Location-aware Query Processing and Optimization: A Tutorial Mohamed

Location-aware Query Processing and Optimization: A Tutorial Mohamed

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 [email protected] Department of Computer Science, Purdue University West Lafayette, Indiana, USA. [email protected] Motivation May 2007 MDM Tutorial 2 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 May 2007 MDM Tutorial 3 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 May 2007 MDM Tutorial 4 Location-based Database Servers Built-in Approach Layered Approach GIS Interface Spatio-temporal GIS DBMS DBMS ST Query Processing ST-Index May 2007

MDM Tutorial 5 Variety of Location-aware Queries Continuously report the number of cars in the freeway Type: Range query Query: Stationary Time: Present Object: Moving Duration: Continuous What are my nearest McDonalds for the next hour? Type: Nearest-Neighbor query Query: Moving Time: Future Object: Stationary Duration: Continuous Send E-coupons to all cars that I am their nearest gas station Type: Reverse NN query Query: Stationary Time: Present Object: Moving Duration: Snapshot What was the closest dist. between Taxi A & me yesterday? Type: Closest-point query Query: Moving Time: Past Object: Moving

Duration: Snapshot May 2007 MDM Tutorial 6 Tutorial Outline Location-aware Environments Location-aware Snapshot Query Processing Snapshot Past Queries Snapshot Present Queries Snapshot Future Queries Spatio-temporal Access Methods Location-aware Continuous Query Processing Scalable Execution of Continuous Queries Location-aware Query Optimization Uncertainty in Location-aware Query Processing Case Study Open Research Issues May 2007 MDM Tutorial 7 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 May 2007 MDM Tutorial 8 Location-aware Snapshot Query Processing Indexing the Time Dimension Historical trajectories are represented by their three-dimensional Minimum Bounding Rectangle (MBR) Time 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 May 2007 MDM Tutorial 9 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 3D-R-tree Timestamp 0 A multi-version R-tree can be combined with a 3D-R-tree to

support interval queries May 2007 MDM Tutorial 10 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 May 2007 MDM Tutorial

11 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 May 2007 MDM Tutorial Hash based on OID 12 Location-aware Snapshot Query Processing Update Memos

Keep a memo with the R-tree Spatio-temporal Queries 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 Raw answer set Update Memo The update memo is reflected to the R-tree once the relevant disk page is retrieved May 2007 Final answer set MDM Tutorial 13

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 May 2007 MDM Tutorial 14 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

May 2007 MDM Tutorial 15 Location-aware Snapshot Query Processing Time-Parameterized Data Structures The Time-parameterized R-tree (TPR-tree) consists of: Minimum bounding rectangles (MBR) Velocity bounding rectangles (VBR) 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 May 2007 MDM Tutorial 16 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 May 2007 MDM Tutorial 17 Spatio-temporal Access Methods RPPF-tree Red: Future Blue: Past Green: Present Brown: All

May 2007 MDM Tutorial 18 Tutorial Outline Location-aware Environments Location-aware Snapshot Query Processing Location-aware Continuous Query Processing Continuous Queries Vs. Snapshot Queries Approaches for Continuous Query Evaluation Scalable Execution of Continuous Queries Location-aware Query Optimizer Uncertainty in Location-aware Query Processing Case Study Open Research Issues May 2007 MDM Tutorial 19 Snapshot vs. Continuous Query Processing

Traditional (Snapshot) Queries Answer Data Query Continuous Queries Answer Query Data Query May 2007 MDM Tutorial Data 20 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 May 2007 MDM Tutorial 21 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 May 2007

MDM Tutorial 22 Location-aware Continuous Query Processing Caching the Result 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 K-NN query Initially, retrieve more than k Range query Evaluate the query with a larger range How much we need to pre-compute? How do we do re-caching? May 2007 MDM Tutorial 23 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 Ai Nearest-Neighbor Query 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 May 2007 MDM Tutorial 24 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 May 2007 MDM Tutorial +_ + 25 Tutorial Outline Location-aware Environments Location-aware Snapshot Query Processing Location-aware Continuous Query Processing Scalable Execution of Continuous Queries Location-aware Centralized Database Systems Location-aware Distributed Database Systems Location-aware Data Stream Management Systems Location-aware Query Optimizer Uncertainty in Location-aware Query Processing Case Study Open Research Issues May 2007

MDM Tutorial 26 Scalability of Location-aware Continuous Queries Motivation Continuous K-NN Query Keep me updated by nearest 3 hospitals Make sure that the nearest 3 airplanes are FRIENDLY Continuous K-NN Query May 2007 Location-aware Database Server Alert me if there are less than 3 police cars within 5 miles Continuous Range Query

MDM Tutorial Continuous Range Query How many cars in the highlighted area? Monitor the traffic in the red areas Continuous Range Query 27 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

May 2007 MDM Tutorial 28 Scalability of Location-aware Continuous Queries Main Concepts (Cont.) One thread for all continuous queries .. . .. . .. . Each query is a single thread Q1 Q2 .. . QN

Split ST Query 1 ST Query 2 DIndex DIndex Data Objects Data Objects .. . ST Query N Shared ST Join DIndex DIndex QIndex

Data Objects Data Objects ST Queries Evaluating a large number of concurrent continuous spatio-temporal queries is abstracted as a spatio-temporal join between moving objects and moving queries May 2007 MDM Tutorial 29 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 May 2007

MDM Tutorial 30 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 May 2007 MDM Tutorial 31

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 May 2007 MDM Tutorial 32

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 May 2007 MDM Tutorial 33 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 Os location Challenge: Discovering that an object becomes insignificant May 2007 MDM Tutorial 34 Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont.) Only significant objects are Cache Area 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 May 2007 MDM Tutorial 35 Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont.) The first k objects are considered an initial answer K-NN query is reduced to a circular range query However, the query area may shrink or grow May 2007 MDM Tutorial K=3

36 Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont.) Each query is a single thread +/Stationary Range QN +/Moving kNN .. . QN +/- +/- +/- +/Moving Range Shared

Operator Shared Memory Buffer among all C. Queries Stream of Moving Objects May 2007 Q2 .. .. . Q1 .. .. Q2 .. . .. .

Q1 One thread for all continuous queries Split Shared Spatiotemporal Join Stream of Moving Stream of Moving Objects Queries MDM Tutorial 37 Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont.) Query Load Shedding Reduce the cache area Possibly reduce the query area Immediately drop insignificant tuples Intuitive and simple to implement 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? 2 1 6 5 3 4 7 K=2 May 2007 MDM Tutorial 38

Tutorial Outline Location-aware Environments Location-aware Snapshot Query Processing Location-aware Continuous Query Processing Scalable Execution of Continuous Queries Location-aware Query Optimization Uncertainty in Location-aware Query Processing Case Study Open Research Issues May 2007 MDM Tutorial 39 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

May 2007 MDM Tutorial 40 Spatio-temporal Query Operators Existing Approaches are Built on Top of DBMS (at the Application Level) Continuously report the trucks in this area Scalar functions (Stored procedure) Only produce objects in the areas of interest The performance of scalar functions is limited Database Engine May 2007 SELECT O. ID FROM Objects O WHERE O.type = truck

INSIDE Area A MDM Tutorial Database Engine Spatio-temporal Operators 41 Spatio-temporal Query Operators Continuously report the Avis cars in a certain area Scalar Function Spatio-temporal Operators JOIN AvisCars Moving Objects May 2007 2500 +/- +/INSIDE 3000

Tuples in the Pipeline SELECT M.ObjectID FROM MovingObjects M, AvisCars A WHERE M.ID = A.ID INSIDE RegionR JOIN 2000 Scalar Function Table Function 1500 1000 500 0 +/AvisCars INSIDE Operator INSIDE 2

4 8 16 32 Query Size Moving Objects MDM Tutorial 42 64 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 INSIDE

SELECT SELECT INSIDE May 2007 MDM Tutorial 43 Spatio-temporal Histograms Moving objects in D-dimensional space are mapped to 2D- dimensional histogram buckets x x t t May 2007 MDM Tutorial 44

Spatio-temporal Histograms with Query Feedback Estimating the selectivity of spatio-temporal operators is crucial in determining the best plan for spatio-temporal queries 10% 6.25% 6.98% 6.98% 6.25% 6.01% Q1 6.25% 6.25% 6.01% 6.25% 6.98% 6.25% 6.98% 6.25% 6.01% 6.25% 6.01% 6.25% 6.01% 6.25% 6.01% 6.25% 6.01%

6.25% 6.01% 6.25% 6.01% 6.25% 6.01% 6.25% 6.01% 6.25% 6.01% Query Query Optimizer Spatio-temporal Histogram Query plan Query Executer May 2007 Feedback MDM Tutorial 45

Adaptive Query Optimization Continuous queries last for SELECT ObjectID FROM MovingObjects M WHERE Type = Truck INSIDE Region R 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 SELECT INSIDE INSIDE SELECT Moving Objects

Moving Objects Need continuous optimization and ability to change the query plan: Training period: Spatio-temporal histogram, periodicity mining Online detection of changes May 2007 MDM Tutorial 46 Tutorial Outline Location-aware Environments Location-aware Snapshot Query Processing Location-aware Continuous Query Processing Scalable Execution of Continuous Queries Location-aware Query Optimizer Uncertainty in Location-aware Query Processing Case Study Open Research Issues May 2007

MDM Tutorial 47 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 objects 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 May 2007 MDM Tutorial

48 Uncertainty in Moving Objects Historical data (Trajectories) Current data T01+210 May 2007 MDM Tutorial 49 Uncertainty in Moving Objects Error in Query Answer Range Queries Nearest Neighbor Queries May 2007 MDM Tutorial 50 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 May 2007 MDM Tutorial 51 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 May 2007 MDM Tutorial

52 Representing Uncertain Data in Road Networks Given: Start and end points Constraints : Deviation threshold r Speed threshold v May 2007 MDM Tutorial 53 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 May 2007 MDM Tutorial 54 Querying Uncertain Data Uncertain Keywords (Cont.) Q4 Q2 O

Q1 Q3 Object O is definitely always in Q1 Object O is possibly always in Q2 Object O is definitely sometimes in Q3 Object O is possibly sometimes in Q4 May 2007 MDM Tutorial 55 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 May 2007 MDM Tutorial 56 Querying Uncertain Data Probabilistic Range Queries A C E D F Query Answer: B (B, 50%) (C, 90%)

D E (F, 30%) May 2007 MDM Tutorial 57 Querying Uncertain Data Probabilistic Nearest-Neighbor Queries A C E D F B Query Answer (k=1): (C, p1) (D, p2) (E, p3) May 2007

MDM Tutorial 58 Tutorial Outline Location-aware Environments Location-aware Snapshot Query Processing Location-aware Continuous Query Processing Scalable Execution of Continuous Queries Location-aware Query Optimizer Uncertainty in Location-aware Query Processing Case Studies DOMINO SECONDO PLACE Open Research Issues May 2007 MDM Tutorial 59 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 May 2007 MDM Tutorial 60 DOMINO Architecture DOMINO Arc-View GIS Object-Relational DBMS Informix/Oracle May 2007

Provide temporal capabilities, uncertainty management, and location prediction Provide capabilities and user interface primitives for storing, querying, and manipulating geographic information Stores the information about each moving object, including each objects plan of motion MDM Tutorial 61 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) May 2007 MDM Tutorial 62 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, objectoriented, 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 May 2007 MDM Tutorial 63 SECONDO Architecture Generic GUI independent of data models. The interface includes command prompt and is extensible by a set of different viewers GUI Java The core functionality is the optimization of conjunctive queries, i.e., producing an efficient query plan Optimizer PROLOG SECONDO Kernel Berkeley DB (C++) On top of the query optimizer, there is a SQL-like language in a notation adopted to PROLOG Built on top of Berkeley DB. Includes specific data models, algebra

modules, and query processors over the implemented algebra. May 2007 MDM Tutorial 64 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 May 2007 MDM Tutorial 65 PLACE Architecture DBMS

PLACE Query Parser INSIDE, kNN Negative updates Query Processor Continuous / Moving Queries Scalable shared operators Relational Operators INSIDE, KNN, operators Storage Engine

Stream of Moving Objects/Queries May 2007 MDM Tutorial 66 PLACE Architecture PLACE A Query Processor for Real-time Spatio-temporal Data Streams NILE A Query Processing Engine for Data Streams PREDATOR SQL Language Query processor Storage engine Abstract data types May 2007 Continuous time-based Sliding Window Queries Continuous Predicate-based

Window Queries Moving Queries WINDOW window_clause INSIDE inside_clause kNN knn_clause W-Expire Operator INSIDE Operator Negative Tuples kNN Operator Stream_Scan Operator Stream of Moving Stream data types MDM Tutorial Objects/Queries 67 Extended SQL Syntax inside_clause: Stationary query: (x ,y ,x ,y ) 1 1 2 2 Moving query: (M,OID, width, length) knn_clause: Stationary query: (k,x,y) Moving query: (M, OID, k) May 2007

MDM Tutorial 68 Tutorial Outline Location-aware Environments Location-aware Snapshot Query Processing Location-aware Continuous Query Processing Scalable Execution of Continuous Queries Location-aware Query Optimizer Uncertainty in Location-aware Query Processing Case Study Open Research Issues May 2007 MDM Tutorial 69 Open Research Issues Location Privacy YOU ARE TRACKED !!!! 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 May 2007 MDM Tutorial 70 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

May 2007 MDM Tutorial 71 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 May 2007 MDM Tutorial 72 References Overview Papers: 1. 2. 3. 4.

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. 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. Christian S. Jensen. Database Aspects of Location-based Services. In Location-based Services, pages 115-148. Morgan Kaufmann, 2004. 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. 6. 7. 8. 9. Mohamed F. Mokbel, Thanaa M. Ghanem, and Walid G. Aref. Spatio-temporal Access Methods. IEEE Data Engineering Bulletin, 26(2):40-49, June 2003. 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. 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. 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. Jamel Tayeb, Ozgur Ulusoy, and Ouri Wolfson. A Quadtree-Based Dynamic Attribute Indexing Method. The Computer Journal, 41(3):185-200, 1998. May 2007 MDM Tutorial 73 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. May 2007 MDM Tutorial 74 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 TrajectoryOriented 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. May 2007 MDM Tutorial 75

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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. 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. 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. May 2007 MDM Tutorial 77 References Location-aware Snapshot Query Processing: 47. 48. 49. 50. 51. 52.

53. 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. 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. 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 334345, Madison, WI, June 2002. Yufei Tao and Dimitris Papadias. Spatial Queries in Dynamic Environments. ACM Transactions on Database Systems, TODS, 28(2):101-139, June 2003. 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. 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. 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. May 2007 MDM Tutorial 78 References

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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. 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. Marios Hadjieleftheriou, George Kollios, Petko Bakalov, and Vassilis J. Tsotras. Complex Spatiotemporal Pattern Queries. In Proceeding of the International Conference on Very Large Data Bases, VLDB, pages 877-888, Trondheim, Norway, August 2005. 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. May 2007 MDM Tutorial 79 References Location-aware Continuous Query Processing: 62. 63. 64. 65.

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Information and Knowledge Management, CIKM, pages 427-436, Washington, DC, November 2004. 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. 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. 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. 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. 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. 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. May 2007 MDM Tutorial 81 References Location-aware Continuous Query Processing (cont.):

77. 78. 79. 80. 81. 82. 83. 84. 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. 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. 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. 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. Yufei Tao, George Kollios, Jerey Considine, Feifei Li, and Dimitris Papadias. Spatio-temporal

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