Locationaware Query Processing and Optimization A Tutorial Mohamed

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Location-aware Query Processing and Optimization: A Tutorial Mohamed F. Mokbel Walid G. Aref Department

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

Motivation May 2007 MDM Tutorial 2

Motivation May 2007 MDM Tutorial 2

Applications Traffic Monitoring n How many cars are in the downtown area? n Send

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

Applications (Cont. ) Location-based Store Finder / Advertisement n Where is my nearest Gas

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

Location-based Database Servers Built-in Approach Layered Approach GIS Interface Spatio-temporal GIS DBMS ST Query

Location-based Database Servers Built-in Approach Layered Approach GIS Interface Spatio-temporal GIS 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 n

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

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Snapshot Past Queries

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Snapshot Past Queries n Snapshot Present Queries n Snapshot Future Queries n Spatio-temporal Access Methods n Location-aware Continuous Query Processing n Scalable Execution of Continuous Queries n Location-aware Query Optimization n Uncertainty in Location-aware Query Processing n Case Study n Open Research Issues May 2007 MDM Tutorial 7

Location-aware Snapshot Query Processing Querying the Past n Examples: n Querying Along the Temporal

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

Location-aware Snapshot Query Processing Indexing the Time Dimension n Historical trajectories are represented by

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

Location-aware Snapshot Query Processing Multi-version Index Structures n Maintain an R-tree for each time

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

Location-aware Snapshot Query Processing Querying the Present n Time is always NOW n Example

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

Location-aware Snapshot Query Processing Updating Index Structures n Traditional R-tree updates are top-down n

Location-aware Snapshot Query Processing Updating Index Structures n Traditional R-tree updates are top-down n Updates translated to delete and insert transactions n To support frequent updates: n Updates can be managed in space without the need for deletion or insertions n 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 n Keep a memo with the R-tree Spatio-temporal

Location-aware Snapshot Query Processing Update Memos n Keep a memo with the R-tree Spatio-temporal Queries n The memo contains the recent updates to the existing R-tree n The query answer returned from the R-tree should be passed through the memo Raw answer set Update Memo n 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 n Examples: n What will my nearestaurant

Location-aware Snapshot Query Processing Querying the Future n Examples: n What will my nearestaurant be after 30 minutes? n Does my path conflict with any other cars for the next hour? n Features: n Predict the movement through a velocity vector n 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 n A line (trajectory) in the two-dimensional space

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

Location-aware Snapshot Query Processing Time-Parameterized Data Structures n The Time-parameterized R-tree (TPR-tree) consists of:

Location-aware Snapshot Query Processing Time-Parameterized Data Structures n The Time-parameterized R-tree (TPR-tree) consists of: n Minimum bounding rectangles (MBR) n Velocity bounding rectangles (VBR) n A bounding rectangle with MBR & VBR is guaranteed to contain all its moving objects as long as they maintain their velocity vector n 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 n A unified index structure

Location-aware Snapshot Query Processing Indexing Past, Present, and Future n A unified index structure for both past, present, and future data n Makes use of the partial-persistent R-tree for past data and the TPR-tree for current and future data n Double Time-Parameterized Bounding rectangles are used to bound moving objects. Double TPBR has two components: n 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 n Head MBR to bound the finite historical trajectories. The head is an optimized TPBR n 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

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

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query

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

Snapshot vs. Continuous Query Processing n Traditional (Snapshot) Queries Answer Data Query n Continuous

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

Location-aware Continuous Query Processing Approaches n Straightforward Approach n Abstract the continuous queries to

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

Location-aware Continuous Query Processing Result Validation n Associate a validation condition with each query

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

Location-aware Continuous Query Processing Caching the Result n Observation: Consecutive evaluations of a continuous

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

Location-aware Continuous Query Processing Predicting the Result n Given a future trajectory movement, the

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

Location-aware Continuous Query Processing Incremental Evaluation n The query is evaluated only once. Then,

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

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query Processing n Scalable Execution of Continuous Queries n Location-aware Centralized Database Systems n Location-aware Distributed Database Systems n Location-aware Data Stream Management Systems n Location-aware Query Optimizer n Uncertainty in Location-aware Query Processing n Case Study n Open Research Issues May 2007 MDM Tutorial 26

Scalability of Location-aware Continuous Queries Motivation Continuous K-NN Query Keep me updated by nearest

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 n Continuous queries last for long times

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

Scalability of Location-aware Continuous Queries Main Concepts (Cont. ) One thread for all continuous

Scalability of Location-aware Continuous Queries Main Concepts (Cont. ) One thread for all continuous queries Q 1 . . Each query is a single thread Q 2 . . . QN Split . . . ST Query N ST Query 1 ST Query 2 DIndex QIndex Data Objects ST Queries Shared ST Join n 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 n Centralized index structures n

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

Scalability of Location-aware Continuous Queries Location-aware Centralized Database Systems (Cont. ) n To accommodate

Scalability of Location-aware Continuous Queries Location-aware Centralized Database Systems (Cont. ) n To accommodate for the continuous movement of both data and queries: n Concurrent continuous queries share a grid structure n Moving objects are hashed to the same grid structure as queries n 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 n Motivation: Centralized location-aware servers

Scalability of Location-aware Continuous Queries Location-aware Distributed Database Systems n Motivation: Centralized location-aware servers will have a bottleneck at the server side n Assumption: Moving objects have devices with the capability of doing some computations n Idea: n n Server will ship some of its processing to the moving objects Server will act as a mediator among moving objects n 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. ) n Each moving

Scalability of Location-aware Continuous Queries Location-aware Distributed Database Systems (Cont. ) n Each moving object O maintains a list of the queries that O may be part of their answer n It is the responsibility of the moving object O to report that O becomes part of the answer of a certain query n Once a query updates its location, it sends the new location to the server, which will propagate the new location to the interested users n 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 n Motivation: Very high

Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems n Motivation: Very high arrival rates that are beyond the system capability to store n 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 n Significant objects: A moving object O is significant if there is at least one query that is interested in O’s location n 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. ) n Only

Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont. ) n Only significant objects are Cache Area stored in-memory n An object is considered significant if it is either in the query area or the cache area n Due to the query and object movements, a stored object may become insignificant at any time n 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. ) n The

Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont. ) n The first k objects are considered an initial answer n 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. ) Stationary Range

Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont. ) Stationary Range +/Moving k. NN Q 1 . . . QN +/- +/- +/Moving Range Shared Operator Shared Memory Buffer among all C. Queries Stream of Moving Objects May 2007 Q 2 . . . +/- QN . . Q 2 One thread for all continuous queries . . . Q 1 . . . Each query is a single thread Split Shared Spatiotemporal Join Stream of Moving Objects Queries MDM Tutorial 37

Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont. ) n Query

Scalability of Location-aware Continuous Queries Location-aware Data Stream Management Systems (Cont. ) n Query Load Shedding n n Reduce the cache area Possibly reduce the query area Immediately drop insignificant tuples Intuitive and simple to implement n Object Load Shedding n n 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 n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query

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

Location-aware Query Optimization n Spatio-temporal pipelinable query operators n n Range queries Nearest-neighbor queries

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

Spatio-temporal Query Operators n Existing Approaches are Built on Top of DBMS (at the

Spatio-temporal Query Operators n 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 n “Continuously report the Avis cars in a certain area” SELECT

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

Spatio-temporal Selectivity Estimation n Estimating the selectivity of spatio-temporal operators is crucial in determining

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

Spatio-temporal Histograms n Moving objects in D-dimensional space are mapped to 2 D- dimensional

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

Spatio-temporal Histograms with Query Feedback n Estimating the selectivity of spatio-temporal operators is crucial

Spatio-temporal Histograms with Query Feedback n Estimating the selectivity of spatio-temporal operators is crucial in determining the best plan for spatio-temporal queries 10% 6. 25% 6. 98% 6. 25% 6. 01% Q 1 6. 25% 6. 01% 6. 25% 6. 98% 6. 25% 6. 01% 6. 25% 6. 01% Query Optimizer Spatio-temporal Histogram Query plan Query Executer May 2007 Feedback MDM Tutorial 45

Adaptive Query Optimization n Continuous queries last for long SELECT Object. ID FROM Moving.

Adaptive Query Optimization n Continuous queries last for long SELECT Object. ID FROM Moving. Objects M WHERE Type = Truck INSIDE Region R 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 SELECT Moving Objects n 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 n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query Processing n Scalable Execution of Continuous Queries n Location-aware Query Optimizer n Uncertainty in Location-aware Query Processing n Case Study n Open Research Issues May 2007 MDM Tutorial 47

Uncertainty in Moving Objects n Location information from moving objects is inherently inaccurate n

Uncertainty in Moving Objects n Location information from moving objects is inherently inaccurate n Sources of uncertainty: n n n 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 May 2007 MDM Tutorial 48

Uncertainty in Moving Objects n Historical data (Trajectories) n Current data T 01+Є210 May

Uncertainty in Moving Objects n Historical data (Trajectories) n Current data T 01+Є210 May 2007 MDM Tutorial 49

Uncertainty in Moving Objects Error in Query Answer n Range Queries n Nearest Neighbor

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

Representing Uncertain Data using Ellipses n Given : Start point n End point n

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

Representing Uncertain Data using Cylinders n Given: n Start and end points n Constraint:

Representing Uncertain Data using Cylinders n Given: n Start and end points n Constraint: n 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 n Given: n Start and end points n

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

Querying Uncertain Data Uncertain Keywords n KEYWORDS: n n n Probability: possibly, definitely Temporal:

Querying Uncertain Data Uncertain Keywords n KEYWORDS: n n n Probability: possibly, definitely Temporal: sometimes, always Spatial: somewhere, everywhere n Examples: n n 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. ) Q 4 Q 2 O Q 1

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

Querying Uncertain Data Probabilistic Queries n With each query answer, associate a probability that

Querying Uncertain Data Probabilistic Queries n With each query answer, associate a probability that this answer is true n The answer set of a query Q is represented as a set of tuples <ID, p> where ID is the tuple identifier and p is the probability that the object ID belongs to the answer set of Q n Assumptions: n 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 B n Query

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

Querying Uncertain Data Probabilistic Nearest-Neighbor Queries A C E D F B n Query

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

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query Processing n Scalable Execution of Continuous Queries n Location-aware Query Optimizer n Uncertainty in Location-aware Query Processing n Case Studies n DOMINO n SECONDO n PLACE n Open Research Issues May 2007 MDM Tutorial 59

Case Study I DOMINO n DOMINO: Databases f. Or Mov. INg Objects tracking n

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

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 object’s plan of motion MDM Tutorial 61

Uncertainty Management in DOMINO n Uncertainty operators are implemented as user- defined functions (UDFs)

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

Case Study II SECONDO n SECONDO: An Extensible DBMS Architecture and Prototype n A

Case Study II SECONDO n SECONDO: An Extensible DBMS Architecture and Prototype n 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) n A database is a set of SECONDO objects of the form (name, type, value), where type is one of the implemented algebras n About 20 implemented algebras, e. g. , standard algebra, relational algebra, R-Tree algebra, and spatial algebra n 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

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 n PLACE: Pervasive Location-Aware Computing Environments n Scalable

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

PLACE Architecture DBMS PLACE Query Parser INSIDE, k. NN Negative updates Query Processor Continuous

PLACE Architecture DBMS PLACE Query Parser INSIDE, k. NN 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

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 ØContinuous Predicate-based Window Queries ØMoving Queries WINDOW window_clause INSIDE inside_clause k. NN knn_clause W-Expire Operator INSIDE Operator Negative Tuples k. NN Operator Stream_Scan Operator Stream of Moving Stream data types MDM Tutorial Objects/Queries 67

Extended SQL Syntax n inside_clause: n Stationary query: (x 1, y 1, x 2,

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

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query

Tutorial Outline n Location-aware Environments n Location-aware Snapshot Query Processing n Location-aware Continuous Query Processing n Scalable Execution of Continuous Queries n Location-aware Query Optimizer n Uncertainty in Location-aware Query Processing n Case Study n Open Research Issues May 2007 MDM Tutorial 69

Open Research Issues Location Privacy YOU ARE TRACKED… !!!! “New technologies can pinpoint your

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 n Mining the history Predicting the future n

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

Open Research Issues Reducing the Gap between ST Databases and DBMSs/DSMSs n What do

Open Research Issues Reducing the Gap between ST Databases and DBMSs/DSMSs n What do Spatio-temporal researchers offer? n n 50+ spatial index structure, 30+ spatio-temporal indexing structure Wide variety of spatio-temporal query processing techniques n What do DBMS designers want? n n Little disturbance to their code Large number of customers n The result is: n n DB 2 and SQLServer do not support the R-tree (and may not be willing to) Oracle supports only R-tree and Quadtree n Can we reduce this gap? n n YES. Think in the minimal additions to the engine Example I: B-tree with SFC Example II: Gi. ST and SP-Gi. ST Example III: Add-in query operators May 2007 MDM Tutorial 72

References Overview Papers: n 1. 2. 3. 4. Ouri Wolfson, Bo Xu, Sam Chamberlain,

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References n Spatio-temporal Access Methods (Cont. ): 10. Dieter Pfoser, Christian S. Jensen, and

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Thank you May 2007 MDM Tutorial 86

Thank you May 2007 MDM Tutorial 86