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 . eduau Todd Mytkowicz Microsoft Research toddmmicrosoftcom Kathryn S McKinley Microsoft Research mckinleymicrosoftcom Abstract Emerging applications increasingly use estimates such as sen sor data GPS probabilistic models machine learning big data and Mehdi Modares and Joshua Bergerson. DEPARTMENT OF CIVIL, ARCHITECTURAL AND EVIRONMENTAL ENGINEERING . Dynamic . Analysis. An essential procedure to design a structure subjected to a system of dynamic loads such as wind or earthquake excitations. 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. Tanvi. . Madgavkar. CSE 7330. FALL 2009. What is Data Warehousing?. Ralph Kimball states . that . :. A . data warehouse . is a . copy of transaction . data . specifically structured for query and analysis. 31.03.2014. (. Muscat. , . Oman. ). OUTLINE. Users Problems and necessity for Data Warehouse. BI . Definition. . and. . Components. Data Warehousing Concepts . DW Goals and Objectives . OLAP and OLTP terms . 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. Pivot Tables. Concept Map. OLAP. Visualization. Pivot Table. Pivot Chart. Charts. BI. Objectives. Define . OLAP & Visualization. Demonstrate Excel’s Pivot Table features.. Segment A:. Visualization. By -. . Pooja. . Sinha. . Kaushalya. . Bakde. Data Warehouse. . “A data warehouse is a . subject-oriented. , . integrated. , . time-variant. , and . nonvolatile. collection of data in support of management’s decision-making process.”—W. H. Inmon. 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. 2. Data, Data everywhere. yet .... I can’t find the data I need. data is scattered over the network. many versions, subtle differences. I can’t get the data I need. need an expert to get the data. Report . in Argos. Agenda. What is the Degree Audit . OLAP Report. ? . OLAP: On-Line . Analytical Processing . Accessing Argos . Running the Degree Audit . OLAP Report. . Interpreting the . Report Data. Chapter 2: . Data . Uncertainty Model. 2. Objectives. In this chapter, you will:. Learn the formal definition of uncertain data. Explore different granularities of data uncertainty. Become familiar with different representations of uncertain data. 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. 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.