Related papers: Data mining : past present and future - a typical …

The current research is focusing on the area of Opinion Mining also called as sentiment analysis due to sheer volume of opinion rich web resources such as discussion forums, review sites and blogs are available in digital form. One…

Anomaly mining is an important problem that finds numerous applications in various real world domains such as environmental monitoring, cybersecurity, finance, healthcare and medicine, to name a few. In this article, I focus on two areas,…

The literature on machine learning in the context of data streams is vast and growing. However, many of the defining assumptions regarding data-stream learning tasks are too strong to hold in practice, or are even contradictory such that…

This has much in common with traditional work in statistics and machine learning. However, there are important new issues which arise because of the sheer size of the data. One of the important problem in data mining is the…

Discovering frequent episodes over event sequences is an important data mining task. In many applications, events constituting the data sequence arrive as a stream, at furious rates, and recent trends (or frequent episodes) can change and…

Nowadays, every device connected to the Internet generates an ever-growing stream of data (formally, unbounded). Machine Learning on unbounded data streams is a grand challenge due to its resource constraints. In fact, standard machine…

Under several emerging application scenarios, such as in smart cities, operational monitoring of large infrastructure, wearable assistance, and Internet of Things, continuous data streams must be processed under very short delays. Several…

In this era of computerization, education has also revamped itself and is not limited to old lecture method. The regular quest is on to find out new ways to make it more effective and efficient for students. Nowadays, lots of data is…

Data is often generated in streams, with new observations arriving over time. A key challenge for learning models from data streams is capturing relevant information while keeping computational costs manageable. We explore intelligent data…

Graphs emerge in almost every real-world application domain, ranging from online social networks all the way to health data and movie viewership patterns. Typically, such real-world graphs are big and dynamic, in the sense that they evolve…

More and more business activities are performed using information systems. These systems produce such huge amounts of event data that existing systems are unable to store and process them. Moreover, few processes are in steady-state and due…

Stream processing has been an active research field for more than 20 years, but it is now witnessing its prime time due to recent successful efforts by the research community and numerous worldwide open-source communities. This survey…

Automated machine learning techniques benefited from tremendous research progress in recently. These developments and the continuous-growing demand for machine learning experts led to the development of numerous AutoML tools. However, these…

We address here two major challenges presented by dynamic data mining: 1) the stability challenge: we have implemented a rigorous incremental density-based clustering algorithm, independent from any initial conditions and ordering of the…

In recent years, social media has become one of the most popular platforms for communication. These platforms allow users to report real-world incidents that might swiftly and widely circulate throughout the whole social network. A social…

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide. Over the years, extensive…

The emerging Fog paradigm has been attracting increasing interests from both academia and industry, due to the low-latency, resilient, and cost-effective services it can provide. Many Fog applications such as video mining and event…

One of the most valuable assets of an organization is its organizational data. The analysis and mining of this potential hidden treasure can lead to much added-value for the organization. Process mining is an emerging area that can be…

Recent advances in data science, machine learning, and artificial intelligence, such as the emergence of large language models, are leading to an increasing demand for data that can be processed by such models. While data sources are…

Emerging applications of machine learning in numerous areas involve continuous gathering of and learning from streams of data. Real-time incorporation of streaming data into the learned models is essential for improved inference in these…