An algorithm for generating model-sensitive search plans for pattern matching on EMF models

In this paper, we propose a new model-sensitive search plan generation algorithm to speed up the process of graph pattern matching. This dynamic programming based algorithm, which is able to handle general n-ary constraints in an integrated manner, collects statistical data from the underlying EMF model, and uses this information for optimization purposes. Additionally, runtime performance measurements have been carried out to quantitatively evaluate the effects of the search plan generation algorithm on the pattern matching engine.

Incremental Graph Pattern Matching (IGPM) offers an elegant approach to find patterns in graph-based models, reporting newly added and recently removed pattern matches. However, analyzing these matches w.r.t. temporal and causal dependencies can in general only be done by extending not just the IGPM engine but also the underlying model, which often is impractical and sometimes even impossible. Therefore, we transform the stream of pattern matches to a stream of events and employ Complex Event Processing (CEP) to detect such dependencies and derive more complex events from them. For this purpose, we introduce GrapeL as a textual language to specify and generate integrated solutions using both IGPM and CEP to benefit from the synergy of both approaches, which we present in the context of a flight and booking scenario. Finally, we show that our solution can compete with an optimized hand-crafted version without GrapeL and CEP while offering a specification that yields a less tedious and error-prone design process.

Prolog is a simple but powerful programming language founded on symbolic logic. The basic computational mechanism is a pattern matching process (“unification”) operating on general record structures (“terms” of logic). We briefly review the language and compare it especially with pure Lisp. The remainder of the paper discusses techniques for implementing Prolog efficiently; in particular we describe how to compile the patterns involved in the matching process. These techniques are as incorporated in our DECsystem-10 Prolog compiler (written in Prolog). The code it generates is comparable in speed with that produced by existing DEC10 Lisp compilers. We argue that pattern matching is a better method for expressing operations on structured data than conventional selectors and constructors - both for the user and for the implementor.

Bead encoding is a key problem central to all multiplexed bead-based microarrays. Most existing bead-based microarrays require complicated and costly bead fabrication and/or sophisticated bead decoding hardware to achieve high multiplexing. Recently, the development of spatial bead encoding techniques have opened up the possibility of using pattern matching and image processing to develop highly multiplexed, high-throughput bead-based arrays. In this thesis, we improve the existing spatial bead encoding scheme, and develop a pipeline of computational methods, which allows for automated spatial bead encoding. Six novel computational methods, that automate the proposed bead encoding scheme, are developed in this thesis. The proposed scheme improves the previously reported spatial encoding schemes by making them better suited for use in real world scenarios. In the spatial bead encoding scheme, a sequential bead deposition method is used to capture the identities of the beads in bright or dark field images of the array. While, a second fluorescence image of the array is used to quantify the target analyte concentrations associated with the beads. By aligning the two images using the patterns formed by the beads, both the bead identities (i.e. the target analyte associate with the bead) and the analyte concentrations are decoded. The six computational methods developed in this thesis, are grouped into three categories: bright and dark field image processing, fluorescence image processing, and bead pattern matching. These methods are developed such that they require minimal parameter tuning, and are able to deal with noisy images acquired in uncontrolled environments As part of the bright and dark field image processing we have developed two novel methods: a fully automatic method for detecting the underlying micro-well grid structure in the images, and an unsupervised learning based method to classify the micro-wells as either empty or containing a bead. We show the ability of the proposed methods to deal with extreme amounts of noise and distortions using multiple datasets. While, microarray image gridding is a well-studied problem in the context of fluorescence images of planar microarrays, to the best of our knowledge no methods have yet been developed for processing either bright or dark field images of bead-based microarrays. Unlike in bright field images, where all the beads are visible, in fluorescence images only those beads that are expressed in the sample are visible. Therefore, in fluorescence image analysis the first step is to detect the beads in the image. To this end, we have developed an unsupervised, simultaneous bead detection and segmentation method which uses the coherence in shape and size of the beads to circumvent the need for parameter optimization. The main challenge in this segmentation is the large dynamic range across which the beads are expressed. We have also developed a probabilistic non-linear gridding method that attempts to establish the micro-well grid structure from the detected beads. While similar gridding techniques have been developed in the past, we make two contributions in this regard: first, we put the gridding method in a probabilistic framework, and second, we show that in certain cases it is impossible to ascertain if the correct grid has been detected without using additional information about the physical properties of the array. The final step in the proposed bead encoding scheme is to establishing one-to-one correspondences between the beads detected in the bright field and fluorescence images. We have developed two methods for this matching. When the micro-well grid structure in the fluorescence image is successfully established, the grid information can be used to reduce the matching problem to a binary grid alignment problem. A grid matching method that is very fast and can handle a large number of beads (>50,000) has been developed for this purpose. Finally, to perform matching when the fluorescence image grid is not available, a novel point pattern matching method that is affine invariant and robust to small non-linear distortions has been developed. The point pattern matching method addresses the problem of matching large point sets in the presence of large amounts of outliers and occlusion. The proposed method is evaluated using several real and simulated datasets.

In Model-driven engineering (MDE), models constitute the basis source of information to specify and automatically generate an executable system. The evolution history analysis of software system models helps us to discover interesting logical couplings between its artifacts; which is not possible with the analysis of a single version of the model. The detection of such couplings earlier in the software design improves the model (software) in a way to increase its modularity. In this paper, we propose an approach for the analysis of a version repository for EMF models (CDO Model Repository) in order to detect logical couplings between the software model artifacts, using the Apriori algorithm. Such logical couplings help us to understand the model evolution, to predict future changes on the model, to identify potential faults, and consequently to produce automatically a coherent and modular software source code.

The minimum requirements in building a multilingual corpus of parallel texts are tags in the texts that establish cross-references between corresponding parts. These tags can be used to merge several independent text files into one, displaying aligned units of text, in order to do research with the help of the TUSTEP word processor. Extensive use is made of the complex possibilities of pattern matching: words, word forms, prefixes, suffixes, and one or more explicit or abstract character strings can be searched, while at the same time excluding others, by entering instructions into the word processor interactively. By doing so, the user does not depend upon predefined tags which contain semantic or grammatical information. Examples taken from the Finnish-German parallel corpus show how such instructions are written and what the results look like. Apart from interactive work, pattern matching and text processing tasks can be done by executing parameter driven program files. Examples show how four text files are aligned to produce a POSTSCRIPT as well as an HTML file.

IBM's newest technology, the Power Edge of Network (PowerEN) processor, merges network and server attributes to create a new class of wire-speed processor. PowerEN is a hybrid computer that employs: massive multithreading capabilities, integrated I/O and unique special-purpose accelerators for compression, cryptography, pattern matching, XML and Network processing. As a novel architecture, the PowerEN processor offers fertile ground for research. It can facilitate the development of faster applications in many fields of computer science such as Networking, Cryptography, Virtualization, Bioinformatics, SOA, and Systems. IBM encourages academic research and has set up the “PowerEN Developer Cloud” infrastructure to allow it. Israeli universities are of the first to perform research on this global infrastructure.

Erie is a name recognition system developed for the Multilingual Entity Task (MET) in MUC-6. The pattern matching engine recognizes organization, person, and place names along with time and numeric expressions in Japanese text. Although our previous information extraction system Tex-tract performed well in MUC-5, the pattern matching engine, which was written in AWK language, was slow[2]. System maintenance was also difficult, since the patterns were defined in both the matching engine and the pattern files. Erie solves these problems by generating a pattern matching engine in C language directly from the defined patterns.

Deep learning-based feature extraction and optimizing pattern matching for intrusion detection using finite state machine

Pattern matching (PM) with gap constraints (or flexible wildcards) is one of the essential tasks in repetitive sequential pattern mining (or sequence pattern mining), since it can compute the support of a pattern in a sequence. Nonoverlapping PM (or PM under nonoverlapping condition) which is a kind of PM with gap constraints methods allows the same position character in the sequence to be reused at different locations in the pattern, but is not allowed to be reused in the same position of the pattern. The researches on nonoverlapping are under non-negative gaps which are more restrictive on the order of each character occurring in the sequence. As we know that it is easy to obtain valuable patterns under the nonoverlapping condition in sequence pattern mining. This paper addresses a nonoverlapping PM problem with general gaps which means that the gap can be a negative value. We proposes an effective algorithm which employs Nettree structure to convert the problem into a general gap Nettree at first. In order to find the nonoverlapping occurrences, the algorithm employs a backtracking strategy to find the leftmost full path in each iteration. This paper also analyzes the time and space complexities of the proposed algorithm. Experimental results verify the proposed algorithm has better performance and demonstrate that the general gap is more flexible than the non-negative gap.

Pattern matching is a very important topic in computer science. It has been used in various applications such as information retrieval, virus scanning, DNA sequence analysis, data mining, machine learning, network security and pattern recognition. This paper has presented a new pattern matching algorithm—Enhanced ERS-A, which is an improvement over ERS-S algorithm. In ERS-A, two sliding windows are used to scan the text from the left and the right simultaneously. The proposed algorithm also scans the text from the left and the right simultaneously as well as making comparisons with the pattern from both sides simultaneously. The comparisons done between the text and the pattern are done from both sides in parallel. The shift technique used in the Enhanced ERS-A is the four consecutive characters in the text immediately following the pattern window. The experimental results show that the Enhanced ERS-A has enhanced the process of pattern matching by reducing the number of comparisons performed.

Pattern matching algorithms have gained a lot of importance in computer science, primarily because they are used in various domains such as computational biology, video retrieval, intrusion detection systems, and fraud detection. Finding one or more patterns in a given text is known as pattern matching. Two important things that are used to judge how well exact pattern matching algorithms work are the total number of attempts and the character comparisons that are made during the matching process. The primary focus of our proposed method is reducing the size of both components wherever possible. Despite sprinting, hash-based pattern matching algorithms may have hash collisions. The Efficient Hashing Method (EHM) algorithm is improved in this research. Despite the EHM algorithm’s effectiveness, it takes a lot of time in the preprocessing phase, and some hash collisions are generated. A novel hashing method has been proposed, which has reduced the preprocessing time and hash collision of the EHM algorithm. We devised the Hashing Approach for Pattern Matching (HAPM) algorithm by taking the best parts of the EHM and Quick Search (QS) algorithms and adding a way to avoid hash collisions. The preprocessing step of this algorithm combines the bad character table from the QS algorithm, the hashing strategy from the EHM algorithm, and the collision-reducing mechanism. To analyze the performance of our HAPM algorithm, we have used three types of datasets: E. coli, DNA sequences, and protein sequences. We looked at six algorithms discussed in the literature and compared our proposed method. The Hash-q with Unique FNG (HqUF) algorithm was only compared with E. coli and DNA datasets because it creates unique bits for DNA sequences. Our proposed HAPM algorithm also overcomes the problems of the HqUF algorithm. The new method beats older ones regarding average runtime, number of attempts, and character comparisons for long and short text patterns, though it did worse on some short patterns.

Pattern matching is essential to a wide range of applications such as network intrusion detection, virus scanning, etc. Pattern matching algorithms normally rely on state machines to detect predefined patterns. Recently, parallel pattern matching engines, based on ASICs, FPGAs or network processors, perform matching with multiple state machines. The state migration in the matching procedure incurs intensive memory accesses. Thus, it is critical to minimize the storage of state machines such that they can be fit in on-chip or other fast memory modules to achieve high-speed pattern matching. This paper proposes novel optimization techniques, namely state re-labeling and memory partition, to reduce state machine storage. The paper also presents architectural designs based on the optimization strategy. We evaluate our design using realistic pattern sets, and the results show state machine memory reduction up to 80.1%.

A strongly connected component (SCC) is a maximal subgraph of a directed graph G in which every pair of nodes are reachable from each other in the SCC. With such a property, a general directed graph can be represented by a directed acyclic graph DAG by contracting an SCC of G to a node in DAG. In many real applications that need graph pattern matching, topological sorting, or reachability query processing, the best way to deal with a general directed graph is to deal with its DAG representation. Therefore, finding all SCCs in a directed graph G is a critical operation. The existing in-memory algorithms based on depth first search (DFS) can find all SCCs in linear time w.r.t. the size of a graph. However, when a graph cannot resident entirely in the main memory, the existing external or semi-external algorithms to find all SCCs have limitation to achieve high I/O efficiency. In this paper, we study new I/O efficient semi-external algorithms to find all SCCs for a massive directed graph G that cannot reside in main memory entirely. To overcome the deficiency of the existing DFS based semi-external algorithm that heavily relies on a total order, we explore a weak order based on which we investigate new algorithms. We propose a new two phase algorithm, namely, tree construction and tree search. In the tree construction phase, a spanning tree of G can be constructed in bounded sequential scans of G. In the tree search phase, it needs to sequentially scan the graph once to find all SCCs. In addition, we propose a new single phase algorithm, which combines the tree construction and tree search phases into a single phase, with three new optimization techniques. They are early acceptance, early rejection, and batch processing. By the single phase algorithm with the new optimization techniques, we can significantly reduce the number of I/Os and CPU cost. We conduct extensive experimental studies using 4 real datasets including a massive real dataset, and several synthetic datasets to confirm the I/O efficiency of our approaches.

Background: Canada is currently experiencing a health workforce crisis, reflective of the global workforce challenges. Integrated models of care such as Ontario Health Team (OHTs), in Ontario, Canada are important examples of large-scale reform focusing on local populations and a regional health workforce. To ensure a workforce that meets the needs of their population, a regional approach to workforce planning is required. While significant work has been published on health workforce planning, there are few examples of how regional integrated care systems can apply workforce planning models to address population needs. The overall aim of this project is to inform policy to support regional workforce planning for integrated care strategies. Approach: An exploratory mixed methods single case study design was used. The study was conducted in partnership with one Ontario Health Team that served as the case. Lived experience patient partners were core members of the research team, providing input throughout the project. The Health Workforce Planning Model was identified in a scoping review and applied to the OHT. A mixed methods approach included multiple regional, provincial, and federal data sources to describe the population, health needs, and service providers. Interviews were conducted with the OHT leaders and community members to obtain insights into workforce planning from the regional context. A document analysis of publicly available workforce planning documents across all OHTs was completed. A deliberative dialogue was held to obtain input from the OHT, provincial and federal decision makers to inform policy recommendations. Discrete analysis was conducted for each data set and the quantitative and qualitative data was merged through a process of pattern matching. Results: Using the Health Workforce Planning Model, we attempted to capture data related to service requirements (population data demographics, health status, health services utilization) and service capacity (health workforce) to examine fit and identify gaps. Data were gathered by a complex web at federal, provincial, and regional levels. Six federal/provincial data sets were used to describe the population. Nineteen of 93 regional organizations completed surveys to obtain workforce data. A review of 54 OHT websites found only 12 OHTs had documents mentioning health workforce planning, including strategic plans (66.7%) and other reports (e.g. year-end). Fourteen interviews were completed with regional OHT leaders and community partners, providing insights into contextual factors including current status of health workforce planning, driving forces, capacity for change and solutions. Key recommendations focused on the need for: 1) health workforce planning governance structures and accountability provincially and within OHTs, 2) standardized and comprehensive data and reporting across all sectors and 3) infrastructure at the provincial level and within regions to support health workforce planning. Implications: Despite the crisis in the health workforce there remains significant challenges in obtaining data to inform workforce planning and little infrastructure within integrated care systems such as Ontario Heath Teams to support regional planning. Ongoing efforts need to focus on consistency and standardization of workforce data and building in targeted resources and infrastructure to support regions to proactively plan for population needs.