PPT-OLAP over Uncertain and Imprecise Data

Doug Burdick Prasad Deshpande T S Jayram Raghu Ramakrishnan Shivakumar Vaithyanathan Presented by Raghav Sagar OLAP Overview Online Analytical Processing OLAP Interactive analysis of data allowing data to be summarized and viewed in different ways in an online . 20 1027200 1048830 1054860 1081860 1126920 1137390 1180200 1192470 1229340 1282710 O7 826440 864840 882600 896730 922290 947580 976770 1005900 1035120 1126920 1204380 O6 612540 672960 717120 717120 719850 750720 754770 754770 797670 873510 918030 O5 Volume. Temperature. Mass. Reading the Meniscus. Always read volume from the bottom of the . meniscus. . The meniscus is the curved surface of a liquid in a narrow cylindrical container.. Try to avoid parallax errors.. Roger Levy. UC San Diego. LSA Institute 2011. 26 . July 2011. Garden Paths: What we don’t understand so well. In previous days we covered “ideal-observer” models of syntactic comprehension. Today we’ll add nuance to this picture. Data Uncertainty: . Modeling and Querying. Mohamed F. Mokbel. Department of Computer Science and Engineering. University of Minnesota. www.cs.umn.edu/~mokbel. mokbel@cs.umn.edu. 2. Talk Outline. Introduction to Uncertain Data. Robust Control Toolbox. Reza . Alinezhad. . Aliakbar. . Afzalian. . Jan 2012. 2. Reza Alinezhad Aliakbar Afzalian. Outline. Modeling uncertainty . Uncertain Elements. Uncertain . School of Computing. National University of Singapore. Department of Computer Science. Aalborg. University. Meihui. Zhang. , Su Chen, Christian S. Jensen, . Beng. Chin . Ooi. , . Zhenjie. Zhang. Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. Finding Top . k. Most Influential Spatial Facilities over Uncertain Objects. Liming Zhan. Ying Zhang . Wenjie. Zhang . Xuemin. Lin. The . University of New South Wales, . Australia. Outline. Motivation. 指導教授：陳良弼 老師. 報告者：鄧雅文 . 97753034. Introduction. Related Work. Problem Formulation. Future Work. Outline. Top-. k. query on certain data. Rank results according to a user-defined score. Chapter 3: Probabilistic Query Answering (1). 2. Objectives. In this chapter, you will:. Learn the challenge of probabilistic query answering on uncertain data. Become familiar with the . framework for probabilistic . Chapter 1: An Overview of Probabilistic Data Management. 2. Objectives. In this chapter, you will:. Get to know what uncertain data look like. Explore causes of uncertain data in different applications. Wherever you roam. And admit that the waters. Around you have grown. And accept it that soon. You'll be drenched to the bone.. If your time to you. Is worth savin'. Then you better start swimmin'. Or you'll sink like a stone. Chapter 7: Probabilistic Query Answering (5). 2. Objectives. In this chapter, you will:. Explore the definitions of more probabilistic query types. Probabilistic skyline query. Probabilistic reverse skyline query. It is further ordered that service shall be deemed made on respondent by sending this