KNR-tree: A novel R-tree-based index for facilitating Spatial Window Queries on any k relations...

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Transcript of KNR-tree: A novel R-tree-based index for facilitating Spatial Window Queries on any k relations...

  • kNR-tree: A novel R-tree-based index for facilitating Spatial Window Queries on any krelations among N spatial relations in Mobile environments

    ANIRBAN MONDALIIS, University of Tokyo.ANTHONY K. H. TUNGSOC,National University of Singapore MASARU KITSUREGAWAIIS, University of Tokyo.Contact E-mail: anirban@tkl.iis.u-tokyo.ac.jp

  • PRESENTATION OUTLINE INTRODUCTION PROBLEM FORMULATION QUERY PROCESSING The kNR-tree index PERFORMANCE STUDY RELATED WORK CONCLUSION AND FUTURE WORK

  • PRESENTATION OUTLINE INTRODUCTION PROBLEM FORMULATION QUERY PROCESSING The kNR-tree index PERFORMANCE STUDY RELATED WORK CONCLUSION AND FUTURE WORK

  • INTRODUCTIONIncreasing popularity of mobile applications Prevalence of spatial data and its applications Efficient processing of spatial queries in mobile environmentsThis work focusses on the processing of spatial select queries on any k relations among Nspatial relations in mobile environments kNW queries

  • Motivation for kNW queriesA single client may be interested in objects from a number of different relations Different clients may be interested in different numbers as well as different kinds of relations. Mobile environments typically have multiple relations and user population demographics may vary considerably.

  • Examples of kNW queriesFind all bookshops, restaurants and car-parks which I will encounter nearby me during my next 10 minutes of travelling. Find all bus stations and shopping centres which I will encounter nearby me during my next 15 minutes of travelling.

  • Examples of kNW queriesFind all bookshops, restaurants and car-parks which I will encounter nearby me during my next 10 minutes of travelling. Find all bus stations and shopping centres which I will encounter nearby me during my next 15 minutes of travelling. kNW queries are beneficial in the real world.

  • MAIN CONTRIBUTIONSProposal of the kNR-tree, a single integrated novel R-tree-based structure for indexing objects from N different spatial relations. kNR-tree facilitates kNW queries.Processing of kNW queries in mobile environments Differences from existing worksThe window of the query is speculative (not known in advance) the processing done by some of the base stations may not contribute to the final results.We examine issues concerning objects from N different spatial relations.

  • PRESENTATION OUTLINE INTRODUCTION PROBLEM FORMULATION QUERY PROCESSING The kNR-tree index PERFORMANCE STUDY RELATED WORK CONCLUSION AND FUTURE WORK

  • PROBLEM FORMULATIONGiven a set of base stations, each of which stores and manages the data (from N spatial relations) of mutually disjoint spatial regions and a set of mobile clients, the mobile client wishes to find the results of spatial window queries (on any k of the N relations) nearby himself within the duration of the next T time units.

  • THE CONTEXTEach object is a point in space represented by its centroid.Each object has a corresponding descriptor bitmap.The descriptor bitmap for each object is exactly the same in terms of the entry positions of relation.Tick entry positions as 1 if object has the relation, otherwise mark as 0.Objects are static, but the clients who issue queries to the objects are mobile. Alternative perspective of this problem: Indexing only one spatial relation with the type of the object as a scalar attribute of the space.

  • ILLUSTRATIVE EXAMPLE OF OBJECT BITMAPS

  • CLIENT QUERIESClient queries are of the form (queryID, clientID, PIssue, SpeedMax, Qbitmap, Delta, Tau) queryID is the unique query identifierclientID is the unique client identifierPIssue is the point of issue of the querySpeedMax specifies clients maximum speed.Qbitmap is the query bitmap (an array of N bits)Structure is exactly same in terms of entry positions of relations as object bitmap. Delta quantifies the distance from clients current location which M considers to be nearby himself. Tau indicates the duration of time (after issuing the query) during which the client would wish to receive the query results.

  • PRESENTATION OUTLINE INTRODUCTION PROBLEM FORMULATION QUERY PROCESSING The kNR-tree index PERFORMANCE STUDY RELATED WORK CONCLUSION AND FUTURE WORK

  • WINDOW QUERY PROCESSING IN MOBILE ENVIRONMENTS We define Qcircle as a circle drawn with PIssue as centre and (Tau SpeedMax + Delta) as radius. MBR of Qcircle is called QMBR. Client cannot be traveling at his maximum speed in all directions at onceQMBR is a speculative and conservative estimate of the query windowQMBR may intersect with the domains of multiple base stations

  • Case 1QMBR falls completely within one base station Bs domainB processes QMBR on its own and sends results to client.

  • Case 2QMBR intersects with the domain of at least one base station other than B B determines the set R of base stations with whose domains QMBR intersects.For each member r of R, B determines the intersecting rectangular part between QMBR and rs domain and sends the intersecting rectangular part to each r. We refer to such intersecting rectangular parts as subQMBRs. After processing its respective subQMBR, each r sends a COMPLETE message to indicate that it has completed processing its subQMBR.

  • PRESENTATION OUTLINE INTRODUCTION PROBLEM FORMULATION QUERY PROCESSING The kNR-tree index PERFORMANCE STUDY RELATED WORK CONCLUSION AND FUTURE WORK

  • The kNR-treekNR-tree is a single integrated Rtree-based structure for indexing objects from N spatial relations.Non-leaf nodes of the kNR-tree contain entries of the form (ptr, mbr, Nbitmap) ptr is a pointer to a child node in the kNR-tree mbr is the MBR that covers all the MBRs in the child node. Nbitmap consists of array of N entry bits, one for each spatial relation.Leaf nodes of the kNR-tree contain entries of the form (oid, loc, Nbitmap) oid is a pointer to an object in the database loc is the location of the object.

  • Illustrative example of kNR-tree

  • Window query processing algorithm for kNR-tree

  • PRESENTATION OUTLINE INTRODUCTION PROBLEM FORMULATION QUERY PROCESSING The kNR-tree index PERFORMANCE STUDY RELATED WORK CONCLUSION AND FUTURE WORK

  • PERFORMANCE STUDYReal dataset Greece Roads used for our experiments The Greece Roads dataset contains 23268 rectangles.We computed the centroid of these rectangles to get 23268 pointsEnlarging this dataset of points by translating and mapping the data. Each of the base stations had more than 200000 points (objects) Each point associated with at least one spatial relation We used 16 base stations kNR-tree used for indexing the points at each base station We define the size of a query QSIZE as the percentage of a base stations domain that a query covers. Example: QSIZE = 20 implies that the query covers 20% of the area associated with the base stations domain.We used a fanout of 64 for the kNR-tree

  • Effect of variations in QSIZE

  • Effect of variations in k

  • PRESENTATION OUTLINE INTRODUCTION PROBLEM FORMULATION QUERY PROCESSING The kNR-tree index PERFORMANCE STUDY RELATED WORK CONCLUSION AND FUTURE WORK

  • RELATED WORKTraditional R-tree-based indexes are not adequate for indexing mobile objects Frequent updates large number of node-splits and/or node-merges.R-tree-based structures for mobile context Time-parameterized R-tree (TPR-tree) Spatio-Temporal R-tree (STR-tree) Trajectory-Bundle tree (TB-tree) Lazy Update R-tree (LUR-tree) Multiversion 3D R-tree(MV3R-tree)

  • PRESENTATION OUTLINE INTRODUCTION PROBLEM FORMULATION QUERY PROCESSING The kNR-tree index PERFORMANCE STUDY RELATED WORK CONCLUSION AND FUTURE WORK

  • CONCLUSION AND FUTURE WORKSummaryWe have addressed efficient processing of kNW queries in mobile environments.Our solution involves the use of our proposed kNR-tree. A single integrated index for N spatial relationsFuture WorkDetailed performance evaluationInvestigation of the effect of spatial density Load-balancing among the base stations.

    Good afternoon everyone.Welcome to my presentation on SPATIAL INDEXING IN PEER-TO-PEER ENVIRONMENTSMy name is yilifu, the master student of kitsuregawa-Laboratory of the University of Tokyo.

    This is the outline of my presentation.First we shall discuss theThen,we shall move on to related worksAfter that,I shall present a brief overview of our proposed system and our strategy for distributed indexing and searching.Next, we shall discuss our performance evaluation base on Theoretical Analysis/simulation experiments.Finally, I shall conclude my presentation with the summary and directions for future research.This is the outline of my presentation.First we shall discuss theThen,we shall move on to related worksAfter that,I shall present a brief overview of our proposed system and our strategy for distributed indexing and searching.Next, we shall discuss our performance evaluation base on Theoretical Analysis/simulation experiments.Finally, I shall conclude my presentation with the summary and directions for future research.This is the outline of my presentation.First we shall discuss theThen,we shall move on to related worksAfter that,I shall present a brief overview of our proposed system and our strategy for distributed indexing and searching.Next, we shall discuss our performance evaluation base on Theoretical Analysis/simulation experiments.Finally, I shall conclude my presentation with the summary and directions for future research.This is the outline of my presentation.First we shall discuss theThen,we shall move on to related worksAfter that,I shall present a brief overview of our proposed system and our strategy for distributed indexing and searching.Next, we shall disc