting High- Performance Data Processing on Flat-Files Xuan Zhang Gagan Agrawal Ohio State University.

DBXplorer: A System for Keyword- Based Search over Relational Databases Sanjay Agrawal, Surajit Chaudhuri, Gautam Das Cathy Wang

EASE: An Effective 3-in-1 Keyword Search Method for Unstructured, Semi-structured and Structured Data Cuoliang Li, Beng Chin Ooi, Jianhua Feng, Jianyong.

The Ohio State University Efficient and Effective Sampling Methods for Aggregation Queries on the Hidden Web Fan Wang Gagan Agrawal Presented By: Venu.

A Model for Fast Web Mining Prototyping Nivio Ziviani UFMG – Brazil Álvaro Pereir a Ricardo Baeza-Yates Jesus Bisbal UPF – Spain.

SEEDEEP: A System for Exploring and Querying Deep Web Data Sources PhD Defense The Ohio State University Summer 2010 SEEDEEP: A System for Exploring and.

SEEDEEP: A System for Exploring and Querying Deep Web Data Sources Gagan Agrawal Fan Wang, Tantan Liu Ohio State University.

Templated Search over Relational Databases Date: 2015/01/15 Author: Anastasios Zouzias, Michail Vlachos, Vagelis Hristidis Source: ACM CIKM’14 Advisor:

Ranking objects based on relationships Computing Top-K over Aggregation Sigmod 2006 Kaushik Chakrabarti et al.

The Structure of the Web. Getting to knowing the Web How big is the web and how do you measure it? How many people use the web? How many use search engines?

Toward Semantic Search: RDFa based facet browser Jin Guang Zheng Tetherless World Constellation.

Raluca Paiu1 Semantic Web Search By Raluca PAIU

SEARCH ENGINE OPTIMIZATION. What is Search Engine Optimization?  Search engine optimization ( SEO ) is the process of affecting the visibility of a website.

Graphs. Contents Terminology Graphs as ADTs Applications of Graphs.

Instance Discovery and Schema Matching With Applications to Biological Deep Web Data Integration Tantan Liu, Fan Wang, Gagan Agrawal {liut, wangfa,

1 A Methodology for automatic retrieval of similarly shaped machinable components Mark Ascher - Dept of ECE.

Differential Analysis on Deep Web Data Sources Tantan Liu, Fan Wang, Jiedan Zhu, Gagan Agrawal December.

How to use Search Engines and Discovery Tools? Salama Khamis Al Mehairi U

Semantic Graph Mining for Biomedical Network Analysis: A Case Study in Traditional Chinese Medicine Tong Yu HCLS

Answering pattern queries using views

CSCI2950-C Lecture 12 Networks

Algorithms for Finding Distance-Edge-Colorings of Graphs

Keyword Search over RDF Graphs

SEEDEEP: A System for Exploring and Querying Deep Web Data Sources

Federated & Meta Search

Associative Query Answering via Query Feature Similarity

LTER Metadata Query Interface – Current Status and Future Challenges

Neural Networks for Vertex Covering

Declarative Creation of Enterprise Applications

Version 3.5 (Citrus) Preview

KDD Reviews 周天烁 2018年5月9日.

9 Algorithms: Indexing Now where did I put that?.

Keyword Searching and Browsing in Databases using BANKS

Stratified Sampling for Data Mining on the Deep Web

Bidirectional Query Planning Algorithm

Results Fusion in Heterogeneous Information Sources

Mathematics for Computer Science MIT 6.042J/18.062J

Simple Graphs: Connectedness, Trees

Supporting High-Performance Data Processing on Flat-Files

Tantan Liu, Fan Wang, Gagan Agrawal The Ohio State University

Chapter 14 Graphs © 2011 Pearson Addison-Wesley. All rights reserved.

Introduction Dataset search

SQL Segment designer Campaign Load Campaign Launch SELECT FROM WHERE

GOBLAN A Graphical Object Language

Presentation transcript:

Answering Cross-Source Keyword Queries Over Biological Data Sources Fan Wang Gagan Agrawal Ohio State University

Motivation Many biological data sources are deep web Only the query interface, and not the contents, on the surface web Easy mechanism needed for finding this information Approach: simple keyword interface Need ontology and query planning

Our Contribution: SEEDEEP System Discover data source metadata Generate query plans for search Fault Tolerance mechanism Query caching mechanism

Query Planning Problem Ordinary query format Entity keywords, attribute keywords, comparison predicates Standard select-project-join SQL query style Formulation Sub-graph set cover problem, NP-hard Target data source subgraph, can have disconnected components which nodes cover what terms the size should be minimal, our cost model This problem is NP hard we have node and edge ranking functions Starting data source