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Significance and Challenges of Big Data Research
Temporal Planning using Subgoal Partitioning and Resolution in SGPlan
This paper presents a partition-and-resolve strategy that looks for locally optimal subplans in constraint-partitioned temporal planning subpro problems and that resolves those inconsistent global constraints across the subproblems.
Editorial: Two Named to Editorial Board of IEEE Transactions on Knowledge and Data Engineering
- B. Wah
- 1 October 1996
Multi-Dimensional Regression Analysis of Time-Series Data Streams
Stream Cube: An Architecture for Multi-Dimensional Analysis of Data Streams
This paper proposes an architecture, called stream_cube, to facilitate on-line, multi-dimensional,multi-level analysis of stream data, and proposes an efficient stream data cubing algorithm which computes only the layers (cuboids) along a popular path and leaves the other cuboids for query-driven, on- line computation.
Algorithms for the satisfiability (SAT) problem: A survey
- Jun Gu, P. Purdom, J. Franco, B. Wah
- Computer ScienceSatisfiability Problem: Theory and Applications
This survey presents a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective and describes sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms.
A Discrete Lagrangian-Based Global-Search Method for Solving Satisfiability Problems
This paper derives new approaches for applying Lagrangian methods in discrete space, shows that an equilibrium is reached when a feasible assignment to the original problem is found and presents heuristic algorithms to look for equilibrium points, and proposes a new discrete Lagrange-multiplier-based global-search method (DLM) for solving satisfiability problems.
Star-Cubing: Computing Iceberg Cubes by Top-Down and Bottom-Up Integration
An Efficient Global-Search Strategy in Discrete Lagrangian Methods for Solving Hard Satisfiability Problems
An efficient global-search strategy in an algorithm based on the theory of discrete Lagrange multipliers for solving difficult SAT instances, which is more general than trap escaping because it tries to avoid visiting the same region repeatedly even when the trajectory is not inside a trap.
Scheduling of Genetic Algorithms in a Noisy Environment
New methods for adjusting configuration parameters of genetic algorithms operating in a noisy environment by model the search process as a statistical selection process and derive equations useful for problems related to the scheduling of resources for tests performed in genetic algorithms are developed.