We propose a new effective method of managing flash memory space for flash memory-specific file systems based on a log-structured file system. Flash memory has attractive features such as non-volatility and fast I/O speed, but it also suffers from inability to update in situ and from limited usage (erase) cycles. These drawbacks necessitate a number of changes to conventional storage (file) management techniques. Our focus is on lowering cleaning cost and evenly utilizing flash memory cells while maintaining a balance between these two often-conflicting goals. The proposed cleaning method performs well especially when storage utilization and the degree of locality are high. The cleaning efficiency is enhanced by dynamically separating cold data and non-cold data, which is called 'collection operation.' The second goal, that of cycle-leveling, is achieved to the degree that the maximum difference between erase cycles is below the error range of the hardware. Experimental results show that the proposed technique provides sufficient performance for reliable flash storage systems.

This paper discusses a new type of semi-supervised document clustering that uses partial supervision to partition a large set of documents. Most clustering methods organizes documents into groups based only on similarity measures. In this paper, we attempt to isolate more semantically coherent clusters by employing the domain-specific knowledge provided by a document analyst. By using external human knowledge to guide the clustering mechanism with some flexibility when creating the clusters, clustering efficiency can be considerably enhanced. Experimental results show that the use of only a little external knowledge can considerably enhance the quality of clustering results that satisfy users' constraint.

Materialized views, which are derived from base relations and stored in the database, offer opportunities for significant performance gain in query evaluation by providing quick access to the pre-computed data. A materialized view can be utilized in evaluating a query if it has pre-computed result of some part of the query plan. Although many approaches to utilizing materialized views in evaluating a query have been proposed, there exist several restrictions in selecting such views. This paper proposes new ways of utilizing materialized views in answering an aggregate query. Views including relations that are not referred to in the given query are utilized. Attributes missing from a view can be recovered under certain conditions. We identify the conditions where a view may be used in evaluating a query and present the algorithm to search for the most efficient query among the equivalent ones. We also report on a simulation based on the TPC-H and GRID databases. Simulation results show that our approach provides impressive performance improvements to the data warehousing environment where aggregate views are often pre-computed and materialized.