ESTRA: An easy streaming data analysis tool

Download
2021-2-28
Savaş Başak, Ecehan
Easy Streaming Data Analysis Tool (ESTRA) is designed with the aim of creating an easy-to-use data stream analysis platform that serves the purpose of a quick and efficient tool to explore and prototype machine learning solutions on various datasets. ESTRA is developed as a web-based, scalable, extensible, and open-source data analysis tool with a user-friendly and easy to use user interface. ESTRA comes with a bundle of datasets (Electricity, KDD Cup’99, and Covertype), dataset generators (Sea and Hyperplane), and implementations of various analysis and learning algorithms (D3, Hoeffding Tree, CluStream, DenStream, kNN, k-means, and StreamKM++). Moreover, ESTRA provides an easy way to investigate various properties of the datasets and to observe the results of executed machine learning algorithms. ESTRA’s straightforward and clean architecture with open source tools allows it to be extensible. Used libraries and frameworks in ESTRA like React, Python and ScikitMultiflow are popular open source tools with broad community support and extensions. ESTRA’s capabilities of easy prototyping and exploring machine learning solutions are demonstrated by repeating the machine learning experiments performed in various studies.

Suggestions

Learning to rank web data using multivariate adaptive regression splines
Altınok, Gülşah; Batmaz, İnci; Karagöz, Pınar; Department of Statistics (2018)
A new trend, called learning to rank, has recently come to light in a wide variety of applications in Information Retrieval (IR), Natural Language Processing (NLP), and Data Mining (DM), to utilize machine learning techniques to automatically build the ranking models. Typical applications are document retrieval, expert search, definition search, collaborative filtering, question answering, and machine translation. In IR, there are three approaches used for ranking. The one is traditional model approaches su...
Mask Combination of Multi-Layer Graphs for Global Structure Inference
Bayram, Eda; Thanou, Dorina; Vural, Elif; Frossard, Pascal (2020-01-01)
Structure inference is an important task for network data processing and analysis in data science. In recent years, quite a few approaches have been developed to learn the graph structure underlying a set of observations captured in a data space. Although real-world data is often acquired in settings where relationships are influenced by a priori known rules, such domain knowledge is still not well exploited in structure inference problems. In this paper, we identify the structure of signals defined in a da...
Data mining analysis of economic indicators of countries
Güngör, Erdem; Yozgatlıgil, Ceylan; Department of Statistics (2020-8)
Data Mining is becoming a famous analysis day by day to reveal the hidden information within big data. In the study, we use data mining techniques on the economic indicators of the countries. The four data mining techniques are to be implemented on the dataset. Making homogenous groups of the countries whose economic characteristics are similar are obtained by the Clustering Algorithm. After the clustering algorithm is performed, we pass to Association Rule Data Mining to investigate the most exported produ...
Using data analytics for collaboration patterns in distributed software team simulations
Dafoulas, Georgios A.; Serce, Fatma C.; SWİGGER, Kathleen; BRAZİLE, Robert; Alpaslan, Ferda Nur; Alpaslan, Ferda Nur; Milewski, Allen (2016-08-05)
This paper discusses how previous work on global software development learning teams is extended with the introduction of data analytics. The work is based on several years of studying student teams working in distributed software team simulations. The scope of this paper is twofold. First it demonstrates how data analytics can be used for the analysis of collaboration between members of distributed software teams. Second it describes the development of a dashboard to be used for the visualization of variou...
PARALLEL COMPUTING IN STATISTICAL METHODS
Oltulu, Orçun; Gökalp Yavuz, Fulya; Department of Statistics (2022-8-17)
Cost-efficient data collection and storage methods enable scientists, companies, and even regular computer users to reach high-dimensional data sets faster and cheaper. Even though personal computers are getting more powerful and efficient, some algorithms, tasks, and problems still require too much computational power and time to run on a personal computer. For a few decades, parallelization in statistical computing had an increasing trend, and researchers put significant effort into converting or adjustin...
Citation Formats
E. Savaş Başak, “ESTRA: An easy streaming data analysis tool,” M.S. - Master of Science, Middle East Technical University, 2021.