Syntactic Pattern Matching Research Articles

SlabCity: Whole-Query Optimization Using Program Synthesis

Query rewriting is often a prerequisite for effective query optimization, particularly for poorly-written queries. Prior work on query rewriting has relied on a set of "rules" based on syntactic pattern-matching. Whether relying on manual rules or auto-generated ones, rule-based query rewriters are inherently limited in their ability to handle new query patterns. Their success is limited by the quality and quantity of the rules provided to them. To our knowledge, we present the first synthesis-based query rewriting technique, SlabCity, capable of whole-query optimization without relying on any rewrite rules. SlabCity directly searches the space of SQL queries using a novel query synthesis algorithm that leverages a new concept called query dataflows. We evaluate SlabCity on four workloads, including a newly curated benchmark with more than 1000 real-life queries. We show that not only can SlabCity optimize more queries than state-of-the-art query rewriting techniques, but interestingly, it also leads to queries that are significantly faster than those generated by rule-based systems.

Top-Down Synthesis for Library Learning

This paper introduces corpus-guided top-down synthesis as a mechanism for synthesizing library functions that capture common functionality from a corpus of programs in a domain specific language (DSL). The algorithm builds abstractions directly from initial DSL primitives, using syntactic pattern matching of intermediate abstractions to intelligently prune the search space and guide the algorithm towards abstractions that maximally capture shared structures in the corpus. We present an implementation of the approach in a tool called Stitch and evaluate it against the state-of-the-art deductive library learning algorithm from DreamCoder. Our evaluation shows that Stitch is 3-4 orders of magnitude faster and uses 2 orders of magnitude less memory while maintaining comparable or better library quality (as measured by compressivity). We also demonstrate Stitch’s scalability on corpora containing hundreds of complex programs that are intractable with prior deductive approaches and show empirically that it is robust to terminating the search procedure early—further allowing it to scale to challenging datasets by means of early stopping.

Mining definitions in Kissat with Kittens.

Bounded variable elimination is one of the most important preprocessing techniques in SAT solving. It benefits from discovering functional dependencies in the form of definitions encoded in the CNF. While the common approach pioneered in SatELite relies on syntactic pattern matching, our new approach uses cores produced by an embedded SAT solver, Kitten. In contrast to a similar semantic technique implemented in Lingeling based on BDD algorithms to generate irredundant CNFs, our new approach is able to generate DRAT proofs. We further discuss design choices for our embedded SAT solver Kitten. Experiments with Kissat show the effectiveness of this approach.

CYK Parsing over Distributed Representations

Parsing is a key task in computer science, with applications in compilers, natural language processing, syntactic pattern matching, and formal language theory. With the recent development of deep learning techniques, several artificial intelligence applications, especially in natural language processing, have combined traditional parsing methods with neural networks to drive the search in the parsing space, resulting in hybrid architectures using both symbolic and distributed representations. In this article, we show that existing symbolic parsing algorithms for context-free languages can cross the border and be entirely formulated over distributed representations. To this end, we introduce a version of the traditional Cocke–Younger–Kasami (CYK) algorithm, called distributed (D)-CYK, which is entirely defined over distributed representations. D-CYK uses matrix multiplication on real number matrices of a size independent of the length of the input string. These operations are compatible with recurrent neural networks. Preliminary experiments show that D-CYK approximates the original CYK algorithm. By showing that CYK can be entirely performed on distributed representations, we open the way to the definition of recurrent layer neural networks that can process general context-free languages.

Extracting causal relations from the literature with word vector mapping

Causal graphs play an essential role in the determination of causalities and have been applied in many domains including biology and medicine. Traditional causal graph construction methods are usually data-driven and may not deliver the desired accuracy of a graph. Considering the vast number of publications with causality knowledge, extracting causal relations from the literature to help to establish causal graphs becomes possible. Current supervised-learning-based causality extraction methods requires sufficient labeled data to train a model, and rule-based causality extraction methods are limited by the predefined patterns. This paper proposes a causality extraction framework by integrating rule-based methods and unsupervised learning models to overcome these limitations. The proposed method consists of three modules, including data preprocessing, syntactic pattern matching, and causality determination. In data preprocessing, abstracts are crawled based on attribute names before sentences are extracted and simplified. In syntactic pattern matching, these simplified sentences are parsed to obtain the part-of-speech tags, and triples are achieved based on these tags by matching the two designed syntactic patterns. In causality determination, four verb seed sets are initialized, and word vectors are constructed for the verbs in both the seed sets and the triples by applying an unsupervised machine learning model. Causal relations are identified by comparing the similarity between the verbs in each triple and that in each seed set to overcome the limitation of the seed sets. Causality extraction results on the attributes from the risk factors for Alzheimer’s disease show that our method outperforms Bui’s method and Alashri’s method in terms of precision, recall, specificity, accuracy and F-score, with increases in the F-score of 8.29% and 5.37%, respectively.

Probabilistic Context-Free Grammars Estimated from Infinite Distributions

In this paper, we consider probabilistic context-free grammars, a class of generative devices that has been successfully exploited in several applications of syntactic pattern matching, especially in statistical natural language parsing. We investigate the problem of training probabilistic context-free grammars on the basis of distributions defined over an infinite set of trees or an infinite set of sentences by minimizing the cross-entropy. This problem has applications in cases of context-free approximation of distributions generated by more expressive statistical models. We show several interesting theoretical properties of probabilistic context-free grammars that are estimated in this way, including the previously unknown equivalence between the grammar cross-entropy with the input distribution and the so-called derivational entropy of the grammar itself. We discuss important consequences of these results involving the standard application of the maximum-likelihood estimator on finite tree and sentence samples, as well as other finite-state models such as Hidden Markov Models and probabilistic finite automata.

컬러 분포와 WordNet상의 유사도 측정을 이용한 의미적 이미지 검색

Semantic interpretation of image is incomplete without some mechanism for understanding semantic content that is not directly visible. For this reason, human assisted content-annotation through natural language is an attachment of textual description to image. However, keyword-based retrieval is in the level of syntactic pattern matching. In other words, dissimilarity computation among terms is usually done by using string matching not concept matching. In this paper, we propose a method for computerized semantic similarity calculation In WordNet space. We consider the edge, depth, link type and density as well as existence of common ancestors. Also, we have introduced method that applied similarity measurement on semantic image retrieval. To combine wi#h the low level features, we use the spatial color distribution model. When tested on a image set of Microsoft`s `Design Gallery Line`, proposed method outperforms other approach.

An Approach for Recovering Distributed System Architectures

Reasoning about software systems at the architectural level is key to effective software development, management, evolution and reuse. All too often, though, the lack of appropriate documentation leads to a situation where architectural design information has to be recovered directly from implemented software artifacts. This is a very demanding process, particularly when involving recovery of runtime abstractions (clients, servers, interaction protocols, etc.) that are typical to the design of distributed software systems. This paper presents an exploratory reverse engineering approach, called X-ray, to aid programmers in recovering architectural runtime information from a distributed system's existing software artifacts. X-ray comprises three domain-based static analysis techniques, namely i>component module classification, i>syntactic pattern matching, and i>structural reachability analysis. These complementary techniques can facilitate the task of identifying a distributed system's implemented i>executable components and their potential i>runtime interconnections. The component module classification technique automatically distinguishes source code modules according to the executables components they implement. The syntactic pattern matching technique in turn helps to recognise specific code fragments that may implement typical component interaction features. Finally, the structural reachability analysis technique aids in the association of those features to the code specific for each executable component. The paper describes and illustrates the main concepts underlying each technique, reports on their implementation as a suit of new and off-the-shelf tools, and, to give evidence of the utility of the approach, provides a detailed account of a successful application of the three techniques to help recover a static approximation of the runtime architecture for Field, a publicly-available distributed programming environment.

The LRPD test: speculative run-time parallelization of loops with privatization and reduction parallelization

Current parallelizing compilers cannot identify a significant fraction of parallelizable loops because they have complex or statically insufficiently defined access patterns. As parallelizable loops arise frequently in practice, we advocate a novel framework for their identification: speculatively execute the loop as a doall and apply a fully parallel data dependence test to determine if it had any cross-iteration dependences; if the test fails, then the loop is reexecuted serially. Since, from our experience, a significant amount of the available parallelism in Fortran programs can be exploited by loops transformed through privatization and reduction parallelization, our methods can speculatively apply these transformations and then check their validity at run-time. Another important contribution of this paper is a novel method for reduction recognition which goes beyond syntactic pattern matching: it detects at run-time if the values stored in an array participate in a reduction operation, even if they are transferred through private variables and/or are affected by statically unpredictable control flow. We present experimental results on loops from the PERFECT Benchmarks, which substantiate our claim that these techniques can yield significant speedups which are often superior to those obtainable by inspector/executor methods.

The LRPD test

Current parallelizing compilers cannot identify a significant fraction of parallelizable loops because they have complex or statically insufficiently defined access patterns. As parallelizable loops arise frequently in practice, we advocate a novel framework for their identification: speculatively execute the loop as a doall, and apply a fully parallel data dependence test to determine if it had any cross-iteration dependences; if the test fails, then the loop is re-executed serially. Since, from our experience, a significant amount of the available parallelism in Fortran programs can be exploited by loops transformed through privatization and reduction parallelization , our methods can speculatively apply these transformations and then check their validity at run-time. Another important contribution of this paper is a novel method for reduction recognition which goes beyond syntactic pattern matching; it detects at run-time if the values stored in an array participate in a reduction operation, even if they are transferred through private variables and/or are affected by statically unpredictable control flow. We present experimental results on loops from the PERFECT Benchmarks which substantiate our claim that these techniques can yield significant speedups which are often superior to those obtainable by inspector/executor methods.