Existing Pattern Matching Algorithms Research Articles

Dynamic pattern matching with multiple queries on large scale data streams

Similarity search in data streams is an important but challenging task in many practical areas where real-time pattern retrieval is required. Dynamic and fast updating data streams are often subject to outliers, noise and potential distortions in amplitude and time dimensions. Such conditions typically lead to a failure of existing pattern matching algorithms and to inability to retrieve required patterns from the stream. The main reason for such failures is the limitation of data normalization utilized in the majority of methods. Another reason is the lack of means to consider multiple examples of the same template to account for possible variations of the query signal. In this paper, we propose a dynamic normalization approach that allows bringing streaming signal subsequences to the scale of the query template. This significantly improves pattern retrieval capabilities, especially when sampling variance or time distortions are present. We further develop a pattern matching approach utilizing the proposed normalization mechanism and extend it for the case when multiple examples of a query template are available. Multiple synthetic and real data experiments demonstrate that this allows to considerably improve the pattern matching rate for distorted data streams, providing real time performance.

Multi-field Pattern Matching based on Sparse Feature Sampling.

We present an approach to pattern matching in 3D multi-field scalar data. Existing pattern matching algorithms work on single scalar or vector fields only, yet many numerical simulations output multi-field data where only a joint analysis of multiple fields describes the underlying phenomenon fully. Our method takes this into account by bundling information from multiple fields into the description of a pattern. First, we extract a sparse set of features for each 3D scalar field using the 3D SIFT algorithm (Scale-Invariant Feature Transform). This allows for a memory-saving description of prominent features in the data with invariance to translation, rotation, and scaling. Second, the user defines a pattern as a set of SIFT features in multiple fields by e.g. brushing a region of interest. Third, we locate and rank matching patterns in the entire data set. Experiments show that our algorithm is efficient in terms of required memory and computational efforts.

Choosing optimal pattern matching algorithm for designing production system

The main purpose of this study is to formalize the principles of representing the features of the production system and antecedents of productions for implementing the optimal pattern matching algorithm. Optimality of this choice determines the production system efficiency since the problem of pattern matching is the most memory and time-consuming. When analyzing the experimental studies of the existing pattern matching algorithms, the main problem is that they involve different performance indicators, i.e. the characteristics for comparison may be selected so that they give preference to one of the algorithms in advance. Sample of universal characteristics of production system, taking into account the features of the solvable application task, including the properties of the antecedents of productions is proposed in the research. Based on the values of these characteristics, the estimate of each of the existing pattern matching algorithms can be calculated using the proposed schemes. The maximum value corresponds to the preferred algorithm. This choice, in turn, allows to determine or modify existing software tools for implementing computational formalism, based on the production model. An example of using the methodology for implementing a software module to define the technical state of NPP facilities is given. The research results can be used in designing the production system, aimed at solving the pre-formalized application task, mainly of technical and medical diagnostics