Regular Expression Search on Compressed Text

We present an algorithm for searching regular expression matches in compressed text. The algorithm reports the number of matching lines in the uncompressed text in time linear in the size of its compressed version. We define efficient data structures that yield nearly optimal complexity bounds and provide a sequential implementation --zearch-- that requires up to 25% less time than the state of the art.Comment: 10 pages, published in Data Compression Conference (DCC'19

Improved Approximate String Matching and Regular Expression Matching on Ziv-Lempel Compressed Texts

We study the approximate string matching and regular expression matching problem for the case when the text to be searched is compressed with the Ziv-Lempel adaptive dictionary compression schemes. We present a time-space trade-off that leads to algorithms improving the previously known complexities for both problems. In particular, we significantly improve the space bounds, which in practical applications are likely to be a bottleneck

Processing SPARQL queries with regular expressions in RDF databases

Background: As the Resource Description Framework (RDF) data model is widely used for modeling and sharing a lot of online bioinformatics resources such as Uniprot (dev.isb-sib.ch/projects/uniprot-rdf) or Bio2RDF (bio2rdf.org), SPARQL - a W3C recommendation query for RDF databases - has become an important query language for querying the bioinformatics knowledge bases. Moreover, due to the diversity of users' requests for extracting information from the RDF data as well as the lack of users' knowledge about the exact value of each fact in the RDF databases, it is desirable to use the SPARQL query with regular expression patterns for querying the RDF data. To the best of our knowledge, there is currently no work that efficiently supports regular expression processing in SPARQL over RDF databases. Most of the existing techniques for processing regular expressions are designed for querying a text corpus, or only for supporting the matching over the paths in an RDF graph. Results: In this paper, we propose a novel framework for supporting regular expression processing in SPARQL query. Our contributions can be summarized as follows. 1) We propose an efficient framework for processing SPARQL queries with regular expression patterns in RDF databases. 2) We propose a cost model in order to adapt the proposed framework in the existing query optimizers. 3) We build a prototype for the proposed framework in C++ and conduct extensive experiments demonstrating the efficiency and effectiveness of our technique. Conclusions: Experiments with a full-blown RDF engine show that our framework outperforms the existing ones by up to two orders of magnitude in processing SPARQL queries with regular expression patterns.X113sciescopu

Analyzing large-scale DNA Sequences on Multi-core Architectures

Rapid analysis of DNA sequences is important in preventing the evolution of different viruses and bacteria during an early phase, early diagnosis of genetic predispositions to certain diseases (cancer, cardiovascular diseases), and in DNA forensics. However, real-world DNA sequences may comprise several Gigabytes and the process of DNA analysis demands adequate computational resources to be completed within a reasonable time. In this paper we present a scalable approach for parallel DNA analysis that is based on Finite Automata, and which is suitable for analyzing very large DNA segments. We evaluate our approach for real-world DNA segments of mouse (2.7GB), cat (2.4GB), dog (2.4GB), chicken (1GB), human (3.2GB) and turkey (0.2GB). Experimental results on a dual-socket shared-memory system with 24 physical cores show speed-ups of up to 17.6x. Our approach is up to 3x faster than a pattern-based parallel approach that uses the RE2 library.Comment: The 18th IEEE International Conference on Computational Science and Engineering (CSE 2015), Porto, Portugal, 20 - 23 October 201

Fast and Compact Regular Expression Matching

We study 4 problems in string matching, namely, regular expression matching, approximate regular expression matching, string edit distance, and subsequence indexing, on a standard word RAM model of computation that allows logarithmic-sized words to be manipulated in constant time. We show how to improve the space and/or remove a dependency on the alphabet size for each problem using either an improved tabulation technique of an existing algorithm or by combining known algorithms in a new way