PPT-Query

by Singing and Humming System LIN CHIAO WEI 20151202 QBSH Retrieve a song when forgetting the names of singer and song Extracting information from the humming input comparing with database and ranking by similarity. Speaker: . Rui-Rui. Li. Supervisor: Prof. Ben Kao. 1. Outline. Introduction. Motivation. Methods. -using click-through data. -using session data. -context aware query suggestion. -other methods. Conclusion. Alexander . Kotov. (UIUC). . Pranam. . Kolari. , Yi . Chang (Yahoo!). Lei . Duan. (Microsoft). Motivation. Improvements in ranking can be achieved in two ways:. Better features/methods for promoting high-quality result pages. Murali. Mani, . Mohamad. . Alawa. , . Arunlal. . Kalyanasundaram. University of Michigan, Flint. Presented at IDEAS 2011.. Provenance Metadata. Data about origins of data. Applications:. Check whether data item is valid – in health records. Outlier Detection. Ayushi Dalmia. *. , Manish Gupta. *+. , Vasudeva Varma. *. 1. IIIT Hyderabad, India* Microsoft, India. +. Introduction. A. B. B. B. B. A. B. B. B. A. C. C. C. X. 1. CSE 8330. Instructor: . Dr.Margaret. H. Dunham. Presenter: . Akshaya. . Aradhya. Introduction. Query optimization in XML databases. Query optimization in Parallel databases. Comparison. Conclusion and Future work. Matt Masson. Tessa Palmer. DBI-B410. Agenda. Overview of Power . Query. Why you would use the Power Query Formula Language (“M. ”). Introduction to the . Language. Types, values, and . functions. Abstract. With the wide deployment of public cloud computing infrastructures, using clouds to host data query services has become an appealing solution for the advantages on scalability and cost-saving. However, some data might be sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed.. Davide Mottin, University of Trento. Francesco . Bonchi. , Yahoo Labs - Francesco . Gullo. , Yahoo Labs. Issues. with Pattern . S. earch. O. OH. O. S. Query. 510 . matches. T. oo. . many. . matches. Query Optimization Process. (simplified a bit). Parse the SQL query into a logical tree:. identify distinct blocks (corresponding to nested sub-queries or views). . Query rewrite phase:. apply . algebraic transformations. Dr. Bjarne Berg . 3. What We’ll Cover …. . Introduction. Performance Issues & Tips. MultiProviders and Partitioning. Aggregates. Query Design & Caching. Hardware & Servers. Designing for Performance. Ruirui. Li, Ben Kao, Bin Bi, . Reynold. Cheng, Eric Lo. Speaker:. . Ruirui. Li. 1. The University of Hong Kong. Outline. Motivation. Problem Statement. DQR Model. Experiments & Evaluation. 2. Date. . :. . 2013/08/20. Source. . :. . SIGIR’13. Authors : . Weize. Kong and James . Allan. Advisor . : . Dr.Jia. -ling, . Koh. Speaker : . Wei, Chang. 1. Outline. Introduction. Approach. Experiment. Steve Thompson, ConfigMgr MVP. Configuration Manager . Advisor, Dell Services. UD-B407. How to solve common questions with queries. Understand the query process. Identify . performance . issues. Statistics . Database System Implementation CSE 507. Some slides . adapted from . S. ilberschatz. , . Korth. and . Sudarshan. Database System Concepts – 6. th. Edition.. And . Elamsri. and . Navathe. , Fundamentals of Database Systems – 6.