Moving Objects and Spatial Data Computing
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This document summarizes a presentation on moving objects and spatial data computing. It discusses spatial data types including location data from GPS, images, videos, sensors and more. Spatial data is increasingly integrated with multimedia content. Technologies like Google Street View, drones, autonomous vehicles, and black boxes in cars generate large amounts of spatial and multimedia data. Hadoop and Spatial databases like PostGIS are important for analyzing spatial big data from social media.
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Moving Objects Patterns
• Moving Together Patterns
• Flock
• 특정 Region r내에서 m 개체이상이 같은 방향으로 움직임
• Leadership
• 가장 먼저 움직인 객체
• Convergence
• m 개체 이상이 같은 방향으로
• Encounter
• r내에서 만나는 지역
[37] J. Gudmundsson and M.V. Kreveld. 2006. Computing longest duration flocks in
trajectory data. In Proceedings of the 14th Annual ACM International Symposium on
Advances in Geographic Information Systems. ACM, 35‐42.
[38] J. Gudmundsson, M.V. Kreveld, and B. Speckmann. 2004. Efficient detection of
motion patterns in spatio‐temporal data sets. In Proceedings of the 12th Annual
ACM International Symposium on Advances in Geographic Information Systems.
ACM, 250–257.](https://image.slidesharecdn.com/kaistv02-170929093337/85/Moving-Objects-and-Spatial-Data-Computing-68-320.jpg)
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• Data Type
• TPoint and TDouble
• TPoint : (Point, Timestamp)
• TDouble : (Double, Timestamp)
• TBool : (Boolean, Timestamp )
• MPoint
• TPoint[]
• MDouble
• TDouble[]
• MBool
• TBool[]
Data Model](https://image.slidesharecdn.com/kaistv02-170929093337/85/Moving-Objects-and-Spatial-Data-Computing-77-320.jpg)
![78
• Data Type and Operations
Data Model
t
x
distance : moving double
MDouble
MPoint
MBool : is Intersect?
false[t1], false[t2], true[t3) …](https://image.slidesharecdn.com/kaistv02-170929093337/85/Moving-Objects-and-Spatial-Data-Computing-78-320.jpg)
![86
• Segment Table
• strategies
• split
• count‐based‐split(*)
• spatial‐based‐split
• temporal‐based‐split
• st‐based‐split
• compression
• no compression : tpoint[]
• naïve compression : zip
• simplification
Storage Model
moid segid tpcount rect start_time end_time tpseg
box2d timestamp tpoint[]](https://image.slidesharecdn.com/kaistv02-170929093337/85/Moving-Objects-and-Spatial-Data-Computing-86-320.jpg)
Moving Objects and Spatial Data Computing
- 1. Moving Objects and Spatial Data Computing (이동체 및 공간 데이터 컴퓨팅 연구 동향) 2017. 09. 28 Kwang Woo NAM kwnam@kunsan.ac.kr Kunsan National University This research was supported by a Grant (14NSIP‐B080144‐01) from National Land Space Information Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government. 2017.09.28 KAIST 조천식녹색교통대학원 발표자료
- 2. 2 Who 남광우 교수/ 국립 군산대학교 ‐ 군산대 컴퓨터정보통신공학부(2004‐현재) ‐ 미래형자동차 R&D 전문인력양성사업단(2017‐) { KEA, 군산대, 한양대, 충북대, 인하대, 부품연} ‐ 前 ETRI 텔레매틱스연구단 선임연구원(2001‐2004) ‐ U. of Minnesota Visiting Scholar(2015‐2016) Projects • 국토해양부, 공간정보 S/W활용을 위한 오픈소스 가공기술 개발(14‐19) : PostTrajectory • NRF, 소셜 미디어 스트림 기반 공간 지식의 연속 마이닝(13‐16): SKSpark, SpatialTajo • ETRI, 시멘틱 공간정보 서비스 프레임워크 및 구조 분석(10) • 국토해양부, U‐GIS 국토정보 컨텐츠 서비스 요소 기술 연구(07‐11) • NRF, geoSensor 센서 스트림 기반 점진적 온라인 패턴 마이닝 기법 연구(09‐12) • NRF, GeoSensor Network를 위한 효율적인 공간 센서 스트림 죠인 및 집계처리 기법 연구(06‐07) • NRF, 상황정보 기반의 Networked GeoVision 기술 개발(08‐11) : GeoVIsion
- 3. 3 • Spatial Data • 지상, 지하, 수상, 공중 등의 공간 상에 존재하는 자연적 또 는 인공적 객체들의 위치정보와 속성정보 및 객체 들간의 위상정보 Introduction (a) GIS (b) BIM(CAD)
- 5. Introduction 공간 데이터 뉴미디어 공간정보 콘텐츠 공간 정보 공간정보 Spectrum의 확대 2D Vector RS Data 2.5D Data DEM Data LBS/TelematicsGeoERPWeb GIS MultiSensor Smart Phone Real 3D Data 360 Panorama Video+GPS (blackbox) Game,Edu,… Closed Box(Public Sector) Open Box( Enterpise Sector) Shared Box(People Sector) Sony Camera (GPS+Pano) iWatch Google Glass GoPro • Evolutions of Spatial Data/Information
- 6. Introduction • 일반적인 공간좌표 및 POI 등의 공간 정보와 웹 문서, 도서, 사진, 동영상, 음성 등의 미디어가 결합된 공간정보 컨텐츠들의 등장 • 예) GoPro, PointCloud, 차량 블랙박스, 360 파노라마, 구글 글래스 GeoTagged Photo(Panoramio) 360 Panorama(StreetView) Car Blackbox Video 자율주행차 PointCloud(LiDAR)
- 7. 7 • Converging into Spatial Data Introduction GNSS - 무선데이터통신망을 이용한 정밀위치정보제공 - 차량항법, 교통정보 제공 GIS SIIS ITS - 정밀 기준점 위치정보에 의한 영상정보 정확도 향상 - GPS 기지국 구축적지 선정 - 차량용 네비게이션 정보제공 - 교통주제도 구축 및 계량적 분석 기술제공 - 교통시설물 관리 및 IT기반 운영기술 제공 - 대용량 공간정보 자동수정 갱신 - 영상지도 제작 - 측량기술 및 관련기술 (위성삼각측량, WGS84)제공 - 실시간 Mobile GIS응용기술 - 영상기반 교통정보 수집기술제공 - 고 정밀 영상검지 시스템 구축 출처 : ETRI
- 8. 공간정보 서비스 개발 플랫폼
- 9. 9 • 버스정보시스템과 스마트폰 앱 Spatial Data Services 버스정보시스템 주변 맛집 앱 정재준, 노영희, 공간정보의 이해, 국토교통부, 2015
- 14. 14 • 자율주행 자동차 • Google Self Driving Car Spatial Data Services https://waymo.com/ Video : https://waymo.com/tech/
- 16. 16 • 항법미사일과 공간정보기술 공간정보 융합 기술 https://topwar.ru/84614‐demony‐treh‐stihiy‐kalibr‐protiv‐tomagavka.html 토마호크 미사일 : http://kwangaeto.egloos.com/m/6250027 Tomahawk Cruise Missile 위성항법과 GIS를 이용한 순항 미사일의 유도
- 17. 17 • 기상 예측과 예보 주요 공간정보 서비스 정재준, 노영희, 공간정보의 이해, 국토교통부, 2015 태풍 위성 사진 태풍의 이동 경로
- 20. 20 • Spatial Queries • top‐K • Spatial operation • Point : kNN (k‐nearest neighbor), distance • Line : buffer, inside, intersect • Polygon : inside, outside Spatial Data Model K=4 kNN N distance buffer inside / intersect inside / outside
- 21. 21 • R‐Tree Spatial Data Indexing a b c d a b c d R S R S root root R-tree 1 2 3 4 5 6 7 8 9 Pointers to geometries 4 5 6 7 8 91 2 3 m = 2; M = 3
- 22. 22 • PostGIS/PostgreSQL : Stonebraker/Berkeley Spatial Database Systems Oracle Ingres PostgresSybaseIngres Illustra Postgres95 SQL Server Relational Theory Ingres Informix (IUS) 1980 1970 1990 2000 PostgreSQL
- 23. 23 • PostGIS • PostgreSQL Spatial Extension • 경부 고속도로와 Cross하는 도로를 검색하라 • SELECT gid, name FROM bc_roads WHERE ST_Crosses( the_geom, ST_GeomFromText(‘..’, 3005) ); • 대전시내안의 도로 총 길이를 구하라 • SELECT Sum( ST_Length( the_geom ) ) / 1000 AS km_roads FROM bc_roads; Spatial Database Systems ST_Intersects(G1,G2) ST_Contains(G1,G2) ST_Within(G1,G2) ST_Touches(G1,G2) ST_DWithin(G1,G2,D)
- 24. 24 • pgRouting • Routing function extension to PostGIS • example page • New York Bike Map : http://www.ridethecity.com/ Spatial Database Systems SELECT * FROM pgr_dijkstra(' SELECT gid AS id, source, target, cost_s / 3600 * 100 AS cost, reverse_cost_s / 3600 * 100 AS reverse_cost FROM ways', 13009, 3986);
- 25. 25 • New York Taxi Data • 1.1 Billion NYC Taxi and Uber Trips • PostGIS + pgRouting Spatial Applications : NY Taxi http://toddwschneider.com/posts/analyzing‐1‐1‐billion‐nyc‐taxi‐and‐uber‐trips‐with‐a‐vengeance/
- 27. 27 • Hadoop • Java 기반의 대용량 데이터 처리 프레임워크 • default 3 replications : 5M files ‐> 15M files • Hadoop HDFS : • 구글의 2003년 논문으로 발표한 GFS(Google File System)의 구현 • 2005년에 더그 커팅(Doug Cutting)이 오픈소스로 구현 • MapReduce : • Google의 MapReduce을 논문을 구현한 결과물 Spatial Big Data : Micro Blogs
- 29. 29 • HDFS block system : Append Only • Default block size : 64MB • Default replications : 3 Spatial Big Data : Micro Blogs 블록1 블록2 블록3 블록4 블록5 320MB 파일 블록1 블록3 블록4 블록2 블록3 블록4 블록1 블록3 블록5 블록2 블록4 블록5 블록1 블록2 블록5 HDFS 파일저장
- 31. 31 • Spatial Social Data : Twitter • Square Union Model Spatial Big Data : Micro Blogs 사람 지역 시간 단어Tweet ① 1차 : 2개 요소의 결합 ex) Tweet + 사람 ② 2차 : 3개 요소의 결합 ex) Tweet + 사람 + 지역 ③ 3차 : 4개 요소의 결합 ex) Tweet + 사람 + 지역 + 단어 ④ 4차 : 5개 요소의 결합 ※ 모든 질의는 top-K를 지원한 다
- 32. 32 • Real‐world Social Data • (lat, long) => key value Spatial Big Data : Micro Blogs o1 o2 o5 o7 o4 o8 o9 o12 o10 o11 o15 o6 o13 o14 o16 o17o18 o19 o3 o20 a b c e d f oid wordset o1 Italian, restaurant, expensive o2 coffee, expensive, restaurant o3 Italian, pizza, expensive o4 restaurant, pizza, expensive o5 Italian, pizza, restaurant, expensive o6 coffee, restaurant, low-priced o7 Italian, coffee, low-priced, restaurant, pizza o8 coffee, restaurant, expensive o9 expensive, restaurant o10 pasta, pizza, expensive o11 pasta, low-priced, restaurant o12 Italian, restaurant, expensive o13 pizza, low-priced o14 tea, expensive, restaurant o15 Italian, restaurant o16 pasta, restaurant, expensive o17 pizza, restaurant, low-priced o18 Italian, pizza, restaurant o19 Italian, pasta, restaurant, expensive o20 pasta, expensive (b) Social Database (a) social data in real world (lat, long)
- 33. 33 • Simplified Tweet Data Model • Simple MapReduce Example : Counting Tweets by User Spatial Big Data : Micro Blogs tweet : (mid, userid, x, y, time, text) tweet : (mid, userid, x, y, time, {word,…}) (mid, userid, x, y, time, {word,…}) Map (userid1, mid1) (userid2, mid2) (userid3, mid3) (userid2, mid4) (userid3, mid5) (userid4, mid6) shuffle Reduce (userid1, {mid1}) (userid3, {mid3,mid5}) (userid2, {mid2,mid4}) (userid4, mid6) (userid1, 1) (userid3, 2) (userid2, 2) (userid4, 1)
- 34. 34 • GeoHash : Z‐Ordering Partition Spatial Big Data : Micro Blogs 0 1 4 5 16 17 20 21 2 3 6 7 18 19 22 23 8 9 12 13 24 25 28 29 10 11 14 15 26 27 30 31 32 33 36 37 48 49 52 53 34 35 38 39 50 51 54 55 40 41 44 45 56 57 60 61 42 43 46 47 58 59 62 63 o1 o2 o5 o7 o4 o8 o9 o12 o10 o11 o15 o6 o13 o14 o16 o17o18 o19 o3 o20 (b) Spatial social data and grid space oid wordset geo o1 Italian, restaurant, expensive 13 o2 coffee, expensive, restaurant 18 o3 Italian, pizza, expensive 12 o4 restaurant, pizza, expensive 60 o5 Italian, pizza, restaurant, expensive 12 o6 coffee, restaurant, low-priced 35 o7 Italian, coffee, low-priced, restaurant, pizza 44 o8 coffee, restaurant, expensive 62 o9 expensive, restaurant 15 o10 pasta, pizza, expensive 12 o11 pasta, low-priced, restaurant 13 o12 Italian, restaurant, expensive 11 o13 pizza, low-priced 44 o14 tea, expensive, restaurant 15 o15 Italian, restaurant 35 o16 pasta, restaurant, expensive 15 o17 pizza, restaurant, low-priced 18 o18 Italian, pizza, restaurant 7 o19 Italian, pasta, restaurant, expensive 60 o20 pasta, expensive 62 (c) Spatial Wordset Transaction Database Spatial (a) social data in real world Single Level Grid Approach
- 35. 35 • GeoHash • Z‐Ordering ID : Array of Bits • Characterizing by Base32 Encoding Spatial Big Data : Micro Blogs Grid Level 1 Grid Level 2 Grid Level 3 Grid Level 4 00 01 10 11 00 01 10 11 Miami, Florida의 ID? 11 11 10 = geohash : 7 Base32 Table
- 36. 36 • Rowkey로서의 GeoHash • 지오해시가 로우키를 위한 최적의 선택인 이유 ① 계산하기 쉽다 ② 접두사가 최근접 이웃을 발견하는데 중요한 역할을 하기 때문 • 단점 : 접두사의 정확도와 경계값 문제 Spatial Big Data : Micro Blogs
- 37. 37 • Spatial Aggregation : Counting Spatial‐Tweets • GeoHash Optimization Spatial Big Data : Micro Blogs (mid, userid, x, y, time, {word,…}) (geoid, mid) Map Shuffle (geoid, {mid, mid, …}) Reduce (geoid, n) (‘aaaaa’, mid1) (‘aaaab’, mid2) (‘aaaac’, mid3) (‘aaaad’, mid4) (‘aaaae’, mid5) (‘aaaba’, mid6) (‘aaabb’, mid7) (‘aaaa’, mid1) (‘aaaa’, mid2) (‘aaaa’, mid3) (‘aaaa’, mid4) (‘aaaa’, mid5) (‘aaab’, mid6) (‘aaab’, mid7) original data Approach 1 (‘aaaa’, mid1) (‘aaaa’, mid2) (‘aaaa’, mid3) (‘aaaa’, mid4) (‘aaab’, mid6) (‘aaab’, mid7) Approach 2 (‘aaae’, mid5)
- 38. 38 • Finding Home/Visitor • Finding Spatial Words Spatial Big Data : Micro Blogs (mid, userid, x, y, time, {word,…}) (<userid,geoid>, mid) Map Shuffle (<userid,geoid>, {mid, mid, …}) Reduce (<userid,geoid>, n) (mid, userid, x, y, time, {word,…}) (<geoid, word>, mid) (<geoid, word>, mid) (<geoid, word>, mid) Map Shuffle (<geoid, word>,{mid, mid, …}) Reduce (<geoid, word>, n)
- 39. 39 • SpatialHadoop • 하둡에 공간 연산자를 지원하기 위하여 개발 • MapReduce를 이용하여 공간 연산자를 처리 • 2레벨 방식의 인덱스 이용 • Grobal index : 그리드 파일 • Local index : R‐tree Spatial Big Data : Systems Ahmed Eldawy, Mohamed F. Mokbel: SpatialHadoop: A MapReduce framework for spatial data. ICDE 2015: 1352‐1363
- 40. 40 • Hadoop‐GIS • HiveSP: 공간 데이터 웨어하우징 시스템 • 공간 분할, 공간 질의 엔진을 포함 • 2레벨 인덱스 사용 Spatial Big Data : Systems Ablimit Aji, Fusheng Wang, Hoang Vo, Rubao Lee, Qiaoling Liu, Xiaodong Zhang, Joel H. Saltz: Hadoop‐GIS: A High Performance Spatial Data Warehousing System over MapReduce. PVLDB 6(11): 1009‐1020 (2013)
- 41. 41 • Spark : In‐memory Computing • GeoSpark • Spark의 RDD를 SpatialRDD로 변형하여 공간 연산을 지원 • 2레벨 인덱스 사용 • 지역 인덱스를 메모리에 상주 시키고 질의를 수행 Spatial Big Data : Systems Jia Yu, Jinxuan Wu, Mohamed Sarwat: GeoSpark: a cluster computing framework for processing large‐scale spatial data. SIGSPATIAL/GIS 2015: 70:1‐70:4
- 42. 42 • SpatialSpark • GPU에서 사용하는 공간 색인 및 공간 조인을 위한 CUDA/Thrust 구현을 참고하여 개발 • Scala의 벡터/콜렉션 함수가 지원하는 병렬 기본 요소 활용 • JTS 라이브러리 사용 • 공간 조인에 대한 성능을 비교 분석할 때 사용 • LocationSpark • Query Scheduler • 데이터가 한쪽으로 몰려 있는 곳의 통신 오버헤드를 완화하기 위 하여 각 파티션의 통계정보를 수집하여 파티션을 다시 분할 • Query Executor • 특정 쿼리 평가를 실행하여 질의가 인덱스를 사용해야 하는지 여 부를 판단 Spatial Big Data : Systems SpatialSpark : http://simin.me/projects/spatialspark/ LocationSpark : MingJie Tang, Yongyang Yu, Qutaibah M. Malluhi, Mourad Ouzzani, Walid G. Aref: LocationSpark: A Distributed In‐Memory Data Management System for Big Spatial Data. PVLDB 9(13): 1565‐1568 (2016)
- 43. 43 • SKSpark : Spatial Keyword Spark • First Spatial Keyword Big Data System • BRQ • 사용자가 질의 영역과 키워드를 입력하면 질의 영역에 해당되는 모 든 객체 중 키워드를 모두 포함하는 객체를 검색 • , • = 질의 단어 • = 질의 범위 • . ∩ . ∈ ∩ . ∩ . ∈ • BkQ • 사용자가 질의 위치와 검색 단어를 입력하면 검색 단어를 모두 포함 하는 문서들 중 사용자의 위치와 가장 가까운 순서대로 k개를 검색 • , , • Spatial‐Keyword In‐memory Indexing Spatial Big Data : Systems
- 44. 44 • SKSpark Spatial Big Data : Systems SpatialWebRDD Layer GeoPartitionedRDD Layer SKIndexedRDD Layer Apache Spark Cluster on HDFS Spark Layer Query Layer 0 5 10 15 20 25 1 2 3 4 5 6 검 색 시 간 ( m s ) 검색 키워드 수 DIR-tree QP-tree 0 5 10 15 20 25 30 35 40 45 50 1 2 3 4 5 6 검 색 시 간 ( m s ) k값 DIR- tree BRQ BkQ
- 46. 46 • Queries for Static Object/Static Map • Shortest path query • Range query : Find the restaurants • k-NN/top-K queries • Queries for Moving Object/Static Map • Continuous range queries • Location-based k-NN/top-K map queries • Queries for Moving Objects/Moving Objects • Internet connected cars • k-NN/top-K moving objects queries • Continuous moving objects queries MO and Spatial Queries
- 47. 47 MO and Spatial Queries • Moving Objects Databases MODB … MO Big Data Cluster/HDFS MO Mining/Analysis MO DBMS MO Queries Map
- 48. 48 Trajectory Refinement • Noise Filtering • Mean(or Median) Filter • Kalman and Particle Filter • Heuristics‐based Outlier Detection • v > 300km/h
- 49. 49 Trajectory Refinement • Stay Point Detection • Semantic Filtering • 예 : 고속도로 평균 속도/시간 계산시에 삭제/보정 • 다른 예
- 50. 50 Trajectory Segmentation • Reasons of Trajectory Segmentation • Too large MBR : false hit in Indexing • Semantical segmentation for mining 도심 구간 고속도로 Marios Hadjieleftherius, et. al, Efficient Indexing of Spatiotemporal Objects, EDBT 02.
- 51. 51 Trajectory Segmentation • Methods of Trajectory Segmentation • Time interval segmentation • Turning points segmentation
- 52. 52 Trajectory Segmentation • Methods of Trajectory Segmentation • Key shape segmentation • Stay point segmentation Yu Zheng, Trajectory Data Mining : An Overview, ACM Trans. on Intelligent System and Technology, Sept. 2015
- 53. 53 Trajectory Segmentation • Methods of Trajectory Segmentation • Semantical change point segmentation Yu Zheng, Trajectory Data Mining : An Overview, ACM Trans. on Intelligent System and Technology, Sept. 2015
- 54. 54 Trajectory Queries • Queries • Spatial Queries • KAIST에 있었던 차량은? • Spatio‐temporal Queries • 오늘 7am‐8am 사이에 KAIST를 지나간 차량 은? • Moving Objects Queries • x지점 10m 이내로 지나간 차량은? • 서로 10m 이내로 스쳐간 차량들은? • 1시간 이상 KAIST에 머문 차량은? • MO Mining Queries • 두 대 이상 함께 움직인 차량들은(flock)?
- 55. 55 Trajectory Queries • Range Queries • k‐NN Queries Yu Zheng, Trajectory Data Mining : An Overview, ACM Trans. on Intelligent System and Technology, Sept. 2015
- 56. 56 Trajectory Queries • k‐NN Trajectory Queries • 예 ) Car Pool App • Find similar trajectory cars with my trajectory Yu Zheng, Trajectory Data Mining : An Overview, ACM Trans. on Intelligent System and Technology, Sept. 2015
- 57. 57 Moving Objects Indexing • Indexing Moving Objects • 3DR‐Tree for Historical Trajectory Data • Historical trajectories are represented by their three‐dimensional MBB(Minimum Bounding Box) Time Mohamed F. Mokbel, Thanaa M. Ghanem, Walid G. Aref: Spatio‐Temporal Access Methods. IEEE Data Eng. Bull. 26(2): 40‐49 (2003) Long‐Van Nguyen‐Dinh, Walid G. Aref, Mohamed F. Mokbel: Spatio‐Temporal Access Methods: Part 2 (2003 ‐ 2010). IEEE Data Eng. Bull. 33(2): 46‐55 (2010)
- 58. 58 Moving Objects Indexing • Indexing Moving Objects • Multi‐version Indexing for Historical Trajectories • Maintain an R‐tree for each time instance • R‐tree nodes that are not changed across consecutive time instances are linked together Timestamp 1Timestamp 0 3D‐R‐tree Mohamed F. Mokbel, Thanaa M. Ghanem, Walid G. Aref: Spatio‐Temporal Access Methods. IEEE Data Eng. Bull. 26(2): 40‐49 (2003) Long‐Van Nguyen‐Dinh, Walid G. Aref, Mohamed F. Mokbel: Spatio‐Temporal Access Methods: Part 2 (2003 ‐ 2010). IEEE Data Eng. Bull. 33(2): 46‐55 (2010)
- 59. 59 Moving Objects Indexing • TB‐Tree : Trajectory Bundle Tree • Hybrid index structure which preserves trajectories • A predominant trajectory index structure for Euclidean spaces • Fast access to the trajectory information of moving objects Dieter Pfoser, Christian S. Jensen, Yannis Theodoridis: Novel Approaches to the Indexing of Moving Object Trajectories. VLDB 2000: 395‐406
- 60. 60 Moving Objects Indexing • Indexing Moving Objects for Current Position Queries • Focus Time : Now • Features • Frequent Insertion of New Data • Continuous Queries about NOW data • Example • A 지역에 현재 있는 차의 수는? • 현재 x 지점 근처에 가장 가까이 있는 차량 3대는? • Problems • 전통적인 R‐tree는 frequent update에 느림
- 61. 61 Update Memo Spatio‐temporal Queries Raw answer set Final answer set • RUM‐tree : R‐tree with Update Memo • Memo : R‐tree의 최근 update 포함 • update 될 때 Memo에 적어놓음 Moving Objects Indexing Access Xiong, Xiaopeng, and Walid G. Aref. "R-trees with update memos." Data Engineering, 2006. ICDE'06. Proceedings of the 22nd International Conference on. IEEE, 2006.
- 62. 62 Moving Objects Indexing • The Time‐parameterized R‐tree (TPR‐tree) • Minimum bounding rectangles (MBR) • Velocity bounding rectangles (VBR) • MBR&VBR은 차량의 속도가 유지되는 한 동일한 MBR 안에 유지 될 수 있음을 보장함으로서 update를 최소화 Simonas Saltenis, Christian S. Jensen, Scott T. Leutenegger, Mario A. López: Indexing the Positions of Continuously Moving Objects. SIGMOD Conference 2000: 331‐342
- 63. 63 Moving Objects Indexing Christian S. Jensen, Dalia Tiesyte, Nerius Tradisauskas: Robust B+‐Tree‐Based Indexing of Moving Objects. MDM 2006: 12 • Bx‐Tree • B+ tree 를 기반으로 time/spatial id를 key로 갖도록 변형 • spatial id는 Hilbert Key를 사용 • Bdual‐Tree, BBx‐tree 등으로 계속 확장
- 64. 64 Moving Objects Indexing • Indexing on Road Network • FNR‐Tree : Fixed Network R‐tree • MON‐Tree : Moving Objects on Network FNR‐Tree : E. Frentzos. Indexing objects moving on fixed networks. SSTD, 2003. MON‐tree : Victor Teixeira de Almeida, et. al, Indexing the Trajectories of Moving Objects in Networks. GeoInformatica 9(1), 2005 R‐tree for Network R‐tree for MO
- 65. 65 Moving Objects Patterns Fosca Giannotti, Mirco Nanni, Fabio Pinelli, Dino Pedreschi: Trajectory pattern mining. KDD 2007: 330‐339 • Region of Interest(ROI) • T‐Patterns • ST Sequence Patterns BA 20 min C 3 min 3 min 6 min 2 min BA 20 min C 3 min
- 66. 66 Moving Objects Patterns • Region of Interest(ROI) : Static ROI • Fixed pre‐defined regions • Find frequent visited regions A B C time
- 67. 67 Moving Objects Patterns • Region of Interest(ROI) : Dynamic ROI • Spatial Clustering and Labeling • Find frequent visited regions cluster(x1,y1) cluster(x2,y2) time
- 68. 68 Moving Objects Patterns • Moving Together Patterns • Flock • 특정 Region r내에서 m 개체이상이 같은 방향으로 움직임 • Leadership • 가장 먼저 움직인 객체 • Convergence • m 개체 이상이 같은 방향으로 • Encounter • r내에서 만나는 지역 [37] J. Gudmundsson and M.V. Kreveld. 2006. Computing longest duration flocks in trajectory data. In Proceedings of the 14th Annual ACM International Symposium on Advances in Geographic Information Systems. ACM, 35‐42. [38] J. Gudmundsson, M.V. Kreveld, and B. Speckmann. 2004. Efficient detection of motion patterns in spatio‐temporal data sets. In Proceedings of the 12th Annual ACM International Symposium on Advances in Geographic Information Systems. ACM, 250–257.
- 69. 69 Moving Objects Patterns • Moving Together Patterns • Convoy • min consecutive timestamps together H. Jeung, M. Yiu, X. Zhou, C. Jensen, and H. Shen. 2008. Discovery of convoys in trajectory databases. Proceedings of the VLDB Endowment 1, 1, 1068–1080.
- 70. 70 Moving Objects Patterns • Moving Together Patterns • Swarm • min timestamps together Z. Li, B. Ding, J. Han, and R. Kays. 2010. Swarm: Mining relaxed temporal moving object clusters. Proceedings of the VLDB Endowment 3, 1-2, 723–734.
- 71. 71 Moving Objects Patterns • Trajectory Clustering • A Partition‐and‐Group Framework J. G. Lee, J. Han, and K. Y. Whang. 2007. Trajectory clustering: A partition‐and‐group framework. In Proceedings of ACM SIGMOD Conference on Management of Data. ACM, 593‐604
- 72. 72 Moving Objects Patterns • Trajectory Clustering • A Partition‐and‐Group Framework J. G. Lee, J. Han, and K. Y. Whang. 2007. Trajectory clustering: A partition‐and‐group framework. In Proceedings of ACM SIGMOD Conference on Management of Data. ACM, 593‐ 604
- 73. 73 Moving Objects Patterns • Trajectory Class • TraClass Algorithm • Classify subtrajectories instead of whole trajectories Jae‐Gil Lee, Jiawei Han, Xiaolei Li, and Hector Gonzalez, “TraClass: Trajectory Classification Using Hierarchical Region‐Based and Trajectory‐Based Clustering”, Proc. 2008 Int. Conf. on Very Large Data Base (VLDB'08), Auckland, New Zealand, Aug. 2008
- 74. 74 Moving Objects Patterns • Travel Recommendation • Find the interesting locations and travel sequences from trajectories Y. Zheng, L. Zhang, Z. Ma, X. Xie, W.‐Y. Ma. 2011. Recommending friends and locations based on individual location history. ACM Transaction on the Web 5, 1, 5‐44. Y. Zheng, L. Zhang, X. Xie, W.‐Y. Ma. 2009. Mining interesting locations and travel sequences from GPS trajectories. In Proceedings of the 18th International Conference on World Wide Web. ACM, 791‐800.
- 76. 76 • PostTrajectory • https://github.com/awarematics/posttrajectory PostTrajectory Architecture 76 PostgreSQL DB PostGIS PostTrajectory Trajectory Type Trajectory Indexing PostGeoPhoto GeoPhoto Type GeoPhoto Indexing Geometry Type R-Tree on GiST BlackBox App for Cars (SkyBox) Apache/Tomcat Web Sever PostGeoVideo GeoVideo Type GeoPhoto Indexing Trajectory Storage Trajectory Queries Trajectory Analysis Trajectory Apps GeoV/P Storage GeoV/P Queries GeoV/P Analysis GeoV/P Apps Trajectory/GeoPhoto/GeoVideo Web User Geospatial Media Contents Management System(GeoCMS)
- 77. 77 • Data Type • TPoint and TDouble • TPoint : (Point, Timestamp) • TDouble : (Double, Timestamp) • TBool : (Boolean, Timestamp ) • MPoint • TPoint[] • MDouble • TDouble[] • MBool • TBool[] Data Model
- 79. 79 • Spatiotemporal Relationship Operations • Enters • Leaves • Crosses • StayIn • Bypass Data Model cross leave stayinenter bypass t x polygon r x x x
- 80. 80 • Slice Operations • Temporal Slicing • Spatial Slicing • Spatiotemporal Slicing Data Model t polygon r temporal slicing spatial slicing spatiotemporal slicing mpoint slice(mpoint, period) mpoint slice(mpoint, geometry) mpoint slice(mpoint, geometry, period) mpoint slice(mpoint, period) mpoint slice(mpoint, geometry) mpoint slice(mpoint, geometry, period)
- 81. 81 • Projection Operations • Spatial Projection • Temporal Projection Data Model LineString sproject( mpoint ) periodtproject( mpoint) LineString sproject( mpoint ) periodtproject( mpoint) temporal projection spatial projection t LineString start timestamp end timestamp
- 82. 82 • Table Creation Storage Model CREATE TABLE taxi ( taxi_id int, taxi_number char(20), taxi_model char(20), taxi_driver char(20) traj MPOINT ); SELECT AddTrajectoryColumn('public', 'taxi', 'traj', 4326, ‘MPOINT', 2, 150);
- 83. 83 • Tables • Meta Data Table : Trajectory_Column • Moving Objects Table • Segment Table • A trajectory is split into segments Storage Model PostGISPostGIS spatial objects table(roads) PostTrajectoryPostTrajectory trajectory_columns(metadata) geometry_columns(metadata) spatial objects table(usstates) moving objects table(taxi) moving objects table(bus) segment table(mpoint) segment table(mpoint)
- 84. 84 • MPoint • (segtable_oid, moid) • One segment table per a trajectory attribute Storage Model PostGISPostGIS ,,, spatial objects table(usstates) geometry geometry geometry geometry geometry geometry on a row PostTrajectoryPostTrajectory ,,, mpoint segment segment segment … mpoint := (segtable_oid, moid) moving objects table(taxi) segment table moid moid moid ,,, mpoint segmentmoid
- 85. 85 • Example Storage Model CREATE TABLE taxi ( taxi_id int, taxi_number char(20), taxi_model char(20), taxi_driver char(20) ); SELECT AddTrajectoryColumn('public', 'taxi', 'traj', 4326, ‘MPOINT', 2, 150); SELECT AddTrajectoryColumn('public', 'taxi', ‘accel1', 4326, ‘MDouble', 2, 150); georeference WGS4326 2 Dminsion 150 tpoint per a segment taxi_id traj accel1 taxi table … traj_segment_table accel1_segment_table
- 86. 86 • Segment Table • strategies • split • count‐based‐split(*) • spatial‐based‐split • temporal‐based‐split • st‐based‐split • compression • no compression : tpoint[] • naïve compression : zip • simplification Storage Model moid segid tpcount rect start_time end_time tpseg box2d timestamp tpoint[]
- 87. 87 • Insert new Moving Object • Append GPS Trajectory for a moving object • Remove GPS Trajectories Trajectory Queries ##Inserting Moving Objects insert into taxi values(1, '57NU2001', 'Optima', 'hongkd7'); insert into taxi values(2, '57NU2002', 'SonataYF', 'hongkd7'); UPDATE taxi SET traj = append(traj, tpoint(st_point(200, 200), timestamp '2010‐01‐25 12:05:30+09')) WHERE taxi_id = 1; UPDATE taxi SET traj = remove(traj, to_timestamp(12345678), to_timestamp(23456789) ) WHERE taxi_id = 1;
- 93. 93 • Trajectory Data : T‐Drive • Microsoft Research Asia, Beijin • 6‐month real dataset of 30,000 taxis in Beijing • Total distance: almost 0.5 billion (446 million) KM • Number of GPS points: almost 1 billion (855 million) Performance Evaluation
- 97. 97 • New Era of Spatial Data/Information • GPS Everywhere! Introduction images from Apple https://www.wired.com/2009/06/black‐box‐for‐the‐car/ http://www.bayareabikeshare.com CarPhone Bike and, life log for human
- 99. GeoMedia : GeoPhoto GeoPhoto GPS Tagged Photo (x, y, z) Geophoto KML Camera, Lookat + Field of View Geophoto GeoPhoto Field of View(FoV)
- 102. 102 • Example : Finding GPS Photos in Flickr Related Work : Flickr 지역/시/주/우편번호 입력지역/시/주/우편번호 입력 컨텐츠의 지정 : face, bike, building, …
- 108. 108 • Solution : PostGeoMedia • PostgreSQL/PostGIS + OpenCV/Tensorflow Our Approach PostgreSQL/PostGIS with Media Processing Metadata Photos OpenCV/ TensorFlow OpenCV/ TensorFlow Geo Media Processing Apache/Tomcat
- 110. 110 • Creation of GeoPhoto Tables GeoPhoto Queries CREATE Table dronePhotos { ID int, owner VARCHAR(20), img GeoPhotos } Select AddGeoPhotoColumns( photos, ‘img’ ); CREATE Type GeoPhotos ( ID INT, uriString VARCHAR(100), width double, height double, time TIMESTAMP, geom GEOMETRY(POINT,4326) ); INSERT INTO dronephotos VALUES( 1, ‘kwnam’, 'file://tmp/drone001.jpg');
- 111. 111 • Face and Eye Detection in SQL • Spatial Queries with Face/Eye Detection GeoPhoto Queries SELECT faces( uriString), eyes(uriString), cars( uriString ) FROM DronePhotos; SSELECT faces( uriString ), eyes( uriString), cars( uriString ) FROM DronePhotos WHERE ST_WITHIN( geom, ST_MakeEnvelope(191232, 243117,191232, 243119,312) );
- 113. 113 • 사람이 찍힌 사진을 검색하시오 • 특정 지역내에 사람이 찍힌 사진을 검색하시오 GeoPhoto Queries SELECT count(*) FROM DronePhotos WHERE NumOfFaces(uriString) >0 OR NumOfCars( uriString ) > 0 ; SELECT count(*) FROM DronePhotos WHERE ( NumOfFaces(uriString) >0 OR NumOfCars( uriString ) > 0 ) AND ST_WITHIN( geom, ST_MakeEnvelope(191232, 243117,191232, 243119,312) );
- 114. 114 • Join GeoPhoto Queries SELECT count(*) FROM DronePhotos dp, ROAD r WHERE ST_WITHIN( dp.geom, ST_BUFFER( r.geom, 10 ) );
- 116. 116 • 번호판 인식 + GPS Applications http://blog.naver.com/emaru2017/221070385130
- 117. 117 Example
- 120. 120 • Real Example : GeoVideo/Trajectories in Car Blackbox • Moving Point and Moving Double GeoVideo *.mp4 : video data *.gps : gps data(NMEA) *.3gf : acceleration sensor
- 121. GeoVideo : Moving Objects • GPS Trajectory and GeoVideo의 분석과 모니터링 차량 3대 센서 데이터 비교 분석
- 122. 122 • MediaQ by USC • GeoUGV : User‐Generated Mobile Video • Since 2014 • MediaQ web: http://mediaq.usc.edu Related Work Ying Lu, Hien To, Abdullah Alfarrarjeh, Seon Ho Kim, Yifang Yin, Roger Zimmermann, and Cyrus Shahabi,GeoUGV: User‐Generated Mobile Video Dataset with Fine Granularity Spatial Metadata, In the 7th ACM Multimedia Systems Conference (MMSys), Klagenfurt am Worthersee, Austria, May 10‐13, 2016 Seon Ho Kim, Ying Lu, Giorgos Constantinou, Cyrus Shahabi, Guanfeng Wang, and Roger Zimmermann,MediaQ: Mobile Multimedia Management System. In the conference of Multimedia Systems (MMSys), In the conference of Multimedia Systems (MMSys), pages: 224 ‐ 235, Singapore, March 19 ‐21, 2014
- 123. 123 • GeoVideoIndex by POSTECH • GeoVideo • MBTR : Minimum Bounding Tilted Rectangle Related Work Dongha Lee, Jinoh Oh, Woong-Kee Loh, Hwanjo Yu: GeoVideoIndex: Indexing for georeferenced videos. Inf. Sci. 374: 210-223 (2016)
- 124. PostGeomedia GPS VideoSensor Trajectory SubTable Blackbox Drone index Moving Feature Table Query UDF Extension PostGeoMedia Integration Layer Sensor SubTable index Video SubTable index • 공간 미디어 인덱스 Indexes : 3DR Tree on GiST SETI* on ORDB Bx* Tree on GiST Storage : Traditional Table Plain Binary Objects Compressed WKB Materialized View
- 125. 125 Conclusion • Trends • Spatial Big Data Computing • Moving Objects Data Computing • Our Systems • SKSpark • PostTrajectory • PostGeoMedia