PPT-Effectively Indexing Uncertain Moving Objects for Predictiv

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. This is the moving business. It’s not easy, but it’s real simple: We take your things and put them on a truck, bring them to your new place, and move them in. Like everyone else, we wrap, we hoist, we pack – it’s just part of the job. We only make two promises. First, that we don’t charge mystery fees. Second, that we’ll work hard, because that’s what we love to do. 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 Natalie Bryant, Director. Practice Development. How many of your waking . hours . are spent communicating with others? . 70% - 80. %. Ways to Communicate:. Reading. Writing. Speaking. Listening. Listening. OT 122. Chapter Two. Intro. Must be a consistent system to work!. Indexing?. Selecting the filing segment under which to store a record and determining the order in which the units should be considered. --Presented By . Sudheer. . Chelluboina. .. Professor: . Dr.Maggie. Dunham. Contents . Outline of Paper. Introduction . Index Structures. Due to rapid increase in the use of location based services applications, large amount of location data of moving object is recorded. Because of that efficient indexing techniques are required to manage these large amounts of trajectory data. All index structures are focused on either indexing past, current and future locations. Every indexing structure or techniques discussed in this paper will make simpler indexing or it will increase the overall query processing performance. . 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. . P . L . Chandrika. . . Advisors: Dr.. . C. V. Jawahar . . . Centre for Visual Information Technology, IIIT- Hyderabad. Problem Setting . Arijit Khan. Systems Group. ETH Zurich. Lei Chen. Hong . Kong University of Science and Technology. Social Network. Transportation Network. Chemical Compound. Biological Network. Graphs are Everywhere. Goals:. Store large files. Support multiple search keys. Support efficient insert, delete, and range queries. 2. Files and Indexing. Entry sequenced file. : Order records by time of insertion.. Search with sequential search. SpatialHadoop. Type: Research Paper (Experimental evaluation). Authors: Ahmed . Eldawy. , . Louai. . Alarabi. , Mohamed F. . Mokbel. Presented by: Siddhant Kulkarni. Term: Fall 2015. Motivation. 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. Listening Skills and Spoken Communication. Objectives:. Identify ways to improve communication and listening skills to help ensure a productive work environment.. Explain the importance of spoken communication to improving understanding.. 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. Responding effectively to stress involves the use of multiple strategies to regulate emotions at different time points of a stressful situation. BEFORE. DURING. AFTER. EVENT. STRATEGIES. . UNDERSTANDING STRESS.