Regular Expression Constraints Research Articles

A CP-based approach for mining sequential patterns with quantities

This paper addresses the problem of mining sequential patterns (SPM) from data represented as a set ofsequences. In this work, we are interested in sequences of items in which each item is associated with its quantity.To the best of our knowledge, existing approaches don’t allow to handle this kind of sequences under constraints.In the other hand, several proposals show the efficiency of constraint programming (CP) to solve SPM problemdealing with several kind of constraints. However, in this paper, we propose the global constraint QSPM whichis an extension of the two CP-based approaches proposed in [5] and [7]. Experiments on real-life datasets showthe efficiency of our approach allowing to specify many constraints like size, membership and regular expressionconstraints.

Mining DNA Sequence Patterns with Constraints Using Hybridization of Firefly and Group Search Optimization

Abstract DNA sequence mining is essential in the study of the structure and function of the DNA sequence. A few exploration works have been published in the literature concerning sequence mining in information mining task. Similarly, in our past paper, an effective sequence mining was performed on a DNA database utilizing constraint measures and group search optimization (GSO). In that study, GSO calculation was utilized to optimize the sequence extraction process from a given DNA database. However, it is apparent that, occasionally, such an arbitrary seeking system does not accompany the optimal solution in the given time. To overcome the problem, we proposed in this work multiple constraints with hybrid firefly and GSO (HFGSO) algorithm. The complete DNA sequence mining process comprised the following three modules: (i) applying prefix span algorithm; (ii) calculating the length, width, and regular expression (RE) constraints; and (iii) optimal mining via HFGSO. First, we apply the concept of prefix span, which detects the frequent DNA sequence pattern using a prefix tree. Based on this prefix tree, length, width, and RE constraints are applied to handle restrictions. Finally, we adopt the HFGSO algorithm for the completeness of the mining result. The experimentation is carried out on the standard DNA sequence dataset, and the evaluation with DNA sequence dataset and the results show that our approach is better than the existing approach.

Efficient parallel algorithm for multiple sequence alignments with regular expression constraints on graphics processing units

Multiple sequence alignments with constraints has become an important problem in computational biology. The concept of constrained sequence alignment is proposed to incorporate the biologist's domain knowledge into sequence alignments such that the user-specified residues/segments are aligned together in the alignment results. Over the past decade, a series of constrained multiple sequence alignment tools were proposed in the literature. RE-MuSiC is the newest tool with the regular expression constraints and useful for a wide range of biological applications. However, the computation time of REMuSiC is large for a large amount of sequences or long sequences and this problem limits the application usage. Therefore, in this paper, a tool, GPU-REMuSiC v1.0, is proposed to reduce the computation time of RE-MuSiC by using the graphics processing units with CUDA. GPU-REMuSiC v1.0 can achieve 29× speedups for overall computation time by the experimental results.

Multiple Sequence Alignments with Regular Expression Constraints on a Cloud Service System

Multiple sequence alignments with constraints are of priority concern in computational biology. Constrained sequence alignment incorporates the domain knowledge of biologists into sequence alignments such that the user-specified residues/segments are aligned together according to the alignment results. A series of constrained multiple sequence alignment tools have been developed in relevant literatures in the recent decade. GPU-REMuSiC is the most advanced method with the regular expression constraints, in which graphics processing units (GPUs) with CUDA are used. GPU-REMuSiC can achieve a speedup ratio of 29x for overall computation time based on the experimental results. However, the execution environment of GPU-REMuSiC must be constructed; it is a threshold for biologists to set up it. Therefore, this work presents an intuitive friendly user interface of GPU-REMuSiC for the potential cloud server with GPUs, called Cloud GPU-REMuSiC. Implementing the user interface via a network allows us to transmit the input data to a remote server without a complex cumbersome setting in a local host. Finally, the alignment results can be obtained from a remote cloud server with GPUs. Cloud GPU-REMuSiC is highly promising as an online application that is accessible without time or location constraints.

A Weighted Closed Sequential Patterns Mining Algorithm Based on Regular Expression Constraints

A Weighted Closed Sequential Patterns Mining Algorithm Based on Regular Expression Constraints

RE-MuSiC: a tool for multiple sequence alignment with regular expression constraints

RE-MuSiC is a web-based multiple sequence alignment tool that can incorporate biological knowledge about structure, function, or conserved patterns regarding the sequences of interest. It accepts amino acid or nucleic acid sequences and a set of constraints as inputs. The constraints are pattern descriptions, instead of exact positions of fragments to be aligned together. The output is an alignment where for each pattern (constraint), an occurrence on each sequence can be found aligned together with those on the other sequences, in a manner that the overall alignment is optimized. Its predecessor, MuSiC, has been found useful by researchers since its release in 2004. However, it is noticed in applications that the pattern formulation adopted in MuSiC, namely, plain strings allowing mismatches, is not expressive and flexible enough. The constraint formulation adopted in RE-MuSiC is therefore enhanced to be regular expressions, which is convenient in expressing many biologically significant patterns like those collected in the PROSITE database, or structural consensuses that often involve variable ranges between conserved parts. Experiments demonstrate that RE-MuSiC can be used to help predict important residues and locate phylogenetically conserved structural elements. RE-MuSiC is available on-line at http://140.113.239.131/RE-MUSIC.

First-order temporal pattern mining with regular expression constraints

Previous studies on mining sequential patterns have focused on temporal patterns specified by some form of propositional temporal logic. However, there are some interesting sequential patterns, such as the multi-sequential patterns, whose specification needs a more expressive formalism, the first-order temporal logic. Multi-sequential patterns appear in different application contexts, for instance in spatial census data mining, which is the target application of the study developed in this paper. We extend a well-known user-controlled tool, based on regular expressions constraints, to the multi-sequential pattern context. This specification tool enables the incorporation of user focus into the mining process. We present MSP-Miner, an Apriori-based algorithm to discover all frequent multi-sequential patterns satisfying a user-specified regular expression constraint.

Mining sequential patterns with regular expression constraints

Discovering sequential patterns is an important problem in data mining with a host of application domains including medicine, telecommunications, and the World Wide Web. Conventional sequential pattern mining systems provide users with only a very restricted mechanism (based on minimum support) for specifying patterns of interest. As a consequence, the pattern mining process is typically characterized by lack of focus and users often end up paying inordinate computational costs just to be inundated with an overwhelming number of useless results. We propose the use of Regular Expressions (REs) as a flexible constraint specification tool that enables user-controlled focus to be incorporated into the pattern mining process. We develop a family of novel algorithms (termed SPIRIT-Sequential Pattern mining with Regular expression consTraints) for mining frequent sequential patterns that also satisfy user-specified RE constraints. The main distinguishing factor among the proposed schemes is the degree to which the RE constraints are enforced to prune the search space of patterns during computation. Our solutions provide valuable insights into the trade-offs that arise when constraints that do not subscribe to nice properties (like anti monotonicity) are integrated into the mining process.