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Hoeffding Trees are well-established decision trees for classifying streaming data. The Hoeffding bound was widely used in a static periodic manner, applying the bound for impurity measures to determine whether leaf nodes should split. However, this ...

Replacing missing features in data streams is an important task in order to enable many machine learning algorithms that require feature-complete instances for training and prediction. Two popular methods for dealing with missing features are ...

Road intersections represent one of the most complex configurations encountered when traversing road networks. It is therefore of vital importance to improve their operational performance, as that can significantly contribute towards the efficiency of ...

Multi-label learning algorithms have attracted more and more attention as of recent. This is mainly because real-world data is generally associated with multiple and non-exclusive labels, which could correspond to different objects, scenes, actions, and ...

The explosive increase in volume, velocity, variety, and veracity of data generated by distributed and heterogeneous nodes such as IoT and other devices, continuously challenge the state of art in big data processing platforms and mining techniques. ...

Recent botnet activities targeting IoT infrastructure and turning computing devices into cryptocurrency miners indicate an increase in the botnet attack surface and capabilities. These facts emphasize the importance of investigating alternative methods ...

Activity recognition aims to provide accurate and opportune information on people’s activities by leveraging sensory data available in today’s sensory rich environments. Nowadays, activity recognition has become an emerging field in the areas of ...

One of the main challenges of Cognitive Computing (CC) is reacting to evolving environments in near-real time. Therefore, it is expected that CC models provide solutions by examining a summary of past history, rather than using full historical data. This ...

Opinion stream mining algorithms learn and adapt a polarity model as new opinionated texts arrive. Text understanding is computationally expensive though, and sensitive to the emergence of new words. In this work, we study polarity prediction for ...

This research presents a new set of techniques to deal with event mining from different text sources, a complex set of NLP tasks which aim to extract events of interest and their components including authors, targets, locations, and event categories. ...

Stream processing has become extremely popular for analyzing huge volumes of data for a variety of applications, including IoT, social networks, retail, and software logs analysis. Streams of data are produced continuously and are mined to extract ...

This paper describes a forgetting technique for the live update of viewer profiles based on individual sliding windows, fading and incremental matrix factorization. The individual sliding window maintains, for each viewer, a queue holding the last n ...

Topic modeling is an important area which aims at indexing and exploring massive data streams. In this paper we introduce a discrete Dynamic Topic Modeling (dDTM) algorithm, which is able to model a dynamic topic that is not necessarily present over all ...

In this paper, we introduce new methods for multiobjective, system-level optimization that have been incorporated into the Lightweight Dataflow for Dynamic Data Driven Application Systems (DDDAS) Environment (LiD4E). LiD4E is a design tool for optimized ...

With the advancement of data generation technologies such as sensor networks, multiple data streams are continuously generated. Clustering multiple data streams is challenging as the requirement of clustering at anytime becomes more critical. We aim to ...

Numerous stream mining algorithms are equipped with forgetting mechanisms, such as sliding windows or fading factors, to make them adaptive to changes. In recommender systems those techniques have not been investigated thoroughly despite the very ...

In the Internet of Things, heterogeneous and distributed streams of sensor events is a driver for context-aware behavior in intelligent environments. However, processing the event data usually cross-cuts the business logic of IoT applications and ...

Over the last decade a vast number of businesses have developed online e-shops in the web. These online stores are supported by sophisticated systems that manage the products and record the activity of customers. There exist many research works that ...

In this paper, we describe our work on extracting entities from the online social messages regarding emergent events for ontology learning, which can contribute to a solution for quick response of emerging disastrous events. Our work started with the ...