This work is motivated by the strong demand for flash-friendly index designs to resolve reliability and performance concerns for data manipulation over flash memory. In comparison to previous work, we propose and explore the impact of hot-data access, sibling-link updates, and different workload types to a tree index structure over flash memory. In particular, a flash-friendly${B^ + }$-tree, referred to as an Adaptive Durable${B^ + }$-tree, is proposed to not only improve the endurance but also the performance of a tree index structure. The capability of the proposed methodology and index design is evaluated through a series of experiments, in which significant improvement on endurance was achieved in comparison to previous reports on the subject.

As flash memory emerges as a high-performance and energy-efficient alternative for storage devices, how to accommodate disk-based storage servers with a flash-memory cache might provide a promising solution to resolve the energy-efficiency concerns of storage servers and their data centers. In this work, we propose a cache design method over flash memory without any additional hardware support for storage servers. In particular, a scalable set-associative flash-cache management design is proposed to significantly improve the flash endurance with excellent caching response time and hit rates. The capability of the proposed scheme was evaluated by extensive experiments over Microsoft’s server traces, in which significant improvement on endurance was achieved, compared to existing work.

Multi-dimensional temporal data set is the common format in sensor network applications to store sampled temporal data. As time goes on, the size of the core tables in the data set may increase to enormous size and the tables become not managable. In order to reduce storage space and allow on-line query, how to trade off data compression effectiveness for on-line query performance is a challenge issue. In this paper, we are concerned with an effective framework for temporal data set that does not scarify on-line query performance and is specifically designed for very large sensor network database. The sampled data are compressed using several candidate approaches including dictionary-base compress and lossless vector quantization. In the mean time, on-line queries are conducted without decompressing the compressed data set so as to enhance the query performance. Experiments are conducted on a power meter database and sonoma database to evaluate the proposed methodologies in terms of data compression rate and data query speed. The results show that the compression rate ranges from 70% for numerical data to 20% for character data. In the mean time, the increased overhead for online query is limited up to 2%.

A large-scaled RFID application often needs a highly efficient database system in data processing. This research is motivated by the strong demand of an efficient index structure design for main-memory database systems of RFID applications. In this paper, a signature-based grid index structure is proposed for efficient data queries and storage. An efficient methodology is proposed to locate duplicates and to execute batch deletions and range queries based on application domain knowhow. The capability of the design is implemented in an open source main-memory database system H2 and evaluated by realistic workloads of RFID applications.