(7) Parallelism for traditional small and mid data set is ok. However, for large data set it may be overkilled in interms of paralleling each sub queries for large volume of co-current accessing.
(8) How to speed up the data accessing of archieving data does parallelization work ? Does archiving have indexing ? if not a sequential scan is required, can this be done over parallelization ?
(9) How to move large amount of data throughout the memory hierarchy of parallel computers ?
(10) Future system needs to deal with search whose part of data does come from archives
(11) Current data storage is used as read/write cache. New algorithm is required for the 3 level system buffering management
(12) Current Tx model is good for short Tx. However, for long run Tx, We need entire new approach to handel data integrity and recovering
(13)Space efficient Algorithm for Versioning and configuration model for DB to handle versions of objects
(14) Extend existing data model to include much more semantic information of data.
(15) Browsing with interrogation the nature of the process that merge data for hetergenerous and distributed database
(16) Current distributed DBMS algorithm for query processing, cocurrency control and support for multiple copies were designed for a few sites. They must be rethink for 1000, 10000 sites
(17) local cache, local replication of remote desktop become important, efficient cache maintenance is an open problem.
(18)
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