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Split incremental clustering algorithm of mixed data stream
Clustering has been recognized as one of the most prominent functions in data mining. It aims to partition a given set of elements into homogeneous...
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Adversarial concept drift detection under poisoning attacks for robust data stream mining
Continuous learning from streaming data is among the most challenging topics in the contemporary machine learning. In this domain, learning...
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Real-Time Streaming Datasets in Power BI
Power BI datasets can use the Import Data, DirectQuery, or Live Connection connection types. However, there is also one specific type of dataset that...
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Discovering Persistent Subgraph Patterns over Streaming Graphs
Streaming graph analysis is gaining importance in various fields due to the natural dynamicity in many real graph applications. Prior subgraph...
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Defining Data Quality Issues in Process Mining with IoT Data
IoT devices supporting business processes (BPs) in sectors like manufacturing, logistics or healthcare collect data on the execution of the...
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Literature review on Intention Mining-oriented Process Mining in information system
Process Mining focused only on the activity-oriented process and neglected the users’ behaviors behind the activities, which led to overlooking the...
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Modeling the public attitude towards organic foods: a big data and text mining approach
This study aims to identify the topics that users post on Twitter about organic foods and to analyze the emotion-based sentiment of those tweets. The...
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Streaming Data Analytics for Feature Importance Measures in Concept Drift Detection and Adaptation
Numerous applications require the ability to detect and adapt to concept drifts in streaming data on the fly. This is challenged by limited...
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New Spark solutions for distributed frequent itemset and association rule mining algorithms
The large amount of data generated every day makes necessary the re-implementation of new methods capable of handle with massive data efficiently....
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An efficient architecture for processing real-time traffic data streams using apache flink
Big Data technologies emerging day by day and are making drastic changes in various real-world applications. Traditional data mining tools adequate...
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CPOCEDS-concept preserving online clustering for evolving data streams
Clustering streaming data is challenging due to many temporal dynamics, such as concept drift, concept evolution, and feature evolution. Concept...
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Data- & compute-efficient deviance mining via active learning and fast ensembles
Detecting deviant traces in business process logs is crucial for modern organizations, given the harmful impact of deviant behaviours (e.g., attacks...
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Discovering Bursting Patterns over Streaming Graphs
A streaming graph is a constantly growing sequence of directed edges, which provides a promising way to detect valuable information in real time. A...
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C-3PA: Streaming Conformance, Confidence and Completeness in Prefix-Alignments
The aim of streaming conformance checking is to find discrepancies between process executions on streaming data and the reference process model. The...
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Mining frequent Itemsets from transaction databases using hybrid switching framework
With the growing volume of data, mining Frequent Itemsets remains of paramount importance. These have applications in various domains such as market...
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LIFOSS: a learned index scheme for streaming scenarios
Recently, researches on dynamic decision-making based on streaming data are in full swing. As an indispensable technology for data management and...
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Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud
Big data is gaining overwhelming attention since the last decade. Almost all the fields of science and technology have experienced a considerable...
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Data Clustering Mining Method of Social Network Talent Recruitment Stream Based on MST Algorithm
In order to solve the problem that the data clustering mining method of social network talent recruitment stream is affected by the score of graph...
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Reinforcement Learning-Based Streaming Process Discovery Under Concept Drift
Streaming process discovery aims to discover a process model that may change over time, co** with the challenges of concept drift in business...
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A Tool for Measuring Energy Consumption in Data Stream Mining
Energy consumption reduction is an increasing trend in machine learning given its relevance in socio-ecological importance. Consequently, it is...