Big data for Natural Language Processing: A streaming approach

Agerri, Rodrigo; Artola, Xabier; Beloki, Zuhaitz; Rigau, Germán; Soroa, Aitor

doi:10.1016/j.knosys.2014.11.007

Cited by 69 publications

(34 citation statements)

References 14 publications

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“…The tool developed by [68] was tested on both car dataset and Wikinews 5 dataset in comparison with sequential processing. It was discovered that their tool (pipeline implementation) performed better and faster.…”

Section: Research Question 5: What Are the Evaluation Techniques Or Bmentioning

confidence: 99%

Big data stream analysis: a systematic literature review

2019

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Advances in information technology have facilitated large volume, high-velocity of data, and the ability to store data continuously leading to several computational challenges. Due to the nature of big data in terms of volume, velocity, variety, variability, veracity, volatility, and value [1] that are being generated recently, big data computing is a new trend for future computing. Big data computing can be generally categorized into two types based on the processing requirements, which are big data batch computing and big data stream computing Abstract Recently, big data streams have become ubiquitous due to the fact that a number of applications generate a huge amount of data at a great velocity. This made it difficult for existing data mining tools, technologies, methods, and techniques to be applied directly on big data streams due to the inherent dynamic characteristics of big data. In this paper, a systematic review of big data streams analysis which employed a rigorous and methodical approach to look at the trends of big data stream tools and technologies as well as methods and techniques employed in analysing big data streams. It provides a global view of big data stream tools and technologies and its comparisons. Three major databases, Scopus, ScienceDirect and EBSCO, which indexes journals and conferences that are promoted by entities such as IEEE, ACM, SpringerLink, and Elsevier were explored as data sources. Out of the initial 2295 papers that resulted from the first search string, 47 papers were found to be relevant to our research questions after implementing the inclusion and exclusion criteria. The study found that scalability, privacy and load balancing issues as well as empirical analysis of big data streams and technologies are still open for further research efforts. We also found that although, significant research efforts have been directed to real-time analysis of big data stream not much attention has been given to the preprocessing stage of big data streams. Only a few big data streaming tools and technologies can do all of the batch, streaming, and iterative jobs; there seems to be no big data tool and technology that offers all the key features required for now and standard benchmark dataset for big data streaming analytics has not been widely adopted. In conclusion, it was recommended that research efforts should be geared towards developing scalable frameworks and algorithms that will accommodate data stream computing mode, effective resource allocation strategy and parallelization issues to cope with the ever-growing size and complexity of data.

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Section: Research Question 5: What Are the Evaluation Techniques Or Bmentioning

confidence: 99%

Big data stream analysis: a systematic literature review

2019

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“…Although the usage of new programming languages (such as Python, Golang, or R) is increasing worldwide, the number of Java developments and developers is clearly greater than other languages [24]. For this reason, this study compiles the following tools implemented in Java: (i) MALLET [25], (ii) GATE [26], (iii) Stanford CoreNLP [27], (iv) OpenNLP [28], (v) UIMA [29], and (vi) DKPro-Core [30] and a study executed by IXA NLP Group using Apache Storm framework for text analysis purposes [31].…”

Section: State Of the Artmentioning

confidence: 99%

Using Natural Language Preprocessing Architecture (NLPA) for Big Data Text Sources

Novo-Lourés

Pavón

Laza

et al. 2020

Scientific Programming

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During the last years, big data analysis has become a popular means of taking advantage of multiple (initially valueless) sources to find relevant knowledge about real domains. However, a large number of big data sources provide textual unstructured data. A proper analysis requires tools able to adequately combine big data and text-analysing techniques. Keeping this in mind, we combined a pipelining framework (BDP4J (Big Data Pipelining For Java)) with the implementation of a set of text preprocessing techniques in order to create NLPA (Natural Language Preprocessing Architecture), an extendable open-source plugin implementing preprocessing steps that can be easily combined to create a pipeline. Additionally, NLPA incorporates the possibility of generating datasets using either a classical token-based representation of data or newer synset-based datasets that would be further processed using semantic information (i.e., using ontologies). This work presents a case study of NLPA operation covering the transformation of raw heterogeneous big data into different dataset representations (synsets and tokens) and using the Weka application programming interface (API) to launch two well-known classifiers.

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“…A similar work to ours is a streaming approach in a distributed architecture for scaling up natural language processing methods [46] which also leverages Storm. This work describes a series of experiments carried out with the goal of analyzing the scaling capabilities of the language processing.…”

Section: P Lib =mentioning

confidence: 99%

Sequence Similarity Parallelization over Heterogeneous Computer Clusters Using Data Parallel Programming Model

Hajibaba¹,

Gorgin²,

Sharifi³

2017

SCPE

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Sequence similarity, as a special case of data intensive applications, is one of the neediest applications for parallelization. Clustered commodity computers as a cost-effective platform for distributed and parallel processing, can be leveraged to parallelize sequence similarity. However, manually designing and developing parallel programs on commodity computers is a time-consuming, complex and error-prone process. In this paper, we present a sequence similarity parallelization technique using the Apache Storm as a stream processing framework with a data parallel programming model. Storm automatically parallelizes computations via a special user-defined topology that is represented as a directed acyclic graph. The proposed technique collects streams of data from a disk and sends them sequence by sequence to clustered computers for parallel processing. We also present a dispatching policy for balancing the cluster workload and managing the cluster heterogeneity to achieve more than 99 percent parallelism. An alignment-free method, known as n-gram modeling, is used to calculate similarities between the sequences. To show the cost-performance superiority of our method on clustered commodity computers over serial processing in powerful computers, we simply use UniProtKB/SwissProt dataset for evaluation of the performance of sequence similarity as an interesting large-scale Bioinformatics application.

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Big data for Natural Language Processing: A streaming approach

Cited by 69 publications

References 14 publications

Big data stream analysis: a systematic literature review

Big data stream analysis: a systematic literature review

Using Natural Language Preprocessing Architecture (NLPA) for Big Data Text Sources

Sequence Similarity Parallelization over Heterogeneous Computer Clusters Using Data Parallel Programming Model

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