A New Progressive Algorithm for a Multiple Longest Common Subsequences Problem and Its Efficient Parallelization

Yang, Jiaoyun; Xu, Yun; Sun, Guoqiang; Shang, Yi

doi:10.1109/tpds.2012.202

Cited by 19 publications

(8 citation statements)

References 27 publications

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“…In practice, for MLCS problems with large number of sequences, the traditional algorithms usually need a long time and large space to find the optimal solution (the complete MLCS), to address this issue, approximate algorithms have been investigated to quickly produce a suboptimal solution (partial MLCS) and gradually improve it when given more time, until an optimal one is found. Yang et al ( 2013 ) proposed an approximate algorithm Pro-MLCS as well as its efficient parallelization based on the dominant point model. Pro-MLCS can find an approximate solution quickly, which only takes around 3% of the entire running time, and then progressively generates better solutions until obtaining the optimal one.…”

Section: Methodsmentioning

confidence: 99%

A Novel Efficient Graph Model for the Multiple Longest Common Subsequences (MLCS) Problem

Zhan

Wang

2017

Front. Genet.

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Searching for the Multiple Longest Common Subsequences (MLCS) of multiple sequences is a classical NP-hard problem, which has been used in many applications. One of the most effective exact approaches for the MLCS problem is based on dominant point graph, which is a kind of directed acyclic graph (DAG). However, the time and space efficiency of the leading dominant point graph based approaches is still unsatisfactory: constructing the dominated point graph used by these approaches requires a huge amount of time and space, which hinders the applications of these approaches to large-scale and long sequences. To address this issue, in this paper, we propose a new time and space efficient graph model called the Leveled-DAG for the MLCS problem. The Leveled-DAG can timely eliminate all the nodes in the graph that cannot contribute to the construction of MLCS during constructing. At any moment, only the current level and some previously generated nodes in the graph need to be kept in memory, which can greatly reduce the memory consumption. Also, the final graph contains only one node in which all of the wanted MLCS are saved, thus, no additional operations for searching the MLCS are needed. The experiments are conducted on real biological sequences with different numbers and lengths respectively, and the proposed algorithm is compared with three state-of-the-art algorithms. The experimental results show that the time and space needed for the Leveled-DAG approach are smaller than those for the compared algorithms especially on large-scale and long sequences.

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Section: Methodsmentioning

confidence: 99%

A Novel Efficient Graph Model for the Multiple Longest Common Subsequences (MLCS) Problem

Zhan

Wang

2017

Front. Genet.

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“…185 posed in the recent literature [21,22]. For each cluster c, we will check for the most common substring occurring in the raw log.…”

Section: Sequence (Mlcs) Technique Solutions Of This Classical Problmentioning

confidence: 99%

Graph clustering and anomaly detection of access control log for forensic purposes

Studiawan

Payne

Sohel

2017

Digital Investigation

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Attacks on operating system access control have become a significant and increasingly common problem. This type of security threat is recorded in a forensic artifact such as an authentication log. Forensic investigators will generally examine the log to analyze such incidents. An anomaly is highly correlated to an attacker's attempts to compromise the system. In this paper, we propose a novel method to automatically detect an anomaly in the access control log of an operating system. The logs will be first preprocessed and then clustered using an improved MajorClust algorithm to get a better cluster. This technique provides parameter-free clustering so that it automatically can produce an analysis report for the forensic investigators. The clustering results will be checked for anomalies based on a score that considers some factors such as the total members in a cluster, the frequency of the events in the log file, and the inter-arrival time of a specific activity. We also provide a graph-based visualization of logs to assist the investigators with easy analysis. Experimental results compiled on an open dataset of a Linux authentication log show that the proposed method achieved the accuracy of 83.14% in the authentication log dataset.

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“…Pro-MLCS is a state-of-the-art anytime algorithm for MLCS [22]. It adopts an iterative best first search strategy to progressively output better and better solutions.…”

Section: Mlcs Problem Formulation and Related Workmentioning

confidence: 99%

A Space-Bounded Anytime Algorithm for the Multiple Longest Common Subsequence Problem

Yang

Shang

et al. 2014

IEEE Trans. Knowl. Data Eng.

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The multiple longest common subsequence (MLCS) problem, related to the identification of sequence similarity, is an important problem in many fields. As an NP-hard problem, its exact algorithms have difficulty in handling large-scale data and time- and space-efficient algorithms are required in real-world applications. To deal with time constraints, anytime algorithms have been proposed to generate good solutions with a reasonable time. However, there exists little work on space-efficient MLCS algorithms. In this paper, we formulate the MLCS problem into a graph search problem and present two space-efficient anytime MLCS algorithms, SA-MLCS and SLA-MLCS. SA-MLCS uses an iterative beam widening search strategy to reduce space usage during the iterative process of finding better solutions. Based on SA-MLCS, SLA-MLCS, a space-bounded algorithm, is developed to avoid space usage from exceeding available memory. SLA-MLCS uses a replacing strategy when SA-MLCS reaches a given space bound. Experimental results show SA-MLCS and SLA-MLCS use an order of magnitude less space and time than the state-of-the-art approximate algorithm MLCS-APP while finding better solutions. Compared to the state-of-the-art anytime algorithm Pro-MLCS, SA-MLCS and SLA-MLCS can solve an order of magnitude larger size instances. Furthermore, SLA-MLCS can find much better solutions than SA-MLCS on large size instances.

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A New Progressive Algorithm for a Multiple Longest Common Subsequences Problem and Its Efficient Parallelization

Cited by 19 publications

References 27 publications

A Novel Efficient Graph Model for the Multiple Longest Common Subsequences (MLCS) Problem

A Novel Efficient Graph Model for the Multiple Longest Common Subsequences (MLCS) Problem

Graph clustering and anomaly detection of access control log for forensic purposes

A Space-Bounded Anytime Algorithm for the Multiple Longest Common Subsequence Problem

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