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Moving Objects Databases

Moving Objects Databases. Worcester Polytechnic Institute Worcester, MA April 8, 2004 Presented by Rimma Kaftanchikova rimma3@wpi.edu. Where are we?. Roadmap. Location-Based Services Why this is a hot technology? Overview Problems with current databases Moving Objects Databases

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Moving Objects Databases

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  1. Moving Objects Databases Worcester Polytechnic Institute Worcester, MA April 8, 2004 Presented by Rimma Kaftanchikova rimma3@wpi.edu

  2. Where are we? CS561

  3. Roadmap • Location-Based Services • Why this is a hot technology? • Overview • Problems with current databases • Moving Objects Databases • Solution • MOST+FTL • Questions CS561

  4. Location Based Services Categories • Information Services • Identify and provide directions to the nearest restaurants, ATMs or gas stations • Allow travelers to obtain other information specific to their location • Corporate applications • Enable enterprises to better manage mobile assets to optimize services or cut costs • E.g., fleet or asset tracking • Entertainment/Community Services • Allow mobile users to create a localized community of people with similar interests • Notify a user when a group member is close-by, e.g. friend-finder CS561

  5. Location Based Services Categories (cont.) • Mobile Commerce Services • Help users shop or purchase goods/services from the retailer closest to their current location. • (Businesses can) send special offers to users in proximity to one of their establishments. • Safety Related Applications • Help public or private safety organizations find or track mobile users in need of assistance. • Help locate stolen property. CS561

  6. Location Based Services Examples Examples: Where is closest gas station? How do I get there? What is the average speed on the highway 1 mile ahead? What are the available parking slots around me? Mobile E-Commerce • Remind me to buy drinks when I’m close to a supermarket • Send a coupon (10% off) to a customer with interest in Nike sneakers that is close to the store • Alert a person entering a bar if two of his “buddies” (wife and girlfriend) are both in the bar; he may want to turn around Generally, queries involve spatial objects (e.g. points, lines, regions, polygons) • “Retrieve the objects that will intersect the polygon within 3 minutes” • “Retrieve the objects that will intersect P within 3 minutes and stay • in the polygon for 1 minute, and 5 minutes later enter another polygon Q” CS561

  7. Why now? • E911 – FCC mandate (In 1996, the Federal Communications Commission (FCC) mandated that all wireless carriers offer a 911 service with the ability to pinpoint the location of callers making emergency requests) • Drop in equipment/service prices • Portable/wearable/wireless device proliferation • Vehicular communication networks • UWB (Ultra Wideband (UWB) systems transmit signals across a much wider frequency than conventional systems and are usually very difficult to detect. ) • 802.11 (Family of wireless data transfer standards (802.11, 802.11a, 802.11b, and 802.11g) CS561

  8. Overview A spatial database is a collection of spatially referenced data that acts as a model of reality A temporal database can store and retrieve temporal data, that is, data which depends on time in some way. Spatial information Temporal information Moving objects databases Location management A moving objects database represents information about moving objects and their location Database Perspective– how to manage and query the data? CS561 Source: http://www.cs.uic.edu/~wolfson/html/mobile.html

  9. Problems With Current Databases Moving Objects: “Objects whose position changes continuously over time” Database Perspective – how to manage and query the data? • Not well equipped to handle • continuously changing data • (e.g position of moving objects) • Reason: data is assumed to • be constant unless it is • explicitly modified CS561

  10. Problems With Current Databases (cont.) • To represent a moving object in a database, you have to update very frequently the position of the moving object • THIS IS A PROBLEM! • Serious performance and wireless-bandwidth overhead • If not, the answer to queries is outdated. • SOLUTION??? CS561

  11. Trajectory • Solution represent the position as a function of time (it changes as time passes, even without an explicit update) CS561

  12. Model of a trajectory Geometrical representation in 3D space(2D spatial + 1D temporal) The resulting line segments comprise a polyline in 3D, the trajectoryof the moving point object. CS561

  13. Trajectory construction • Moving objects create trajectories • Trajectory: a sequence of 2 or 3-dim locations • Based on GPS points (x1,y1,z1,t1), (x2,y2,z2,t2),… • “Snap” points on road network • Find shortest path on map between consecutive gps points • Usually we can sample the positions of the objects at periodic time intervals Dt • Linear Interpolation: easy and usually accurate enough • For vehicles moving on road networks, construction uses a map. CS561

  14. Map • A relation tuple <----> block, i.e. section of street between two intersections CS561

  15. Trajectory Reduction • Line simplification: approximate a trajectory by another which is not farther than ε. e e CS561

  16. Avoid continuous trajectory revision • Solution idea: filter + refinement at query time • Rimma’s idea (for simplicity purposes): • During the process of moving, when the deviation of the actual location from the anticipative location (calculated via the trajectory) exceeds a certain threshold (that we define in the system), a location update should be triggered CS561

  17. Three Time-series Prediction Methods • Two widely used methods: • Moving Averages: the next predicted value is the average of the latest h values of the series • Exponential Smoothing: The next predicted value is the weighted average of the latest h values, and the weights decrease geometrically with the age of the values • Neural-Fuzzy Inference Systems (NFIS) • Fuzzy rule based inference + • Neural back-propagation rule base learning CS561

  18. Moving Objects Spatio-Temporal (MOST) data model • MOST model is designed for databases with dynamic attributes, i.e. attributes that change continuously as a function of time, without being explicitly updated. • Answer to a query depends on: • Database content • Time at which the query entered • Advantage of this model: • Future queries • Example: Retrieve all the airplanes that will come within 30 miles of the airport in the next 10 minutes. CS561

  19. The MOST data model • Database=set of object-classes • Special database object time • Object class=set of attributes (MOTELS(name, location, num_of_rooms, price_per_room)) • Some object-classes are spatial w/ 3 attributes: • X.POSITION, Y.POSITION, Z.POSITION • Set of spatial methods associated w/ them (e.g. INSIDE(o,P), OUTSIDE(o,P), DIST(o1,o2)) CS561

  20. Dynamic Attributes • Attributes: • Static (changes only when an explicit update of the database occurs) • Dynamic (changes over time according to some given function, even if it is not explicitly updated) • Example: a moving object whose position in 3D space at any point in time( x, y, z are dynamic attributes) • Dynamic attribute A is represented by 3 sub-attributes: • A.value (depends on time) • A.updatetime • A.function (function of a single variable t that has a value 0 at t=0) • A.value: • At time A.updatetime the value of A is A.value, and • until the next update of A the value of A at time A.time+t0 is given by A.value+A.function(t0) • An explicit update of a dynamic attribute can change • A.value, • A.function • or both. • Advantage to this approach: • user can query each sub-attribute independently • can ask “Retrieve all objects for which X.POSITION.function = 5*t, i.e. the objects whose speed in the X direction is 5” CS561

  21. Three types of queries in MOST • Instantaneous - answer as of that time • Example: the motels within 5 miles of my current location • Continuous - the answer of the query is needed at each of the future instances. Query pertains to snapshot database • Persistent - Like a continuous query but uses past as well future history. CS561

  22. Database Histories • Traditional databases: queries refer to the current database state (i.e. query languages are nontemporal, i.e. limites to accessing a single (current) database state) • MOB database: database implicitly represents future states of the system being modeled (e.g future positions of moving objects) • Can have queries pertaining to the future (a moving car can request all the motels it will reach in the next 20 minutes) • To interpret this type of queries , authors use the notion of a database history, i.e. a sequence of database states (abstract concept). • A database state is a mapping that associates a set of objects of the appropriate type to each object class. CS561

  23. Database Histories (cont.) • Each database state has an associated time stamp. • In the state, the value of a dynamic attribute is • value of the attribute at the time t=time stamp. • Queries are interpreted over database histories (A database history is an infinite sequence of database states, one for each clock tick) • The time stamps along the database history are strictly increasing • At a particular point in time t, • the database states with a lower time-stamp than t are called past-database history • states with a time-stamp higher than the current time t are called future database history. • Each state in the future history is identical to the state at time t, except for the value of the dynamic attributes. CS561

  24. Future Temporal Logic (FTL) Language How many cars will arrive to WPI’s parking lot in the next 5 minutes? • Motivation: Expressing temporal queries on moving objects using SQL and OQL are cumbersome • New query language for moving objects database ->FTL (query in FTL is simpler and more intuitive) • Answer to future queries is tentative What are the traffic conditions 2 miles ahead of me in the next 3 minutes? CS561

  25. FTL (cont.) • Syntax:Retrieve <target-list> where <condition> • FTL employs spatial and temporal predicates and operators INSIDE(O,P), DISTANCE(O1,O2)<=5 EVENTUALLY-WITHIN-C, UNTIL g, ALWAYS-FOR-C, etc. CS561

  26. Future Temporal Logic (FTL) Language • The answer is regarded as correct according to what is currentlyknown about the real world, but this knowledge (the motion vector) can change. • Query=“Retrieve the pairs of objects o and n such that the distance between o and n stays 5 miles until they both enter polygon P” FTL syntax: RETRIEVE o,n WHERE DIST(o,n)≤5 Until(INSIDE(o,P)^INSIDE(n,P)) CS561

  27. Problems with FTL • Can only query about the future states, not the "past" states. • For example, if we want to query: " who has been in polygon area A one hour ago?" • The General idea of solution is • for the "immediate" past history, we can using the same indexing function to trace back. • for the "longer" past history, What can we do? • Question not addressed in the paper:how to record the past states of the database efficiently and accurately and how the query logic would look like? CS561

  28. Continuous Queries • Example: • Consider a relation MOTELS (geographic_coordinates, room_price, availability) • Consider a moving car issuing a query “Display motels (with availability and cost) within a radius of 5 miles” • Query is continuous!!! The car requests the answer to the query to be continously updated. • As the car moves, the answer changes, so WHEN and HOW often should the query be reevaluated? • Authors’ Solution: • Single evaluation of the query • Reevaluation has to occur only if the motion vector of the car changes. CS561

  29. Indexing • For performance consideration, in answering the queries we would like to avoid examining each moving object in the database (i.e. would like to index dynamic attributes) • Problem: since objects are continuously moving, the spatial index has to be continusly updated UNNACCEPTABLE SOLUTION! • Authors’ solution: a method of indexing dynamic attributes which guarantees logarithmic (in the # of objects) access time. CS561

  30. Indexing dynamic attributes • Objective: enable answering queries of the form “Retrieve the objects that are currently in the polygon” without examining all the objects. • Authors’ solution: • Plot all the functions representing the way a dynamic attribute A changes with time. • Use spatial index for each dynamic attribute A • Spatial indexes use a hierarchical recursive decomposition of space, usually into rectangles; • The id for each object o is stored in the records representing the rectangles crossed by the A.function of o. • Example: “Retrieve the objects for which currently 4<A<5” is entered at time 1:00AM • Then using the index we retrieve the records representing the rectangles that intersect the rectangle • 4<A<5 • And 1-ε<t<1+ ε • For each object id in these records we check whether “currently” 4<A<5. CS561

  31. Indexing dynamic attributes (cont.) • Update of o.A causes the following: • Updating the records representing rectangles ending after time t; • O is removed from the records representing rectangles crossed by the old function-line • And is added to the records representing rectangles crossed by the new function-line. CS561

  32. Indexing of dynamic attribute • Note: spatial indexing is limited to finite space. • In order to use this scheme we have to consider • the time dimension starting at 0 and ending at some time-point T. • Consequently, the index needs to be reconstructed every T time units. • Choosing an appropriate value for T is an important future-research question. CS561

  33. Implementing MOST on top of DBMS Assumption: the relational model and SQL for the underlying DBMS (can be extended to OO model). Store each dynamic attribute A as three DBMS attributes A.value, A.updatetime, A.function • If the query does not contain a reference to a dynamic attribute nor does it contain temporal operators the query is simply passed to the DBMS and the answer returned to the user. • Query contains references to dynamic attributes, but not temporal operators: • “SELECT A” : The MOST system retrieves the attributes for A and computes the value of A for each retrieved object before returning it to the user CS561

  34. Implementing MOST on top of DBMS (cont.) • “WHERE clause F” (e.g. A>5): • F is a boolean combination of atoms • DBMS replaces Q (original query) by two queries Q1 and Q2. • Transformation is F=(F’^p) and (F’’ ^ ~p) i.e. (true) and (false) • Q1 and Q2 are defined as follows: • The target list of Q1 and Q2 consists of the target list of Q, plus the subattributes of the dynamic attributes in p. • The FROM clause of Q1 and Q2 is identical to that of Q. The WHERE clause of Q1 is F’ and that of Q2 is F’’. • Q1 and Q2 are submitted to the underlying DBMS, and the results are processed as follows before returning them to the user. CS561

  35. Implementing MOST on top of DBMS (cont.) • The atom p is evaluated on each tuple in the result of Q1 , and the atom ~p is evaluated on each tuple in the result of Q2 • The tuples that do not satisfy the respective atoms are eliminated, and the projection of the union of the resulting tuples on the original target list is returned to the user. • If the WHERE clause has multiple atoms referencing dynamic attributes then we can give a function EVAL(Q) that performs the above procedure recursively, each time eliminating one of the atoms containing a dynamic variable. • If the original query has k atoms referring to a dynamic variable then, in the worst case, this might mean evaluating up to 2^k queries that do not contain dynamic variables. However, if k is small this may not be a serious problem. CS561

  36. Evaluating a Query (using the index) • Observe that the above procedure does not use indexing of the dynamic attributes. In other words, the results of Q1 and Q2 are are examined in their entirety. If indexing on the dynamic attributes is available, then we can modify the above procedure as follows. Instead of evaluating the atoms p and ~p on each tuple retrieved by Q1 and Q2 respectively, we retrieve the tuples that satisfy p and ~p respectively. • Then we join the relation returned by Q1 with the relation that satisfies p; similarly, we join the relation returned by Q2 with the relation that satisfies ~p. • Observe that in order for this procedure to produce correct results, we must ensure that F’ and p are satisfied for the same tuple in the cartesian product of the FROM relations. We ensure this by including in the target list of all four queries, a key of each relation in the FROM clause. The above method can extended to nested SQL queries as well. CS561

  37. New Research Topics in Moving Objects Database • Distributed/Mobile query and trigger processingwith incomplete/imprecise location information • Extensible and visual languages • Comparison of indexing methods • Uncertainty for moving objects that do not report their location • Scalability • Data Mining • Privacy/Security • Location prediction • Performance/indexing for join queries • …and many more (not listed here) CS561

  38. Thank you CS561

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