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. Unlike objects in free fall, or objects that are simply thrown or have no power to maintain their motion, flying objects (such as birds and airplanes) and powered vehicles (such as cars) exert a force known as . Roeland . Scheepens. , . Huub. . van de . Wetering. , . Jarke. . J. van . Wijk. Presented by: David Sheets. Problem. Address Visual Clutter in…. High density areas. Low resolution screens (e.g. mobile phones). --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.  Abstract — especially if the objects are of various shape and size. In this paper we use machine learning to learn in - hand manipulation of such various sized and shaped objects. iPTF. Frank . Masci, Adam . Waszczak. , Russ . Laher. & James Bauer. iPTF. workshop, August 2014. Goals. To complement search for streaked objects in . iPTF. exposures (fast movers), implemented software to search for non-streaking objects: those that move with . 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. 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. Current Status and Role in Improving Access. to Biomedical Information. A Report to the Board of Scientific Counselors. April 5, 2012. Alan R. Aronson . (Principal Investigator). James G. . Mork. 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. 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.