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.
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.
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.
9345376
Finding recently frequent itemsets adaptively over online transactional 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, especially for an online data stream, can provide valuable information for the analysis of the data stream. However, most of mining algorithms or frequency approximation algorithms over a data stream do not differentiate the information of recently generated data elements from the obsolete information of old data elements which may be no longer useful or possibly invalid at present. Therefore, they are not able to extract the recent change of information in a data stream adaptively. This paper proposes a data mining method for finding recently frequent itemsets adaptively over an online transactional data stream. The effect of old transactions on the current mining result of a 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 memory usage. Finally, the performance of the proposed method is analyzed by a series of experiments 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.
17498259
estWin: adaptively monitoring the recent change of frequent itemsets over online data streams
Knowledge embedded in a data stream is likely to be changed as time goes by. Consequently, identifying the recent change of the knowledge quickly can provide valuable information for the analysis of the data stream. However, most of mining 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-based method that finds recently frequent itemsets over an online data stream adaptively. The size of a window defines a desired life-time of the information in a newly generated transaction. Consequently, only recently generated transactions in the range of the window are considered to find the frequent itemsets of a data stream.
1021056
A performance perspective on the Active Memory System
Abstract One major problem of using Java in real-time and embedded devices is the non-deterministic turnaround time of dynamic memory management systems (memory allocation and garbage collection). For the allocation, the non-determinism is often contributed by the time to perform searching, splitting, and coalescing. For the garbage collection, the turnaround time is usually determined by the size of the heap, the number of live objects, the number of object collected, and the amount of garbage collected. Even with the current state-of-the-art garbage collectors (generational and incremental schemes), they may or may not guarantee the worst-case latency. Moreover, such schemes often prolong overall garbage collection time. In this paper, the performance analysis of the proposed Active Memory Module (AMM) for embedded systems is presented. Unlike the software counterparts, the AMM can perform a memory allocation in a predictable and bounded fashion (14 cycles). Moreover, it can also yield a bounded sweeping time regardless of the number of live objects or heap size. By utilizing the proposed system, the overall speedup can be as high as 23% compared to the garbage collection system of the JDK 1.2.2 running in classic mode.
6354183
A multithreaded concurrent garbage collector parallelizing the new instruction in Java
Parallel, multithreaded Java applications such as Web servers, database servers, and scientific applications are becoming increasingly prevalent. Most of them have high object instantiation rates through the new bytecode that is implemented in a garbage collection subsystem typically. For aforementioned applications, traditional garbage collectors are often the bottleneck that limits program performance and processor utilization on multiprocessor systems. They suffer from long garbage collection pauses (stop-the-world mark-sweep algorithm) or inability of collecting cyclic garbage (reference counting approach). Generational garbage collection, however, is based only on the weak generational hypothesis that most objects die young. In this paper, a new multithreaded concurrent generational garbage collector (MCGC) based on mark-sweep with the assistance of reference counting is proposed. The MCGC can take advantage of multiple CPUs in an SMP system and the merits or light weight processes. Furthermore, the long garbage collection pause can be reduced and the garbage collection efficiency can be enhanced.
34107715
Estimating internal memory fragmentation for Java programs
Dynamic memory management has been an important part of a large class of computer programs and with the recent popularity of object oriented programming languages, more specifically Java, high performance dynamic memory management algorithms continue to be of great importance. In this paper, an analysis of Java programs, provided by the SPECjvm98 benchmark suite, and their behavior, as this relates to fragmentation, is performed. Based on this analysis, a new model is proposed which allows the estimation of the total internal fragmentation that Java systems will incur prior to the programs execution. The proposed model can also accommodate any variation of segregated lists implementation. A comparison with a previously introduced fragmentation model is performed as well as a comparison with actual fragmentation values that were extracted from SPECjvm98. Finally the idea of a test-bed application that will use the proposed model to provide to programmers/developers the ability to know, prior to a programs execution, the fragmentation and memory utilization of their programs, is also introduced. With this application at hand developers as well as designers of applications could better assess the stability, efficiency as well reliability of their applications at compile time.
17161223
Speculative incoherent cache protocols
Multiprocessing and multithreading are becoming ubiquitous even on single chips. With increasing cache sizes, coherence misses in such systems will account for a larger fraction of all cache misses. As communication latencies increase, this larger fraction of coherence misses will cause significant and increased performance losses. Tuning coherence protocols for specific communication patterns and applications can reduce communication latencies. However, these optimizations increase a protocol's design complexity, making the protocol difficult to verify. A competing approach requires parallel programmers to tune applications to work well with simpler protocols. Speculative execution has successfully improved performance in various scenarios. We propose a new type of load speculation, called coherence decoupling. Coherence decoupling is a microarchitectural mechanism that implements separate protocols for speculative use and for the eventual verification of values. The technique reduces the effect of long communication latencies while mitigating the burdens on the coherence protocol designer and the parallel programmer
234543
Reusable Internal Hardware Templates
This paper describes the framework of internal hardware templates. These reusable templates can be instantiated, inside the FPGA, to the required precision. Thus, the resource utilization of the target RCMs can be improved. Moreover, the configuration time can be eliminated after the first use of the template. The detail design is presented.