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Moving Objects Databases

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Discussion of various storage & retrieval strategies ... the other hand, PLIC manages spatiotemporal index about the past location information. ... – PowerPoint PPT presentation

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Title: Moving Objects Databases


1
Moving Objects Databases
  • Nilanshu Dharma
  • Shalva Singh

2
Agenda
  • Introduce the topic of Moving Objects (MOs).
  • Discuss challenges regarding the storage of MOs.
  • Discussion of various storage retrieval
    strategies
  • Perform a comparative analysis on all the 3
    strategies and suggest a solution.
  • Conclusion

3
Introduction
  • Global Positioning System (GPS) makes use of a
    network of satellites provide
  • aid to navigation,
  • land surveying, and
  • scientific studies of various kinds by
    determining receivers location, directions, and
    speed.
  • These functionalities are used by Location Based
    Services (LBS) for tourists, mobile commerce,
    digital battlefield and emergency responses
  • It involves tracking of the transient location of
    a mobile caller or a vehicle, also termed as
    Moving Objects (MOs).

4
Introduction
  • Since the data of millions of MOs changes
    incessantly, it has become inevitable to store
    and manage the voluminous and by devising
    scalable data management system.
  • The DBMS for MOs would deal with data mining,
    location propagation, privacy, and
    synchronization, efficiently.
  • This paper analyses 3 different strategies to
    store and retrieve data. We support one of the
    strategies as the solution for a better database
    approach.

5
Challenges with DBMS of MOs
  • Modeling of location information, uncertainty
    management, indexing scalability, data mining,
    location dissemination, privacy of data and
    location fusion synchronization.
  • Distributing, replication, and caching of
    database for efficient execution.
  • The issues to be addressed also involve- how to
    search database and how frequently the database
    needs to be updated.

6
Strategies for Database Management in MOs
7
Moving Object Management System Based on a File
  • This system stores both the current location and
    the past location of the moving object to store
    and search data efficiently, as location of MOs
    change intermittently.
  • MOMS architecture consists 3 major components,
  • Namely Query Processor Component,
  • Location Storage Component, and
  • Index Component.
  • An additional module, Gateway

8
Past Current Location Index
9
Architecture of File-based Location Storage System
10
The Design of File-based System
  • Location Query Component carries out query
    depending upon MOs model and its operator.
  • Index Component comprises two indexes
    simultaneously
  • Current Location Index Component (CLIC), that
    takes only current locations into consideration
    and
  • Past Location Index Component (PLIC) which
    processes time interval and trajectories queries.
  • Location Storage Component is used to store MOs
    and search the ones that associate with query
    results of location.
  • CLIC adopts the approach of spatial based
    indexing on current location information and
    object based indexing on MO Identification. On
    the other hand, PLIC manages spatiotemporal index
    about the past location information.

11
One Update for all Moving Objects at a Timestamp
  • It is an updating technique applied for indexing
    methods developed from R-Tree. This updates the
    indexes at one time and has considerably improved
    the quality of queries.
  • R-tree is a height balanced external memory data
    structure. It is an efficient method for
    indexing, but requires deletion of obsolete state
    and then insert new state in top-down manner. The
    features of this approach are as follows
  • Support for both deletion and update queries.
  • Updates process for all new states at one
    timestamp, which means it tries to access a disk
    block at most once in a process.
  • It does not deteriorate the quality of the tree
    while providing improved performance.

12
Contd.
  • It is not dependent on a specific type of new
    data distribution.
  • Capacity of main memory used in algorithm is not
    large and can be easily estimated.

13
Deletion Update Query Processing
  • The deletion takes place from the leaf level,
    i.e. deletes all the obsolete states at leafs
    using a parent of pointer.
  • This also saves memory as instead of loading the
    entire tree, only the pointer is needed.
  • For the insertion process the rule is, if leaf
    node is underflow the process will not reinsert
    its entries immediately instead it would move
    them into a stack in main memory for being
    inserted together with insertion process. If
    internal is underflow normal insertion process is
    used.

14
Use of information table and parent_of pointer
15
  • The experiments aim to compare update and insert
    query performance compared to other R-Tree update
    methods.
  • The algorithm proposed outperforms its
    competitors in two sets of experiments conducted.
    One was update queries randomly generated for set
    of 10,000 cars for timestamps 1 to 4 at rates 1
    and 5. Other experiment was on different data
    sizes, 5k, 10k, 20k and 30k cars. Updates were
    taken at 1 and 5 rates and the algorithm proved
    to give most stable results for all loads

16
Comparative Analysis
  • Both the strategies mentioned in the paper are
    unique methods.
  • Method using R-Tree is better approach.
  • It gives a detailed organized algorithm to store
    and retrieve indexes.
  • The experiment results are quite convincing to
    convey the claim.
  • The R-Tree model is scalable and consistent in
    performance.
  • Also it is less cumbersome in terms of resource
    use as compared to file based location storage.

17
Hybrid Model
  • A novel approach where a model can be designed
    that incorporates features based on heuristics.
  • A problem exists with moving objects. If no
    update is received the position of the object
    cannot be declared.
  • We propose a model which would use the past
    information from a moving object to predict its
    current location. Incorporating such
    intelligence would help further reduce the use
    of database resources and improve efficiency of
    the entire system. This model would be
    implemented on the one update at a timestamp
    concept.

18
Conclusion
  • We insinuate that one update at a timestamp is a
    better database approach than index file method.
  • We also propose that it would be beneficial if
    this concept is used under a model which also
    uses heuristics to determine the position of an
    object even if no update is provided. This model
    would work best for objects whose path is
    predetermined.

19
Questions?
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