1. Location-aware Query Processing and Optimization: A Tutorial Mohamed F. Mokbel Walid G. Aref Department of Computer Science and Engineering, University of Minnesota Minneapolis, MN, USA mokbel@cs.umn.edu Department of Computer Science, Purdue University West Lafayette, Indiana, USA. aref@cs.purdue.edu

2. 2 May 2007 MDM Tutorial Motivation

3. 3 May 2007 MDM Tutorial Applications Traffic Monitoring How many cars are in the downtown area? Send an alert if a non-friendly vehicle enters a restricted region Report any congestion in the road network Once an accident is discovered, immediately send alarm to the nearest police and ambulance cars Make sure that there are no two aircrafts with nearby paths

4. 4 May 2007 MDM Tutorial Applications (Cont.) Location-based Store Finder / Advertisement Where is my nearest Gas station? What are the fast food restaurants within 3 miles from my location? Let me know if I am near to a restaurant while any of my friends are there Send E-coupons to all customers within 3 miles of my stores Get me the list of all customers that I am considered their nearest restaurant

5. 5 May 2007 MDM Tutorial Location-based Database Servers Layered Approach DBMS GIS Spatio-temporal Built-in Approach GIS Interface ST-Index ST Query Processing DBMS

6. 6 May 2007 MDM Tutorial Variety of Location-aware Queries Query: Stationary Object: Moving Continuously report the number of cars in the freeway Type: Range query Time: Present Duration: Continuous What are my nearest McDonalds for the next hour? Type: Nearest-Neighbor query Time: Future Duration: Continuous Query: Moving Object: Stationary Send E-coupons to all cars that I am their nearest gas station Type: Reverse NN query Time: Present Duration: Snapshot Query: Stationary Object: Moving What was the closest dist. between Taxi A & me yesterday? Type: Closest-point query Time: Past Duration: Snapshot Query: Moving Object: Moving

7. 7 May 2007 MDM Tutorial Tutorial Outline

8. 8 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Querying the Past Examples: Querying Along the Temporal Dimension: What was the location of a certain object from 7:00 AM to 10:00 AM yesterday? Querying Along the Spatial Dimension: Find all objects that were in a certain area at 7:00 AM yesterday Querying Along the Spatio-temporal Dimension: Find all objects that were close to each other from 7:00 AM to 8:00 AM yesterday Features: Large number of historical trajectories Persistent read-only data The ability to query the spatial and/or temporal dimensions

9. 9 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Indexing the Time Dimension Historical trajectories are represented by their three-dimensional Minimum Bounding Rectangle (MBR) 3D-R-tree is used to index the MBRs Technique simple and easy to implement Does not scale well Does not provide efficient query support Time

10. 10 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Multi-version Index Structures Maintain an R-tree for each time instance R-tree nodes that are not changed across consecutive time instances are linked together Timestamp 1 Timestamp 0 3D-R-tree A multi-version R-tree can be combined with a 3D-R-tree to support interval queries

11. 11 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Querying the Present Time is always NOW Example Queries: Find the number of objects in a certain area What is the current location of a certain object? Features: Continuously changing data Real-time query support is required Index structures should be update-tolerant Present data is always accessed through continuous queries

12. 12 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Updating Index Structures Traditional R-tree updates are top-down Updates translated to delete and insert transactions To support frequent updates: Updates can be managed in space without the need for deletion or insertions Bottom-up approaches through auxiliary index structures to locate the object identifier Hash based on OID

13. 13 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Update Memos Keep a memo with the R-tree The memo contains the recent updates to the existing R-tree The query answer returned from the R-tree should be passed through the memo The update memo is reflected to the R-tree once the relevant disk page is retrieved Update Memo Spatio-temporal Queries Raw answer set Final answer set

14. 14 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Querying the Future Examples: What will my nearest restaurant be after 30 minutes? Does my path conflict with any other cars for the next hour? Features: Predict the movement through a velocity vector Prediction could be valid for only a limited time horizon in the future

15. 15 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Duality Transformation A line (trajectory) in the two-dimensional space can be transformed into a point in another dual two-dimensional space Trajectory: x(t) = vt + a  Point: (v,a) All queries will need to be transformed into the dual space Rectangular queries will be represented as polygons

16. 16 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Time-Parameterized Data Structures A bounding rectangle with MBR & VBR is guaranteed to contain all its moving objects as long as they maintain their velocity vector High degree of overlap when the velocity vector is not updated The Time-parameterized R-tree (TPR-tree) consists of: Minimum bounding rectangles (MBR) Velocity bounding rectangles (VBR)

17. 17 May 2007 MDM Tutorial Location-aware Snapshot Query Processing Indexing Past, Present, and Future A unified index structure for both past, present, and future data Makes use of the partial-persistent R-tree for past data and the TPR-tree for current and future data Double Time-Parameterized Bounding rectangles are used to bound moving objects. Double TPBR has two components: Tail MBR that starts at the time of the last update and extends to infinity. The tail is a regular TPBR of the TPR-tree Head MBR to bound the finite historical trajectories. The head is an optimized TPBR Querying is similar to regular PPR-tree search with the exception of redefining the intersection function to accommodate for the double TPBR

18. 18 May 2007 MDM Tutorial Spatio-temporal Access Methods Red: Future Blue: Past Green: Present Brown: All R PPF -tree

19. 19 May 2007 MDM Tutorial Tutorial Outline

20. 20 May 2007 MDM Tutorial Continuous Queries Data Query Snapshot vs. Continuous Query Processing Traditional (Snapshot) Queries Data Query Answer Query Answer Data

21. 21 May 2007 MDM Tutorial Location-aware Continuous Query Processing Approaches Straightforward Approach Abstract the continuous queries to a series of snapshot queries evaluated periodically Result Validation Result Caching Result Prediction Incremental Evaluation

22. 22 May 2007 MDM Tutorial Location-aware Continuous Query Processing Result Validation Associate a validation condition with each query answer Valid time (t): The query answer is valid for the next t time units Valid region (R) The query answer is valid as long as you are within a region R It is challenging to maintain the computation of valid time/region for querying moving objects Once the associated validation condition expires, the query will be reevaluated

23. 23 May 2007 MDM Tutorial Location-aware Continuous Query Processing Caching the Result K-NN query Initially, retrieve more than k Range query Evaluate the query with a larger range Observation: Consecutive evaluations of a continuous query yield very similar results Idea: Upon evaluation of a continuous query, retrieve more data that can be used later How much we need to pre-compute? How do we do re-caching?

24. 24 May 2007 MDM Tutorial Location-aware Continuous Query Processing Predicting the Result Given a future trajectory movement, the query answer can be pre-computed in advance The trajectory movement is divided into N intervals, each with its own query answers A i The query is evaluated once (as a snapshot query). Yet, the answer is valid for longer time periods Once the trajectory changes, the query will be reevaluated Nearest-Neighbor Query

25. 25 May 2007 MDM Tutorial Location-aware Continuous Query Processing Incremental Evaluation The query is evaluated only once. Then, only the updates of the query answer are evaluated There are two types of updates. Positive and Negative updates + _ + Query Result Only the objects that cross the query boundary are taken into account Need to continuously listen for notifications that someone cross the query boundary

26. 26 May 2007 MDM Tutorial Tutorial Outline

27. 27 May 2007 MDM Tutorial How many cars in the highlighted area? Range Query Continuous Monitor the traffic in the red areas Range Query Continuous Alert me if there are less than 3 police cars within 5 miles Range Query Continuous Keep me updated by nearest 3 hospitals K-NN Query Continuous Make sure that the nearest 3 airplanes are FRIENDLY K-NN Query Continuous Location-aware Database Server Scalability of Location-aware Continuous Queries Motivation

28. 28 May 2007 MDM Tutorial Scalability of Location-aware Continuous Queries Main Concepts Continuous queries last for long times at the server side  While a query is active in the server, other queries will be submitted  Shared execution among multiple queries Should we index data OR queries?  Data and queries may be stationary or moving  Data and queries are of large size  Data and queries arrive to the system with very high rates  Treat data and queries similarly Queries are coming to data OR data are coming to queries?  Both data and queries are subjected to each other  Join data with queries

29. 29 May 2007 MDM Tutorial Scalability of Location-aware Continuous Queries Main Concepts (Cont.) Evaluating a large number of concurrent continuous spatio- temporal queries is abstracted as a spatio-temporal join between moving objects and moving queries Each query is a single thread ST Query 1... ST Query 2 ST Query N Data Objects D- Index Data Objects D- Index Data Objects D- Index Shared ST Join Q1Q1 Split Q2Q2 QNQN One thread for all continuous queries ST Queries... Data Objects D- Index Q- Index

30. 30 May 2007 MDM Tutorial Scalability of Location-aware Continuous Queries Location-aware Centralized Database Systems Centralized index structures Index the queries instead of data Moving Objects (Stationary) ST Queries in an R-tree index structure Valid only for stationary queries

31. 31 May 2007 MDM Tutorial Scalability of Location-aware Continuous Queries Location-aware Centralized Database Systems (Cont.) To accommodate for the continuous movement of both data and queries: Concurrent continuous queries share a grid structure Moving objects are hashed to the same grid structure as queries The spatio-temporal join is done by overlaying the two grid structures

32. 32 May 2007 MDM Tutorial Scalability of Location-aware Continuous Queries Location-aware Distributed Database Systems Motivation: Centralized location-aware servers will have a bottleneck at the server side Assumption: Moving objects have devices with the capability of doing some computations Idea: Server will ship some of its processing to the moving objects Server will act as a mediator among moving objects Implementation: Moving objects should welcome cooperation in such environments

33. 33 May 2007 MDM Tutorial Scalability of Location-aware Continuous Queries Location-aware Distributed Database Systems (Cont.) Each moving object O maintains a list of the queries that O may be part of their answer It is the responsibility of the moving object O to report that O becomes part of the answer of a certain query Once a query updates its location, it sends the new location to the server, which will propagate the new location to the interested users The server is responsible in determining which objects will be interested in which queries

34. 34 May 2007 MDM Tutorial Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems Motivation: Very high arrival rates that are beyond the system capability to store Idea: Only store those objects that are likely to produce query results, i.e., only significant objects are stored, all other data are simply dropped Significant objects: A moving object O is significant if there is at least one query that is interested in O’s location Challenge: Discovering that an object becomes insignificant

35. 35 May 2007 MDM Tutorial Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont.) Cache Area Only significant objects are stored in-memory An object is considered significant if it is either in the query area or the cache area Due to the query and object movements, a stored object may become insignificant at any time Larger cache area indicates more storage overhead and more accurate answer

36. 36 May 2007 MDM Tutorial The first k objects are considered an initial answer K-NN query is reduced to a circular range query K = 3 However, the query area may shrink or grow Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont.)

37. 37 May 2007 MDM Tutorial Shared Spatio- temporal Join. Q1Q1 +/- Split. Q2Q2 +/-. QNQN +/- Stream of Moving Objects Stream of Moving Queries Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont.) Each query is a single thread One thread for all continuous queries Shared Operator Shared Memory Buffer among all C. Queries Stationary Range +/-... +/-... Q1Q1 Q2Q2 QNQN Stream of Moving Objects Moving kNN Moving Range

38. 38 May 2007 MDM Tutorial Query Load Shedding Reduce the cache area Possibly reduce the query area Immediately drop insignificant tuples Intuitive and simple to implement Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont.) Object Load Shedding Objects that satisfy less than k queries are insignificant Lazily drop insignificant tuples Challenge I: How to choose k? Challenge II: How to provide a lower bound for the query accuracy? 1 4 5 6 2 3 7 K = 2

39. 39 May 2007 MDM Tutorial Tutorial Outline

40. 40 May 2007 MDM Tutorial Location-aware Query Optimization Spatio-temporal pipelinable query operators Range queries Nearest-neighbor queries Selectivity estimation for spatio-temporal queries/operators Spatio-temporal histograms Sampling Adaptive query optimization for continuous queries

41. 41 May 2007 MDM Tutorial Continuously report the trucks in this area Scalar functions (Stored procedure) Database Engine Only produce objects in the areas of interest The performance of scalar functions is limited Spatio-temporal Query Operators Existing Approaches are Built on Top of DBMS (at the Application Level) Database Engine Spatio-temporal Operators SELECT O. ID FROM Objects O WHERE O.type = truck INSIDE Area A

42. 42 May 2007 MDM Tutorial SELECT M.ObjectID FROM MovingObjects M, AvisCars A WHERE M.ID = A.ID INSIDE RegionR INSIDE JOIN +/- AvisCars +/- Moving Objects Spatio-temporal Query Operators “Continuously report the Avis cars in a certain area” Scalar Function INSIDE JOIN +/- AvisCars Moving Objects Scalar Function Spatio-temporal Operators

43. 43 May 2007 MDM Tutorial Spatio-temporal Selectivity Estimation Estimating the selectivity of spatio-temporal operators is crucial in determining the best plan for spatio-temporal queries SELECT ObjectID FROM MovingObjects M WHERE Type = Truck INSIDE Region R SELECT INSIDE SELECT INSIDE

44. 44 May 2007 MDM Tutorial Spatio-temporal Histograms Moving objects in D-dimensional space are mapped to 2D- dimensional histogram buckets x t x t

45. 45 May 2007 MDM Tutorial Spatio-temporal Histograms with Query Feedback Estimating the selectivity of spatio-temporal operators is crucial in determining the best plan for spatio-temporal queries Query Optimizer Spatio-temporal Histogram Query Executer Query Query plan Feedback 6.98% 6.01% 6.25% 10% Q1

46. 46 May 2007 MDM Tutorial Adaptive Query Optimization SELECT ObjectID FROM MovingObjects M WHERE Type = Truck INSIDE Region R Continuous queries last for long time (hours, days, weeks)  Environment variables are likely to change  The initial decision for building a query plan may not be valid after a while Need continuous optimization and ability to change the query plan:  Training period: Spatio-temporal histogram, periodicity mining  Online detection of changes SELECT Moving Objects INSIDE SELECT Moving Objects INSIDE

47. 47 May 2007 MDM Tutorial Tutorial Outline

48. 48 May 2007 MDM Tutorial Uncertainty in Moving Objects Location information from moving objects is inherently inaccurate Sources of uncertainty: Sampling. A moving object sends its location information once every t time units. Within any two consecutive locations, we have no clue about the object’s exact location Reading accuracy. Location-aware devices do not provide the exact location Object movement and network delay. By the time that a certain reading is received by the server, the moving object has already changed its location

49. 49 May 2007 MDM Tutorial Uncertainty in Moving Objects Historical data (Trajectories) Current data T0+Є0T0+Є0 T0+Є1T0+Є1 T0+Є2T0+Є2 T0T0 T1T1

50. 50 May 2007 MDM Tutorial Uncertainty in Moving Objects Error in Query Answer Range Queries Nearest Neighbor Queries

51. 51 May 2007 MDM Tutorial Representing Uncertain Data using Ellipses Given : Start point End point Maximum possible speed  Maximum traveling distance S If S is greater than the distance between the two end points, then the moving object may have deviated from the given route

52. 52 May 2007 MDM Tutorial Representing Uncertain Data using Cylinders Given: Start and end points Constraint: An object would report its location only if it is deviated by a certain distance r from the predicted trajectory r

53. 53 May 2007 MDM Tutorial Representing Uncertain Data in Road Networks Given: Start and end points Constraints : Deviation threshold r Speed threshold v

54. 54 May 2007 MDM Tutorial Querying Uncertain Data Uncertain Keywords KEYWORDS: Probability: possibly, definitely Temporal: sometimes, always Spatial: somewhere, everywhere Examples: What are the objects that are possibly sometimes within area R at time interval T? What are the objects that definitely passed through a certain region? Retrieve all the objects that are always inside a certain region Retrieve all the objects that are sometimes definitely inside region R

55. 55 May 2007 MDM Tutorial Querying Uncertain Data Uncertain Keywords (Cont.) Object O is definitely always in Q 1 Q1Q1 Q2Q2 Q3Q3 Q4Q4 O Object O is possibly always in Q 2 Object O is definitely sometimes in Q 3 Object O is possibly sometimes in Q 4

56. 56 May 2007 MDM Tutorial Querying Uncertain Data Probabilistic Queries With each query answer, associate a probability that this answer is true The answer set of a query Q is represented as a set of tuples where ID is the tuple identifier and p is the probability that the object ID belongs to the answer set of Q Assumptions: Objects can lie anywhere uniformly within their uncertainty region

57. 57 May 2007 MDM Tutorial Querying Uncertain Data Probabilistic Range Queries Query Answer: (B, 50%) (C, 90%) D E (F, 30%) A C B E D F

58. 58 May 2007 MDM Tutorial Query Answer (k=1): (C, p 1 ) (D, p 2 ) (E, p 3 ) A C B E D F Querying Uncertain Data Probabilistic Nearest-Neighbor Queries

59. 59 May 2007 MDM Tutorial Tutorial Outline

60. 60 May 2007 MDM Tutorial Case Study I DOMINO DOMINO: Databases fOr MovINg Objects tracking Built on top of database management systems using a three- layers approach; the DBMS layer, the GIS layer, and the DOMINO layer Utilize dynamic attributes for future predicted locations Manage uncertainty that is inherent in future motion plans Support various location models: Exact point location An area in which the object is located in An approximate motion plan A complete motion plan

61. 61 May 2007 MDM Tutorial DOMINO Architecture Object-Relational DBMS Arc-View GIS DOMINO Informix/Oracle Stores the information about each moving object, including each object’s plan of motion Provide capabilities and user interface primitives for storing, querying, and manipulating geographic information Provide temporal capabilities, uncertainty management, and location prediction

62. 62 May 2007 MDM Tutorial Uncertainty Management in DOMINO Uncertainty operators are implemented as user- defined functions (UDFs) in Oracle Uncertainty operators: E.g., Always_Definitely_Inside, Sometime_Definitely_Inside, Possibly_Always_Inside, Possibly_Sometime_Inside Example: SELECT oid FROM MovingObjects WHERE Possibly_Always_Inside (trajectory, region, time interval)

63. 63 May 2007 MDM Tutorial Case Study II SECONDO SECONDO: An Extensible DBMS Architecture and Prototype A generic database system frame that can be filled with implementation of various data models (relational, object- oriented, or XML) and data types (spatial data, moving objects) A database is a set of SECONDO objects of the form (name, type, value), where type is one of the implemented algebras About 20 implemented algebras, e.g., standard algebra, relational algebra, R-Tree algebra, and spatial algebra Query optimizer includes optimization of conjunctive queries, selectivity estimation, and implementation of an SQL-like query language

64. 64 May 2007 MDM Tutorial SECONDO Architecture SECONDO Kernel Berkeley DB (C++) Built on top of Berkeley DB. Includes specific data models, algebra modules, and query processors over the implemented algebra. The core functionality is the optimization of conjunctive queries, i.e., producing an efficient query plan Generic GUI independent of data models. The interface includes command prompt and is extensible by a set of different viewers Optimizer PROLOG GUI Java On top of the query optimizer, there is a SQL-like language in a notation adopted to PROLOG

65. 65 May 2007 MDM Tutorial Case Study III The PLACE Server PLACE: Pervasive Location-Aware Computing Environments Scalable execution of continuous queries over spatio-temporal data streams Shared execution among concurrent continuous queries Built inside a database engine Incremental evaluation of continuous queries Spatio-temporal query operators

66. 66 May 2007 MDM Tutorial Storage Engine Relational Operators Query Processor Query Parser Continuous / Moving Queries INSIDE, kNN Negative updates Stream of Moving Objects/Queries INSIDE, KNN, operators Scalable shared operators PLACE Architecture DBMS PLACE

67. 67 May 2007 MDM Tutorial PLACE Architecture A Query Processor for Real-time Spatio-temporal Data Streams A Query Processing Engine for Data Streams PREDATOR Abstract data types Storage engine Query processor SQL Language Continuous time-based Sliding Window Queries  Continuous Predicate-based Window Queries  Moving Queries Stream data types Stream of Moving Objects/Queries Stream_Scan Operator W-Expire Operator Negative Tuples INSIDE Operator kNN Operator WINDOW window_clause kNN knn_clause PLACE NILE INSIDE inside_clause

68. 68 May 2007 MDM Tutorial Extended SQL Syntax inside_clause: Stationary query: (x 1,y 1,x 2,y 2 ) Moving query: (‘M’,OID, width, length) knn_clause: Stationary query: (k,x,y) Moving query: (‘M’, OID, k)

69. 69 May 2007 MDM Tutorial Tutorial Outline

70. 70 May 2007 MDM Tutorial Open Research Issues Location Privacy “New technologies can pinpoint your location at any time and place. They promise safety and convenience but threaten privacy and security” Cover story, IEEE Spectrum, July 2003 YOU ARE TRACKED… !!!!

71. 71 May 2007 MDM Tutorial Open Research Issues Spatio-temporal Data Mining Mining the history  Predicting the future Online outlier detection for moving objects Suspicious movement in video surveillance Analysis of tsunami, hurricanes, or earthquakes Phenomena detection and tracking

72. 72 May 2007 MDM Tutorial Open Research Issues Reducing the Gap between ST Databases and DBMSs/DSMSs What do Spatio-temporal researchers offer? 50+ spatial index structure, 30+ spatio-temporal indexing structure Wide variety of spatio-temporal query processing techniques What do DBMS designers want? Little disturbance to their code Large number of customers The result is: DB2 and SQLServer do not support the R-tree (and may not be willing to) Oracle supports only R-tree and Quadtree Can we reduce this gap? YES. Think in the minimal additions to the engine Example I: B-tree with SFC Example II: GiST and SP-GiST Example III: Add-in query operators

73. 73 May 2007 MDM Tutorial References Overview Papers: 1. Ouri Wolfson, Bo Xu, Sam Chamberlain, and Liqin Jiang. Moving Objects Databases: Issues and Solutions. In Proceeding of the International Conference on Scientific and Statistical Database Management, SSDBM, pages 111-122, Capri, Italy, July 1998. 2. Mohamed F. Mokbel, Walid G. Aref, Susanne E. Hambrusch, and Sunil Prabhakar. Towards Scalable Location-aware Services: Requirements and Research Issues. In Proceeding of the ACM Symposium on Advances in Geographic Information Systems, ACM GIS, pages 110-117, New Orleans, LA, November 2003. 3. Christian S. Jensen. Database Aspects of Location-based Services. In Location-based Services, pages 115-148. Morgan Kaufmann, 2004. 4. Dik Lun Lee, Manli Zhu, and Haibo Hu. When Location-based Services Meet Databases. Mobile Information Systems, 1(2):81-90, 2005. Spatio-temporal Access Methods: 5. Mohamed F. Mokbel, Thanaa M. Ghanem, and Walid G. Aref. Spatio-temporal Access Methods. IEEE Data Engineering Bulletin, 26(2):40-49, June 2003. 6. X. Xu, Jiawei Han, and W. Lu. RT-Tree: An Improved R-Tree Indexing Structure for Temporal Spatial Databases. In Proceeding of the International Symposium on Spatial Data Handling, SSDH, pages 1040-1049, Zurich, Switzerland, July 1990. 7. Yannis Theodoridis, Michael Vazirgiannis, and Timos Sellis. Spatio-temporal Indexing for Large Multimedia Applications. In Proceeding of the IEEE Conference on Multimedia Computing and Systems, ICMCS, pages 441-448, Hiroshima, Japan, June 1996. 8. Mario A. Nascimento and Jeerson R. O. Silva. Towards Historical R-Trees. In Proceeding of the ACM Sympo-sium on Applied Computing, SAC, pages 235-240, Atlanta, GA, February 1998. 9. Jamel Tayeb, Ozgur Ulusoy, and Ouri Wolfson. A Quadtree-Based Dynamic Attribute Indexing Method. The Computer Journal, 41(3):185-200, 1998.

74. 74 May 2007 MDM Tutorial References Spatio-temporal Access Methods (Cont.): 10. Dieter Pfoser, Christian S. Jensen, and Yannis Theodoridis. Novel Approaches in Query Processing for Moving Object Trajectories. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 395-406, Cairo, Egypt, September 2000. 11. Yufei Tao and Dimitris Papadias. MV3R-Tree: A Spatio-temporal Access Method for Timestamp and Interval Queries. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 431-440, Roma, Italy, September 2001. 12. Yufei Tao and Dimitris Papadias. Efficient Historical R-Trees. In Proceeding of the International Conference on Scientific and Statistical Database Management, SSDBM, pages 223-232, Fairfax, VA, July 2001. 13. George Kollios, Vassilis J. Tsotras, Dimitrios Gunopulos, Alex Delis, and Marios Hadjieleftheriou. Indexing Animated Objects Using Spatiotemporal Access Methods. IEEE Transactions on Knowledge and Data Engineering, TKDE, 13(5):758-777, 2001. 14. Marios Hadjieleftheriou, George Kollios, Vassilis J. Tsotras, and Dimitrios Gunopulos. Efficient Indexing of Spatiotemporal Objects. In Proceeding of the International Conference on Extending Database Technology, EDBT, pages 251-268, Prague, Czech Republic, March 2002. 15. Zhexuan Song and Nick Roussopoulos. SEB-Tree: An Approach to Index Continuously Moving Objects. In Proceeding of the International Conference on Mobile Data Management, MDM, pages 340-344, Melbourne, Australia, January 2003. 16. Elias Frentzos. Indexing Objects Moving on Fixed Networks. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 289-305, Santorini Island, Greece, July 2003. 17. V. Prasad Chakka, Adam Everspaugh, and Jignesh M. Patel. Indexing Large Trajectory Data Sets with SETI. In Proceeding of the International Conference on Innovative Data Systems Research, CIDR, Asilomar, CA, January 2003. 18. Yuhan Cai and Raymond T. Ng. Indexing Spatio-temporal Trajectories with Chebyshev Polynomials. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 599-610, Paris, France, June 2004.

75. 75 May 2007 MDM Tutorial References Spatio-temporal Access Methods (Cont.): 19. Dieter Pfoser and Christian S. Jensen. Trajectory Indexing Using Movement Constraints. GeoInformatica, 9(2):93-115, June 2005. 20. Jinfeng Ni and Chinya V. Ravishankar. PA-Tree: A Parametric Indexing Scheme for Spatio-temporal Trajectories. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 254-272, Angra dos Reis, Brazil, August 2005. 21. Mario A. Nascimento, Jeerson R. O. Silva, and Yannis Theodoridis. Evaluation of Access Structures for Discretely Moving Points. In Proceeding of the International Workshop on Spatio-temporal Database Management, STDBM, pages 171-188, Edinburgh, UK, September 1999. 22. Zhexuan Song and Nick Roussopoulos. Hashing Moving Objects. In Proceeding of the International Conference on Mobile Data Management, MDM, pages 161-172, Hong Kong, January 2001. 23. Dongseop Kwon, Sangjun Lee, and Sukho Lee. Indexing the Current Positions of Moving Objects Using the Lazy Update R-Tree. In Proceeding of the International Conference on Mobile Data Management, MDM, pages 113-120, Singapore, January 2002. 24. Mahdi Abdelguer, Julie Givaudan, Kevin Shaw, and Roy Ladner. The 2-3 TR-Tree, A Trajectory- Oriented Index Structure for Fully Evolving Valid-time Spatio-temporal Datasets. In Proceeding of the ACM Symposium on Advances in Geographic Information Systems, ACM GIS, pages 29-34, McLean, VA, November 2002. 25. Mong-Li Lee, Wynne Hsu, Christian S. Jensen, Bin Cui, and Keng Lik Teo. Supporting Frequent Updates in R-Trees: A Bottom-Up Approach. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 608-619, Berlin, Germany, September 2003. 26. Yuni Xia and Sunil Prabhakar. Q+R-Tree: Efficient Indexing for Moving Object Database. In Proceeding of the International Conference on Database Systems for Advanced Applications, DASFAA, pages 175- 182, Kyoto, Japan, March 2003. 27. Christian S. Jensen, Dan Lin, and Beng Chin Ooi. Query and Update Efficient B+-Tree Based Indexing of Moving Objects. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 768-779, Toronto, Canada, August 2004.

76. 76 May 2007 MDM Tutorial References Spatio-temporal Access Methods (Cont.): 28. Reynold Cheng, Yuni Xia, Sunil Prabhakar, and Rahul Shah. Change Tolerant Indexing for Constantly Evolving Data. In Proceeding of the International Conference on Data Engineering, ICDE, pages 391- 402, Tokyo, Japan, April 2005. 29. Bin Lin and Jianwen Su. Handling Frequent Updates of Moving Objects. In Proceeding of the International Conference on Information and Knowledge Management, CIKM, pages 493-500, Bremen, Germany, October 2005. 30. Xiaopeng Xiong, Mohamed F. Mokbel, and Walid G. Aref. LUGrid: Update-tolerant Grid-based Indexing for Moving Objects. In Proceeding of the International Conference on Mobile Data Management, MDM, Nara, Japan, May 2006. 31. Xiaopeng Xiong and Walid G. Aref. R-Trees with Update Memos. In Proceeding of the International Conference on Data Engineering, ICDE, Atlanta, GA, April 2006. 32. George Kollios, Dimitrios Gunopulos, and Vassilis J. Tsotras. On Indexing Mobile Objects. In Proceeding of the ACM Symposium on Principles of Database Systems, PODS, pages 261-272, Philadelphia. PA, May 1999. 33. Simonas Saltenis, Christian S. Jensen, Scott T. Leutenegger, and Mario A. Lopez. Indexing the Positions of Continuously Moving Objects. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 331-342, Dallas, TX, May 2000. 34. Pankaj K. Agarwal, Lars Arge, and Je Erickson. Indexing Moving Points. In Proceeding of the ACM Symposium on Principles of Database Systems, PODS, pages 175-186, Dallas, TX, May 2000. 35. Mengchu Cai and Peter Revesz. Parametric R-Tree: An Index Structure for Moving Objects. In International Conference on Management of Data, COMAD, 57-64, Pune, India, December 2000. 36. Hae Don Chon, Divyakant Agrawal, and Amr El Abbadi. Storage and Retrieval of Moving Objects. In International Conference on Mobile Data Management, MDM, 173-184, Hong Kong, January 2001. 37. Kriengkrai Porkaew, Iosif Lazaridis, and Sharad Mehrotra. Querying Mobile Objects in Spatio-temporal Databases. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 59-78, Redondo Beach, CA, July 2001.

77. 77 May 2007 MDM Tutorial References Spatio-temporal Access Methods (Cont.): 38. Cecilia Magdalena Procopiuc, Pankaj K. Agarwal, and Sariel Har-Peled. STAR-Tree: An Efficient Self-Adjusting Index for Moving Objects. In Proceeding of the International Workshop on Algorithm Engineering and Experimentation, ALENEX, pages 178-193, San Francisco, CA, January 2002. 39. Simonas Saltenis and Christian S. Jensen. Indexing of Moving Objects for Location-based Services. In Proceeding of the International Conference on Data Engineering, ICDE, pages 463- 472, San Jose, CA, February 2002. 40. Khaled M. Elbassioni and Ibrahim Kamel Amr Elmasry. An Efficient Indexing Scheme for Multi- dimensional Moving Objects. In Proceeding of the International Conference on Database Theory, ICDT, pages 425-439, Siena, Italy, January 2003. 41. Yufei Tao, Dimitris Papadias, and Jimeng Sun. The TPR*-Tree: An Optimized Spatio-temporal Access Method for Predictive Queries. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 129-140, Berlin, Germany, September 2003. 42. Jignesh M. Patel, Yun Chen, and V. Prasad Chakka. STRIPES: An Efficient Index for Predicted Trajectories. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 637-646, Paris, France, June 2004. 43. George Kollios, Dimitris Papadopoulos, Dimitrios Gunopulos, and Vassilis J. Tsotras. Indexing Mobile Objects Using Dual Transformations. VLDB Journal, 14(2):238-256, April 2005. 44. Dan Lin, Christian S. Jensen, Beng Chin Ooi, and Simonas Saltenis. Efficient Indexing of the Historical, Present, and Future Positions of Moving Objects. In Proceeding of the International Conference on Mobile Data Management, MDM, pages 59-66, Ayia Napa, Cyprus, May 2005. 45. Zhao-Hong Liu, Xiao-Li Liu, Jun-Wei Ge, and Hae-Young Bae. Indexing Large Moving Objects from Past to Future with PCFI+-Index. In Proceeding of the International Conference on Management of Data, COMAD, pages 131-137, January 2005. 46. Mindaugas Pelanis, Simonas Saltenis, and Christian Jensen. Indexing the Past, Present, and Anticipated Future Positions of Moving Objects. ACM Transactions of Database Systems, TODS, 31(1), 255-298, March 2006.

78. 78 May 2007 MDM Tutorial References Location-aware Snapshot Query Processing: 47. Ouri Wolfson, Bo Xu, and Sam Chamberlain. Location Prediction and Queries for Tracking Moving Objects. In Proceeding of the International Conference on Data Engineering, ICDE, pages 687-688, San Diego, CA, February 2000. 48. Rimantas Benetis, Christian S. Jensen, Gytis Karciauskas, and Simonas Saltenis. Nearest Neighbor and Reverse Nearest Neighbor Queries for Moving Objects. In Proceeding of the International Database Engineering and Applications Symposium, IDEAS, pages 44-53, Alberta, Canada, July 2002. 49. Yufei Tao and Dimitris Papadias. Time Parameterized Queries in Spatio-temporal Databases. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 334-345, Madison, WI, June 2002. 50. Yufei Tao and Dimitris Papadias. Spatial Queries in Dynamic Environments. ACM Transactions on Database Systems, TODS, 28(2):101-139, June 2003. 51. Yufei Tao, Jimeng Sun, and Dimitris Papadias. Analysis of Predictive Spatio-temporal Queries. ACM Transactions on Database Systems, TODS, 28(4):295-336, December 2003. 52. Dimitris Papadias, Qiongmao Shen, Yufei Tao, and Kyriakos Mouratidis. Group Nearest Neighbor Queries. In Proceeding of the International Conference on Data Engineering, ICDE, pages 301{312, Boston, MA, March 2004. 53. Jimeng Sun, Dimitris Papadias, Yufei Tao, and Bin Liu. Querying about the Past, the Present and the Future in Spatio-temporal Databases. In Proceeding of the International Conference on Data Engineering, ICDE, pages 202-213, Boston, MA, March 2004.

79. 79 May 2007 MDM Tutorial References Location-aware Snapshot Query Processing (Cont.): 54. Bin Lin and Jianwen Su. Shapes Based Trajectory Queries for Moving Objects. In Proceeding of the ACM Symposium on Advances in Geographic Information Systems, ACM GIS, pages 21-30, Bremen, Germany, November 2005. 55. Panfeng Zhou, Donghui Zhang, Betty Salzberg, Gene Cooperman, and George Kollios. Close Pair Queries in Moving Object Databases. In Proceeding of the ACM Symposium on Advances in Geographic Information Systems, ACM GIS, pages 2-11, Bremen, Germany, November 2005. 56. Yufei Tao and Dimitris Papadias. Historical Spatio-temporal Aggregation. ACM Transactions on Information Systems, TOIS, 23(1):61-102, January 2005. 57. Man Lung Yiu, Nikos Mamoulis, and Dimitris Papadias. Aggregate Nearest Neighbor Queries in Road Networks. IEEE Transactions on Knowledge and Data Engineering, TKDE, 17(6):820-833, June 2005. 58. Elias Frentzos, Kostas Gratsias, Nikos Pelekis, and Yannis Theodoridis. Nearest Neighbor Search on Moving Object Trajectories. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 328-345, Angra dos Reis, Brazil, August 2005. 59. Hyung-Ju Cho and Chin-Wan Chung. An Efficient and Scalable Approach to CNN Queries in a Road Network. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 865-876, Trondheim, Norway, August 2005. 60. Marios Hadjieleftheriou, George Kollios, Petko Bakalov, and Vassilis J. Tsotras. Complex Spatio- temporal Pattern Queries. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 877-888, Trondheim, Norway, August 2005. 61. Lei Chen, M. Tamer Ozsu, and Vincent Oria. Robust and Fast Similarity Search for Moving Object Trajectories. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 491-502, Baltimore, MD, June 2005.

80. 80 May 2007 MDM Tutorial References Location-aware Continuous Query Processing: 62. Baihua Zheng and Dik Lun Lee. Semantic Caching in Location-Dependent Query Processing. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 97-116, Redondo Beach, CA, July 2001. 63. Zhexuan Song and Nick Roussopoulos. K-Nearest Neighbor Search for Moving Query Point. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 79-96, Redondo Beach, CA, July 2001. 64. Iosif Lazaridis, Kriengkrai Porkaew, and Sharad Mehrotra. Dynamic Queries over Mobile Objects. In Proceeding of the International Conference on Extending Database Technology, EDBT, pages 269-286, Prague, Czech Republic, March 2002. 65. Sunil Prabhakar, Yuni Xia, Dmitri V. Kalashnikov, Walid G. Aref, and Susanne E. Hambrusch. Query Indexing and Velocity Constrained Indexing: Scalable Techniques for Continuous Queries on Moving Objects. IEEE Transactions on Computers, 51(10):1124-1140, October 2002. 66. Yufei Tao, Dimitris Papadias, and Qiongmao Shen. Continuous Nearest Neighbor Search. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 287-298, Hong Kong, August 2002. 67. Marios Hadjieleftheriou, George Kollios, Dimitrios Gunopulos, and Vassilis J. Tsotras. On-Line Discovery of Dense Areas in Spatio-temporal Databases. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 306-324, Santorini Island, Greece, July 2003. 68. Glenn S. Iwerks, Hanan Samet, and Ken Smith. Continuous K-Nearest Neighbor Queries for Continuously Moving Points with Updates. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 512-523, Berlin, Germany, September 2003.

81. 81 May 2007 MDM Tutorial References Location-aware Continuous Query Processing (Cont.): 69. Jun Zhang, Manli Zhu, Dimitris Papadias, Yufei Tao, and Dik Lun Lee. Location-based Spatial Queries. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 443-454, San Diego, CA, June 2003. 70. Bugra Gedik, Kun-Lung Wu, Philip S. Yu, and Ling Liu. Motion Adaptive Indexing for Moving Continual Queries over Moving Objects. In Proceeding of the International Conference on Information and Knowledge Management, CIKM, pages 427-436, Washington, DC, November 2004. 71. Mohamed F. Mokbel, Xiaopeng Xiong, and Walid G. Aref. SINA: Scalable Incremental Processing of Continuous Queries in Spatio-temporal Databases. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 623-634, Paris, France, June 2004. 72. Ying Cai, Kien A. Hua, and Guohong Cao. Processing Range-Monitoring Queries on Heterogeneous Mobile Objects. In Proceeding of the International Conference on Mobile Data Management, MDM, page January, Berkeley, CA, 2004. 73. Bugra Gedik and Ling Liu. MobiEyes: Distributed Processing of Continuously Moving Queries on Moving Objects in a Mobile System. In Proceeding of the International Conference on Extending Database Technology, EDBT, Crete, Greece, March 2004. 74. Xiaopeng Xiong, Mohamed F. Mokbel, Walid G. Aref, Susanne Hambrusch, and Sunil Prabhakar. Scalable Spatio-temporal Continuous Query Processing for Location-aware Services. In Proceeding of the International Conference on Scientific and Statistical Database Management, SSDBM, pages 317-328, Santorini Island, Greece, June 2004. 75. Haibo Hu, Jianliang Xu, and Dik Lun Lee. A Generic Framework for Monitoring Continuous Spatial Queries over Moving Objects. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 479-490, Baltimore, MD, June 2005. 76. Kyriakos Mouratidis, Dimitris Papadias, and Marios Hadjieleftheriou. Conceptual Partitioning: An Efficient Method for Continuous Nearest Neighbor Monitoring. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 634-645, Baltimore, MD, June 2005.

82. 82 May 2007 MDM Tutorial References Location-aware Continuous Query Processing (cont.): 77. Mohammad R. Kolahdouzan and Cyrus Shahabi. Alternative Solutions for Continuous K Nearest Neighbor Queries in Spatial Network Databases. GeoInformatica, 9(4):321-341, December 2005. 78. Xiaopeng Xiong, Mohamed F. Mokbel, and Walid G. Aref. SEA-CNN: Scalable Processing of Continuous K-Nearest Neighbor Queries in Spatio-temporal Databases. In Proceeding of the International Conference on Data Engineering, ICDE, pages 643-654, Tokyo, Japan, April 2005. 79. Donghui Zhang, Dimitrios Gunopulos, Vassilis J. Tsotras, and Bernhard Seeger. Temporal and Spatio-temporal Aggregations over Data Streams Using Multiple Time Granularities. Journal of Information Systems, 28(1-2):61-84, March 2003. 80. Xuegang Huang and Christian S. Jensen. Towards A Streams-Based Framework for Defining Location-based Queries. In Proceedings of the International Workshop on Spatio-temporal Database Management, STDBM, pages 73-80, Toronto, Canada, August 2004. 81. Yufei Tao, George Kollios, Jerey Considine, Feifei Li, and Dimitris Papadias. Spatio-temporal Aggregation Using Sketches. In Proceeding of the International Conference on Data Engineering, ICDE, pages 214-226, Boston, MA, March 2004. 82. Mohamed F. Mokbel and Walid G. Aref. SOLE: Scalable Online Execution of Continuous Queries on Spatiotemporal Data Streams. Technical Report TR CSD-05-016, submitted for a journal publication, Purdue University Department of Computer Science, July 2005. 83. Mohamed F. Mokbel and Walid G. Aref. GPAC: Generic and Progressive Processing of Mobile Queries over Mobile Data. In Proceeding of the International Conference on Mobile Data Management, MDM, pages 155-163, Ayia Napa, Cyprus, May 2005. 84. Rimma Nehme and Elke Rundensteiner. SCUBA: Scalable Cluster-Based Algorithm for Evaluating Continuous Spatio-Temporal Queries on Moving Objects. In Proceeding of the International Conference on Extending Database Technology, EDBT, Munich, Germany, March 2006.

83. 83 May 2007 MDM Tutorial References Location-aware Query Optimization: 85. Yong-Jin Choi and Chin-Wan Chung. Selectivity Estimation for Spatio-temporal Queries to Moving Objects. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 440-451, Madison, WI, June 2002. 86. Yufei Tao, Jimeng Sun, and Dimitris Papadias. Selectivity Estimation for Predictive Spatio-temporal Queries. In Proceeding of the International Conference on Data Engineering, ICDE, pages 417-428, Bangalore, India, March 2003. 87. Marios Hadjieleftheriou, George Kollios, and Vassilis J. Tsotras. Performance Evaluation of Spatio- temporal Selectivity Estimation Techniques. In Proceeding of the International Conference on Scientific and Statistical Database Management, SSDBM, pages 202-211, Cambridge, MA, July 2003. 88. Qing Zhang and Xuemin Lin. Clustering Moving Objects for Spatio-temporal Selectivity Estimation. In Proceedings of the Australasian Database Conference, pages 123-130, Dunedin, New Zealand, January 2004. 89. Yufei Tao, Dimitris Papadias, Jian Zhai, and Qing Li. Venn Sampling: A Novel Prediction Technique for Moving Objects. In Proceeding of the International Conference on Data Engineering, ICDE, pages 680-691, Tokyo, Japan, April 2005. 90. Hicham G. Elmongui, Mohamed F. Mokbel, and Walid G. Aref. Spatio-temporal Histograms. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 19-36, Angra dos Reis, Brazil, August 2005. 91. Slobodan Rasetic, Jorg Sander, James Elding, and Mario A. Nascimento. A Trajectory Splitting Model for Efficient Spatio-temporal Indexing. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 934-945, Trondheim, Norway, August 2005.

84. 84 May 2007 MDM Tutorial References Uncertainty and Probabilistic Queries: 92. Dieter Pfoser and Christian S. Jensen. Capturing the Uncertainty of Moving-Object Representations. In Proceeding of the International Symposium on Advances in Spatial Databases, SSD, pages 111{132, Hong Kong, July 1999. 93. Reynold Cheng, Dmitri V. Kalashnikov, and Sunil Prabhakar. Evaluating Probabilistic Queries over Imprecise Data. In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 551{562, San Diego, CA, June 2003. 94. Reynold Cheng, Dmitri V. Kalashnikov, and Sunil Prabhakar. Querying Imprecise Data in Moving Object Environments. IEEE Transactions on Knowledge and Data Engineering, TKDE, 16(9):1112{1127, September 2004. 95. Jinfeng Ni, Chinya V. Ravishankar, and Bir Bhanu. Probabilistic Spatial Database Operations. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 140{158, Santorini Island, Greece, July 2003. 96. Goce Trajcevski, Ouri Wolfson, Fengli Zhang, and Sam Chamberlain. The Geometry of Uncertainty in Moving Objects Databases. In Proceeding of the International Conference on Extending Database Technology, EDBT, pages 233{250, Prague, Czech Republic, March 2002. 97. Ouri Wolfson and Huabei Yin. Accuracy and Resource Concumption in Tracking and Location Prediction. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 325{343, Santorini Island, Greece, July 2003. 98. Goce Trajcevski, OuriWolfson, Klaus Hinrichs, and Sam Chamberlain. Managing Uncertainty in Moving Objects Databases. ACM Transactions on Database Systems, TODS, 29(3):463{507, September 2004. 99. Victor Teixeira de Almeida and Ralf Hartmut Guting. Supporting Uncertainty in Moving Objects in Network Databases. In Proceeding of the ACM Symposium on Advances in Geographic Information Systems, ACM GIS, pages 31{40, Bremen, Germany, November 2005. 100. Dieter Pfoser, Nectaria Tryfona, and Christian S. Jensen. Indeterminacy and Spatiotemporal Data: Basic Denitions and Case Study. GeoInformatica, 9(3):211{236, September 2005. 101. Xiangyuan Dai, Man Lung Yiu, Nikos Mamoulis, Yufei Tao, and Michail Vaitis. Probabilistic Spatial Queries on Existentially Uncertain Data. In Proceeding of the International Symposium on Advances in Spatial and Temporal Databases, SSTD, pages 400{417, Angra dos Reis, Brazil, August 2005.

85. 85 May 2007 MDM Tutorial References Case Studies: 102. Mohamed F. Mokbel, Xiaopeng Xiong, Walid G. Aref, Susanne Hambrusch, Sunil Prabhakar, and Moustafa Hammad. PLACE: A Query Processor for Handling Real-time Spatio-temporal Data Streams (Demo). In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 1377{1380, Toronto, Canada, August 2004. 103. Mohamed F. Mokbel, Xiaopeng Xiong, Moustafa A. Hammad, and Walid G. Aref. Continuous Query Processing of Spatio- temporal Data Streams in PLACE. In Proceedings of the International Workshop on Spatio-temporal Database Management, STDBM, pages 57{64, Toronto, Canada, August 2004. 104. Mohamed F. Mokbel and Walid G. Aref. PLACE: A Scalable Location-aware Database Server for Spatiotemporal Data Streams. IEEE Data Engineering Bulletin, 28(3):3{10, September 2005. 105. Mohamed F. Mokbel, Xiaopeng Xiong, Moustafa A. Hammad, and Walid G. Aref. Continuous Query Processing of Spatio- temporal Data Streams in PLACE. GeoInformatica, 9(4):343{365, December 2005. 106. Stefan Dieker and Ralf Hartmut Guting. Plug and Play with Query Algebras: SECONDO-A Generic DBMS Development Environment. In Proceeding of the International Database Engineering and Applications Symposium, IDEAS, pages 380{392, Yokohoma, Japan, September 2000. 107. Ralf Hartmut Guting, Thomas Behr, Victor Teixeira de Almeida, Zhiming Ding, Frank Homann, and Markus Spiekermann. SECONDO: An Extensible DBMS Architecture and Prototype. Technical Report Informatik- Report 313, Fernuniversitat Hagen, March 2004. 108. Thomas Behr and Ralf Hartmut Guting. Fuzzy Spatial Objects: An Algebra Implementation in SECONDO. In Proceeding of the International Conference on Data Engineering, ICDE, pages 1137{1139, Tokyo, Japan, April 2005. 109. Ralf Hartmut Guting, Victor Teixeira de Almeida, Dirk Ansorge, Thomas Behr, Zhiming Ding, Thomas Hose, Frank Homann, Markus Spiekermann, and Ulrich Telle. SECONDO: An Extensible DBMS Platform for Research Prototyping and Teaching. In Proceeding of the International Conference on Data Engineering, ICDE, pages 1115{1116, Tokyo, Japan, April 2005. 110. Goce Trajcevski, Ouri Wolfson, Hu Cao, Hai Lin, Fengli Zhang, and Naphtali Rishe. Managing Uncertain Trajectories of Moving Objects with Domino. In Proceeding of the International Conference on Enterprise Information Systems, ICEIS, pages 218{225, Ciudad Real, Spain, April 2002. 111. Ouri Wolfson, A. Prasad Sistla, Bo Xu, Jutai Zhou, and Sam Chamberlain. DOMINO: Databases fOr MovINg Objects tracking (Demo). In Proceeding of the ACM International Conference on Management of Data, SIGMOD, pages 547{549, Philadephia, PA, June 1999. 112. Ouri Wolfson, Hu Cao, Hai Lin, Goce Trajcevski, Fengli Zhang, and Naphtali Rishe. Management of Dynamic Location Information in DOMINO (Demo). In Proceeding of the International Conference on Extending Database Technology, EDBT, pages 769{771, Prague, Czech Republic, March 2002.

86. 86 May 2007 MDM Tutorial Thank you