Moving Objects Databases Nilanshu Dharma Shalva Singh

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

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 receiver’s 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).

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.

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.

Strategies for Database Management in MOs

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

Past & Current Location Index

Architecture of File-based Location Storage System

The Design of File-based System Location Query Component carries out query depending upon MO’s 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.

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.

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.

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.

Use of information table and parent_of pointer

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

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.

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.

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.

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