Query evaluation techniques for large databases
ACM Computing Surveys (CSUR)
“Honey, I shrunk the database”: footprint, mobility, and beyond
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Access path selection in a relational database management system
SIGMOD '79 Proceedings of the 1979 ACM SIGMOD international conference on Management of data
Implementation techniques for main memory database systems
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
IBM DB2 Everyplace: A Small Footprint Relational Database System
Proceedings of the 17th International Conference on Data Engineering
PicoDMBS: Scaling Down Database Techniques for the Smartcard
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Memory requirements for query execution in highly constrained devices
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A database striptease or how to manage your personal databases
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
| Hi-index | 0.00 |
Lightweight computing devices are becoming ubiquitous and an increasing number of applications are being developed for these devices. Many applications deal with a significant amount of data and involve complex joins and aggregate operations which necessitate a local database management system on the device. However, scaling down the DBMS is a challenge as these devices are constrained by limited stable storage and main memory. Optimum utilization of these limited resources is a must for such a database system. New storage models that reduce storage costs are needed and the best storage scheme should be selected based on data characteristics and nature of queries. Memory should be optimally allocated among the database operators and the best query plan should be chosen depending on the amount of available memory and the underlying storage scheme. We propose a novel storage model, ID based Storage, which reduces storage costs considerably. We present an exact algorithm for allocating memory among the database operators. Due to its high complexity, we also propose a heuristic solution based on the benefit of an operator per unit memory allocation. Our storage management and query processing strategy ensures the best storage scheme and query execution plan for a given handheld device.