17166482
Benchmarking the effectiveness of sequential pattern mining methods
Recently, there is an increasing interest in new intelligent mining methods to find more meaningful and compact results. In intelligent data mining research, accessing the quality and usefulness of the results from different mining methods is essential. However, there is no general benchmarking criteria to evaluate whether these new methods are indeed more effective compared to the traditional methods. Here we propose a novel benchmarking criteria that can systematically evaluate the effectiveness of any sequential pattern mining method under a variety of situations. The benchmark evaluates how well a mining method finds known common patterns in synthetic data. Such an evaluation provides a comprehensive understanding of the resulting patterns generated from any mining method empirically. In this paper, the criteria are applied to conduct a detailed comparison study of the support-based sequential pattern model with an approximate pattern model based on sequence alignment. The study suggests that the alignment model will give a good summary of the sequential data in the form of a set of common patterns in the data. In contrast, the support model generates massive amounts of frequent patterns with much redundancy. This suggests that the results of the support model require more post processing before it can be of actual use in real applications.
38826941
Effect of Count Estimation in Finding Frequent Itemsets over Online Transactional Data Streams
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Due to this reason, most algorithms for data streams sacrifice the correctness of their results for fast processing time. The processing time is greatly influenced by the amount of information that should be maintained. This issue becomes more serious in finding frequent itemsets or frequency counting over an online transactional data stream since there can be a large number of itemsets to be monitored. We have proposed a method called theestDec method for finding frequent itemsets over an online data stream. In order to reduce the number of monitored itemsets in this method, monitoring the count of an itemset is delayed until its support is large enough to become a frequent itemset in the near future. For this purpose, the count of an itemset should be estimated. Consequently, how to estimate the count of an itemset is a critical issue in minimizing memory usage as well as processing time. In this paper, the effects of various count estimation methods for finding frequent itemsets are analyzed in terms of mining accuracy, memory usage and processing time.
7532286
Finding recent frequent itemsets adaptively 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 more likely to be changed as time goes by. Identifying the recent change of a data stream, specially for an online data stream, can provide valuable information for the analysis of the data stream. In addition, monitoring the continuous variation of a data stream enables to find the gradual change of embedded knowledge. However, most of mining algorithms over a data stream do not differentiate the information of recently generated transactions from the obsolete information of old transactions which may be no longer useful or possibly invalid at present. This paper proposes a data mining method for finding recent frequent itemsets adaptively over an online data stream. The effect of old transactions on the mining result of the data steam is diminished by decaying the old occurrences of each itemset as time goes by. Furthermore, several optimization techniques are devised to minimize processing time as well as main memory usage. Finally, the proposed method is analyzed by a series of experiments.
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.
37599184
Finding frequent itemsets over online data streams
Abstract Conventional data mining methods for finding frequent itemsets require considerable computing time to produce their results from a large data set. Due to this reason, it is almost impossible to apply them to an analysis task in an online data stream where a new transaction is continuously generated at a rapid rate. An algorithm for finding frequent itemsets over an online data stream should support flexible trade-off between processing time and mining accuracy. Furthermore, the most up-to-date resulting set of frequent itemsets should be available quickly at any moment. To satisfy these requirements, this paper proposes a data mining method for finding frequent itemsets over an online data stream. The proposed method examines each transaction one-by-one without any candidate generation process. The count of an itemset that appears in each transaction is monitored by a lexicographic tree resided in main memory. The current set of monitored itemsets in an online data stream is minimized by two major operations: delayed-insertion and pruning . The former is delaying the insertion of a new itemset in recent transactions until the itemset becomes significant enough to be monitored. The latter is pruning a monitored itemset when the itemset turns out to be insignificant. The number of monitored itemsets can be flexibly controlled by the thresholds of these two operations. As the number of monitored itemsets is decreased, frequent itemsets in the online data stream are more rapidly traced while they are less accurate. The performance of the proposed method is analyzed through a series of experiments in order to identify its various characteristics.
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.
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.
14086063
A modular eigen subspace scheme for high-dimensional data classification
In this paper, a novel filter-based greedy modular subspace (GMS) technique is proposed to improve the accuracy of high-dimensional data classification. The proposed approach initially divides the whole set of high-dimensional features into several arbitrary number of highly correlated subgroups by performing a greedy correlation matrix reordering transformation for each class. These GMS can be treated as not only a preprocess of GMS filter-based classifiers but also a unique feature extractor to generate a particular feature subspaces for each different class presented in high-dimensional data. The similarity measures are next calculated by projecting the samples into different modular feature subspaces. Finally, a GMS filter-based architecture based on the mean absolute errors criterion is adopted to build a non-linear multi-class classifier. The proposed GMS filter-based classification scheme is developed to find non-linear boundaries of different classes for high-dimensional data. It not only significantly improves the classification accuracy but also dramatically reduces the computational complexity of feature extraction compared with the conventional principal components analysis. Experimental results demonstrate that the proposed GMS feature extraction method suits the GMS filter-based classifier best as a classification preprocess. It significantly improves the precision of high-dimensional data classification.
43225887
DMMX: Dynamic memory management extensions
Dynamic memory management allows programmers to be more productive and increases system reliability and functionality. However, software algorithms for memory management are slow and non-deterministic. It is well known that object-oriented applications tend to be dynamic memory intensive. This has led programmers to eschew dynamic memory allocation for many real-time and embedded systems. Instead, programmers using Java or C++ as a development language frequently decide to allocate memory statically instead of dynamically. In this paper, we present the design of a bitmap-based memory allocator implemented primarily in combinational logic to allocate memory in a small, predictable amount of time. It works in conjunction with an application-specific instruction-set extension called the dynamic memory management extension (DMMX). Allocation is done through a complete binary tree of combinational logic, which allows constant-time object creation. The garbage collection algorithm is mark sweep, where the sweeping phase can be accomplished in constant time. This hardware scheme can greatly improve the speed and predictability of dynamic memory management. The proposed DMMX is an add-on approach, which allows easy integration into any CPU, hardware-implemented Java virtual machine, or processor in memory.
27069893
Advanced POC model-based Java instruction folding mechanism
Ever since Java was introduced world wide, the execution performance has always been a problem. As one of the solutions, a bytecode instruction folding process for Java processors was developed in a PicoJava model and a Producer, Operator and Consumer (POC) model, but it could not handle certain types of instruction sequences. In this paper, a new instruction folding scheme based on a new, advanced POC model is proposed and demonstrates improvement in bytecode execution. The proposed POC model is able to detect and fold all possible instruction sequence types dynamically in hardware, including a sequence that is separated by other bytecode instructions. SPEC JMV98 benchmark results show that the proposed POC model-based folder can save more than 90% of folding operations. In addition, a design of the proposed POC model-based folding process in hardware is much smaller and more efficient than traditional folding mechanisms.
27355217
Java Bytecode Optimization with Advanced Instruction Folding Mechanism
The execution performance of Java has been a problem since it was introduced world wide. As one of the solutions, a bytecode instruction folding process for Java processors was developed in a PicoJava model and a Producer, Operator and Consumer (POC) model. Although the instruction folding process in these models saved extra stack operations, it could not handle certain types of instruction sequences. In this paper, a new instruction folding scheme based on a new, advanced POC model is proposed and demonstrates improvement in byte-code execution. The proposed POC model is able to detect and fold all possible instruction sequence types, including a sequence that is separated by other bytecode instructions. SPEC JMV98 benchmark results show that the proposed POC model-based folder can save more than 90% of folding operations. In addition, a design of the proposed POC model-based folding process in hardware is much smaller and more efficient than traditional folding mechanisms. In this research, the proposed instruction folding technique can eliminate most of the stack operations and the use of a physical operand stack, and can thereby achieve the performance of high-end RISC processors.