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Interactive software can run not only independently but also often collaboratively to perform tasks thus forming a larger group of software networks. Hence the analysis of interactions is essential as a way to measure the stability of the entire software group network, i.e. the interactive patterns and frequency. However, current studies rarely investigate the performance of software as groups but as individuals thus omitting their interactions. Especially, the performance of some traditional measurement algorithms which execute in nondistributed runtime environments is poor. In this paper, we proposed a new software group stability model concentrating on software network level behaviors as a group. An algorithm is proposed to extract key nodes and critical interactive items based on frequent interaction pattern, then the stability of software group can be assessed based on the loss of connectivity caused by removing key nodes and key edges from the network, using the algorithm SG-StaMea. Furthermore, our algorithms can quantify the stability. To validate the efficacy of our model, the Spark and Hadoop platforms have been selected as targets systems. Both experiments and experimental data showed that our algorithms have significantly improved the accuracy of software stability measurement compared to classical algorithm such as Apriori of frequent pattern.

Software dynamic birthmark techniques construct birthmarks using the captured execution traces from running the programs, which serve as one of the most promising methods for obfuscation-resilient software plagiarism detection. However, due to the perturbation caused by non-deterministic thread scheduling in multi-threaded programs, such dynamic approaches optimized for sequential programs may suffer from the randomness in multi-threaded program plagiarism detection. In this paper, we propose a new dynamic thread-aware birthmark FPBirth to facilitate multi-threaded program plagiarism detection. We first explore dynamic monitoring to capture multiple execution traces with respect to system calls for each multi-threaded program under a specified input, and then leverage the Apriori algorithm to mine frequent patterns to formulate our dynamic birthmark, which can not only depict the program’s behavioral semantics, but also resist the changes and perturbations over execution traces caused by the thread scheduling in multi-threaded programs. Using FPBirth, we design a multi-threaded program plagiarism detection system. The experimental results based on a public software plagiarism sample set demonstrate that the developed system integrating our proposed birthmark FPBirth copes better with multi-threaded plagiarism detection than alternative approaches. Compared against the dynamic birthmark System Call Short Sequence Birthmark (SCSSB), FPBirth achieves 12.4%, 4.1% and 7.9% performance improvements with respect to union of resilience and credibility (URC), F-Measure and matthews correlation coefficient (MCC) metric, respectively.

In many real-life datasets, the number of extracted frequent patterns was shown to be huge, hampering the effective exploitation of such amount of knowledge by human experts. To overcome this limitation, exact condensed representations were introduced in order to offer a small-sized set of elements from which the faithful retrieval of all frequent patterns is possible. In this paper, we introduce a new exact condensed representation only based on particular elements from the disjunctive search space. In this space, a pattern is characterized by its disjunctive support, i.e., the frequency of complementary occurrences – instead of the ubiquitous co-occurrence link – of its items. For several benchmark datasets, this representation has been shown interesting in compactness terms compared to the pioneering approaches of the literature. In this respect, we mainly focus here on proposing an efficient tool for mining this representation. For this purpose, we introduce an algorithm, called DSSRM, dedicated to this task. We also propose several techniques to optimize its mining time as well as its memory consumption. The carried out empirical study on benchmark datasets shows that DSSRM is faster by several orders of magnitude than the MEP algorithm.

Many modern applications such as sensor networks produce probabilistic data. These data are collected into an uncertain database. To interpret uncertainty and to mine frequent patterns in an uncertain database, all possible certain databases are considered, which generates an exponential number of combinations and makes the mining problem highly complicated. In practice, mining is interactive, which makes the discovery of frequent itemsets in an uncertain database even more challenging. The objective of interactive mining is to shorten the time that is required to obtain the desired patterns in the iterated lengthy mining process. The time-consuming mining process in an uncertain database is exacerbated by repeated processing if the mining is performed from scratch. Therefore, we propose an interactive mining algorithm called iDIP to solve this problem. The iDIP algorithm adopts an approximation mechanism to mine the patterns and prunes candidates by using the existing patterns. Comprehensive experiments using both real and synthetic datasets show that iDIP outperforms the well-known re-mining-based MB algorithm for 4.3 times faster on average. In addition, iDIP has good linear scalability.

In this paper, we improve a very famous algorithm Apriori for data mining. Algorithm Apriori is to mine motifs, i.e., maximal frequent patterns or itemsets. The algorithm assumes that all items are totally ordered, e.g., A < B < C < D < for items A,B,C,D etc. Furthermore, items within an itemset are sorted in lexicograghic order. We improve the algorithm, present a new one without assuming any ordering. The new algorithm looks sound and more efficient.

Information disclosure system is the law that listed companies must obey in order to insure the benefits of investors and hold up to public scrutiny. Information like financial statement changes and operation condition of listed companies must be to the public according to the system. XBRL is an XML-based extensible language for exchanging business information. The language is widely used in financial information disclosure system and becomes the standard data format of the system. The specification, taxonomy and instance documents of XBRL are researched in this paper and the method of parallel data mining for the frequent pattern of massive XBRL data is proposed based on MapReduce and HDFS. The XBRL instances of the listed companies in China are processed by using this method and proved it to be effective.