14320982
Sequential Pattern Mining in Multi-Databases via Multiple Alignment
To efficiently find global patterns from a multi-database, information in each local database must first be mined and summarized at the local level. Then only the summarized information is forwarded to the global mining process. However, conventional sequential pattern mining methods based on support cannot summarize the local information and is ineffective for global pattern mining from multiple data sources. In this paper, we present an alternative local mining approach for finding sequential patterns in the local databases of a multi-database. We propose the theme of approximate sequential pattern mining roughly defined as identifying patterns approximately shared by many sequences. Approximate sequential patterns can effectively summerize and represent the local databases by identifying the underlying trends in the data. We present a novel algorithm, ApproxMAP, to mine approximate sequential patterns, called consensus patterns, from large sequence databases in two steps. First, sequences are clustered by similarity. Then, consensus patterns are mined directly from each cluster through multiple alignment. We conduct an extensive and systematic performance study over synthetic and real data. The results demonstrate that ApproxMAP is effective and scalable in mining large sequences databases with long patterns. Hence, ApproxMAP can efficiently summarize a local database and reduce the cost for global mining. Furthremore, we present an elegant and uniform model to identify both high vote sequential patterns and exceptional sequential patterns from the collection of these consensus patterns from each local databases.
14591723
A Sliding Window Method for Finding Recently Frequent Itemsets over Online Data Streams
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is likely to be changed as time goes by. However, most of miming algorithms or frequency approximation algorithms for a data stream do not able to extract the recent change of information in a data stream adaptively. This paper proposes a sliding window method of finding recently frequent itemsets over an online data stream. The size of a window defines a desired life-time of the information of a transaction in a data stream.
1050294
Towards pairing Java applications on SMT processors
This paper investigates various issues of pairing Java applications for multithreaded execution on Intel's hyper-threading Pentium 4 processor. We first quantify the overall performance of multiprogrammed Java applications using a metric called combined speedup. Using the performance counters provided by the Pentium 4, we then quantitatively evaluate the performance of underneath micro-architecture components and their implications to the combined speedup. A statistical model is proposed to analyze the collected data. This novel approach reveals that trace cache is the major factor determining the pairing performance. In particular, we find that the trace cache miss rates of Java applications can be utilized to predict the combined speedups. Three new scheduling strategies are proposed based on these observations and then evaluated. The experimental results show that the proposed strategies have better performance than the conventional round-robin scheduling scheme. Overall, our best strategy enables a reduction in execution time of 10.5% over the serial execution, comparing with a reduction of 5.92% achieved by the round-robin scheduling. The improvement will be increasingly significant on future SMT processors.
34576803
Multi-modal gray-level histogram modeling and decomposition
Abstract In this paper, we present a novel multi-modal histogram thresholding method in which no a priori knowledge about the number of clusters to be extracted is needed. The proposed method combines regularization and statistical approaches. By converting the approaching histogram thresholding problem to the mixture Gaussian density modeling problem, threshold values can be estimated precisely according to the parameters belonging to each contiguous cluster. Computational complexity has been greatly reduced since our method does not employ conventional iterative parameter refinement. Instead, an optimal parameter estimation interval was defined before the estimation procedure. This predefined optimal estimation interval reduces time consumption while other histogram decomposition based methods search all feature space to locate an estimation interval for each candidate cluster. Experimental results with both simulated data and real images demonstrate the robustness of our method.
115245987
Object caching for performance in object-oriented systems
Object-oriented systems exhibit a very high rate of object creation, but most objects are short-lived. As a result, memory-management overhead is significant. An application-specific coprocessor architecture to speed up object creation and memory reclamation in object-oriented systems is described. The architecture supports a bit-vector approach to dynamic storage allocation and liberation. Novel created objects reside in a cache that is reference counted. Most objects are expected to die before they age out of the cache, drastically reducing the number of references to main memory. Many existing computer architectures would require only minor compiler modification to incorporate and benefit from this coprocessor.<<ETX>>
11259233
The design and analysis of a quantitative simulator for dynamic memory management
The use of object-oriented programming in software development allows software systems to be more robust and more maintainable. At the same time, the development time and expense are also reduced. To achieve these benefits, object-oriented applications use dynamic memory management (DMM) to create generic objects that can be reused. Consequently, these applications are often highly dynamic memory intensive. For the last three decades, several DMM schemes have been proposed. Such schemes include first fit, best fit. segregated fit, and buddy systems. Because the performance (e.g., speed, memory utilization, etc.) of each scheme differs, it becomes a difficult choice in selecting the most suitable approach for an application and what parameters (e.g., block size. etc.) should be adopted. 
 
In this paper, a DMM simulation tool and its usage are presented. This tool receives DMM traces of C/C++ or Java programs and performs simulation according to the scheme (first fit, best fit, buddy system, and segregated fit) defined by the user. Techniques required to obtain memory traces are presented. At the end of each simulation run, a variety of performance metrics are reported to the users. By using this tool, software engineers can evaluate system performance and decide which algorithm is the most suitable. Moreover, hardware engineers can perform a system analysis before hardware (e.g., modified buddy system, first fit, etc.) is fabricated.
9862925
Powers of Asteroidal Triple-free Graphs with Applications
An asteroidal triple is an independent set of three vertices in a graph such that every two of them are joined by a path avoiding the closed neighborhood of the third. Graphs without asteroidal triples are called AT-free graphs. In this paper, we show that every AT-free graph admits a vertex ordering that we call a 2-cocomparability ordering. The new suggested ordering generalizes the cocomparability ordering achievable for cocomparability graphs. According to the property of this ordering, we show that every proper power G (k ≥ 2) of an AT-free graph G is a cocomparability graph. Moreover, we demonstrate that our results can be exploited for algorithmic purposes on AT-free graphs.