1868308
Plan Mining by Divide-and-Conquer
Plans or sequences of actions are an important form of data With the proliferation of database technology plan databases or planbases are increasingly common E cient discovery of important patterns of actions in plan databases presents a challenge to data mining In this paper we present a method for mining signi cant patterns of successful actions in a large planbase using a divide and conquer strategy The method exploits multi dimensional generalization of sequences of actions and extracts the inherent hierarchical structure and sequential patterns of plans at di erent levels of abstraction These patterns are used in turn to subsequently narrow down the search for more speci c patterns The process is analogous to the use of divide and conquer methods in hierarchical planning We illustrate our approach using a travel planning database
5296157
A Novel Scalable Algorithm for Supervised Subspace Learning
Subspace learning approaches aim to discover important statistical distribution on lower dimensions for high dimensional data. Methods such as principal component analysis (PCA) do not make use of the class information, and linear discriminant analysis (LDA) could not be performed efficiently in a scalable way. In this paper, we propose a novel highly scalable supervised subspace learning algorithm called as supervised Kampong measure (SKM). It assigns data points as close as possible to their corresponding class mean, simultaneously assigns data points to be as far as possible from the other class means in the transformed lower dimensional subspace. Theoretical derivation shows that our algorithm is not limited by the number of classes or the singularity problem faced by LDA. Furthermore, our algorithm can be executed in an incremental manner in which learning is done in an online fashion as data streams are received. Experimental results on several datasets, including a very large text data set RCV1, show the outstanding performance of our proposed algorithm on classification problems as compared to PCA, LDA and a popular feature selection approach, information gain (IG).
9645876
Handling feature interactions in process-planning
Abstract The development of a successful approach to the integration of computer-aided design and computer-aided process-planning will require solutions to several fundamental problems in planning and geometric reasoning. In this article, we consider two such problems, both of which arise from interactions among machinable features. The first problem is that interactions can lead to several alternative interpretations of what the machinable features are. We address this problem by developing an algebra of features. The second problem is that interactions among features can cause problems in optimizing the process plans. For this problem, we have developed a branch-and-bound optimization algorithm with good heuristics to guide the search. This article presents the theory and mathematical framework for both problems and also the details of the implementation and experimental results.
13595177
Multiple-Goal Recognition from Low-Level Signals
Researchers and practitioners from both the artificial intelligence and pervasive computing communities have been paying increasing attention to the task of inferring users' high-level goals from low-level sensor readings. A common assumption made by most approaches is that a user either has a single goal in mind, or achieves several goals sequentially. However, in real-world environments, a user often has multiple goals that are concurrently carried out, and a single action can serve as a common step towards multiple goals. In this paper, we formulate the multiple-goal recognition problem and exemplify it in an indoor environment where an RF-based wireless network is available. We propose a goal-recognition algorithm based on a dynamic model set and show how goal models evolve over time based on pre-defined states. Experiments with real data demonstrate that our method can accurately and efficiently recognize multiple interleaving goals in a user's trace.
17216165
Cut-and-Pick Transactions for Proxy Log Mining
Web logs collected by proxy servers, referred to as proxy logs or proxy traces, contain information about Web document accesses by many users against many Web sites. This "many-to-many" characteristic poses a challenge to Web log mining techniques due to the difficulty in identifying individual access transactions. This is because in a proxy log, user transactions are not clearly bounded and are sometimes interleaved with each other as well as with noise. Most previous work has used simplistic measures such as a fixed time interval as a determination method for the transaction boundaries, and has not addressed the problem of interleaving and noisy transactions. In this paper, we show that this simplistic view can lead to poor performance in building models to predict future access patterns. We present a more advanced cut-and-pick method for determining the access transactions from proxy logs, by deciding on more reasonable transaction boundaries and by removing noisy accesses. Our method takes advantage of the user behavior that in most transactions, the same user typically visits multiple, related Web sites that form clusters. These clusters can be discovered by our algorithm based on the connectivity among Web sites. By using real-world proxy logs, we experimentally show that this cut-and-pick method can produce more accurate transactions that result in Web-access prediction models with higher accuracy.
15099164
A scalable supervised algorithm for dimensionality reduction on streaming data
Algorithms on streaming data have attracted increasing attention in the past decade. Among them, dimensionality reduction algorithms are greatly interesting due to the desirability of real tasks. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two of the most widely used dimensionality reduction approaches. However, PCA is not optimal for general classification problems because it is unsupervised and ignores valuable label information for classification. On the other hand, the performance of LDA is degraded when encountering limited available low-dimensional spaces and singularity problem. Recently, Maximum Margin Criterion (MMC) was proposed to overcome the shortcomings of PCA and LDA. Nevertheless, the original MMC algorithm could not satisfy the streaming data model to handle large-scale high-dimensional data set. Thus an effective, efficient and scalable approach is needed. In this paper, we propose a supervised incremental dimensionality reduction algorithm and its extension to infer adaptive low-dimensional spaces by optimizing the maximum margin criterion. Experimental results on a synthetic dataset and real datasets demonstrate the superior performance of our proposed algorithm on streaming data.
27730185
Parallel All-Row Preconditioned Interval Linear Solver for Nonlinear Equations on Multiprocessors
Interval Newton methods in conjunction with generalized bisection form the basis of algorithms that find all real roots within a specified X ⊂ Rn of a system of nonlinear equations F(X) = 0 with mathematical certainty, even in finite-precision arithmetic. One of the major computational cost in such methods is solving a correspondent linear interval system. This paper proposes parallel implementations of the all-row preconditioned interval linear solver on two multiprocessor architectures: shared bus multiprocessor and hypercube connected multiprocessor. Efficient parallel algorithms that are specifically tailored for these architectures are presented. The algorithms effectively use the hardware features to minimize communication overheads. Performance evaluation is carried out by means of actual measurements of the algorithms running on real parallel computers. Our numerical results show that data partitioning, data allocation and processor scheduling play important roles in the performance of the parallel computation. In both systems, significant speedup can be obtained by properly overlapping computation and communication.
2657730
Approximate Analysis of Single and Multiple Ring Networks
Asynchronous packet-switched interconnection networks with decentralized control are very appropriate for multiprocessing and data-flow architectures. The authors present performance models of single- and multiple-ring networks based on token-ring, slotted-ring, and register-insertion-ring protocols. The multiple ring networks have the advantage of being reliable, expandable, and cost effective. An approximate and uniform analysis, based on the gate M/G/1 queuing model, has been developed to evaluate the performance of both existing single-ring networks and the proposed multiple-ring networks. Approximations are good for low and medium load. The analyses are based on symmetric ring structure with nonexhaustive service policy and infinite queue length at each station. They essentially involve modeling of queues with single- and multiple-walking servers. The results obtained from the analytical models are compared to those obtained from simulation. >
22339085
An adaptive cache coherence scheme for hierarchical shared-memory multiprocessors
The paper considers a large scale, cache-based multiprocessor that is interconnected by a hierarchical network such as hierarchical buses or a multistage interconnection network (MIN). An adaptive cache coherence scheme for the system is proposed based on a hardware approach that handles multiple shared reads efficiently while reducing the network traffic. The new protocol allows multiple copies of a shared data block in the hierarchical network, but minimizes the cache coherence overhead by dynamically partitioning the network into sharing and non-sharing regions based on program behaviors. The new cache coherence scheme effectively utilizes the bandwidth of the hierarchical networks and exploits the locality properties of parallel algorithms. The simulation results show that the new protocol gives 15% to 30% performance improvement over some existing cache coherence schemes for the similar systems over a wide range of workload parameters.<<ETX>>
31570297
Performance of Multiple-Bus Interconnections for Multiprocessors
Abstract Bus structures, in general, are easily understood and therefore preferred by manufactures for implementation. Multiple-bus systems can be viewed as an incremental expansion of single-bus architectures that can provide high bandwidth and reliability. This research note provides a brief overview of various analytical techniques suitable for performance evaluation of the multiple-bus multiprocessors. Some results and comparisons based on these techniques are also provided.
854297
Performance of Cache Memories for Vector Computers
Abstract Recent studies [5] have shown that the memory system is the major bottleneck of vector computers. Performance measurements on CRAY-2 computers indicate that as much as a factor of 4.4 performance degradation results from memory latency. In this paper, we study the performance of incorporating cache memories into vector computers. Two cache organizations that we proposed [22] are considered: direct-mapped cache and prime-mapped cache. We analyze the caching behavior of three typical blocked algorithms for numerical applications: matrix multiplication, Gaussian elimination, and FFT. By analyzing the algorithm structures in conjunction with system architectures, we develop analytical models based on real applications rather than on statistical estimates. Our performance models give the expected value of execution time of an algorithm averaged over a wide range of problem sizes. Performance measurements of the algorithms on a real machine are carried out to validate our analysis. Numerical results show that the prime-mapped cache minimizes the cache miss ratio caused by line interferences that are critical for numerical applications.