10 research outputs found
A Logical Model and Data Placement Strategies for MEMS Storage Devices
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
Region-Sector (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.Comment: 37 page
Teaching Tip: Teaching NoSQL Databases in a Database Course for Business Students
NoSQL databases have been used in organizations for decades. Few database textbooks on the market, however, have suitable materials about NoSQL beyond general introductions for typical business students. In fact, users of the typical NoSQL systems on the software market need to have certain computer programming skills. This teaching tip introduces a small unit on NoSQL databases in a traditional database course for students in all business majors. The unit uses a Microsoft Excel-based NoSQL database example to explain the basis of NoSQL, describes the four essential types of NoSQL databases, and discusses representative NoSQL database management systems on the software market. As this unit does not require computer programming skills, it can be easily integrated into an existing relational database course for business students. The unit was tested twice. Students have demonstrated positive first-hand practice experiences of NoSQL beyond general concepts of NoSQL
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Physical Plan Instrumentation in Databases: Mechanisms and Applications
Database management systems (DBMSs) are designed with the goal set to compile SQL queries to physical plans that, when executed, provide results to the SQL queries. Building on this functionality, an ever-increasing number of application domains (e.g., provenance management, online query optimization, physical database design, interactive data profiling, monitoring, and interactive data visualization) seek to operate on how queries are executed by the DBMS for a wide variety of purposes ranging from debugging and data explanation to optimization and monitoring. Unfortunately, DBMSs provide little, if any, support to facilitate the development of this class of important application domains. The effect is such that database application developers and database system architects either rewrite the database internals in ad-hoc ways; work around the SQL interface, if possible, with inevitable performance penalties; or even build new databases from scratch only to express and optimize their domain-specific application logic over how queries are executed.
To address this problem in a principled manner in this dissertation, we introduce a prototype DBMS, namely, Smoke, that exposes instrumentation mechanisms in the form of a framework to allow external applications to manipulate physical plans. Intuitively, a physical plan is the underlying representation that DBMSs use to encode how a SQL query will be executed, and providing instrumentation mechanisms at this representation level allows applications to express and optimize their logic on how queries are executed.
Having such an instrumentation-enabled DBMS in-place, we then consider how to express and optimize applications that rely their logic on how queries are executed. To best demonstrate the expressive and optimization power of instrumentation-enabled DBMSs, we express and optimize applications across several important domains including provenance management, interactive data visualization, interactive data profiling, physical database design, online query optimization, and query discovery. Expressivity-wise, we show that Smoke can express known techniques, introduce novel semantics on known techniques, and introduce new techniques across domains. Performance-wise, we show case-by-case that Smoke is on par with or up-to several orders of magnitudes faster than state-of-the-art imperative and declarative implementations of important applications across domains.
As such, we believe our contributions provide evidence and form the basis towards a class of instrumentation-enabled DBMSs with the goal set to express and optimize applications across important domains with core logic over how queries are executed by DBMSs
An Experimental Study Into the Effect of Varying the Join Selectivity Factor on the Performance of Join Methods in Relational Databases
Relational database systems use join queries to retrieve data from two relations. Several join methods can be used to execute these queries. This study investigated the effect of varying join selectivity factors on the performance of the join methods. Experiments using the ORACLE environment were set up to measure the performance of three join methods: nested loop join, sort merge join and hash join. The performance was measured in terms of total elapsed time, CPU time and the number of I/O reads. The study found that the hash join performs better than the nested loop and the sort merge under all varying conditions. The nested loop competes with the hash join at low join selectivity factor. The results also showed that the sort merge join method performs better than the nested loop when a predicate is applied to the inner table
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Decision making theory with geographic information systems support
Decisions are made with varying degrees of effectiveness and efficiency and are influenced by a myriad of internal and external forces. Decision Support Systems (DSS) software can effectively aid decision making through processing the facts and producing meaningful outputs for use by the person or team in making the final choice. Geographic Information Systems (GIS), a form of DSS, are very effective when locational data are present. This thesis talks about using GIS software in decision making procedures