Pattern Matching Framework Research Articles

PimPam: Efficient Graph Pattern Matching on Real Processing-in-Memory Hardware

Graph pattern matching is powerful and widely applicable to many application domains. Despite the recent algorithm advances, matching patterns in large-scale real-world graphs still faces the memory access bottleneck on conventional computing systems. Processing-in-memory (PIM) is an emerging hardware architecture paradigm that puts computing cores into memory devices to alleviate the memory wall issues. Real PIM hardware has recently become commercially accessible to the public. In this work, we leverage the real PIM hardware platform to build a graph pattern matching framework, PimPam, to benefit from its abundant computation and memory bandwidth resources. We propose four key optimizations in PimPam to improve its efficiency, including (1) load-aware task assignment to ensure load balance, (2) space-efficient and parallel data partitioning to prepare input data for PIM cores, (3) adaptive multi-threading collaboration to automatically select the best parallelization strategy during processing, and (4) dynamic bitmap structures that accelerate the key operations of set intersection. When evaluated on five patterns and six real-world graphs, PimPam outperforms the state-of-the-art CPU baseline system by 22.5x on average and up to 71.7x, demonstrating significant performance improvements.

The Consequences of Hate Crime Victimization: Considering Prejudicial Attitudes as an Outcome of Interracial Bias-Motivated Conflict

Objectives: While extant research has largely framed prejudicial attitudes as a precursor to hate crime offending, the current research considers the possibility that negative outgroup attitudes may also be an important consequence of hate crime victimization as well. Methods: Using survey data from 3,183 respondents across the United States, this research employs a series of regression models to examine the relationship between hate crime victimization and three different types of prejudicial attitudes: anti-Asian xenophobia, anti-Hispanic xenophobia, and anti-Black prejudice. Results: Results indicate that hate crime victimization is significantly and positively associated with all three prejudicial attitudes, such that those who experienced a hate crime are more likely to report higher levels of anti-Asian xenophobia ( b = .455; p < .001), anti-Hispanic xenophobia ( b = .408; p < .001), and anti-Black prejudice ( b = .360; p < .001). Results also indicate that these patterns are both offender race-specific and stronger for more recent victimization, compared to less recent victimization. Conclusions: When interpreting these results within a pattern matching framework, we argue that they suggest important initial evidence that prejudice may not be just a precursor to hate crime offending, but also a possible consequence of hate crime victimization, thus implying a potentially cyclical relationship between intergroup offending and victimization that should be the subject of future research.

Locating patterns in Nanopore currents using time-warped signal representation of consensus nucleotides for demultiplexing and motif detection.

Nanopore-based approaches for the sequencing of DNA and RNA molecules are promising technologies with potential applications in clinical genomics. These approaches have generated large numbers of time series objects over the years, however, it remains a challenge to accurately decipher the underlying nucleotide sequence corresponding to a given signal. By using a combination of consensus signal averaging and stream monitoring of variable-length motifs, we outline an online pattern matching framework that can efficiently locate consensus sequences in real world Nanopore datasets. We demonstrate the applicability of our proposed framework across two use-cases: demultiplexing of DNA barcodes and multiple motif site identification in RNA transcripts.

OpenForensics: A digital forensics GPU pattern matching approach for the 21st century

Pattern matching is a crucial component employed in many digital forensic (DF) analysis techniques, such as file-carving. The capacity of storage available on modern consumer devices has increased substantially in the past century, making pattern matching approaches of current generation DF tools increasingly ineffective in performing timely analyses on data seized in a DF investigation. As pattern matching is a trivally parallelisable problem, general purpose programming on graphic processing units (GPGPU) is a natural fit for this problem. This paper presents a pattern matching framework – OpenForensics – that demonstrates substantial performance improvements from the use of modern parallelisable algorithms and graphic processing units (GPUs) to search for patterns within forensic images and local storage devices.

Efficient graph pattern matching framework for network-based in-vehicle fault detection

Abnormal messages propagated from faulty operations in an in-vehicle network may severely harm a vehicular system, but they cannot be easily detected when their information is not known in advance. To support an efficient detection of faulty message patterns propagated in the in-vehicle network, this paper presents a novel graph pattern matching framework built upon a message log-driven graph modeling. Our framework models the unknown operation of the in-vehicle network as a query graph and the reference database of normal operations as data graphs. Given a query graph and data graphs, we determine whether the query graph represents normal or fault operation by using the distance measure on the data graphs. The analysis of the faulty message propagation requires to consider the sequence of events in the distance measure, and thus, using the conventional graph distance measures can generate false negatives due to the lack of consideration of the sequence relationships among the events. We therefore propose a novel distance metric based on the maximum common subgraph (MCS) between two graphs and the sequence numbers of messages, which works robustly even for the abnormal faulty patterns and can avoid false negatives in large databases. Since the problem of MCS computation is NP-hard, we also propose two efficient filtering techniques, one based on the lower bound of the MCS distance for a polynomial-time approximation and the other based on edge pruning. Experiments performed on real and synthetic datasets to assess our framework show that ours significantly outperforms the previously existing methods in terms of both performance and accuracy of query responses.

Towards program theory validation: Crowdsourcing the qualitative analysis of participant experiences

This exploratory study examines a novel tool for validating program theory through crowdsourced qualitative analysis. It combines a quantitative pattern matching framework traditionally used in theory-driven evaluation with crowdsourcing to analyze qualitative interview data. A sample of crowdsourced participants are asked to read an interview transcript and identify whether program theory components (Activities and Outcomes) are discussed and to highlight the most relevant passage about that component. The findings indicate that using crowdsourcing to analyze qualitative data can differentiate between program theory components that are supported by a participant’s experience and those that are not. This approach expands the range of tools available to validate program theory using qualitative data, thus strengthening the theory-driven approach.

A Unified Framework for Both Graph and Pattern Matching Using IPaMWAG

A Unified Framework for Both Graph and Pattern Matching Using IPaMWAG

Index-supported pattern matching on tuples of time-dependent values

Lately, the amount of mobility data recorded by GPS-enabled (and other) devices has increased drastically, entailing the necessity of efficient processing and analysis methods. In many cases, not only the geographic position, but also additional time-dependent information are traced and/or generated, according to the purpose of the evaluation. For example, in the field of animal behavior research, besides the position of the monitored animal, biologists are interested in further data like the altitude or the temperature at every measuring point. Other application domains comprise the names of streets, places of interest, or transportation modes that can be recorded along with the geographic position of a person. In this paper, we present in detail a framework for analyzing datasets with arbitrarily many time-dependent attributes. This can be considered as a major extension of our previous work, a comprehensive framework for pattern matching on symbolic trajectories with index support. For an efficient processing of different data types, a variable number of indexes of four different types that correspond to the data types of the attributes are applied. We demonstrate the expressiveness and efficiency of our approach by querying a real dataset representing taxi trips in Rome and, particularly, with a broad series of experiments using trajectories generated by BerlinMOD combined with geological raster data.

An attribute detection based approach to automatic speech processing

State-of-the-art automatic speech and speaker recognition systems are often built with a pattern matching framework that has proven to achieve low recognition error rates for a variety of resource-rich tasks when the volume of speech and text examples to build statistical acoustic and language models is plentiful, and the speaker, acoustics and language conditions follow a rigid protocol. However, because of the “blackbox” top-down knowledge integration approach, such systems cannot easily leverage a rich set of knowledge sources already available in the literature on speech, acoustics and languages. In this paper, we present a bottom-up approach to knowledge integration, called automatic speech attribute transcription (ASAT), which is intended to be “knowledge-rich”, so that new and existing knowledge sources can be verified and integrated into current spoken language systems to improve recognition accuracy and system robustness. Since the ASAT framework offers a “divide-and-conquer” strategy and a “plug-andplay” game plan, it will facilitate a cooperative speech processing community that every researcher can contribute to, with a view to improving speech processing capabilities which are currently not easily accessible to researchers in the speech science community.

Selective memory generalization by spatial patterning of protein synthesis.

Protein synthesis is crucial for both persistent synaptic plasticity and long-term memory. De novo protein expression can be restricted to specific neurons within a population, and to specific dendrites within a single neuron. Despite its ubiquity, the functional benefits of spatial protein regulation for learning are unknown. We used computational modeling to study this problem. We found that spatially patterned protein synthesis can enable selective consolidation of some memories but forgetting of others, even for simultaneous events that are represented by the same neural population. Key factors regulating selectivity include the functional clustering of synapses on dendrites, and the sparsity and overlap of neural activity patterns at the circuit level. Based on these findings, we proposed a two-step model for selective memory generalization during REM and slow-wave sleep. The pattern-matching framework we propose may be broadly applicable to spatial protein signaling throughout cortex and hippocampus.

Incremental graph pattern matching

Graph pattern matching is commonly used in a variety of emerging applications such as social network analysis. These applications highlight the need for studying the following two issues. First, graph pattern matching is traditionally defined in terms of subgraph isomorphism or graph simulation. These notions, however, often impose too strong a topological constraint on graphs to identify meaningful matches. Second, in practice a graph is typically large, and is frequently updated with small changes. It is often prohibitively expensive to recompute matches starting from scratch via batch algorithms when the graph is updated. This article studies these two issues. (1) We propose to define graph pattern matching based on a notion of bounded simulation , which extends graph simulation by specifying the connectivity of nodes in a graph within a predefined number of hops. We show that bounded simulation is able to find sensible matches that the traditional matching notions fail to catch. We also show that matching via bounded simulation is in cubic time, by giving such an algorithm. (2) We provide an account of results on incremental graph pattern matching, for matching defined with graph simulation, bounded simulation, and subgraph isomorphism. We show that the incremental matching problem is unbounded , that is, its cost is not determined alone by the size of the changes in the input and output, for all these matching notions. Nonetheless, when matching is defined in terms of simulation or bounded simulation, incremental matching is semibounded , that is, its worst-time complexity is bounded by a polynomial in the size of the changes in the input, output, and auxiliary information that is necessarily maintained to reuse previous computation, and the size of graph patterns. We also develop incremental matching algorithms for graph simulation and bounded simulation, by minimizing unnecessary recomputation. In contrast, matching based on subgraph isomorphism is neither bounded nor semibounded. (3) We experimentally verify the effectiveness and efficiency of these algorithms, and show that: (a) the revised notion of graph pattern matching allows us to identify communities commonly found in real-life networks, and (b) the incremental algorithms substantially outperform their batch counterparts in response to small changes. These suggest a promising framework for real-life graph pattern matching.

Computing metamorphoses between discrete measures

Metamorphosis is a mathematical framework for diffeomorphic pattern matching in which one defines a distanceon a space of images or shapes. In the case of image matching, this distance involves computing the energetically optimalway in which one image can be morphed into the other, combining both smooth deformations and changes in the image intensity.In [12], Holm, Trouvé and Younes studied the metamorphosis of more singular deformable objects, in particular measures.In this paper, we present results on the analysis and computation of discrete measure metamorphosis, building upon the work in [12].We show that, when matching sums of Dirac measures, minimizing evolutions can include other singular distributions, which complicates the numerical approximation of such solutions.We then present an Eulerian numerical scheme that accounts for these distributions, as well as some numerical experiments using this scheme.

A framework for traversing dense annotation lattices

Pattern matching, or querying, over annotations is a general purpose paradigm for inspecting, navigating, mining, and transforming annotation repositories—the common representation basis for modern pipelined text processing architectures. The open-ended nature of these architectures and expressiveness of feature structure-based annotation schemes account for the natural tendency of such annotation repositories to become very dense, as multiple levels of analysis get encoded as layered annotations. This particular characteristic presents challenges for the design of a pattern matching framework capable of interpreting ‘flat’ patterns over arbitrarily dense annotation lattices. We present an approach where a finite state device applies (compiled) pattern grammars over what is, in effect, a linearized ‘projection’ of a particular route through the lattice. The route is derived by a mix of static grammar analysis and runtime interpretation of navigational directives within an extended grammar formalism; it selects just the annotations sequence appropriate for the patterns at hand. For expressive and efficient pattern matching in dense annotations stores, our implemented approach achieves a mix of lattice traversal and finite state scanning by exposing a language which, to its user, provides constructs for specifying sequential, structural, and configurational constraints among annotations.

Virtual double-sided image probing: A unifying framework for non-linear grayscale pattern matching

This paper focuses on non-linear pattern matching transforms based on mathematical morphology for gray level image processing. Our contribution is on two fronts. First, we unify the existing and a priori unconnected approaches to this problem by establishing their theoretical links with topology. Setting them within the same context allows to highlight their differences and similarities, and to derive new variants. Second, we develop the concept of virtual double- sided image probing (VDIP), a broad framework for non-linear pattern matching in grayscale images. VDIP extends our work on the multiple object matching using probing (MOMP) transform we previously defined to locate multiple grayscale patterns simultaneously. We show that available methods as well as the topological approach can be generalized within the VDIP framework. They can be formulated as particular variants of a general transform designed for virtual probing. Furthermore, a morphological metric, called SVDIP (single VDIP), is deduced from the VDIP concept. Some results are presented and compared with those obtained with classical methods.

Discovering gene annotations in biomedical text databases.

BackgroundGenes and gene products are frequently annotated with Gene Ontology concepts based on the evidence provided in genomics articles. Manually locating and curating information about a genomic entity from the biomedical literature requires vast amounts of human effort. Hence, there is clearly a need forautomated computational tools to annotate the genes and gene products with Gene Ontology concepts by computationally capturing the related knowledge embedded in textual data.ResultsIn this article, we present an automated genomic entity annotation system, GEANN, which extracts information about the characteristics of genes and gene products in article abstracts from PubMed, and translates the discoveredknowledge into Gene Ontology (GO) concepts, a widely-used standardized vocabulary of genomic traits. GEANN utilizes textual "extraction patterns", and a semantic matching framework to locate phrases matching to a pattern and produce Gene Ontology annotations for genes and gene products.In our experiments, GEANN has reached to the precision level of 78% at therecall level of 61%. On a select set of Gene Ontology concepts, GEANN either outperforms or is comparable to two other automated annotation studies. Use of WordNet for semantic pattern matching improves the precision and recall by 24% and 15%, respectively, and the improvement due to semantic pattern matching becomes more apparent as the Gene Ontology terms become more general.ConclusionGEANN is useful for two distinct purposes: (i) automating the annotation of genomic entities with Gene Ontology concepts, and (ii) providing existing annotations with additional "evidence articles" from the literature. The use of textual extraction patterns that are constructed based on the existing annotations achieve high precision. The semantic pattern matching framework provides a more flexible pattern matching scheme with respect to "exactmatching" with the advantage of locating approximate pattern occurrences with similar semantics. Relatively low recall performance of our pattern-based approach may be enhanced either by employing a probabilistic annotation framework based on the annotation neighbourhoods in textual data, or, alternatively, the statistical enrichment threshold may be adjusted to lower values for applications that put more value on achieving higher recall values.

An approach to automated particle picking from electron micrographs based on reduced representation templates

Reduced representation templates are used in a real-space pattern matching framework to facilitate automatic particle picking from electron micrographs. The procedure consists of five parts. First, reduced templates are constructed either from models or directly from the data. Second, a real-space pattern matching algorithm is applied using the reduced representations as templates. Third, peaks are selected from the resulting score map using peak-shape characteristics. Fourth, the surviving peaks are tested for distance constraints. Fifth, a correlation-based outlier screening is applied. Test applications to a data set of keyhole limpet hemocyanin particles indicate that the method is robust and reliable.

Collage system: a unifying framework for compressed pattern matching

We introduce a general framework which is suitable to capture the essence of compressed pattern matching according to various dictionary-based compressions. It is a formal system to represent a string by a pair of dictionary D and sequence S of phrases in D . The basic operations are concatenation, truncation, and repetition. We also propose a compressed pattern matching algorithm for the framework. The goal is to find all occurrences of a pattern in a text without decompression, which is one of the most active topics in string matching. Our framework includes such compression methods as Lempel–Ziv family (LZ77, LZSS, LZ78, LZW), RE-PAIR, SEQUITUR, and the static dictionary-based method. The proposed algorithm runs in O((|| D||+| S|)·height( D)+m 2+r) time with O(|| D||+m 2) space, where || D|| is the size of D , | S| is the number of tokens in S , height( D) is the maximum dependency of tokens in D , m is the pattern length, and r is the number of pattern occurrences. For a subclass of the framework that contains no truncation, the time complexity is O(|| D||+| S|+m 2+r) .

2D-pattern matching image and video compression: theory, algorithms, and experiments

In this paper, we propose a lossy data compression framework based on an approximate two-dimensional (2D) pattern matching (2D-PMC) extension of the Lempel-Ziv (1977, 1978) lossless scheme. This framework forms the basis upon which higher level schemes relying on differential coding, frequency domain techniques, prediction, and other methods can be built. We apply our pattern matching framework to image and video compression and report on theoretical and experimental results. Theoretically, we show that the fixed database model used for video compression leads to suboptimal but computationally efficient performance. The compression ratio of this model is shown to tend to the generalized entropy. For image compression, we use a growing database model for which we provide an approximate analysis. The implementation of 2D-PMC is a challenging problem from the algorithmic point of view. We use a range of techniques and data structures such as k-d trees, generalized run length coding, adaptive arithmetic coding, and variable and adaptive maximum distortion level to achieve good compression ratios at high compression speeds. We demonstrate bit rates in the range of 0.25-0.5 bpp for high-quality images and data rates in the range of 0.15-0.5 Mbps for a baseline video compression scheme that does not use any prediction or interpolation. We also demonstrate that this asymmetric compression scheme is capable of extremely fast decompression making it particularly suitable for networked multimedia applications.

Concept mapping: Soft science or hard art?

Is concept mapping “science” or “art”? Can we legitimately claim that concept maps represent reality, or are they primarily suggestive devices which might stimulate new ways to look at our experiences? Here, the scientific side of concept mapping is viewed as “soft science” and the artistic one as “hard art” to imply that the process has some qualities of both, but probably does not fall exclusively within either's domain. In the spirit of hard art, a “gallery” of final concept maps from twenty projects is presented, partly to illustrate more examples of the process when used in a variety of subject areas and for different purposes, and partly for their aesthetic value alone. In the spirit of soft science, two major issues are considered. First, the evidence for the validity and reliability of concept mapping is introduced, along with some suggestions for further research which might be undertaken to examine those characteristics. Second, the role of concept mapping is discussed, with special emphasis on its use in a pattern matching framework.