PPT-1 Ranked Queries over sources

with Boolean Query Interfaces without Ranking Support Vagelis Hristidis Florida International University Yuheng Hu Arizona State University Panos Ipeirotis New York University. Smith ChungSheng Li Jeffrey Scott Vitter Duke University IBM T J Watson IBM T J Watson IBM T J Watson Duke University natsevcsdukeedu yuanchiusibmcom jsmithusibmcom csliusibmcom jsvcsdukeedu Abstract This paper investigates the problem of incremen t joint work with Dan . Suciu. (University of Washington). Alexandra Meliou. Hypothetical (What-if. ). Queries. Brokerage company. DB. Key Performance Indicators (KPI). Example from [. Balmin. et al. VLDB’00]:. Sampling Design. By . Abdul . Haq. Ph.D. Student,. Department of Mathematics and Statistics,. University of Canterbury, Christchurch, NZ.. 1. Outline. 2. Simple random sampling.. Ranked set sampling.. INST 734. Module 3. Doug . Oard. Agenda. Ranked retrieval. Similarity-based ranking. Probability-based ranking. Boolean Retrieval. Strong points. Accurate, . if you know the right strategies. Efficient for the computer. Ben . Carterette. Paul Clough. Evangelos. . Kanoulas. Mark Sanderson. Vision. Vision. Put user in the evaluation . loop. Cranfield. Paradigm. Simple user model. Controlled experiments. Reusable but static test collections. Junior Info Session. Speaker: Mr. Giordano. Agenda. The application process. The college selection process. The cost. Realistic choices “Will I get in?”. Getting to know the college. The SAT and ACT. Abstract. Keyword search is an intuitive paradigm for searching linked data sources on the web. We propose to route keywords only to relevant sources to reduce the high cost of processing keyword search queries over all sources. . Dallas . Commission on Homelessness. August 2016. CONFIDENTIAL. INTRODUCTION. 1. The Dallas Commission On Homelessness developed and conducted a survey over the course of several weeks. Upon reviewing the results, they reached out to The Dallas Marketing Group for assistance in interpreting those results. This summary addresses the closed-ended survey questions. . Sinn und . Bedeutung. . 21. 4 September 2016. Drew Reisinger. Johns Hopkins University . 1. Summary. Review . Medicine Problem, Prof. Procrastinate. Introduce new solution to . PrP. Introduce Embedded MP, . Ranked retrieval. Thus far, our queries have all been Boolean.. Documents either match or . don’t. .. Good for expert users with precise understanding of their needs and the collection.. Also good for applications: Applications can easily consume 1000s of results.. Power . Query. Chris Webb. chris@crossjoin.co.uk. @technitrain. Who Am I?. Chris Webb. chris@crossjoin.co.uk. Twitter @Technitrain. UK-based consultant and trainer: . www.crossjoin.co.uk. . www.technitrain.com. Raju. . Balakrishnan. , . Subbarao. . Kambhampati. Arizona State University. Funding from. Deep Web Integration Scenario. Web DB. Mediator. ←. query. Web DB. Web DB. Web DB. Web DB. Millions of sources containing structured tuples. Information Integration on the Web (SA-2). 1. Information Integration. 4/5. Kambhampati & Knoblock. Information Integration on the Web (SA-2). 2. Information Integration on the Web. AAAI Tutorial (SA2). Tessa . West. Katherine Thorson. New York University. Physiological Linkage. A. B. HR = 77. HR = 78. HR = 72. HR = 78. Thorson, West, and Mendes (2018, Psych Methods). Thorson & West (2018, Bio Psych).