| Budget Amount *help |
¥2,710,000 (Direct Cost: ¥2,500,000、Indirect Cost: ¥210,000)
Fiscal Year 2007: ¥910,000 (Direct Cost: ¥700,000、Indirect Cost: ¥210,000)
Fiscal Year 2006: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2005: ¥900,000 (Direct Cost: ¥900,000)
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| Research Abstract |
A central research theme for constraint query processing includes a study on methods for solving constraint satisfaction problems in constraint databases. At the start of this study, we investigated many frameworks related to the processing of constraint databases. In order to proceed effectively, we focused on the following constraints : character sequence, network structure, spatial data, computational geometry, and finite domain. The fruitful productions of the study are below :
(1) Character Sequence Constraints
In order to solve the character sequence constraints that are represented as extracting frequent subsequences from databases storing character or word sequences, we focused on molecular sequence databases and revealed methods for extracting frequent sequential patterns with a regular expression that has a capability to represent variable wildcard regions and ambiguous characters. Moreover, we revealed a dynamic load-balancing technique on distributed parallel processing to sp
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eed up the processing of frequent variable pattern extraction.
(2) Network Structure Constraints
To solve the network structure constraints that are represented as understanding databases with a network relationship, we revealed a method for decomposing the network relation of a Blog user space using a clustering technique. Sub-networks extracted by the decomposition denote Blog user communities.
(3) Spatial Data Constraints
To solve special data constraints included in constraint databases that store special information, we focused on logs of Web services that are accessed from cellular phones tracked by GPS, and revealed a method to speed up the extraction of frequent neighboring attribute patterns from constraint databases that store spatial objects located in two-dimensional space.
(4) Computational Geometry Constraints
To solve computational geometry constraints, we focused on a reconciliation graph that is constructed by connecting the leaf layers of two ordered heterogeneous trees, i.e., genetic and taxonomic trees. As a result, we revealed a method that drastically reduces the number of crossovers on a reconciliation graph.
(5) Finite Domain Constraints
To solve finite domain constraints defined over finite sets, we focused on a job-shop scheduling problem that is defined by n jobs J_1, J_2, ., J_n of varying sizes, which need to be scheduled on m identical machines, while trying to minimize the total length of the schedule. As a result, we revealed a method to speed up the computation of a constraint solver in PC cluster environments. Less
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