Time Series Data Mining: A Case Study With Big Data Analytics Approach

Wang, Fang; Li, Menggang; Mei, Yiduo; Li, Wenrui

doi:10.1109/access.2020.2966553

Cited by 35 publications

(21 citation statements)

References 24 publications

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“…Abedjan et al [12] mainly investigates data profiling for relational data. However, in addition to relational databases, many non-relational databases need data profiling [16], such as time series data [18]- [20], graph data [21]- [23], or heterogeneous data in dataspaces [24]- [26].…”

Section: Preliminariesmentioning

confidence: 99%

Sampling for Big Data Profiling: A Survey

Liu

Zhang

2020

IEEE Access

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Due to the development of internet technology and computer science, data is exploding at an exponential rate. Big data brings us new opportunities and challenges. On the one hand, we can analyze and mine big data to discover hidden information and get more potential value. On the other hand, the 5V characteristic of big data, especially Volume which means large amount of data, brings challenges to storage and processing. For some traditional data mining algorithms, machine learning algorithms and data profiling tasks, it is very difficult to handle such a large amount of data. The large amount of data is highly demanding hardware resources and time consuming. Sampling methods can effectively reduce the amount of data and help speed up data processing. Sampling technology has been widely used in big data context. Data profiling is the activity that finds metadata of data set and has many use cases, e.g., performing data profiling tasks on relational data, graph data, and time series data for anomaly detection and data repair. However, data profiling is computationally expensive, especially for large data sets. Hence this article focuses on researching sampling for data profiling tasks in big data context and investigates the application of sampling in different categories of data profiling. From the experimental results of these studies, the results got from the sampled data are close to or even exceed the results of the full amount of data. Therefore, sampling technology plays an important role in the era of big data, and we also have reason to believe that sampling technology will become an indispensable step in big data processing in the future.INDEX TERMS Big data, large amount, sampling, data profiling.

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Section: Preliminariesmentioning

confidence: 99%

Sampling for Big Data Profiling: A Survey

Liu

Zhang

2020

IEEE Access

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“…Currently, BDA is the new big data technology that has become widely embraced across sectors, companies, geographic areas, as well as among individuals, to help businesses and individuals make data-driven decisions to accomplish desired business goals [5,[15][16][17][18] . Of late, BDA can be enabled by several analytic platforms and tools, including those based on Structured Query Language (SQL) queries, fact clustering, data mining, natural language processing statistical analysis, data visualisation, AI, ML, text analytics, MongoDB, Hadoop, and MapReduce.…”

Section: Concept Of Big Data and Big Data Analyticsmentioning

confidence: 99%

A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

Nti

Quarcoo

Aning

et al. 2022

Big Data Min. Anal.

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The availability of digital technology in the hands of every citizenry worldwide makes an available unprecedented massive amount of data. The capability to process these gigantic amounts of data in real-time with Big Data Analytics (BDA) tools and Machine Learning (ML) algorithms carries many paybacks. However, the high number of free BDA tools, platforms, and data mining tools makes it challenging to select the appropriate one for the right task. This paper presents a comprehensive mini-literature review of ML in BDA, using a keyword search; a total of 1512 published articles was identified. The articles were screened to 140 based on the study proposed novel taxonomy. The study outcome shows that deep neural networks (15%), support vector machines (15%), artificial neural networks (14%), decision trees (12%), and ensemble learning techniques (11%) are widely applied in BDA.The related applications fields, challenges, and most importantly the openings for future research, are detailed.

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“…Five concepts are important for time-series: the starting time, pattern similarity, period range, confidence, and the endpoint time. Many techniques [2][3][4][5][6][7][8] have been applied for time-series prediction based on the period and data type. The DTW model has been effectively used to automatically deal with time deformations in different time-series ranges with time-dependent data, for pattern recognition and similarity analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

Safari¹,

Hosseini²,

Mazinani³

2021

IJFIS

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Prediction of time series is associated with nondeterministic pattern analysis for uncertain conditions. Therefore, it is necessary to develop high-quality prediction methods for real-world applications. Type-2 fuzzy systems can handle high-order uncertainties, such as sequential dependencies associated with time series. Precise and reliable prediction can help to develop reasonable strategies and assist specialists in planning the best policies for modeling events in uncertain time series. In this study, a hybrid model (dynamic type-2 fuzzy time warping [DT2FTW]) was proposed for handling high-order uncertainties in time-series prediction. A type-2 fuzzy intelligent system was developed alongside a dynamic time warping algorithm for predicting the patterns' similarity in long-time series for time-series prediction. The results demonstrate that the proposed DT2FTW model yields more reliable predictions on global standard benchmarks such as the Mackey-Glass, Dow Jones, and NASDAQ time-series. The results also confirm that the proposed DT2FTW model has lower error rates than its counterpart algorithms in terms of the root mean square error (RMSE), mean absolute error (MAE), and mean percentage error (MPE). In addition, the results confirm the superiority of the proposed model with an average area under the ROC curve (AUC) of 94%, with the 95% confidence interval (92%-95%).

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Time Series Data Mining: A Case Study With Big Data Analytics Approach

Cited by 35 publications

References 24 publications

Sampling for Big Data Profiling: A Survey

Sampling for Big Data Profiling: A Survey

A mini-review of machine learning in big data analytics: Applications, challenges, and prospects

Dynamic Type-2 Fuzzy Time Warping (DT2FTW): A Hybrid Model for Uncertain Time-Series Prediction

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