6717527
An Efficient and Scalable Algorithm for Multi-Relational Frequent Pattern Discovery
We propose MRFPDA, an efficient and scalable algorithm for multi-relational frequent pattern discovery. We incorporate in the algorithm an optimal refinement operator to provide an improvement of the efficiency of candidate generation. Furthermore, MRFPDA utilizes a new strategy of sharing computations to avoid redundant computations in the candidate evaluation. In our experiments, it is shown that on small datasets the performance of MRFPDA is comparable with the performance of the state-of-the-art of multi-relational frequent pattern discovery, and on large datasets MRFPDA is more scalable than two existing approaches
12895493
Improvement of HITS-based algorithms on web documents
In this paper, we present two ways to improve the precision of HITS-based algorithms on Web documents. First, by analyzing the limitations of current HITS-based algorithms, we propose a new weighted HITS-based method that assigns appropriate weights to in-links of root documents. Then, we combine content analysis with HITS-based algorithms and study the effects of four representative relevance scoring methods, VSM, Okapi, TLS, and CDR, using a set of broad topic queries. Our experimental results show that our weighted HITS-based method performs significantly better than Bharat's improved HITS algorithm. When we combine our weighted HITS-based method or Bharat's HITS algorithm with any of the four relevance scoring methods, the combined methods are only marginally better than our weighted HITS-based method. Between the four relevance-scoring methods, there is no significant quality difference when they are combined with a HITS-based algorithm.
9354212
High-Performance Job-Shop Scheduling With A Time-Delay TD-lambda Network
Job-shop scheduling is an important task for manufacturing industries. We are interested in the particular task of scheduling payload processing for NASA's space shuttle program. This paper summarizes our previous work on formulating this task for solution by the reinforcement learning algorithm T D(). A shortcoming of this previous work was its reliance on hand-engineered input features. This paper shows how to extend the time-delay neural network (TDNN) architecture to apply it to irregular-length schedules. Experimental tests show that this TDNN-TD() network can match the performance of our previous hand-engineered system. The tests also show that both neural network approaches signiicantly out-perform the best previous (non-learning) solution to this problem in terms of the quality of the resulting schedules and the number of search steps required to construct them.
43025995
Processing Probabilistic Range Query over Imprecise Data Based on Quality of Result
Sensors are employed in many applications to monitor entities such as environment temperature or illumination. Because of limited resources (e.g., battery power and wireless bandwidth), it is impractical for the database to track continuous changing readings from sensors at all times. Consequently, database may produce incorrect query results based on recorded data. However, if the error between the recorded value and the actual value is bounded, probabilistic approach can be applied to process queries over the imprecise data in database. The paper proposes a quality of result based probabilistic range query processing method. Two measures are defined to evaluate the quality of result. A probability model is proposed to estimate the quality of returned results. Based on the model, an algorithm is developed to choose the probability threshold according to user’s requirement on the quality of result.
17633305
The Impact of Cache Organization in Optimizing Microprocessor Power Consumption
In the recent years, power consumption has become increasingly an important design concern as silicon area and performance in modern computer systems design. Several factors have contributed to this trend. Perhaps the most visible have been the remarkable success and growth of Personal Digital Assistants (PDA’s), Cellular phones and pagers, etc .This work is an attempt to explore the impact of feature size shrinkage and cache configuration on optimizing power consumption in modern processors. The work studies two commercially known RISC microprocessors. The StrongARM and the Alpha-21064 microprocessors. The latter is the ancestor of the former and is therefore used here as the baseline in our analysis. Our analysis has illustrated quantitatively great power saving when technology is downsized and cache is well organized
14422830
Compiler-guided register reliability improvement against soft errors
With the scaling of technology, transient errors caused by external particle strikes have become a critical challenge for microprocessor design. As embedded processors are widely used in reliability-sensitive environments, it becomes increasingly important to develop cost-effective techniques to improve the processor reliability against soft errors. This paper focuses on studying the register file immunity against soft errors since modern processors typically employ a large number of registers, which are accessed very frequently. As a result, soft errors occurred in registers can easily propagate to functional units or the memory system, leading to silent data error (SDC) or system crash.To develop cost-effective techniques to fight soft errors for embedded processors, the first step is to understand the register file susceptibility to soft errors and its impact on the system reliability accurately. Toward this goal, this paper proposes the concept of register vulnerability factor (RVF) to characterize the probability that register transient errors can escape the register file and thus potentially impact the system reliability. Built upon the RVF concept, we then propose two cost-effective compiler-guided techniques to improve the register file reliability by lowering the RVF value. Our experiments indicate that on average, the RVF can be reduced to 9.1% and 9.5% by the hyperblock-based instruction re-scheduling and the reliability-oriented register assignment respectively, which can potentially lower the reliability cost significantly while protecting register files against transient errors.
10336575
Discovering accurate interclass test dependences
Knowledge of interclass test dependences is crucial to decide class test order, facilitating the design of an efficient integration test plan. In order to discover precise interclass test dependences, we proposed a semantic-based definition and designed a safe approximation algorithm to calculate the dependence according to the given definition. The algorithm propagates semantic dependences at method-level granularity and is parameterized by the precision of the corresponding program analysis. We have experimented with nine benchmarks and showed that the algorithm is rather accurate in that it discovers dependence cycles precisely in 6 out of 9 benchmarks (as evaluated by human inspection). The algorithm uncovers additional opportunities for concurrent testing in each of the benchmarks, with an on average expected 52.5% time savings over the ORD-based definition.
2581690
Distributed breakout revisited
Distributed breakout algorithm (DBA) is an efficient method for solving distributed constraint satisfaction problems (CSP). Inspired by its potential of being an efficient, low-overhead agent coordination method for problems in distributed sensor networks, we study DBA's properties in this paper. We specifically show that on an acyclic graph of <i>n</i> nodes, DBA can find a solution in <i>O(n<sup>2</sup>)</i> synchronized distributed steps. This completeness result reveals DBA's superiority over conventional local search on acyclic graphs and implies its potential as a simple self-stabilization method for tree-structured distributed systems. We also show a worst case of DBA in a cyclic graph where it never terminates. To overcome this problem on cyclic graphs, we propose two stochastic variations to DBA. Our experimental analysis shows that stochastic DBAs are able to avoid DBA's worst-case scenarios and has similar performance as that of DBA.
40080812
Efficient Strategies for (Weighted) Maximum Satisfiability
It is well known that the Davis-Putnam-Logemann-Loveland (DPLL) algorithm for satisfiability (SAT) can be extended to an algorithm for maximum SAT (max-SAT). In this paper, we propose a number of strategies to significantly improve this max-SAT method. The first strategy is a set of unit propagation rules; the second is an effective lookahead heuristic based on linear programming; and the third strategy is a dynamic variable ordering that exploits problem constrainedness during search. We integrate these strategies in an efficient complete solver for both max-SAT and weighted max-SAT. Our experimental results on random problem instances and many instances from SATLIB demonstrate the efficacy of these strategies and show that the new solver is able to significantly outperform most of the existing complete max-SAT solvers, with a few orders of magnitude of improvement in running time in many cases. 
 
This research was supported in part by NSF grants IIS-0196057 and ITR/EIA-0113618, and in part by DARPA Cooperative Agreement F30602-00-2-0531. We thank Zhongsheng Guo for an early implementation of the DPLL algorithm and Fadi Aloul, Javier Larrosa and Jordi Plane for making their programs available to us for this research.
34866255
An Average-Case Analysis of Branch-and-Bound with Applications: Summary of Results
Motivated by an anomaly in branch-and-bound (BnB) search, we analyze its average-case complexity. We first delineate exponential vs polynomial average-case complexities of BnB. When best-first BnB is of linear complexity, we show that depth-first BnB has polynomial complexity. For problems on which best-first BnB has exponential complexity, we obtain an expression for the heuristic branching factor. Next, we apply our analysis to explain an anomaly in lookahead search on sliding-tile puzzles, and to predict the existence of an average-case complexity transition of BnB on the Asymmetric Traveling Salesman Problem. Finally, by formulating IDA* as costbounded BnB, we show its average-case optimality, which also implies that RBFS is optimal on average.
1151096
MaxSolver: An efficient exact algorithm for (weighted) maximum satisfiability
Maximum Boolean satisfiability (max-SAT) is the optimization counterpart of Boolean satisfiability (SAT), in which a variable assignment is sought to satisfy the maximum number of clauses in a Boolean formula. A branch and bound algorithm based on the Davis-Putnam-Logemann-Loveland procedure (DPLL) is one of the most competitive exact algorithms for solving max-SAT. In this paper, we propose and investigate a number of strategies for max-SAT. The first strategy is a set of unit propagation or unit resolution rules for max-SAT. We summarize three existing unit propagation rules and propose a new one based on a nonlinear programming formulation of max-SAT. The second strategy is an effective lower bound based on linear programming (LP). We show that the LP lower bound can be made effective as the number of clauses increases. The third strategy consists of a binary-clause first rule and a dynamic-weighting variable ordering rule, which are motivated by a thorough analysis of two existing well-known variable orderings. Based on the analysis of these strategies, we develop an exact solver for both max-SAT and weighted max-SAT. Our experimental results on random problem instances and many instances from the max-SAT libraries show that our new solver outperforms most of the existing exact max-SAT solvers, with orders of magnitude of improvement in many cases.