TERM PAPER RELATED TO SPATIO TEMPORAL DATABASES

1. TERM PAPERRELATED TO SPATIO TEMPORAL DATABASES By GOPIKRISHNA KURRA SRIKANTH GOLI SIVATEJA KOTIPALLI

2. OUTLINE • Introduction • Motivation • Problem statement • Conclusion and Future work • Comparisons • References

3. PAPER 1 Data Types and Operations for Spatio-Temporal Data Streams

4. Introduction • In the field of spatio-temporal databases, moving objects databases and field of data stream management systems are separately researched over the last ten years. • In the recent years, researchers focusing on a system which studies both moving objects and data stream together.

5. Motivation • Consider an example of aeroplane tracking system used by airport authorities • inform the aeroplane pilot if he is within 10 km from a bad weather area • inform the aeroplane pilot if his current position differs form the expected position by more then 1km • alert an emergency service if an aeroplane enters a tornado or a hurricane

6. Problem Statement • All the previous questions can be answered using present DBMS or DSMS systems. • Existing commercial DBMS support geospatial data but cannot handle streaming data and continuous queries. • while existing DSMS support data streams and continuous queries and have only rudimentary support for spatial data.

7. Conclusion and Future work • This paper, has implemented a model which consists of data types and operations needed to support streaming spatio-temporal data. • This paper has outlined a query which supports both one time queries and continuous queries. • The future work proposed by the author is to implement a DSMS prototype which fully supports spatial streaming data and enables spatio-temporal data stream processing.

8. Paper-2 Hierarchical Data Management for Spatial-Temporal Information in WSNs

9. Introduction: • The aim of this paper is to create a hierarchical data management system for spatial temporal information in Wireless Sensor Networks (WSNs). • Utilizing limited local storage capacity at individual sensor nodes efficiently to store historical data and produce fast and approximate responses to user queries is one of the significant design challenge in the sensor networks • A hierarchical data caching technique which consists of sensor nodes, cache nodes, and the base station to efficiently answer user queries with different data quality levels id proposed in this paper.

10. Introduction(continued): • In the proposed system, each sensor node forecasts and summarizes sensed data. • Then cache nodes receive and merge the forecasting parameters from sensor nodes, and provide approximate answers to spatial-temporal user queries. • Thus transmission cost can be significantly saved. • The query is forwarded to lower levels if the accuracy of query does not satisfy user-specified confidence bound.

11. Motivation: • Transmission between sensor nodes using raw time-series data is not feasible in sensor network applications as it has been demonstrated that energy consumption due to packet transmission cost is the dominant source of energy depletion and consequent loss of data in wireless sensor networks. • For the above mentioned reason, authors propose data compression and data aggregation techniques to reduce the amount of data size and minimize the communication overhead .

12. Problem statement: • The main problem in sensor networks is utilizing the limited local storage capacity at individual sensor nodes efficiently to store historical data and produce fast and approximate responses to user queries. • A hierarchical data caching technique which consists of sensor nodes, cache nodes, and the base station to efficiently answer user queries with different data quality levels id proposed in this paper.

13. Conclusion • Within user specified accuracy requirements, the proposed cache management strategies can produce satisfactory responses to injected user queries and incur much less time and computational overheads. • The simulation results illustrate that the proposed caching system can further reduce the transmission overhead. • Finally the results from the simulation show that the online linear data forecasting methods are feasible to be a data caching technique that can be utilized in wireless sensor networks.

14. Future work: • Balancing the storage utilization to maximize the effective storage capacity in the data caching system and implementation of data migration at each cache node to redistribute existing data summaries to other cache nodes can be taken up for future work .

15. PAPER 3 A Spatio-Temporal Database Prototype for Managing Moving Objects in GIS

16. INTRODUCTION : • In this paper, author presents the design and implementation of a spatio-temporal database prototype for managing moving objects. Proposed methods : • A Trajectory Model. • S-TB tree is proposed in the prototype.

17. Motivation : • The location technologies, such as GPS and telegraphy, are producing more and more data of moving objects. • Spatiotemporal database is needed to manage these data, so as to solve the problems in spatio-temporal applications. • However, there are few prototypes of spatio-temporal database systems yet. This is because attention is always paid on some specified aspects not the whole system. • To overcome these drawbacks author proposes a spatio-temporal database prototype .

18. PROBLEM DEFINITION : • A time dimension is added to the moving objects two dimension spatial attribute. So moving objects’ movement in the plane can be treated as a trajectory in the 3- Dimension spatio-temporal space. According to this idea, we propose the trajectory model. • In the trajectory model, each moving object’s movement is divided into continuous sections/ Fragments.

19. CONCLUSION AND FUTURE WORK : • This paper has provided an overview of the design of a spatio-temporal database prototype for managing moving objects. • A S-TB tree index is built on this trajectory model and it is used in the filter of query processing.

20. REFERNCES : • [1] A. P. Sistla, O. Wolfson, S. Chamberlain, S. Dao. “Modeling and Querying Moving Objects”. Proc. Of ICDE 1997. • [2] R. Guting, M. Bohlen, M. Erwig, C. Jensen, Lorentzos N, M. Schneider,and M. Vazirgianis. “A foundation for representing and querying moving objects”. Technical Report 238, FerUniversitat das Hagen(Germany), 1998. • [3] S. Yoon and C. Shahabi, “The Clustered Aggregation (CAG) Technique Leveraging Spatial and Temporal Correlations in Wireless Sensor Networks,” ACM Transactions on Sensor Networks (TOSN’07) archive, vol. 3, no. 1, 2007. • [4] S. Ratnasamy, B. Karp, L. Yin, F. Yu, D. Estrin, R. Govindan, and S.Shenker, “GHT: A Geographical Hash-Table for Data-Centric Storage,” 1st ACM International Workshop on Wireless Sensor Networks and their Applications, 2002. • A. Arasu, S. Babu, and J. Widom. The CQL continuous query language: semantic foundations and query execution. The International Journal on Very Large Databases. • L. Forlizzi, R. H. G¨uting, E. Nardelli, and M. Schneider. A data model and data structures for moving objects databases. In SIGMOD Conference.

21. COMPARISONS :

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23. Thank You http://students.cse.unt.edu/~gk0096/ 23/16