MEMS based storage architecture for relational databases

Yu, Hailing; Agrawal, Divyakant; Abbadi, Amr El

doi:10.1007/s00778-005-0176-2

Cited by 2 publications

(24 citation statements)

References 9 publications

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“…Yu et al [22] places relational data on the MEMS storage device such that projection queries are performed efficiently. Yu et al [21] places two-dimensional spatial data such that spatial range queries are performed efficiently.…”

Section: Device-specific Approachesmentioning

confidence: 99%

“…non-simultaneous-access sector group in the direction of the Sector axis. We map the tip sectors in the non-simultaneous-access sector group (i.e., tip sectors in a region) in the column-prime order as shown in Figure 7 since it is the fastest order to access all the tip sectors in a region [17,22]. We call an ordered The RS model reads or writes data by performing the following three steps repeatedly (as compared to the physical MEMS storage device described in Section 2).…”

Section: Definition Of the Rs Modelmentioning

confidence: 99%

“…The same holds for the MEMS storage device. For good data retrieval performance, we need to place data on the MEMS storage device taking advantage of its structure and access characteristics [6,18,21,22,23]. In this section, we present heuristic data placement strategies that help us efficiently find good data placements.…”

Section: Data Placement Strategies In the Rs Modelmentioning

confidence: 99%

“…As the measure of data retrieval performance, we use the time to read the data being retrieved by a query as was done by Yu et al [21,22]. We call it the retrieval time.…”

Section: Data Placement Strategies In the Rs Modelmentioning

confidence: 99%

“…These methods allow us to use the MEMS storage device easily just like a disk, but provide relatively poor data retrieval performance because they do not take full advantage of the characteristics of MEMS storage devices [18]. In the database field, methods have been proposed to directly place data on the MEMS storage device based on data access patterns of applications [21,22]. These methods provide relatively good data retrieval performance [18], but are quite sophisticated because they directly manage MEMS storage devices having a complicated structure.…”

Section: Introductionmentioning

confidence: 99%

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A Logical Model and Data Placement Strategies for MEMS Storage Devices

Kim

Whang

Kim

et al. 2009

IEICE Trans. Inf. & Syst.

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MEMS storage devices are new non-volatile secondary storages that have outstanding advantages over magnetic disks. MEMS storage devices, however, are much different from magnetic disks in the structure and access characteristics. They have thousands of heads called probe tips and provide the following two major access facilities: (1) flexibility : freely selecting a set of probe tips for accessing data, (2) parallelism : simultaneously reading and writing data with the set of probe tips selected.Due to these characteristics, it is nontrivial to find data placements that fully utilize the capability of MEMS storage devices. In this paper, we propose a simple logical model called the RegionSector (RS) model that abstracts major characteristics affecting data retrieval performance, such as flexibility and parallelism, from the physical MEMS storage model. We also suggest heuristic data placement strategies based on the RS model and derive new data placements for relational data and two-dimensional spatial data by using those strategies. Experimental results show that the proposed data placements improve the data retrieval performance by up to 4.0 times for relational data and by up to 4.8 times for two-dimensional spatial data of approximately 320 Mbytes compared with those of existing data placements. Further, these improvements are expected to be more marked as the database size grows.

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Section: Device-specific Approachesmentioning

confidence: 99%

Section: Definition Of the Rs Modelmentioning

confidence: 99%

Section: Data Placement Strategies In the Rs Modelmentioning

confidence: 99%

“…As the measure of data retrieval performance, we use the time to read the data being retrieved by a query as was done by Yu et al [21,22]. We call it the retrieval time.…”

Section: Data Placement Strategies In the Rs Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Logical Model and Data Placement Strategies for MEMS Storage Devices

Kim

Whang

Kim

et al. 2009

IEICE Trans. Inf. & Syst.

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The ubiquitous DBMS

et al. 2010

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Advancement in mobile computing technologies has prompted strong needs for database systems that can be used in small devices such as sensors, cellular phones, PDAs, car navigators, and Ultra Mobile PCs (UMPCs). We term the database systems that are customizable for small computing devices as Ubiquitous Database Management Systems (UDBMSs). In this paper, we first review the requirements of the UDBMS. The requirements identified include lightweight DBMSs, selective convergence, flash-optimized storage systems, data synchronization, support of unstructured/semistructured data, complex database operations, selfmanagement, and security. Next, we review existing systems and research prototypes. We review the functionality of UDBMSs including the footprint size, support of standard SQL, transaction management, concurrency control, recovery, indexing, and access control. We then review the supportability of the requirements by those UDBMSs surveyed. We finally present research issues related to the UDBMS.

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MEMS based storage architecture for relational databases

Cited by 2 publications

References 9 publications

A Logical Model and Data Placement Strategies for MEMS Storage Devices

A Logical Model and Data Placement Strategies for MEMS Storage Devices

The ubiquitous DBMS

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