A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data

Richter, Aaron; Khoshgoftaar, Taghi M.; Landset, Sara; Hasanin, Tawfiq

doi:10.1109/iri.2015.12

Cited by 33 publications

(13 citation statements)

References 22 publications

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“…New reviews are constantly being added to large repositories of reviews across various websites at a high rate, over 1.5 million per month in the case of Yelp 4 . Consequently, distributed and streaming applications of machine learning algorithms 14 should be explored to effectively model the large corpus of online reviews which exist in the real world [38]. Mahout has been used for large-scale recommendation systems [39], which would be useful to apply to review spam detection, as reviewers may be related to each other on different review websites.…”

Section: Comparative Analysis and Suggestionsmentioning

confidence: 99%

Survey of review spam detection using machine learning techniques

Crawford

Khoshgoftaar

Prusa

et al. 2015

Journal of Big Data

Self Cite

369

176

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Online reviews are often the primary factor in a customer's decision to purchase a product or service, and are a valuable source of information that can be used to determine public opinion on these products or services. Because of their impact, manufacturers and retailers are highly concerned with customer feedback and reviews. Reliance on online reviews gives rise to the potential concern that wrongdoers may create false reviews to artificially promote or devalue products and services. This practice is known as Opinion (Review) Spam, where spammers manipulate and poison reviews (i.e., making fake, untruthful, or deceptive reviews) for profit or gain. Since not all online reviews are truthful and trustworthy, it is important to develop techniques for detecting review spam. By extracting meaningful features from the text using Natural Language Processing (NLP), it is possible to conduct review spam detection using various machine learning techniques. Additionally, reviewer information, apart from the text itself, can be used to aid in this process. In this paper, we survey the prominent machine learning techniques that have been proposed to solve the problem of review spam detection and the performance of different approaches for classification and detection of review spam. The majority of current research has focused on supervised learning methods, which require labeled data, a scarcity when it comes to online review spam. Research on methods for Big Data are of interest, since there are millions of online reviews, with many more being generated daily. To date, we have not found any papers that study the effects of Big Data analytics for review spam detection. The primary goal of this paper is to provide a strong and comprehensive comparative study of current research on detecting review spam using various machine learning techniques and to devise methodology for conducting further investigation.

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Section: Comparative Analysis and Suggestionsmentioning

confidence: 99%

Survey of review spam detection using machine learning techniques

Crawford

Khoshgoftaar

Prusa

et al. 2015

Journal of Big Data

Self Cite

369

176

View full text Add to dashboard Cite

show abstract

“…As these tools have advantages and drawbacks, and many have overlapping features, deciding on which framework to use is not easy. In this regard, several papers provide comparisons between some of these tools including MLlib [51,69].…”

Section: Research Highlightsmentioning

confidence: 99%

Big data analytics on Apache Spark

Salloum

Dautov

Chen

et al. 2016

Int J Data Sci Anal

319

121

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Apache Spark has emerged as the de facto framework for big data analytics with its advanced in-memory programming model and upper-level libraries for scalable machine learning, graph analysis, streaming and structured data processing. It is a general-purpose cluster computing framework with language-integrated APIs in Scala, Java, Python and R. As a rapidly evolving open source project, with an increasing number of contributors from both academia and industry, it is difficult for researchers to comprehend the full body of development and research behind Apache Spark, especially those who are beginners in this area. In this paper, we present a technical review on big data analytics using Apache Spark. This review focuses on the key components, abstractions and features of Apache Spark. More specifically, it shows what Apache Spark has for designing and implementing big data algorithms and pipelines for machine learning, graph analysis and stream processing. In addition, we highlight some research and development directions on Apache Spark for big data analytics.

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“…They implemented IAL (Independent Assisted Living) and patient monitoring system. Richter et al, (2015) have conducted several comparative studies on toolkits for machine learning in big data, including Mahout MapReduce, Mahout Samsara, Spark MLlib, H2O, and SAMOA. The study showed that on average, Spark MLlib and H2O has better performances than other toolkits in terms of the extensibility, scalability, usability, fault tolerance and speed.…”

Section: Related Workmentioning

confidence: 99%

Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system

Arridha

Sukaridhoto

Pramadihanto

et al. 2017

IJSSC

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Abstract:Monitoring water conditions in real-time is a critical mission to preserve the water ecosystem in maritime and archipelagic countries, such as Indonesia that is relying on the wealth of water resources. To integrate the water monitoring system into the big data technology for realtime analysis, we have engaged in the ongoing project named SEMAR (Smart Environment Monitoring and Analytic in Real-time system), which provides the IoT-Big Data platform for water monitoring. However, SEMAR does not have an analytical system yet. This paper proposes the analytical system for water quality classification using Pollution Index method, which is an extension of SEMAR. Besides, the communication protocol is updated from REST to MQTT. Furthermore, the real-time user interface is implemented for visualisation. The evaluations confirmed that the data analytic function adopting the linear SVM and Decision Tree algorithms achieves more than 90% for the estimation accuracy with 0.019075 for the MSE.

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A Multi-dimensional Comparison of Toolkits for Machine Learning with Big Data

Cited by 33 publications

References 22 publications

Survey of review spam detection using machine learning techniques

Survey of review spam detection using machine learning techniques

Big data analytics on Apache Spark

Classification extension based on IoT-big data analytic for smart environment monitoring and analytic in real-time system

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