전산학과 황규영 교수님의 초청으로 Shashi Shekhar 교수님을 모시고
아래와 같이 ICC Global Lecture를 개최하오니 관심있는 분들의 많은 참석 바랍니다.
- 아 래 -
Title : Spatial Data Analytics
Lecturer : Prof. Shashi Shekhar
Affiliation : Department of Computer Science and Engineering, University of Minnesota
Time : 2013/7/15(Mon)~7/18(Thu), PM 13:00~17:15 (with 15minute break)
Place : Main Campus, CS Building, Oh Sang-Su Seminar Room (E3-1, #4443)
Host : Prof. Whang, Kyu-Young
Spatial data analytics is concerned with analysis of data describing geographic phenomena (e.g., climate) or instrumented physical environment (e.g., roads, building). It is important for societal applications in sustainable development, energy, mobility, public safety, public health, as well as emerging location-based services such as local advertisement, mobile commerce and augmented reality.
However, traditional computation models often abstract out physical locations in space and time. This leads to blind-spots, semantic gaps, and inefficiencies. For example, a prominent e-commerce company claimed that geography is dead in Internet era only to discover logistics and distribution challenges. Data-types (e.g. numbers, text) in many programming languages have a semantic gap with spatial computing needs to represent geometry and topology. Sorting is often used to speed-up searches in relational databases, but is not intuitive in spatial data. Independence assumption may simplify data mining and statistical reasoning, but is often inappropriate for spatial datasets. Graph models provide a simple shortest path algorithm (e.g. Dijkstra's, A*), but may not be straightforward for geometric restriction on turns, etc.
This course introduces the fundamental ideas underlying the emerging spatial data analytics systems for spatial database management, spatial data mining, and spatial network engines to address above mentioned challenges.
Day 1 (1 hour): Keynote: Spatial Computing 2020 Vision
Day 1 (3 hours): Module 1: Spatial Databases I - Overview, Spatial Models
Day 2 (4 hours): Module 2: Spatial Databases II - Storage, Query Processing, Trends
Day 3 (4 hours): Module 3: Location Based Services and Spatial Networks
Day 4 (4 hours): Module 4: Spatial (and Spatio-temporal) Data Mining
Keynote: Spatial Computing 2020 Vision
Module 1: Spatial Databases I - Motivation, Overview, Spatial Models
1A. Motivation: Societal Use Cases, Intellectual challenges to relational DBMS
1B. Course Overview: learning Goals, Schedule, Activities
1C. Relevant Mathematics: Set Theory, Topology, Vector Spaces, Euclidean Geometry
1D. Conceptual Model: Raster vs. Vector, Map Algebra, Entity Relationship with Pictograms
1E. Logical Model: SQL3 + OGIS Data-types and Operations
Module 2: Spatial Databases II - Storage, Query Processing, Trends
2A. File Structures: Space Filling Curves
2B. Spatial Index Families: R-tree, Grid-File
2C. Algorithms for range query, nearest neighbor query, spatial join
2D. Spatial Query Processing and Optimization
2E. Trends: Spatio-temporal Databases
Module 3: Location Based Services (LBS) and Spatial Networks
3A. LBS: (Reverse) Geo-coding, Map-matching, Routing, Location, Allocation
3B. Conceptual Models: Graphs, Flow Networks
3C. Network Storage Models: Topological Ordering, Connectivity Clustered Access Method
3D. Algorithms: Shortest Path, Network Vornoi, Capacity-constrained Network Vornoi
3E. Spatio-temporal Networks: Time-evolving Graphs, Evacuation Route Planning
Module 4: Spatial (and Spatio-temporal) Data Mining
4A. Motivation: cancer cluster, crime hot-spots, understanding climate change, etc.
4B. Unique Challenges: Spatial Auto-correlation, Heterogeneity, Edge Effect
4C. Spatial Anomalies: common tests and algorithms
4D. Hot-spots: areal (e.g., K-means) or linear (e.g., K-Main Routes)
4E. Frequent Patterns: Colocation, Co-occurrences, Cascades
4F. Location Prediction: Spatial Auto-regression, Spatial Decision Trees,
1. Spatial Databases, Wiley Encyclopedia of Computer Science and Engineering (Ed. Benjamin Wah), John Wiley and Sons Inc, 2009. (www.spatial.cs.umn.edu/paper_ps/ecse408.pdf)
2. Identifying patterns in spatial information: a survey of methods, Wiley Interdisciplinary. Reviews: Data Mining and Knowledge Discovery, 1(3), April/May 2011. (www.cs.umn.edu/~shekhar/talk/2011/sdm_wiley2011.pdf)
3. Selected articles, Encyclopedia of GIS, Springer, 2008, isbn 978-0-387-30858-6. ( books.google.com/books?id=6q2lOfLnwkAC)
4. Spatial Databases: A Tour, Prentice Hall, 2003, ISBN 0-13-017480-7.
Databases, geographic information systems (GIS) and spatial databases.
Ph.D. 1989, M.S. 1987, Computer Science, University of California, Berkeley
B.Tech. 1985, Computer Science, Indian Institute of Technology, Kanpur, India
McKnight Distinguished University Professor
Faculty of Computer Science and Engineering, University of Minnesota
Prof. Shekhar was elected an IEEE Fellow and received the IEEE Technical Achievement Award for contributions to spatial database storage methods, data mining, and Geographic Information Systems (GIS). He has a distinguished academic record that includes 200+ refereed papers and multiple books including a textbook on Spatial Databases (Prentice Hall, 2003, ISBN 0-13-017480-7) as well as an Encyclopedia of GIS (Springer, 2008, isbn 978-0-387-30858-6). He is serving as a member of the mapping science committee of the NRC/NAS (National Research Council National Academy of Sciences) (2004-9), and the steering committee of the ACM Workshop on GIS. He is also serving as a co-Editor-in-Chief of Geo-Informatica: An Intl. Journal on Adv. in Computer Sc. for GIS. He has served as a member of the NRC/NAS Committee to review basic and applied research at National Geo-spatial-Intelligence Agency, the Board of Directors of University Consortium on GIS (2003-4), the editorial boards of IEEE Transactions on Knowledge and Data Eng. as well as the IEEE-CS Computer Sc. & Eng. Practice Board. He served as a program co-chair for ACM Intl. Workshop on Adv. in GIS (1996).
Prof. Shekhar is a leading researcher in the area of spatial databases and spatial data mining, an interdisciplinary area at the intersection of Computer Science and GIS. A major goal of his research is to understand the computational structure of very large spatial computations (e.g. data analysis via spatial querying and spatial data mining) needed by social and physical sciences as well as engineering disciplines. Earlier his research developed core technologies behind in-vehicle navigation devices as well as web-based routing services, which revolutionized outdoor navigation in urban environment in the last decade. His research results are now playing a critical role in evacuation route planning for homeland security and were recognized via the CTS Partnership Award (2006) for significant impact on transportation.