Data Warehouses (DWs) with large quantities of data present major performance and scalability challenges, and parallelism can be used for major performance improvement in such context. However, instead of costly specialized parallel hardware and interconnections, we focus on low-cost standard computing nodes, possibly in a non-dedicated local network. In this environment, special care must be taken with partitioning and processing. We use experimental evidence to analyze the shortcomings of a basic horizontal partitioning strategy designed for that environment, then propose and test improvements to allow efficient placement for the low-cost Node Partitioned Data Warehouse. We show experimentally that extra overheads related to processing large replicated relations and repartitioning requirements between nodes can significantly degrade speedup performance for many query patterns. We analyze a simple, easy-to-apply partitioning and placement decision that achieves good performance improvement results. Our experiments and discussion provide important insight into partitioning and processing issues for data warehouses in shared-nothing environments.
TopLearning Objectives
- 1.
Distinguish between “learning objects,” “learning resources,” “instructional devices,” and “instructional artifacts.”
- 2.
Distinguish between student vs. teacher-made artifacts.
- 3.
Summarize in your own words the current market on jobs in artifacts in your area.
- 4.
Classify primary from secondary artifacts, social artifacts and idea artifacts.
- 5.
Describe the identifying characteristics of the multipurpose frame as an instructional artifact.
- 6.
List four factors that contribute to the mental restructuring of knowledge.
TopInstructional Artifacts
Most educational and psychological researchers prefer to describe “artifact” without actually defining it, explaining its origin in the world, nor even who designed it. For example there are two profound articles by Deborah Nelson about what children know and want to know about “artifacts” (Nelson et al., 2004) and the observations that two-year-olds name artifacts by their functions (Nelson et al., 2000). Both never actually define “artifacts,” explaining their origin, how they were obtained, or who designed them. Similarly, for Waltz (2004) “artifact” is described as another way of objectifying educational technology and subjectifying the children that use it. No mention of what it an “artifact” is, no explanation of where it came from, how it can be obtained, nor even who designed it. Haryu and Imai used the term in a recent experiment.
In Study 1, three 12-year-old children were tested to determine whether they had interpreted a new noun associated with a familiar artifact to be a material name, or a new label for the object. (Haryu & Imai, 2002, p. 1378)
Although these and many other researchers describe “artifact” without actually defining it, they all ascribe importance to the term “artifact.” Its widespread use begs certain questions that arise for us in conducting Web-based educational research with or about artifacts. What is an “artifact”? Is an “artifact” something concrete or can an “artifact” be imagined, or felt, or an idea? Can we ascribe qualitative criteria to an “artifact,” such as “well-preserved,” or “rare,” or “unique”? Is it something developed by a student, by the teacher, or generated from system activity?
TopStudent- And Teacher-Made Artifacts
Teacher/Designer-Made Artifacts
Some educational researchers classify “artifacts” as products developed by a teacher, instructional designer or software developer. Bannan-Ritland (2003) says that “artifacts” are things designed by the teacher or researcher “…to engineer and construct effective learning environments (using software and other artifacts) that allow teachers and learners to make these propositions actionable” (p. 21). Bannan-Ritland’s conceptualization of “artifacts” as teacher- or designer-made devices is consistent with “resource-based teaching,” the second phase of online teaching (Mann, 2000, 1999a, 1999b).